JP2026018020A - system - Google Patents

Abstract

A system is provided.
A means for inputting basic information of a subject;
a means for determining cognitive characteristics of a subject;
A means for proposing an optimal learning method based on the judgment results;
a means of delivering learning content;
A means to track learning progress and administer comprehension tests;
a means of providing individualized feedback to subjects;
A way to resolve questions during learning in real time,
A system including:
[Selected Figure] Figure 1

Description

The technology disclosed herein relates to a system.

Patent document 1 discloses a persona chatbot control method executed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Japanese Patent Application Laid-Open No. 2022-180282

In traditional education systems, uniform learning methods centered around textbooks are the norm, making it difficult to select learning methods that suit individual students' characteristics. This has led to problems such as a decline in motivation to learn and low learning effectiveness. Furthermore, it has not adequately addressed social issues such as educational disparities, school absenteeism, and support for students with disabilities. To solve these problems, it is necessary to propose optimal learning methods based on students' cognitive characteristics and provide a flexible learning environment that responds to their individual progress.

The present invention solves these problems by the following means. First, it provides a means for inputting basic information about the subject, followed by a means for assessing the subject's cognitive characteristics. Next, it provides a means for suggesting the optimal learning method based on the assessment results, and further includes a means for providing learning content. In addition, it incorporates means for tracking learning progress and conducting comprehension tests, thereby providing individual feedback to the subject. By utilizing these means in a comprehensive manner, it is possible to provide an optimal educational experience tailored to each individual's learning needs, solving the problems faced by conventional education systems. Furthermore, by adding a means for suggesting discussion topics to the subject and encouraging the exchange of opinions between subjects, it is possible to stimulate mutual communication.

"Means for entering basic information about the subject" refers to an interface that allows users to collect basic information such as their name, age, grade, and areas of interest, and enter it into the system.

"Means for determining a subject's cognitive characteristics" refers to a system that presents questions and tasks to determine whether the subject has a visual, verbal, or auditory dominance, and collects and analyzes the subject's responses.

"Means for suggesting optimal learning methods" refers to a system that selects and suggests the most suitable learning method (video materials, text materials, audio materials, etc.) for each individual based on the results of their assessed cognitive characteristics.

"Means for providing learning content" refers to a system that provides relevant educational materials and learning materials (video, text, audio, etc.) to the target person based on their proposed learning method.

"Means for tracking learning progress" refers to a system that records and analyzes the learning content and progress of the subject in real time.

"Means for conducting comprehension tests" refers to a system for periodically administering short quizzes on the content that the subject has learned to measure their level of comprehension.

"Means for providing individualized feedback" refers to a system that suggests learning advice and next assignments specific to each individual based on the results of a comprehension test.

"A means of resolving questions during learning in real time" is a system that provides immediate answers to any doubts or questions that students may have while learning.

"Means for suggesting discussion topics" is an interface that periodically suggests topics to participants to encourage discussion and promotes the exchange of opinions among participants.

1 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a first embodiment. 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. 10 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a second embodiment. FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. FIG. 10 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a third embodiment. FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. 1 shows an emotion map onto which multiple emotions are mapped. 1 shows an emotion map onto which multiple emotions are mapped. FIG. 2 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in application example 1. FIG. 10 is a sequence diagram showing the flow of processing of the data processing system in the second embodiment when an emotion engine is combined. FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined.

Below, an example of an embodiment of a system relating to the technology disclosed herein will be described with reference to the accompanying drawings.

First, let me explain the terminology used in the following explanation.

In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, a processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose Computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

In the following embodiments, coded random access memory (RAM) is memory in which information is temporarily stored and is used by the processor as work memory.

In the following embodiments, the coded storage refers to one or more non-volatile storage devices that store various programs, parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

In the following embodiments, a communication I/F (Interface) with a symbol is an interface that includes a communication processor, an antenna, etc. The communication I/F controls communication between multiple computers. Examples of communication standards that can be applied to the communication I/F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

In the following embodiments, "A and/or B" is synonymous with "at least one of A and B." In other words, "A and/or B" means that it may be just A, just B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and/or B" also applies when three or more things are expressed connected by "and/or."

[First embodiment]

Figure 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and/or a LAN (Local Area Network).

The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I/F 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

The reception device 38 is equipped with a touch panel 38A, a microphone 38B, etc., and receives user input. The touch panel 38A detects contact with an indicator (e.g., a pen or finger) to receive user input via the indicator. The microphone 38B detects the user's voice to receive user input via voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

The output device 40 is equipped with a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible by the user 20 (e.g., audio and/or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54.

Figure 2 shows an example of the main functions of the data processing device 12 and smart device 14.

As shown in FIG. 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

Storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

Next, we will explain the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

This invention is an AI system designed to support education. It assesses the cognitive characteristics of each individual and proposes individually appropriate learning methods to maximize learning effectiveness and address educational disparities, school absenteeism, and support for people with disabilities. This system functions between a server, a terminal, and a user.

Overview of the system

This system provides educational support tailored to the target individual by performing the following processes:

1. Obtain basic information about the subject.

2. Determine the subject's cognitive characteristics.

3. Suggest the optimal learning method based on the assessment results.

4. Provide learning content.

5. Track learning progress and administer comprehension tests.

6. Provide personalized feedback.

7. Resolve questions while studying in real time.

8. Suggest discussion topics and encourage exchange of opinions.

Program processing

Below is a natural language explanation of the program processing at each step of this system.

1. User Registration

The server provides a user registration page.

Users use the device to enter basic information such as name, age, grade, and areas of interest.

The server stores the user's basic information in a database.

2. Cognitive trait assessment test

The server displays the cognitive characteristics assessment test interface on the terminal.

The user clicks the Start Test button to begin the test.

The server sends a series of questions and tasks to the device in sequence.

The device displays each question or task to the user and accepts their response.

Users answer each question on their device.

The server receives responses from users and stores them in a database in real time.

The server analyzes the collected data and determines the user's visual, language, and auditory cognitive characteristics.

3. Learning Method Suggestions

Based on the results of the assessment, the server selects the optimal learning method for the user.

The server will present the selected learning method and related learning materials to the device.

The device will display learning method suggestions and introductions to learning materials to the user.

4. Providing learning content

The server provides learning content appropriate to the user (video, text, audio materials, etc.).

Users can check the learning content on their device and begin learning.

The device records the user's learning progress in real time.

5. Real-time support

The server provides an interface via the terminal that accepts questions the user may have while studying.

The user enters a question on the device and submits it.

The server analyzes the question and provides an appropriate answer instantly.

The device displays the response from the server to the user.

6. Progress Tracking and Comprehension Testing

The server periodically displays an interface on the terminal that conducts comprehension tests.

Users take the test on their device.

The device sends the user's response to the server.

The server scores the answers, stores the results in a database, and provides feedback to the user.

7. Individual feedback and suggestions for next steps

The server analyzes the results of the user's comprehension test and suggests the next learning content and assignments.

The device will display feedback and new challenges to the user.

Users will work on the proposed tasks on their devices.

8. Promoting discussion and communication

The server will provide regular discussion topics.

The device displays discussion topics to the user and provides a form where they can enter their responses and opinions.

Users can enter their opinions on their devices and exchange them with other users.

Specific examples

For example, let's say Mr. C is a second-year junior high school student who wants to study history.

1. User Registration

Mr. C types "Second year junior high school student, interested in history" into his device.

The server stores this information.

2. Cognitive trait assessment test

Mr. C clicks the start test button on his device.

The server will begin the validation test.

Questions such as "Is it easy to remember with an image?" will be displayed on the device.

Mr. C answers the questions.

The server analyzes Mr. C's answers and determines that he is visually dominant.

3. Learning Method Suggestions

The server suggests, "Learn about historical events through videos."

A link to the video material will appear on your device.

4. Providing learning content

Mr. C starts watching the video on his device.

5. Real-time support

Mr. C types the question into his terminal: "I don't understand the background of this war."

The server provides the answer, which is displayed on the device.

6. Progress Tracking and Comprehension Testing

After watching the video, the server will conduct a comprehension test.

Mr. C takes the test on a terminal.

7. Individual feedback and suggestions for next steps

The server analyzes the test results and suggests, "Next, let's learn more about the causes of this war."

The next learning content will be displayed to Mr. C on his device.

8. Promoting discussion and communication

The server suggests that next week's theme is 'Important Figures in Modern History' and provides a platform for users to exchange opinions.

Mr. C can enter his opinions on the device and discuss them with his classmates.

In this way, the AI system can provide an optimal educational experience tailored to individual learning needs, creating an environment in which Mr. C can learn independently.

The processing flow is explained below.

Step 1:

The server provides a user registration page. Users use their terminal to enter basic information such as name, age, grade, and areas of interest. The server stores the entered basic information in a database.

Step 2:

The server displays the cognitive characteristics assessment test interface on the terminal. The user clicks the Start Test button to begin the test.

Step 3:

The server sequentially sends a series of questions and tasks to the terminal. The terminal displays the questions and tasks to the user and accepts answers. The user answers each question on the terminal.

Step 4:

The server receives the user's responses and stores them in a database in real time. The server analyzes the collected data and determines the user's visual, language, and auditory cognitive characteristics.

Step 5:

Based on the results of the assessment, the server selects the most suitable learning method for the user. The server then presents the selected learning method and related learning materials to the device. The device then displays learning method suggestions and learning material introductions to the user.

Step 6:

The server provides learning content (video, text, audio materials, etc.) appropriate for the user. The user checks the learning content on their device and begins learning. The device records the user's learning progress in real time.

Step 7:

The server provides an interface that accepts questions that users have while studying via their terminal. The user enters and submits a question on the terminal. The server analyzes the question and instantly generates and provides an appropriate answer. The terminal displays the answer from the server to the user.

Step 8:

The server periodically displays an interface on the device that conducts comprehension tests. The user takes the test on the device. The device sends the user's answers to the server. The server scores the answers, stores the results in a database, and provides feedback to the user.

Step 9:

The server analyzes the results of the user's comprehension test and suggests the next learning content and assignments. The device displays feedback and new assignments to the user. The user then works on the suggested assignments.

Step 10:

The server periodically provides discussion topics. The terminal displays the discussion topics to the user and provides a form where they can enter their responses and opinions. Users enter their opinions on the terminal and exchange opinions with other users.

(Example 1)

Next, we will explain Example 1. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

Modern education requires providing optimal learning methods based on individual cognitive characteristics. However, many current educational systems only offer uniform learning methods, making it difficult to address individual cognitive characteristics. Furthermore, they lack features such as learning progress tracking, comprehension tests, and real-time question resolution, making it impossible to adequately address individual learning needs. Furthermore, there are few systems that automatically suggest learning content or provide feedback, preventing the effectiveness of education from being maximized.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for inputting basic information about the subject, means for determining the cognitive characteristics of the subject, means for proposing the optimal learning method based on the determination results, means for providing learning content, means for tracking learning progress and conducting comprehension tests, means for providing individual feedback to the subject, means for resolving questions during learning in real time, means for saving the subject's input and learning progress in a database, means for collecting the subject's questions and generating answers using a generative AI model, and means for evaluating the subject's answers and dynamically suggesting the next learning content. This makes it possible to provide the optimal learning method according to individual cognitive characteristics, track learning progress, resolve questions in real time, and dynamically suggest learning content.

"Target" refers to individuals who use educational support AI systems to advance their studies.

"Basic information" refers to basic information necessary for education, such as the subject's name, age, grade, and areas of interest.

"Cognitive characteristics" refer to the information processing characteristics of a subject, such as vision, language, and hearing, and indicate individual characteristics in learning.

"Learning methods" are the optimal learning tools and approaches suggested based on cognitive characteristics, including teaching materials and learning formats.

"Learning content" refers to learning videos, texts, audio materials, etc. provided to the target audience, and is an information resource to support learning.

"Learning progress" refers to the progress a subject makes as they progress through their studies.

A "comprehension test" is a test used to measure the subject's level of understanding of the learning content.

"Individualized feedback" refers to specific assessments and guidance provided to students based on their comprehension test results and learning progress.

"Real-time question resolution" means providing immediate and appropriate answers to questions that students have while studying.

A "database" is a system that stores and manages data such as basic information about the subject, their learning progress, and their responses.

A "generative AI model" is an artificial intelligence model used to generate appropriate answers to user questions.

"Dynamic suggestions" means generating and suggesting the next content or assignment to be learned on an ongoing basis based on the student's learning progress and level of understanding.

This invention relates to an educational support AI system that maximizes learning effectiveness by providing optimal learning methods based on individual cognitive characteristics, and addresses educational disparities, school absenteeism, and support for people with disabilities. The system functions between a server, a terminal, and a user.

Overall system overview

This system provides tailored educational support by performing the following processes:

1. Obtain basic information about the subject.

2. Determine the subject's cognitive characteristics.

3. Suggest the optimal learning method based on the assessment results.

4. Provide learning content.

5. Track learning progress and administer comprehension tests.

6. Provide personalized feedback.

7. Resolve questions while studying in real time.

8. Suggest discussion topics and encourage exchange of opinions.

Hardware and software used

The hardware used includes a server and user devices. The server uses a database (e.g., MySQL, PostgreSQL) to manage and analyze user information, an execution environment for machine learning models (e.g., Python, Scikit-learn), a real-time chat system (e.g., Node.js), and a generative AI model (e.g., OpenAI GPT-3).

The user device provides an interface that runs on a web browser and handles user operations and learning progress using HTML, CSS, JavaScript, React, etc.

Program processing overview

User Registration

The server generates a user registration page using HTML and CSS and sends it to the device. The user enters basic information such as name, age, grade, and areas of interest on the device and submits it. The server receives the information in JSON format and stores it in a database.

Cognitive trait assessment test

The server uses JavaScript and React to generate the cognitive trait assessment test interface and display it on the device. The user starts the test and answers a series of questions. The server receives the answers, stores them in a database, and uses a Python machine learning model to analyze the data and determine cognitive traits.

Suggested learning methods

The server determines the optimal learning method based on cognitive characteristics and selects relevant learning materials. This is then sent to the device, which then presents it to the user.

Providing learning content

The server provides selected learning content, which users use on their devices to progress through their studies. The devices record learning progress in real time and send it to the server.

Real-time support

The server accepts the user's question through the device and generates an appropriate answer using a generative AI model. This is then sent to the device and displayed.

Progress tracking and comprehension testing

The server periodically conducts comprehension tests and stores the results in a database. It provides feedback to the user and suggests what to study next.

Individual feedback and suggestions for next steps

The server analyzes the next learning content based on the test results and provides feedback and new challenges to the user, thereby presenting the optimal learning path for each individual user.

Fostering discussion and communication

The server periodically generates new discussion topics and presents them to users. Users can then enter their opinions on their devices and exchange them with other users.

Specific examples

For example, let's say Mr. C is a second-year junior high school student who wants to study history.

1. Mr. C types "Second year junior high school student, interested in history" into his device and submits the information. The server stores this information.

2. Mr. C starts the cognitive characteristics assessment test on his device. The server displays the questions, and Mr. C enters the answers. The server analyzes the answers and determines that he is visually dominant.

3. The server suggests, "Let's learn about historical events through videos," and displays a link to the video material on the device.

4. Mr. C begins watching the video on his device. The device records his learning progress and sends it to the server.

5. When Mr. C enters a question such as "I don't understand the background of this war," the server uses a generative AI model to generate and display an answer.

6. After watching the video, the server conducts a comprehension test, and Mr. C takes the test. The results are sent to the server, which then suggests the next learning content.

7. The server will suggest a new task, saying, "Next, let's learn more about the causes of this war," and display this on the terminal.

8. The server suggests that next week's topic is "Important Figures in Modern History" and provides a discussion platform. C can enter his opinions and discuss them with his classmates.

Example prompt

"If you have a second-year junior high school student who wants to study history, how would you use AI to suggest the best learning method? Please explain the specific process."

In this way, AI systems can provide optimal educational experiences tailored to individual learning needs and create an environment in which students can learn independently.

The flow of the identification process in Example 1 will be explained using Figure 11.

Step 1:

User Registration

The server generates a user registration page using HTML and CSS and sends it to the device. The user enters basic information such as name, age, grade, and areas of interest on the device and submits it. The server receives the received information in JSON format and stores it in a database. This registers the user's basic information in the database. The input is the user's basic information, and the output is the user's basic information stored in the database.

Step 2:

Cognitive trait assessment test

The server uses JavaScript and React to generate an interface for the cognitive trait assessment test and displays it on the device. The user clicks a button to start the test and answers a series of questions. The device displays each question and accepts the user's answers. The server stores the received answers in a database and analyzes the data using a Python machine learning model to determine the user's cognitive traits. The input is the user's answers, and the output is the determined cognitive traits. This reveals the user's cognitive traits.

Step 3:

Study method suggestions

The server selects the optimal learning method based on cognitive characteristics and selects related learning materials. The server sends the selected learning method and learning materials to the terminal via a REST API. The terminal displays this to the user. The input is cognitive characteristics and a learning materials database, and the output is a link to the learning method and learning materials presented to the user. This allows the user to be presented with the optimal learning method.

Step 4:

Providing learning content

The server dynamically generates learning content appropriate for the user and sends it to the device. The user checks the learning content on the device and begins learning. The device records learning progress and periodically sends it to the server. The input is the user's cognitive characteristics and progress, and the output is the learning content provided and a record of learning progress. This allows users to study with content that suits them.

Step 5:

Real-time support

The server provides an interface that accepts user questions via the terminal. The user enters the question on the terminal and sends it to the server. The server analyzes the question and generates the optimal answer using a generative AI model. The server sends the answer to the terminal, which displays it to the user. The input is the user's question, and the output is the generated answer. This allows the user's question to be resolved instantly.

Step 6:

Progress tracking and comprehension testing

The server periodically displays an interface on the device that conducts comprehension tests. The user takes the test on the device and sends their answers to the server. The server scores the answers and stores the results in a database. It then generates feedback based on the results and sends it to the device. The input is the comprehension test answers, and the output is the test results and feedback. This evaluates the user's level of understanding and determines the next learning content.

Step 7:

Individual feedback and suggestions for next steps

The server analyzes the test results and determines the next learning content. The server sends feedback and new assignments to the device, which displays them to the user. The user then works on the suggested assignments. The input is the test results and learning goals, and the output is feedback and new suggested assignments. This allows the user to continuously work on appropriate assignments.

Step 8:

Fostering discussion and communication

The server periodically generates discussion topics and sends them to the device. Users input their opinions on their device and send them to the server. The server collects the opinions and displays them to other users, allowing for an exchange of opinions. The input is the user's opinion, and the output is the shared opinion and discussion results. This promotes communication between users and leads to deeper understanding.

(Application Example 1)

Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

With conventional educational systems, it was difficult to efficiently grasp the cognitive characteristics of individual students and propose optimal learning methods based on those characteristics. Furthermore, there was a lack of educational support for industrial product operation methods and control technologies in industrial settings, and automatic and effective measures were needed to improve engineers' skills.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

In this invention, the server includes means for inputting basic information about the subject, means for assessing the cognitive characteristics of the subject, means for proposing the optimal learning method based on the assessment results, means for providing learning content, means for tracking learning progress and conducting comprehension tests, means for providing individual feedback and proposing the next assignment to the subject, means for resolving questions during learning in real time, means for promoting the exchange of opinions between subjects, and means for supporting learning of industrial product operation methods and control technology. This makes it possible to propose the optimal learning method for each subject's cognitive characteristics and to automatically and effectively educate them on industrial product operation methods and control technology.

1. "Basic information of the subject" refers to personal information such as the subject's name, age, years of experience, and area of expertise.

2. "Cognitive characteristics" refers to the individual characteristics of how a subject receives, processes, and understands information. Examples include visual dominance, auditory dominance, and linguistic dominance.

3. "Optimal learning method" refers to the most effective learning method based on the cognitive characteristics of the individual.

4. "Learning Content" refers to the learning materials and information provided to the target audience for learning, including video, text, and audio materials.

5. "Tracking learning progress" refers to recording and managing a student's learning progress in real time.

6. "Comprehension test" refers to a test used to assess the extent to which a subject has understood the learning content.

7. "Individualized feedback" refers to specific advice and next steps provided based on the learner's learning progress and level of understanding.

8. "Resolving questions during learning in real time" refers to providing immediate answers to questions that arise when a student is learning.

9. "Promoting the exchange of opinions" refers to support provided to stimulate information sharing and discussion among participants.

10. "Supporting learning about industrial product operation methods and control technologies" refers to educational support to promote understanding of the correct operation methods and control technologies of industrial products and equipment used in industry.

Overall system configuration

This invention describes a specific form for implementing an educational support system consisting of a server, a terminal, and a user. This system proposes optimal learning methods based on the cognitive characteristics of each individual, and supports education on industrial product operation methods and control technologies.

Hardware and software used

This invention uses the following hardware and software:

Server: High-performance server (e.g., AWS, Google Cloud)

Devices: Smartphones, factory robot HMIs (Human-Machine Interfaces), PCs

Front-end: Angular, HTML5, CSS3, JavaScript

Backend: Node.js, MySQL

AI modeling: Python, TensorFlow, Socket.IO

Program processing

1. User Registration

The server provides a user registration page using Angular on the front end.

Users use the terminal to enter basic information such as their name, age, years of experience, and area of expertise.

The server receives this information via Node.js and stores it in a MySQL database.

2. Assessment of cognitive characteristics

The server uses an Angular front-end to provide an interface for questions and tasks that assess cognitive characteristics.

The user starts the test on their device and answers the questions.

The server receives the responses and analyzes the data using Python and TensorFlow to determine cognitive characteristics.

3. Learning Method Suggestions

Based on the analysis results, the server selects the optimal learning method and suggests corresponding learning materials.

The device will display the suggestions and teaching materials to the user.

4. Providing learning content

The server provides learning content (video, audio, text) that is best suited to the user's cognitive characteristics.

Users can view this content on their devices and progress as they study. Progress is recorded in real time.

5. Real-time support

If users have any questions while studying, they can enter them using their smartphone or tablet.

The server uses Socket.IO to receive questions in real time, processes them in Python, and provides instant answers.

6. Progress Tracking and Comprehension Testing

The server periodically conducts comprehension tests, analyzes the results, stores them in a database, and provides feedback to the user.

7. Individual feedback and suggestions for next steps

The server suggests the next learning task based on the test results and provides related learning content.

8. Promoting discussion and communication

The server will regularly provide discussion topics and an interface to encourage the exchange of opinions between users.

Users can enter their opinions on their devices and discuss with other users.

Examples and prompts

As a concrete example, consider the case where engineer A is learning how to operate a new industrial robot.

If Engineer A enters "Years of experience: 5 years, Specialty: Industrial robot control" and has visually dominant cognitive characteristics, the system will suggest, "Let's learn the basic operation of industrial robots through video materials." Engineer A will begin learning by watching the video materials, and if he has any questions, he can ask, "Please tell me in detail about how the sensors on this robot work." The server will immediately provide an appropriate answer to this question.

Examples of prompt statements include:

"For an engineer with five years of robot operation experience, please suggest a method of operating an industrial robot that is easy for someone with visually dominant cognitive characteristics to learn. Also, please suggest related video teaching materials."

Some examples include:

In this way, it is possible to propose learning methods that are optimal for each individual's cognitive characteristics, and to automatically and effectively educate them on how to operate industrial products and control technologies.

The flow of the specific processing in Application Example 1 will be explained using Figure 12.

Step 1:

User Registration

The server provides a user registration page using Angular on the front end.

Users use the terminal to enter basic information such as their name, age, years of experience, and area of expertise.

The basic information entered is sent from the terminal to the server, which receives this information via Node.js and stores it in a MySQL database.

Enter basic information such as name, age, years of experience, and area of expertise.

Output: Basic user information stored in the database

Step 2:

Assessing cognitive characteristics

The server uses an Angular front-end to provide an interface for questions and tasks that assess cognitive characteristics.

The user starts the test on their device and answers the questions.

The server receives the response data and analyzes it using Python and TensorFlow to determine the user's cognitive characteristics. Categories of cognitive characteristics such as visual, auditory, and language are determined.

Input: User response data

Output: Cognitive characteristics (visual, auditory, language, etc.)

Step 3:

Study method suggestions

Based on the results of the cognitive characteristic analysis, the server selects the optimal learning method and suggests corresponding learning materials.

The device will display the suggestions and teaching materials to the user.

For example, video learning materials are suggested to visually-oriented users.

Input: Cognitive trait analysis results

Output: Suggested learning methods and materials

Step 4:

Providing learning content

The server provides learning content (video, audio, text) that is best suited to the user's cognitive characteristics.

Users can view this content on their devices and progress through their studies. Progress is recorded in real time from the device to the server.

Input: Suggested learning methods and materials

Output: Providing learning content and recording progress

Step 5:

Real-time support

If users have any questions while studying, they can enter them using their smartphone or tablet.

The server uses Socket.IO to receive questions in real time, processes them in Python, and provides instant answers.

Input: User question

Output: Answers provided in real time

Step 6:

Progress tracking and comprehension testing

The server periodically conducts comprehension tests, analyzes the results, stores them in a database, and provides feedback to the user.

The terminal displays the test results to the user.

Input: Comprehension test response data

Output: Analysis of test results and feedback

Step 7:

Individual feedback and suggestions for next steps

Based on the results of the comprehension test, the server suggests the next learning task and provides related learning content.

The device will display the next task and related content to the user.

Input: Comprehension test results

Output: Providing the next assignment and related learning content

Step 8:

Fostering discussion and communication

The server will regularly provide discussion topics and an interface to encourage the exchange of opinions between users.

Users can enter their opinions on their devices and discuss with other users.

Input: Discussion topic and user opinions

Output: Facilitated exchange of ideas and shared opinions

In this way, by clarifying the roles of the server, terminal, and user at each step and organizing the specific operations and inputs/outputs, the educational support system is designed to function effectively.

Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

This invention is an AI system designed to support education. It maximizes learning effectiveness by assessing the cognitive characteristics of each individual and proposing individually appropriate learning methods. Furthermore, by combining it with an emotion engine, it provides an optimal learning experience by taking into account the emotional state of the individual. This system functions among four parties: a server, a terminal, a user, and an emotion engine.

Overall system overview

This system provides educational support tailored to the target individual by performing the following processes:

1. Obtain basic information about the subject.

2. Determine the subject's cognitive characteristics.

3. Suggest the optimal learning method based on the assessment results.

4. Provide learning content.

5. Track learning progress and administer comprehension tests.

6. Provide personalized feedback.

7. Resolve questions while studying in real time.

8. Use an emotion engine to recognize and respond to the subject's emotional state.

9. Suggest discussion topics and encourage exchange of ideas.

Program processing

Below is a natural language explanation of the program processing at each step of this system.

1. User Registration

The server provides a user registration page.

Users use the device to enter basic information such as name, age, grade, and areas of interest.

The server stores the basic information entered in a database.

2. Cognitive trait assessment test

The server displays the cognitive characteristics assessment test interface on the terminal.

The user clicks the Start Test button to begin the test.

3. Conduct cognitive assessment tests

The server sends a series of questions and tasks to the device in sequence.

The device displays questions and tasks to the user and accepts answers.

Users answer each question on their device.

The server receives responses from users and stores them in a database in real time.

The server analyzes the collected data and determines the user's visual, language, and auditory cognitive characteristics.

4. Learning Method Suggestions

Based on the results of the assessment, the server selects the optimal learning method for the user.

The server will present the selected learning method and related learning materials to the device.

The device will display learning method suggestions and introductions to learning materials to the user.

5. Providing learning content

The server provides learning content appropriate to the user (video, text, audio materials, etc.).

Users can check the learning content on their device and begin learning.

The device records the user's learning progress in real time.

6. Real-time support

The server provides an interface via the terminal that accepts questions the user may have while studying.

The user enters a question on the device and submits it.

The server analyzes the question and instantly generates and provides an appropriate answer.

The device displays the response from the server to the user.

7. Progress Tracking and Comprehension Testing

The server periodically displays an interface on the terminal that conducts comprehension tests.

Users take the test on their device.

The device sends the user's response to the server.

The server scores the answers, stores the results in a database, and provides feedback to the user.

8. Individual feedback and suggestions for next steps

The server analyzes the results of the user's comprehension test and suggests the next learning content and assignments.

The device will display feedback and new challenges to the user.

Users will work on the proposed tasks.

9. Emotion Recognition and Response

The server uses an emotion engine to monitor the user's emotional state in real time through the device while they are learning.

The emotion engine identifies emotions from the user's facial expressions and tone of voice, and stores the emotional state in a database.

The server provides optimal feedback and adjusts learning content based on the user's emotional state.

10. Promoting discussion and communication

The server will provide regular discussion topics.

The device displays discussion topics to the user and provides a form where they can enter their responses and opinions.

Users can enter their opinions on their devices and exchange them with other users.

Specific examples

For example, let's say Mr. D is a first-year high school student who wants to study mathematics.

1. User Registration

Mr. D types "First year high school student, interested in mathematics" into his device.

The server stores this information.

2. Cognitive trait assessment test

Mr. D clicks the start test button on his device.

The server will begin the validation test.

Questions such as "Is it easy to remember with an image?" will be displayed on the device.

Mr. D answers the questions.

The server analyzes Mr. D's answers and determines that he is visually dominant.

3. Learning Method Suggestions

The server suggests, "Learn math concepts through videos."

A link to the video material will appear on your device.

4. Providing learning content

Mr. D starts watching the video on his device.

5. Real-time support

Mr. D types a question into his terminal: "I don't know how to use this formula."

The server provides the answer, which is displayed on the device.

6. Progress Tracking and Comprehension Testing

After watching the video, the server will conduct a comprehension test.

Mr. D takes the test on the terminal.

7. Individual feedback and suggestions for next steps

The server analyzes the test results and suggests, "Next, try solving some real problems."

The next learning content will be displayed to Mr. D on his device.

8. Emotion Recognition and Response

The server uses an emotion engine to analyze Mr. D's facial expressions and tone of voice while he is studying.

If it is determined that Mr. D is feeling tired, the server will display a message on his device saying, "Let's take a short break."

Also, if Mr. D is in a state of joy or adaptation, he will be given feedback such as "Good luck," encouraging him to continue learning.

9. Promoting discussion and communication

The server suggests that next week's theme is 'Applications of Functions' and provides a platform where users can exchange opinions.

Mr. D can enter his opinions on the device and discuss them with his classmates.

In this way, the AI system can provide an optimal educational experience tailored to individual learning needs, creating an environment in which D can learn independently. By combining it with an emotion engine, it is possible to respond according to the user's emotional state, further improving learning effectiveness.

The processing flow is explained below.

Step 1:

The server provides a user registration page. Users use their terminal to enter basic information such as name, age, grade, and areas of interest. The server stores the entered basic information in a database.

Step 2:

The server displays the cognitive characteristics assessment test interface on the terminal. The user clicks the Start Test button to begin the test.

Step 3:

The server sequentially sends a series of questions and tasks to the terminal. The terminal displays the questions and tasks to the user and accepts answers. The user answers each question on the terminal.

Step 4:

The server receives the user's responses and stores them in a database in real time. The server analyzes the collected data and determines the user's visual, language, and auditory cognitive characteristics.

Step 5:

Based on the results of the assessment, the server selects the most suitable learning method for the user. The server then presents the selected learning method and related learning materials to the device. The device then displays learning method suggestions and learning material introductions to the user.

Step 6:

The server provides learning content (video, text, audio materials, etc.) appropriate for the user. The user checks the learning content on their device and begins learning. The device records the user's learning progress in real time.

Step 7:

The server provides an interface that accepts questions that users have while studying via their terminal. The user enters and submits a question on the terminal. The server analyzes the question and instantly generates and provides an appropriate answer. The terminal displays the answer from the server to the user.

Step 8:

The server periodically displays an interface on the device that conducts comprehension tests. The user takes the test on the device. The device sends the user's answers to the server. The server scores the answers, stores the results in a database, and provides feedback to the user.

Step 9:

The server analyzes the results of the user's comprehension test and suggests the next learning content and assignments. The device displays feedback and new assignments to the user. The user then works on the suggested assignments.

Step 10:

The server uses an emotion engine to monitor the user's emotional state in real time via the device while they are learning. The emotion engine identifies emotions from the user's facial expressions and tone of voice, and stores the emotional state in a database. The server then provides optimal feedback and adjusts the learning content based on the user's emotional state.

Step 11:

The server periodically provides discussion topics. The terminal displays the discussion topics to the user and provides a form where they can enter their responses and opinions. Users enter their opinions on the terminal and exchange opinions with other users.

(Example 2)

Next, we will explain Example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

Traditional educational systems have struggled to provide learning methods that fully consider the cognitive characteristics and emotional state of each individual student. They also lacked functionality for resolving questions that arise during learning in real time and for facilitating the exchange of opinions between students. This has made it difficult to provide an optimal learning experience for each student, resulting in the issue of not maximizing learning effectiveness.

The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting basic information about the subject, means for determining the cognitive characteristics of the subject, means for proposing an optimal learning method based on the determination results, means for providing learning content, means for tracking learning progress and conducting comprehension tests, means for providing individual feedback to the subject, means for resolving questions during learning in real time, and means for recognizing and responding to the subject's emotional state using an emotion engine. This makes it possible to provide an optimal learning experience tailored to the subject's individual needs, maximizing learning effectiveness. Furthermore, by encouraging the exchange of opinions between subjects as needed, improvement in the quality of learning can also be expected.

"Target audience" refers to learners who use the educational system.

"Basic information" refers to basic data such as the subject's name, age, grade, and areas of interest.

"Cognitive characteristics" refers to the subject's cognitive style, such as visual, linguistic, and auditory.

"Decision testing" refers to a series of questions or tasks designed to assess a subject's cognitive characteristics.

"Feedback" refers to individual evaluations and advice provided based on the subject's learning results.

"Learning method" refers to the learning process and techniques proposed based on the cognitive characteristics of the target individual.

"Learning content" includes learning materials and resources, such as videos, texts, and audio materials.

An "emotion engine" refers to a system that has the ability to recognize the emotional state of a subject and process that data.

"Real-time support" refers to the ability to instantly respond to questions or concerns that students may have while studying.

"Discussion Topics" refer to topics that the server periodically provides for the exchange of opinions among participants.

"Opinion exchange" refers to the activity of sharing opinions and thoughts among participants.

"Server" refers to the central computer that runs the entire system and provides each function.

"Terminal" refers to the computer or mobile device used by a Subject to access the System.

This invention is an AI system designed to support education, maximizing learning effectiveness by determining the cognitive characteristics of each individual and proposing individually appropriate learning methods. Furthermore, by combining it with an emotion engine, it provides an optimal learning experience by taking into account the emotional state of the individual. This system functions among four parties: a server, a terminal, a user, and an emotion engine.

Overall system overview

This system provides educational support tailored to each individual through the following steps:

1. Obtain basic information about the subject.

2. Determine the subject's cognitive characteristics.

3. Suggest the optimal learning method based on the assessment results.

4. Provide learning content.

5. Track learning progress and administer comprehension tests.

6. Provide personalized feedback.

7. Resolve questions while studying in real time.

8. Use an emotion engine to recognize and respond to the subject's emotional state.

9. Suggest discussion topics and encourage exchange of ideas.

Program processing

Processing in this system is carried out through cooperation between the server and the terminal. Details are provided below.

Hardware and Software Use

The server uses a MySQL database to store and manage data.

The server uses Python to implement data analysis and decision algorithms.

The server provides a front end consisting of JavaScript and HTML.

Devices access the server using a browser such as Google Chrome.

The emotion engine uses a camera and microphone to analyze the user's facial expressions and tone of voice.

Specific examples

For example, if Mr. D is a first-year high school student who wants to study mathematics, consider the following:

1. User Registration

D enters "First year high school student, interested in mathematics" into the terminal. The form displayed on the terminal has fields for entering name, age, grade, and areas of interest.

When Mr. D clicks the "Register" button, the server receives the input data and saves it in the MySQL database.

2. Cognitive trait assessment test

Mr. D clicks the start test button on his device. The server sends the assessment test UI, composed of HTML and JavaScript, to the device and asks a series of questions.

Questions such as "Is it easy to remember with an image?" are displayed on the device. Mr. D answers the questions, and the answers are saved in a database in real time.

3. Learning Method Suggestions

Based on Mr. D's cognitive characteristics, the server suggests, "Let's learn mathematical concepts through videos." A link to the video material is displayed on the device.

4. Providing learning content

Mr. D begins watching the video on his device. The server provides the video material via a video streaming service.

5. Real-time support

While learning, Mr. D enters a question into his device, such as "I don't know how to use this formula." The server receives the question, analyzes it using natural language processing (NLP), and instantly generates an appropriate answer, which is displayed on the device.

6. Progress Tracking and Comprehension Testing

After watching the video, the server periodically conducts comprehension tests. Mr. D takes the test on his device and his answers are sent to the server. The server uses an automatic scoring system to score the answers and stores the results in a database.

7. Individual feedback and suggestions for next steps

Based on the test results, the server suggests, "Next, try solving some real problems." The next learning content is displayed to Mr. D on his device.

8. Emotion Recognition and Response

The server uses an emotion recognition algorithm to monitor D's facial expressions and tone of voice in real time while he studies.

If D is identified as feeling tired, the server will display a message on his device saying, "Take a short break." If he is in a state of joy or adaptation, the server will provide feedback saying, "Keep it up," encouraging him to continue learning.

9. Promoting discussion and communication

The server suggests that next week's theme is 'Application of Functions' and provides a platform where users can exchange opinions. Mr. D can enter his opinion on his device and discuss it with his classmates.

Example prompt

"Please explain how to register by entering basic user information."

"Please explain with specific examples what types of questions will be asked during the cognitive assessment test."

"Please explain how you handle real-time support when users have questions while learning."

"Please explain the process of recognizing and responding to user emotions using the emotion engine."

This allows the AI system to provide an optimal educational experience tailored to the user's individual learning needs, improving learning outcomes. By combining it with an emotion engine, it becomes possible to respond according to the user's emotional state, further improving learning outcomes.

The flow of the identification process in Example 2 will be explained using Figure 13.

Step 1: User Registration

The server provides a user registration page. The user uses their terminal to enter basic information such as name, age, grade, and areas of interest. When the user clicks the "Register" button, the terminal sends the data to the server. The server receives the entered basic information and stores it in a database. The input here is the user's basic information, and the output is the data stored in the database.

Step 2: Cognitive trait assessment test

The server displays the cognitive ability assessment test interface on the terminal. The user clicks the test start button and answers the questions and tasks displayed on the terminal in sequence. The server receives each answer and stores them sequentially in a database. Based on the collected answer data, the server performs data analysis using Python to determine the user's visual, linguistic, and auditory cognitive abilities. The input is the user's test answers, and the output is the assessed cognitive abilities.

Step 3: Suggest a learning method

Based on the assessment results, the server selects the optimal learning method and related learning materials. It sends the selected information to the terminal and displays links to the learning method and learning materials to the user. The input is the assessed cognitive characteristics, and the output is the suggested learning method and links to the learning materials.

Step 4: Provide learning content

The server provides learning content such as videos, texts, and audio materials via a streaming service. Users check the learning content on their device and begin studying. The device records the user's learning progress (e.g., video viewing time and text completion status) in real time and sends it to the server. The input is the learning content, and the output is learning progress data.

Step 5: Real-time support

The server provides an interface through the terminal for users to input questions they have while studying. The user enters the question on the terminal and sends it. The server uses NLP (natural language processing) functions to analyze the question, generates an appropriate answer, and sends it to the terminal. The terminal displays the answer to the user. The input is the user's question, and the output is the generated answer.

Step 6: Progress Tracking and Comprehension Testing

The server displays an interface on the terminal that periodically conducts comprehension tests. The user takes the test on the terminal and submits their answers. The server scores the answers using an automatic scoring system and stores the results in a database. The server then provides feedback on the results to the user. The input is the test answers, and the output is the scoring results and feedback.

Step 7: Individual feedback and suggestions for next steps

The server selects the next learning content and assignments based on the results of the comprehension test. The selected information is sent to the device, and feedback and new assignments are displayed to the user. The user then works on the suggested assignments. The input is the test results, and the output is feedback and suggested assignments.

Step 8: Emotion Recognition and Responding

The server uses an emotion engine to monitor the user's facial expressions and tone of voice in real time via the device while they are studying. It identifies the user's emotional state and stores it in a database. The server then provides optimal feedback and adjusts the learning content based on the user's emotional state. For example, if the user is identified as tired, it will suggest a break, and if they are in an adaptive state, it will display an encouraging message. The input is the user's facial expression and tone of voice data, and the output is the emotion analysis results and feedback based on them.

Step 9: Facilitate discussion and communication

The server periodically provides discussion topics and displays them on the terminal. Users enter their opinions on the topics and exchange them with other users. The server stores the posted opinions in a database and displays them to other participants in real time. The input is the discussion topic and user opinions, and the output is a log of the exchange of opinions.

(Application Example 2)

Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart device 14 will be referred to as the "terminal."

Conventional educational support systems have difficulty providing individual learning methods based on the cognitive characteristics of each student, and in particular, do not provide a learning experience that takes into account their emotional state, resulting in insufficient learning outcomes. Furthermore, they are unable to effectively resolve questions in real time during learning or exchange opinions with other students, resulting in a lack of progress management and feedback provision. It is necessary to solve these issues and provide the optimal learning environment for each student.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes means for inputting basic information about the subject, means for determining the cognitive characteristics of the subject, means for proposing an optimal learning method based on the determination results, means for providing learning content, means for tracking learning progress and administering comprehension tests, means for providing individual feedback to the subject, means for resolving questions during learning in real time, means for recognizing the subject's emotional state and providing feedback accordingly, and means for delivering educational content via a head-mounted display. This makes it possible to provide an optimal learning experience that takes into account the subject's cognitive characteristics and emotional state. Furthermore, by encouraging real-time question resolution and opinion exchange, learning efficiency can be improved.

"Means for entering basic information about the subject" refers to a function that allows basic learning-related information, such as the subject's name, age, grade, and areas of interest, to be entered through a user interface and saved in a database.

"Means for assessing the subject's cognitive characteristics" refers to a function that provides questions and tasks to assess the subject's cognitive features and characteristics, and collects and analyzes the responses.

"Means for proposing optimal learning methods based on assessment results" refers to a function that recommends the most suitable learning style and learning materials for each individual based on the assessment results of their cognitive characteristics.

"Means for providing learning content" refers to the function of selecting and delivering learning materials to be provided to the target audience, such as videos, texts, and audio materials.

"Means for tracking learning progress and conducting comprehension tests" refers to a function that monitors the learning progress of participants and periodically conducts tests to assess their comprehension.

"Means for providing individual feedback to the subject" refers to a function for providing appropriate advice and guidance to the subject individually based on their learning outcomes and progress.

"Means to resolve questions during learning in real time" refers to a function that provides immediate answers to questions or queries that students encounter while learning.

"Means for recognizing the subject's emotional state and providing feedback accordingly" refers to a function that monitors the subject's facial expressions, tone of voice, etc. in real time, analyzes their emotional state, and provides appropriate feedback.

"Means for delivering educational content through a head-mounted display" refers to a function that uses a head-mounted display (HMD) to effectively deliver visual and auditory educational content to the target audience.

This invention is an AI system designed to support education, determining the cognitive characteristics and emotional state of the subject and providing optimal learning methods and content based on that. This system functions among four parties: a server, a terminal, a user, and an emotion engine.

First, the user enters basic information. This is done through a user interface provided by the server, and includes information such as name, age, grade, and areas of interest. The entered information is saved using a database management system such as SQLite. This information is used for subsequent cognitive characteristic assessment and learning content customization.

Next, a cognitive trait assessment test is conducted to determine the subject's cognitive traits. The CognitiveTest module is used, and a series of questions and tasks are displayed in the user interface. The user answers these questions, and the responses are collected and analyzed by the server. This identifies visual, linguistic, and auditory cognitive traits.

Based on the assessment results, the server suggests the most appropriate learning method. For example, video materials may be suggested for visually dominant students, and text materials for verbally dominant students. These learning methods and content are provided via devices such as head-mounted displays (HMDs). Using an HMD makes it possible to make the learning experience visual and interactive.

As learning progresses, the server tracks learning progress and periodically administers comprehension tests. The UserTracking module monitors the student's learning progress, and comprehension tests are administered based on that data. Test results are stored in a database and used to adjust subsequent learning plans.

Furthermore, an emotion engine is used to monitor the user's emotional state in real time. The camera and microphone installed in the HMD are used to analyze facial expressions and tone of voice to recognize the user's emotional state. For example, if the server determines that the subject is feeling tired, it will provide feedback such as "Take a short break."

Real-time question resolution is also an important feature. The server accepts questions via the device and provides instant answers generated by AI. This is effective in immediately resolving any difficulties users may have while studying and maintaining continuity in their learning.

Finally, the server periodically provides discussion topics to encourage the exchange of ideas between users. This functionality is realized through the DiscussionModule, allowing users to deepen their understanding by exchanging ideas with other users about what they have learned.

Below is an example of a prompt:

"D is a first-year high school student who is interested in math. Please suggest the best study method for D based on his cognitive characteristics and emotional state. What should we do if D shows no interest while studying?"

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

The server provides a user interface to the terminal for entering basic user information. The user enters basic information such as name, age, grade, and areas of interest. This entered data is sent to the server and stored in a database. The input here is the user's basic information, and the output is the user information stored in the database.

Step 2:

The server displays the cognitive characteristics assessment test interface on the terminal. The user clicks the test start button on the terminal to begin the cognitive characteristics assessment test. The terminal displays the questions received from the server to the user and accepts the user's answers. The user inputs answer data, which the server collects and analyzes in real time. The output is the user's cognitive characteristics assessment results.

Step 3:

The server suggests the optimal learning method based on the cognitive characteristic assessment results. Based on the assessment results, the server selects the most appropriate learning method from various options (video, text, audio materials, etc.). This selected learning method is displayed from the server on the user's device. The input is the cognitive characteristic assessment results, and the output is a suggestion of the optimal learning method.

Step 4:

The server provides the user with the most suitable learning content. The user receives the video and text materials provided by the server via their terminal. The input here is information on the most suitable learning method, and the output is the learning content itself. The learning content is delivered via a head-mounted display (HMD), which the user uses to progress through their studies.

Step 5:

The server tracks learning progress and conducts comprehension tests. Each time a user uses learning content, the server monitors their progress in real time. It also periodically presents tests to assess comprehension, which the user answers. Based on the response data, the server evaluates the level of comprehension and stores this in a database. The input is learning progress data and test answers, and the output is the comprehension assessment results.

Step 6:

The server provides the ability to resolve questions during learning in real time. When a user enters a question into their device, the question is sent to the server. The server analyzes the question and instantly generates an answer, which is sent to the device. The input is the user's question data, and the output is the answer to that question.

Step 7:

The server uses an emotion engine to monitor the user's emotional state in real time. The HMD's built-in camera and microphone collect the user's facial expressions and tone of voice, which are then analyzed by the emotion engine. If the server determines that the user is tired based on this, it provides feedback such as "Take a short break." The input is the user's facial expressions and voice data, and the output is the emotional state analysis results and feedback.

Step 8:

The server periodically provides users with discussion topics and encourages exchange of opinions between users. The server selects the topics and displays them on the user's device. Users use their device to exchange opinions with other users, and the content is sent to the server. The input is the discussion topic and user opinion data, and the output is the result of the exchange of opinions.

The specific processing unit 290 transmits the results of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the results of the specific processing. The microphone 38B acquires audio indicating the user input regarding the results of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

Data generation model 58 is what is known as generative AI (artificial intelligence). Examples of data generation model 58 include generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>) and Gemini (Internet search <URL: https://gemini.google.com/?hl=ja>). Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input. Data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

In the above embodiment, an example was given in which the specific processing was performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the smart device 14.

[Second embodiment]

Figure 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

As shown in FIG. 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and/or a LAN (Local Area Network).

The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I/F 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user 20's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I/Fs 44 and 26 is carried out in a secure manner.

Figure 4 shows an example of the main functions of the data processing device 12 and smart glasses 214. As shown in Figure 4, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32.

The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

Storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

Next, we will explain the identification process performed by the identification processing unit 290 of the data processing device 12. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

This invention is an AI system designed to support education. It assesses the cognitive characteristics of each individual and proposes individually appropriate learning methods to maximize learning effectiveness and address educational disparities, school absenteeism, and support for people with disabilities. This system functions between a server, a terminal, and a user.

Overall system overview

This system provides educational support tailored to each individual by performing the following processes:

1. Obtain basic information about the subject.

2. Determine the subject's cognitive characteristics.

3. Suggest the optimal learning method based on the assessment results.

4. Provide learning content.

5. Track learning progress and administer comprehension tests.

6. Provide personalized feedback.

7. Resolve questions while studying in real time.

8. Suggest discussion topics and encourage exchange of opinions.

Program processing

Below is a natural language explanation of the program processing at each step of this system.

1. User Registration

The server provides a user registration page.

Users use the device to enter basic information such as name, age, grade, and areas of interest.

The server stores the user's basic information in a database.

2. Cognitive trait assessment test

The server displays the cognitive characteristics assessment test interface on the terminal.

The user clicks the Start Test button to begin the test.

The server sends a series of questions and tasks to the device in sequence.

The device displays each question or task to the user and accepts their response.

Users answer each question on their device.

The server receives responses from users and stores them in a database in real time.

The server analyzes the collected data and determines the user's visual, language, and auditory cognitive characteristics.

3. Learning Method Suggestions

Based on the results of the assessment, the server selects the optimal learning method for the user.

The server will present the selected learning method and related learning materials to the device.

The device will display learning method suggestions and introductions to learning materials to the user.

4. Providing learning content

The server provides learning content appropriate to the user (video, text, audio materials, etc.).

Users can check the learning content on their device and begin learning.

The device records the user's learning progress in real time.

5. Real-time support

The server provides an interface via the terminal that accepts questions the user may have while studying.

The user enters a question on the device and submits it.

The server analyzes the question and provides an appropriate answer instantly.

The device displays the response from the server to the user.

6. Progress Tracking and Comprehension Testing

The server periodically displays an interface on the terminal that conducts comprehension tests.

Users take the test on their device.

The device sends the user's response to the server.

The server scores the answers, stores the results in a database, and provides feedback to the user.

7. Individual feedback and suggestions for next steps

The server analyzes the results of the user's comprehension test and suggests the next learning content and assignments.

The device will display feedback and new challenges to the user.

Users will work on the proposed tasks on their devices.

8. Promoting discussion and communication

The server will provide regular discussion topics.

The device displays discussion topics to the user and provides a form where they can enter their responses and opinions.

Users can enter their opinions on their devices and exchange them with other users.

Specific examples

For example, let's say Mr. C is a second-year junior high school student who wants to study history.

1. User Registration

Mr. C types "Second year junior high school student, interested in history" into his device.

The server stores this information.

2. Cognitive trait assessment test

Mr. C clicks the start test button on his device.

The server will begin the validation test.

Questions such as "Is it easy to remember with an image?" will be displayed on the device.

Mr. C answers the questions.

The server analyzes Mr. C's answers and determines that he is visually dominant.

3. Learning Method Suggestions

The server suggests, "Learn about historical events through videos."

A link to the video material will appear on your device.

4. Providing learning content

Mr. C starts watching the video on his device.

5. Real-time support

Mr. C types the question into his terminal: "I don't understand the background of this war."

The server provides the answer, which is displayed on the device.

6. Progress Tracking and Comprehension Testing

After watching the video, the server will conduct a comprehension test.

Mr. C takes the test on a terminal.

7. Individual feedback and suggestions for next steps

The server analyzes the test results and suggests, "Next, let's learn more about the causes of this war."

The next learning content will be displayed to Mr. C on his device.

8. Promoting discussion and communication

The server suggests that next week's theme is 'Important Figures in Modern History' and provides a platform for users to exchange opinions.

Mr. C can enter his opinions on the device and discuss them with his classmates.

In this way, the AI system can provide an optimal educational experience tailored to individual learning needs, creating an environment in which Mr. C can learn independently.

The processing flow is explained below.

Step 1:

The server provides a user registration page. Users use their terminal to enter basic information such as name, age, grade, and areas of interest. The server stores the entered basic information in a database.

Step 2:

The server displays the cognitive characteristics assessment test interface on the terminal. The user clicks the Start Test button to begin the test.

Step 3:

The server sequentially sends a series of questions and tasks to the terminal. The terminal displays the questions and tasks to the user and accepts answers. The user answers each question on the terminal.

Step 4:

The server receives the user's responses and stores them in a database in real time. The server analyzes the collected data and determines the user's visual, language, and auditory cognitive characteristics.

Step 5:

Based on the results of the assessment, the server selects the most suitable learning method for the user. The server then presents the selected learning method and related learning materials to the device. The device then displays learning method suggestions and learning material introductions to the user.

Step 6:

The server provides learning content (video, text, audio materials, etc.) appropriate for the user. The user checks the learning content on their device and begins learning. The device records the user's learning progress in real time.

Step 7:

The server provides an interface that accepts questions that users have while studying via their terminal. The user enters and submits a question on the terminal. The server analyzes the question and instantly generates and provides an appropriate answer. The terminal displays the answer from the server to the user.

Step 8:

The server periodically displays an interface on the device that conducts comprehension tests. The user takes the test on the device. The device sends the user's answers to the server. The server scores the answers, stores the results in a database, and provides feedback to the user.

Step 9:

The server analyzes the results of the user's comprehension test and suggests the next learning content and assignments. The device displays feedback and new assignments to the user. The user then works on the suggested assignments.

Step 10:

The server periodically provides discussion topics. The terminal displays the discussion topics to the user and provides a form where they can enter their responses and opinions. Users enter their opinions on the terminal and exchange opinions with other users.

(Example 1)

Next, we will explain Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Modern education requires providing optimal learning methods based on individual cognitive characteristics. However, many current educational systems only offer uniform learning methods, making it difficult to address individual cognitive characteristics. Furthermore, they lack features such as learning progress tracking, comprehension tests, and real-time question resolution, making it impossible to adequately address individual learning needs. Furthermore, there are few systems that automatically suggest learning content or provide feedback, preventing the effectiveness of education from being maximized.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for inputting basic information about the subject, means for determining the cognitive characteristics of the subject, means for proposing the optimal learning method based on the determination results, means for providing learning content, means for tracking learning progress and conducting comprehension tests, means for providing individual feedback to the subject, means for resolving questions during learning in real time, means for saving the subject's input and learning progress in a database, means for collecting the subject's questions and generating answers using a generative AI model, and means for evaluating the subject's answers and dynamically suggesting the next learning content. This makes it possible to provide the optimal learning method according to individual cognitive characteristics, track learning progress, resolve questions in real time, and dynamically suggest learning content.

"Target" refers to individuals who use educational support AI systems to advance their studies.

"Basic information" refers to basic information necessary for education, such as the subject's name, age, grade, and areas of interest.

"Cognitive characteristics" refer to the information processing characteristics of a subject, such as vision, language, and hearing, and indicate individual characteristics in learning.

"Learning methods" are the optimal learning tools and approaches suggested based on cognitive characteristics, including teaching materials and learning formats.

"Learning content" refers to learning videos, texts, audio materials, etc. provided to the target audience, and is an information resource to support learning.

"Learning progress" refers to the progress a subject makes as they progress through their studies.

A "comprehension test" is a test used to measure the subject's level of understanding of the learning content.

"Individualized feedback" refers to specific assessments and guidance provided to students based on their comprehension test results and learning progress.

"Real-time question resolution" means providing immediate and appropriate answers to questions that students have while studying.

A "database" is a system that stores and manages data such as basic information about the subject, their learning progress, and their responses.

A "generative AI model" is an artificial intelligence model used to generate appropriate answers to user questions.

"Dynamic suggestions" means generating and suggesting the next content or assignment to be learned on an ongoing basis based on the student's learning progress and level of understanding.

This invention relates to an educational support AI system that maximizes learning effectiveness by providing optimal learning methods based on individual cognitive characteristics, and addresses educational disparities, school absenteeism, and support for people with disabilities. The system functions between a server, a terminal, and a user.

Overall system overview

This system provides tailored educational support to each individual by performing the following processes:

1. Obtain basic information about the subject.

2. Determine the subject's cognitive characteristics.

3. Suggest the optimal learning method based on the assessment results.

4. Provide learning content.

5. Track learning progress and administer comprehension tests.

6. Provide personalized feedback.

7. Resolve questions while studying in real time.

8. Suggest discussion topics and encourage exchange of opinions.

Hardware and software used

The hardware used includes a server and user devices. The server uses a database (e.g., MySQL, PostgreSQL) to manage and analyze user information, an execution environment for machine learning models (e.g., Python, Scikit-learn), a real-time chat system (e.g., Node.js), and a generative AI model (e.g., OpenAI GPT-3).

The user device provides an interface that runs on a web browser and handles user operations and learning progress using HTML, CSS, JavaScript, React, etc.

Program processing overview

User Registration

The server generates a user registration page using HTML and CSS and sends it to the device. The user enters basic information such as name, age, grade, and areas of interest on the device and submits it. The server receives the information in JSON format and stores it in a database.

Cognitive trait assessment test

The server uses JavaScript and React to generate the cognitive trait assessment test interface and display it on the device. The user starts the test and answers a series of questions. The server receives the answers, stores them in a database, and uses a Python machine learning model to analyze the data and determine cognitive traits.

Suggested learning methods

The server determines the optimal learning method based on cognitive characteristics and selects relevant learning materials. This is then sent to the device, which then presents it to the user.

Providing learning content

The server provides selected learning content, which users use on their devices to progress through their studies. The devices record learning progress in real time and send it to the server.

Real-time support

The server accepts the user's question through the device and generates an appropriate answer using a generative AI model. This is then sent to the device and displayed.

Progress tracking and comprehension testing

The server periodically conducts comprehension tests and stores the results in a database. It provides feedback to the user and suggests what to study next.

Individual feedback and suggestions for next steps

The server analyzes the next learning content based on the test results and provides feedback and new challenges to the user, thereby presenting the optimal learning path for each individual user.

Fostering discussion and communication

The server periodically generates new discussion topics and presents them to users. Users can then enter their opinions on their devices and exchange them with other users.

Specific examples

For example, let's say Mr. C is a second-year junior high school student who wants to study history.

1. Mr. C types "Second year junior high school student, interested in history" into his device and submits the information. The server stores this information.

2. Mr. C starts the cognitive characteristics assessment test on his device. The server displays the questions, and Mr. C enters the answers. The server analyzes the answers and determines that he is visually dominant.

3. The server suggests, "Let's learn about historical events through videos," and displays a link to the video material on the device.

4. Mr. C begins watching the video on his device. The device records his learning progress and sends it to the server.

5. When Mr. C enters a question such as "I don't understand the background of this war," the server uses a generative AI model to generate and display an answer.

6. After watching the video, the server conducts a comprehension test, and Mr. C takes the test. The results are sent to the server, which then suggests the next learning content.

7. The server will suggest a new task, saying, "Next, let's learn more about the causes of this war," and display this on the terminal.

8. The server suggests that next week's topic is "Important Figures in Modern History" and provides a discussion platform. C can enter his opinions and discuss them with his classmates.

Example prompt

"If you have a second-year junior high school student who wants to study history, how would you use AI to suggest the best learning method? Please explain the specific process."

In this way, AI systems can provide optimal educational experiences tailored to individual learning needs and create an environment in which students can learn independently.

The flow of the identification process in Example 1 will be explained using Figure 11.

Step 1:

User Registration

The server generates a user registration page using HTML and CSS and sends it to the device. The user enters basic information such as name, age, grade, and areas of interest on the device and submits it. The server receives the received information in JSON format and stores it in a database. This registers the user's basic information in the database. The input is the user's basic information, and the output is the user's basic information stored in the database.

Step 2:

Cognitive trait assessment test

The server uses JavaScript and React to generate an interface for the cognitive trait assessment test and displays it on the device. The user clicks a button to start the test and answers a series of questions. The device displays each question and accepts the user's answers. The server stores the received answers in a database and analyzes the data using a Python machine learning model to determine the user's cognitive traits. The input is the user's answers, and the output is the determined cognitive traits. This reveals the user's cognitive traits.

Step 3:

Study method suggestions

The server selects the optimal learning method based on cognitive characteristics and selects related learning materials. The server sends the selected learning method and learning materials to the terminal via a REST API. The terminal displays this to the user. The input is cognitive characteristics and a learning materials database, and the output is a link to the learning method and learning materials presented to the user. This allows the user to be presented with the optimal learning method.

Step 4:

Providing learning content

The server dynamically generates learning content appropriate for the user and sends it to the device. The user checks the learning content on the device and begins learning. The device records learning progress and periodically sends it to the server. The input is the user's cognitive characteristics and progress, and the output is the learning content provided and a record of learning progress. This allows users to study with content that suits them.

Step 5:

Real-time support

The server provides an interface that accepts user questions via the terminal. The user enters the question on the terminal and sends it to the server. The server analyzes the question and generates the optimal answer using a generative AI model. The server sends the answer to the terminal, which displays it to the user. The input is the user's question, and the output is the generated answer. This allows the user's question to be resolved instantly.

Step 6:

Progress tracking and comprehension testing

The server periodically displays an interface on the device that conducts comprehension tests. The user takes the test on the device and sends their answers to the server. The server scores the answers and stores the results in a database. It then generates feedback based on the results and sends it to the device. The input is the comprehension test answers, and the output is the test results and feedback. This evaluates the user's level of understanding and determines the next learning content.

Step 7:

Individual feedback and suggestions for next steps

The server analyzes the test results and determines the next learning content. The server sends feedback and new assignments to the device, which displays them to the user. The user then works on the suggested assignments. The input is the test results and learning goals, and the output is feedback and new suggested assignments. This allows the user to continuously work on appropriate assignments.

Step 8:

Fostering discussion and communication

The server periodically generates discussion topics and sends them to the device. Users input their opinions on their device and send them to the server. The server collects the opinions and displays them to other users, allowing for an exchange of opinions. The input is the user's opinion, and the output is the shared opinion and discussion results. This promotes communication between users and leads to deeper understanding.

(Application Example 1)

Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

With conventional educational systems, it was difficult to efficiently grasp the cognitive characteristics of individual students and propose optimal learning methods based on those characteristics. Furthermore, there was a lack of educational support for industrial product operation methods and control technologies in industrial settings, and automatic and effective measures were needed to improve engineers' skills.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

In this invention, the server includes means for inputting basic information about the subject, means for assessing the cognitive characteristics of the subject, means for proposing the optimal learning method based on the assessment results, means for providing learning content, means for tracking learning progress and conducting comprehension tests, means for providing individual feedback and proposing the next assignment to the subject, means for resolving questions during learning in real time, means for promoting the exchange of opinions between subjects, and means for supporting learning of industrial product operation methods and control technology. This makes it possible to propose the optimal learning method for each subject's cognitive characteristics and to automatically and effectively educate them on industrial product operation methods and control technology.

1. "Basic information of the subject" refers to personal information such as the subject's name, age, years of experience, and area of expertise.

2. "Cognitive characteristics" refers to the individual characteristics of how a subject receives, processes, and understands information. Examples include visual dominance, auditory dominance, and linguistic dominance.

3. "Optimal learning method" refers to the most effective learning method based on the cognitive characteristics of the individual.

4. "Learning Content" refers to the learning materials and information provided to the target audience for learning, including video, text, and audio materials.

5. "Tracking learning progress" refers to recording and managing a student's learning progress in real time.

6. "Comprehension test" refers to a test used to assess the extent to which a subject has understood the learning content.

7. "Individualized feedback" refers to specific advice and next steps provided based on the learner's learning progress and level of understanding.

8. "Resolving questions during learning in real time" refers to providing immediate answers to questions that arise when a student is learning.

9. "Promoting the exchange of opinions" refers to support provided to stimulate information sharing and discussion among participants.

10. "Supporting learning about industrial product operation methods and control technologies" refers to educational support to promote understanding of the correct operation methods and control technologies of industrial products and equipment used in industry.

Overall system configuration

This invention describes a specific form for implementing an educational support system consisting of a server, a terminal, and a user. This system proposes optimal learning methods based on the cognitive characteristics of each individual, and supports education on industrial product operation methods and control technologies.

Hardware and software used

This invention uses the following hardware and software:

Server: High-performance server (e.g., AWS, Google Cloud)

Devices: Smartphones, factory robot HMIs (Human-Machine Interfaces), PCs

Front-end: Angular, HTML5, CSS3, JavaScript

Backend: Node.js, MySQL

AI modeling: Python, TensorFlow, Socket.IO

Program processing

1. User Registration

The server provides a user registration page using Angular on the front end.

Users use the terminal to enter basic information such as their name, age, years of experience, and area of expertise.

The server receives this information via Node.js and stores it in a MySQL database.

2. Assessment of cognitive characteristics

The server uses an Angular front-end to provide an interface for questions and tasks that assess cognitive characteristics.

The user starts the test on their device and answers the questions.

The server receives the responses and analyzes the data using Python and TensorFlow to determine cognitive characteristics.

3. Learning Method Suggestions

Based on the analysis results, the server selects the optimal learning method and suggests corresponding learning materials.

The device will display the suggestions and teaching materials to the user.

4. Providing learning content

The server provides learning content (video, audio, text) that is best suited to the user's cognitive characteristics.

Users can view this content on their devices and progress as they study. Progress is recorded in real time.

5. Real-time support

If users have any questions while studying, they can enter them using their smartphone or tablet.

The server uses Socket.IO to receive questions in real time, processes them in Python, and provides instant answers.

6. Progress Tracking and Comprehension Testing

The server periodically conducts comprehension tests, analyzes the results, stores them in a database, and provides feedback to the user.

7. Individual feedback and suggestions for next steps

The server suggests the next learning task based on the test results and provides related learning content.

8. Promoting discussion and communication

The server will regularly provide discussion topics and an interface to encourage the exchange of opinions between users.

Users can enter their opinions on their devices and discuss with other users.

Examples and prompts

As a concrete example, consider the case where engineer A is learning how to operate a new industrial robot.

If Engineer A enters "Years of experience: 5 years, Specialty: Industrial robot control" and has visually dominant cognitive characteristics, the system will suggest, "Let's learn the basic operation of industrial robots through video materials." Engineer A will begin learning by watching the video materials, and if he has any questions, he can ask, "Please tell me in detail about how the sensors on this robot work." The server will immediately provide an appropriate answer to this question.

Examples of prompt statements include:

"For an engineer with five years of robot operation experience, please suggest a method of operating an industrial robot that is easy for someone with visually dominant cognitive characteristics to learn. Also, please suggest related video teaching materials."

Some examples include:

In this way, it is possible to propose learning methods that are optimal for each individual's cognitive characteristics, and to automatically and effectively educate them on how to operate industrial products and control technologies.

The flow of the identification process in Application Example 1 will be explained using Figure 12.

Step 1:

User Registration

The server provides a user registration page using Angular on the front end.

Users use the terminal to enter basic information such as their name, age, years of experience, and area of expertise.

The basic information entered is sent from the terminal to the server, which receives this information via Node.js and stores it in a MySQL database.

Enter basic information such as name, age, years of experience, and area of expertise.

Output: Basic user information stored in the database

Step 2:

Assessing cognitive characteristics

The server uses an Angular front-end to provide an interface for questions and tasks that assess cognitive characteristics.

The user starts the test on their device and answers the questions.

The server receives the response data and analyzes it using Python and TensorFlow to determine the user's cognitive characteristics. Categories of cognitive characteristics such as visual, auditory, and language are determined.

Input: User response data

Output: Cognitive characteristics (visual, auditory, language, etc.)

Step 3:

Study method suggestions

Based on the results of the cognitive characteristic analysis, the server selects the optimal learning method and suggests corresponding learning materials.

The device will display the suggestions and teaching materials to the user.

For example, video learning materials are suggested to visually-oriented users.

Input: Cognitive trait analysis results

Output: Suggested learning methods and materials

Step 4:

Providing learning content

The server provides learning content (video, audio, text) that is best suited to the user's cognitive characteristics.

Users can view this content on their devices and progress through their studies. Progress is recorded in real time from the device to the server.

Input: Suggested learning methods and materials

Output: Providing learning content and recording progress

Step 5:

Real-time support

If users have any questions while studying, they can enter them using their smartphone or tablet.

The server uses Socket.IO to receive questions in real time, processes them in Python, and provides instant answers.

Input: User question

Output: Answers provided in real time

Step 6:

Progress tracking and comprehension testing

The server periodically conducts comprehension tests, analyzes the results, stores them in a database, and provides feedback to the user.

The terminal displays the test results to the user.

Input: Comprehension test response data

Output: Analysis of test results and feedback

Step 7:

Individual feedback and suggestions for next steps

Based on the results of the comprehension test, the server suggests the next learning task and provides related learning content.

The device will display the next task and related content to the user.

Input: Comprehension test results

Output: Providing the next assignment and related learning content

Step 8:

Fostering discussion and communication

The server will regularly provide discussion topics and an interface to encourage the exchange of opinions between users.

Users can enter their opinions on their devices and discuss with other users.

Input: Discussion topic and user opinions

Output: Facilitated exchange of ideas and shared opinions

In this way, by clarifying the roles of the server, terminal, and user at each step and organizing the specific operations and inputs/outputs, the educational support system is designed to function effectively.

It is also possible to further combine an emotion engine that estimates the user's emotion. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

This invention is an AI system designed to support education. It maximizes learning effectiveness by assessing the cognitive characteristics of each individual and proposing individually appropriate learning methods. Furthermore, by combining it with an emotion engine, it provides an optimal learning experience by taking into account the emotional state of the individual. This system functions among four parties: a server, a terminal, a user, and an emotion engine.

Overview of the system

This system provides educational support tailored to the target individual by performing the following processes:

1. Obtain basic information about the subject.

2. Determine the subject's cognitive characteristics.

3. Suggest the optimal learning method based on the assessment results.

4. Provide learning content.

5. Track learning progress and administer comprehension tests.

6. Provide personalized feedback.

7. Resolve questions while studying in real time.

8. Use an emotion engine to recognize and respond to the subject's emotional state.

9. Suggest discussion topics and encourage exchange of ideas.

Program processing

Below is a natural language explanation of the program processing at each step of this system.

1. User Registration

The server provides a user registration page.

Users use the device to enter basic information such as name, age, grade, and areas of interest.

The server stores the basic information entered in a database.

2. Cognitive trait assessment test

The server displays the cognitive characteristics assessment test interface on the terminal.

The user clicks the Start Test button to begin the test.

3. Conduct cognitive assessment tests

The server sends a series of questions and tasks to the device in sequence.

The device displays questions and tasks to the user and accepts answers.

Users answer each question on their device.

The server receives responses from users and stores them in a database in real time.

The server analyzes the collected data and determines the user's visual, language, and auditory cognitive characteristics.

4. Learning Method Suggestions

Based on the results of the assessment, the server selects the optimal learning method for the user.

The server will present the selected learning method and related learning materials to the device.

The device will display learning method suggestions and introductions to learning materials to the user.

5. Providing learning content

The server provides learning content appropriate to the user (video, text, audio materials, etc.).

Users can check the learning content on their device and begin learning.

The device records the user's learning progress in real time.

6. Real-time support

The server provides an interface via the terminal that accepts questions the user may have while studying.

The user enters a question on the device and submits it.

The server analyzes the question and instantly generates and provides an appropriate answer.

The device displays the response from the server to the user.

7. Progress Tracking and Comprehension Testing

The server periodically displays an interface on the terminal that conducts comprehension tests.

Users take the test on their device.

The device sends the user's response to the server.

The server scores the answers, stores the results in a database, and provides feedback to the user.

8. Individual feedback and suggestions for next steps

The server analyzes the results of the user's comprehension test and suggests the next learning content and assignments.

The device will display feedback and new challenges to the user.

Users will work on the proposed tasks.

9. Emotion Recognition and Response

The server uses an emotion engine to monitor the user's emotional state in real time through the device while they are learning.

The emotion engine identifies emotions from the user's facial expressions and tone of voice, and stores the emotional state in a database.

The server provides optimal feedback and adjusts learning content based on the user's emotional state.

10. Promoting discussion and communication

The server will provide regular discussion topics.

The device displays discussion topics to the user and provides a form where they can enter their responses and opinions.

Users can enter their opinions on their devices and exchange them with other users.

Specific examples

For example, let's say Mr. D is a first-year high school student who wants to study mathematics.

1. User Registration

Mr. D types "First year high school student, interested in mathematics" into his device.

The server stores this information.

2. Cognitive trait assessment test

Mr. D clicks the start test button on his device.

The server will begin the validation test.

Questions such as "Is it easy to remember with an image?" will be displayed on the device.

Mr. D answers the questions.

The server analyzes Mr. D's answers and determines that he is visually dominant.

3. Learning Method Suggestions

The server suggests, "Learn math concepts through videos."

A link to the video material will appear on your device.

4. Providing learning content

Mr. D starts watching the video on his device.

5. Real-time support

Mr. D types a question into his terminal: "I don't know how to use this formula."

The server provides the answer, which is displayed on the device.

6. Progress Tracking and Comprehension Testing

After watching the video, the server will conduct a comprehension test.

Mr. D takes the test on the terminal.

7. Individual feedback and suggestions for next steps

The server analyzes the test results and suggests, "Next, try solving some real problems."

The next learning content will be displayed to Mr. D on his device.

8. Emotion Recognition and Response

The server uses an emotion engine to analyze Mr. D's facial expressions and tone of voice while he is studying.

If it is determined that Mr. D is feeling tired, the server will display a message on his device saying, "Let's take a short break."

Also, if Mr. D is in a state of joy or adaptation, he will be given feedback such as "Good luck," encouraging him to continue learning.

9. Promoting discussion and communication

The server suggests that next week's theme is 'Applications of Functions' and provides a platform where users can exchange opinions.

Mr. D can enter his opinions on the device and discuss them with his classmates.

In this way, the AI system can provide an optimal educational experience tailored to individual learning needs, creating an environment in which D can learn independently. By combining it with an emotion engine, it is possible to respond according to the user's emotional state, further improving learning effectiveness.

The processing flow is explained below.

Step 1:

The server provides a user registration page. Users use their terminal to enter basic information such as name, age, grade, and areas of interest. The server stores the entered basic information in a database.

Step 2:

The server displays the cognitive characteristics assessment test interface on the terminal. The user clicks the Start Test button to begin the test.

Step 3:

The server sequentially sends a series of questions or tasks to the terminal. The terminal displays the questions or tasks to the user and accepts answers. The user answers each question on the terminal.

Step 4:

The server receives the user's responses and stores them in a database in real time. The server analyzes the collected data and determines the user's visual, language, and auditory cognitive characteristics.

Step 5:

Based on the results of the assessment, the server selects the most suitable learning method for the user. The server then presents the selected learning method and related learning materials to the device. The device then displays learning method suggestions and learning material introductions to the user.

Step 6:

The server provides learning content (video, text, audio materials, etc.) appropriate for the user. The user checks the learning content on their device and begins learning. The device records the user's learning progress in real time.

Step 7:

The server provides an interface that accepts questions that users have while studying via their terminal. The user enters and submits a question on the terminal. The server analyzes the question and instantly generates and provides an appropriate answer. The terminal displays the answer from the server to the user.

Step 8:

The server periodically displays an interface on the device that conducts comprehension tests. The user takes the test on the device. The device sends the user's answers to the server. The server grades the answers, stores the results in a database, and provides feedback to the user.

Step 9:

The server analyzes the results of the user's comprehension test and suggests the next learning content and assignments. The device displays feedback and new assignments to the user. The user then works on the suggested assignments.

Step 10:

The server uses an emotion engine to monitor the user's emotional state in real time via the device while they are learning. The emotion engine identifies emotions from the user's facial expressions and tone of voice, and stores the emotional state in a database. The server then provides optimal feedback and adjusts the learning content based on the user's emotional state.

Step 11:

The server periodically provides discussion topics. The terminal displays the discussion topics to the user and provides a form where they can enter their responses and opinions. Users enter their opinions on the terminal and exchange opinions with other users.

(Example 2)

Next, we will explain Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Traditional educational systems have struggled to provide learning methods that fully consider the cognitive characteristics and emotional state of each individual student. They also lacked functionality for resolving questions that arise during learning in real time and for facilitating the exchange of opinions between students. This has made it difficult to provide an optimal learning experience for each student, resulting in the issue of not maximizing learning effectiveness.

The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting basic information about the subject, means for determining the cognitive characteristics of the subject, means for proposing an optimal learning method based on the determination results, means for providing learning content, means for tracking learning progress and conducting comprehension tests, means for providing individual feedback to the subject, means for resolving questions during learning in real time, and means for recognizing and responding to the subject's emotional state using an emotion engine. This makes it possible to provide an optimal learning experience tailored to the subject's individual needs, maximizing learning effectiveness. Furthermore, by encouraging the exchange of opinions between subjects as needed, improvement in the quality of learning can also be expected.

"Target audience" refers to learners who use the educational system.

"Basic information" refers to basic data such as the subject's name, age, grade, and areas of interest.

"Cognitive characteristics" refers to the subject's cognitive style, such as visual, linguistic, and auditory.

"Decision testing" refers to a series of questions or tasks designed to assess a subject's cognitive characteristics.

"Feedback" refers to individual evaluations and advice provided based on the subject's learning results.

"Learning method" refers to the learning process and techniques proposed based on the cognitive characteristics of the target individual.

"Learning content" includes learning materials and resources, such as videos, texts, and audio materials.

An "emotion engine" refers to a system that has the ability to recognize the emotional state of a subject and process that data.

"Real-time support" refers to the ability to instantly respond to questions or concerns that students may have while studying.

"Discussion Topics" refer to topics that the server periodically provides for the exchange of opinions among participants.

"Opinion exchange" refers to the activity of sharing opinions and thoughts among participants.

"Server" refers to the central computer that runs the entire system and provides each function.

"Terminal" refers to the computer or mobile device used by a Subject to access the System.

This invention is an AI system designed to support education, maximizing learning effectiveness by determining the cognitive characteristics of each individual and proposing individually appropriate learning methods. Furthermore, by combining it with an emotion engine, it provides an optimal learning experience by taking into account the emotional state of the individual. This system functions among four parties: a server, a terminal, a user, and an emotion engine.

Overall system overview

This system provides educational support tailored to each individual through the following steps:

1. Obtain basic information about the subject.

2. Determine the subject's cognitive characteristics.

3. Suggest the optimal learning method based on the assessment results.

4. Provide learning content.

5. Track learning progress and administer comprehension tests.

6. Provide personalized feedback.

7. Resolve questions while studying in real time.

8. Use an emotion engine to recognize and respond to the subject's emotional state.

9. Suggest discussion topics and encourage exchange of ideas.

Program processing

Processing in this system is carried out through cooperation between the server and the terminal. Details are provided below.

Hardware and Software Use

The server uses a MySQL database to store and manage data.

The server uses Python to implement data analysis and decision algorithms.

The server provides a front end consisting of JavaScript and HTML.

Devices access the server using a browser such as Google Chrome.

The emotion engine uses a camera and microphone to analyze the user's facial expressions and tone of voice.

Specific examples

For example, if Mr. D is a first-year high school student who wants to study mathematics, consider the following:

1. User Registration

D enters "First year high school student, interested in mathematics" into the terminal. The form displayed on the terminal has fields for entering name, age, grade, and areas of interest.

When Mr. D clicks the "Register" button, the server receives the input data and saves it in the MySQL database.

2. Cognitive trait assessment test

Mr. D clicks the start test button on his device. The server sends the assessment test UI, composed of HTML and JavaScript, to the device and asks a series of questions.

Questions such as "Is it easy to remember with an image?" are displayed on the device. Mr. D answers the questions, and the answers are saved in a database in real time.

3. Learning Method Suggestions

Based on Mr. D's cognitive characteristics, the server suggests, "Let's learn mathematical concepts through videos." A link to the video material is displayed on the device.

4. Providing learning content

Mr. D begins watching the video on his device. The server provides the video material via a video streaming service.

5. Real-time support

While learning, Mr. D enters a question into his device, such as "I don't know how to use this formula." The server receives the question, analyzes it using natural language processing (NLP), and instantly generates an appropriate answer, which is displayed on the device.

6. Progress Tracking and Comprehension Testing

After watching the video, the server periodically conducts comprehension tests. Mr. D takes the test on his device and his answers are sent to the server. The server uses an automatic scoring system to score the answers and stores the results in a database.

7. Individual feedback and suggestions for next steps

Based on the test results, the server suggests, "Next, try solving some real problems." The next learning content is displayed to Mr. D on his device.

8. Emotion Recognition and Response

The server uses an emotion recognition algorithm to monitor D's facial expressions and tone of voice in real time while he studies.

If D is identified as feeling tired, the server will display a message on his device saying, "Take a short break." If he is in a state of joy or adaptation, the server will provide feedback saying, "Keep it up," encouraging him to continue learning.

9. Promoting discussion and communication

The server suggests that next week's theme is 'Application of Functions' and provides a platform where users can exchange opinions. Mr. D can enter his opinion on his device and discuss it with his classmates.

Example prompt

"Please explain how to register by entering basic user information."

"Please explain with specific examples what types of questions will be asked during the cognitive assessment test."

"Please explain how you handle real-time support when users have questions while learning."

"Please explain the process of recognizing and responding to user emotions using the emotion engine."

This allows the AI system to provide an optimal educational experience tailored to the user's individual learning needs, improving learning outcomes. By combining it with an emotion engine, it becomes possible to respond according to the user's emotional state, further improving learning outcomes.

The flow of the identification process in Example 2 will be explained using Figure 13.

Step 1: User Registration

The server provides a user registration page. The user uses their terminal to enter basic information such as name, age, grade, and areas of interest. When the user clicks the "Register" button, the terminal sends the data to the server. The server receives the entered basic information and stores it in a database. The input here is the user's basic information, and the output is the data stored in the database.

Step 2: Cognitive trait assessment test

The server displays the cognitive ability assessment test interface on the terminal. The user clicks the test start button and answers the questions and tasks displayed on the terminal in sequence. The server receives each answer and stores them sequentially in a database. Based on the collected answer data, the server performs data analysis using Python to determine the user's visual, linguistic, and auditory cognitive abilities. The input is the user's test answers, and the output is the assessed cognitive abilities.

Step 3: Suggest a learning method

Based on the assessment results, the server selects the optimal learning method and related learning materials. It sends the selected information to the terminal and displays links to the learning method and learning materials to the user. The input is the assessed cognitive characteristics, and the output is the suggested learning method and links to the learning materials.

Step 4: Provide learning content

The server provides learning content such as videos, texts, and audio materials via a streaming service. Users check the learning content on their device and begin studying. The device records the user's learning progress (e.g., video viewing time and text completion status) in real time and sends it to the server. The input is the learning content, and the output is learning progress data.

Step 5: Real-time support

The server provides an interface through the terminal for users to input questions they have while studying. The user enters the question on the terminal and sends it. The server uses NLP (natural language processing) functions to analyze the question, generates an appropriate answer, and sends it to the terminal. The terminal displays the answer to the user. The input is the user's question, and the output is the generated answer.

Step 6: Progress Tracking and Comprehension Testing

The server displays an interface on the terminal that periodically conducts comprehension tests. The user takes the test on the terminal and submits their answers. The server scores the answers using an automatic scoring system and stores the results in a database. The server then provides feedback on the results to the user. The input is the test answers, and the output is the scoring results and feedback.

Step 7: Individual feedback and suggestions for next steps

The server selects the next learning content and assignments based on the results of the comprehension test. The selected information is sent to the device, and feedback and new assignments are displayed to the user. The user then works on the suggested assignments. The input is the test results, and the output is feedback and suggested assignments.

Step 8: Emotion Recognition and Responding

The server uses an emotion engine to monitor the user's facial expressions and tone of voice in real time via the device while they are studying. It identifies the user's emotional state and stores it in a database. The server then provides optimal feedback and adjusts the learning content based on the user's emotional state. For example, if the user is identified as tired, it will suggest a break, and if they are in an adaptive state, it will display an encouraging message. The input is the user's facial expression and tone of voice data, and the output is the emotion analysis results and feedback based on them.

Step 9: Facilitate discussion and communication

The server periodically provides discussion topics and displays them on the terminal. Users enter their opinions on the topics and exchange them with other users. The server stores the posted opinions in a database and displays them to other participants in real time. The input is the discussion topic and user opinions, and the output is a log of the exchange of opinions.

(Application Example 2)

Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

Conventional educational support systems have difficulty providing individual learning methods based on the cognitive characteristics of each student, and in particular, do not provide a learning experience that takes into account their emotional state, resulting in insufficient learning outcomes. Furthermore, they are unable to effectively resolve questions in real time during learning or exchange opinions with other students, resulting in a lack of progress management and feedback provision. It is necessary to solve these issues and provide the optimal learning environment for each student.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes means for inputting basic information about the subject, means for determining the cognitive characteristics of the subject, means for proposing an optimal learning method based on the determination results, means for providing learning content, means for tracking learning progress and administering comprehension tests, means for providing individual feedback to the subject, means for resolving questions during learning in real time, means for recognizing the subject's emotional state and providing feedback accordingly, and means for delivering educational content via a head-mounted display. This makes it possible to provide an optimal learning experience that takes into account the subject's cognitive characteristics and emotional state. Furthermore, by encouraging real-time question resolution and opinion exchange, learning efficiency can be improved.

"Means for entering basic information about the subject" refers to a function that allows basic learning-related information, such as the subject's name, age, grade, and areas of interest, to be entered through a user interface and saved in a database.

"Means for assessing the subject's cognitive characteristics" refers to a function that provides questions and tasks to assess the subject's cognitive features and characteristics, and collects and analyzes the responses.

"Means for proposing optimal learning methods based on assessment results" refers to a function that recommends the most suitable learning style and learning materials for each individual based on the assessment results of their cognitive characteristics.

"Means for providing learning content" refers to the function of selecting and delivering learning materials to be provided to the target audience, such as videos, texts, and audio materials.

"Means for tracking learning progress and conducting comprehension tests" refers to a function that monitors the learning progress of participants and periodically conducts tests to assess their comprehension.

"Means for providing individual feedback to the subject" refers to a function for providing appropriate advice and guidance to the subject individually based on their learning outcomes and progress.

"Means to resolve questions during learning in real time" refers to a function that provides immediate answers to questions or queries that students encounter while learning.

"Means for recognizing the subject's emotional state and providing feedback accordingly" refers to a function that monitors the subject's facial expressions, tone of voice, etc. in real time, analyzes their emotional state, and provides appropriate feedback.

"Means for delivering educational content through a head-mounted display" refers to a function that uses a head-mounted display (HMD) to effectively deliver visual and auditory educational content to the target audience.

This invention is an AI system designed to support education, determining the cognitive characteristics and emotional state of the subject and providing optimal learning methods and content based on that. This system functions among four parties: a server, a terminal, a user, and an emotion engine.

First, the user enters basic information. This is done through a user interface provided by the server, and includes information such as name, age, grade, and areas of interest. The entered information is saved using a database management system such as SQLite. This information is used for subsequent cognitive characteristic assessment and learning content customization.

Next, a cognitive trait assessment test is conducted to determine the subject's cognitive traits. The CognitiveTest module is used, and a series of questions and tasks are displayed in the user interface. The user answers these questions, and the responses are collected and analyzed by the server. This identifies visual, linguistic, and auditory cognitive traits.

Based on the assessment results, the server suggests the most appropriate learning method. For example, video materials may be suggested for visually dominant students, and text materials for verbally dominant students. These learning methods and content are provided via devices such as head-mounted displays (HMDs). Using an HMD makes it possible to make the learning experience visual and interactive.

As learning progresses, the server tracks learning progress and periodically administers comprehension tests. The UserTracking module monitors the student's learning progress, and comprehension tests are administered based on that data. Test results are stored in a database and used to adjust subsequent learning plans.

Furthermore, an emotion engine is used to monitor the user's emotional state in real time. The camera and microphone installed in the HMD are used to analyze facial expressions and tone of voice to recognize the user's emotional state. For example, if the server determines that the subject is feeling tired, it will provide feedback such as "Take a short break."

Real-time question resolution is also an important feature. The server accepts questions via the device and provides instant answers generated by AI. This is effective in immediately resolving any difficulties users may have while studying and maintaining continuity in their learning.

Finally, the server periodically provides discussion topics to encourage the exchange of ideas between users. This functionality is realized through the DiscussionModule, allowing users to deepen their understanding by exchanging ideas with other users about what they have learned.

Below is an example of a prompt:

"D is a first-year high school student who is interested in math. Please suggest the best study method for D based on his cognitive characteristics and emotional state. What should we do if D shows no interest while studying?"

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

The server provides a user interface to the terminal for entering basic user information. The user enters basic information such as name, age, grade, and areas of interest. This entered data is sent to the server and stored in a database. The input here is the user's basic information, and the output is the user information stored in the database.

Step 2:

The server displays the cognitive characteristics assessment test interface on the terminal. The user clicks the test start button on the terminal to begin the cognitive characteristics assessment test. The terminal displays the questions received from the server to the user and accepts the user's answers. The user inputs answer data, which the server collects and analyzes in real time. The output is the user's cognitive characteristics assessment results.

Step 3:

The server suggests the optimal learning method based on the cognitive characteristic assessment results. Based on the assessment results, the server selects the most appropriate learning method from various options (video, text, audio materials, etc.). This selected learning method is displayed from the server on the user's device. The input is the cognitive characteristic assessment results, and the output is a suggestion of the optimal learning method.

Step 4:

The server provides the user with the most suitable learning content. The user receives the video and text materials provided by the server via their terminal. The input here is information on the most suitable learning method, and the output is the learning content itself. The learning content is delivered via a head-mounted display (HMD), which the user uses to progress through their studies.

Step 5:

The server tracks learning progress and conducts comprehension tests. Each time a user uses learning content, the server monitors their progress in real time. It also periodically presents tests to assess comprehension, which the user answers. Based on the response data, the server evaluates the level of comprehension and stores this in a database. The input is learning progress data and test answers, and the output is the comprehension assessment results.

Step 6:

The server provides the ability to resolve questions during learning in real time. When a user enters a question into their device, the question is sent to the server. The server analyzes the question and instantly generates an answer, which is sent to the device. The input is the user's question data, and the output is the answer to that question.

Step 7:

The server uses an emotion engine to monitor the user's emotional state in real time. The HMD's built-in camera and microphone collect the user's facial expressions and tone of voice, which are then analyzed by the emotion engine. If the server determines that the user is tired based on this, it provides feedback such as "Take a short break." The input is the user's facial expressions and voice data, and the output is the emotional state analysis results and feedback.

Step 8:

The server periodically provides users with discussion topics and encourages exchange of opinions between users. The server selects the topics and displays them on the user's device. Users use their device to exchange opinions with other users, and the content is sent to the server. The input is the discussion topic and user opinion data, and the output is the result of the exchange of opinions.

The specific processing unit 290 transmits the results of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

Data generation model 58 is what is known as generative AI (artificial intelligence). Examples of data generation model 58 include generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>) and Gemini (Internet search <URL: https://gemini.google.com/?hl=ja>). Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input. Data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

In the above embodiment, an example was given in which the specific processing was performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the smart glasses 214.

[Third embodiment]

Figure 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

As shown in FIG. 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and/or a LAN (Local Area Network).

The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I/F 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user 20's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I/Fs 44 and 26 is carried out in a secure manner.

Figure 6 shows an example of the main functions of the data processing device 12 and headset terminal 314. As shown in Figure 6, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32.

The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

Storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

In the headset terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

Next, we will explain the identification process performed by the identification processing unit 290 of the data processing device 12. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

This invention is an AI system designed to support education. It assesses the cognitive characteristics of each individual and proposes individually appropriate learning methods to maximize learning effectiveness and address educational disparities, school absenteeism, and support for people with disabilities. This system functions between a server, a terminal, and a user.

Overall system overview

This system provides educational support tailored to each individual by performing the following processes:

1. Obtain basic information about the subject.

2. Determine the subject's cognitive characteristics.

3. Suggest the optimal learning method based on the assessment results.

4. Provide learning content.

5. Track learning progress and administer comprehension tests.

6. Provide personalized feedback.

7. Resolve questions while studying in real time.

8. Suggest discussion topics and encourage exchange of opinions.

Program processing

Below is a natural language explanation of the program processing at each step of this system.

1. User Registration

The server provides a user registration page.

Users use the device to enter basic information such as name, age, grade, and areas of interest.

The server stores the user's basic information in a database.

2. Cognitive trait assessment test

The server displays the cognitive characteristics assessment test interface on the terminal.

The user clicks the Start Test button to begin the test.

The server sends a series of questions and tasks to the device in sequence.

The device displays each question or task to the user and accepts their response.

Users answer each question on their device.

The server receives responses from users and stores them in a database in real time.

The server analyzes the collected data and determines the user's visual, language, and auditory cognitive characteristics.

3. Learning Method Suggestions

Based on the results of the assessment, the server selects the optimal learning method for the user.

The server will present the selected learning method and related learning materials to the device.

The device will display learning method suggestions and introductions to learning materials to the user.

4. Providing learning content

The server provides learning content appropriate to the user (video, text, audio materials, etc.).

Users can check the learning content on their device and begin learning.

The device records the user's learning progress in real time.

5. Real-time support

The server provides an interface via the terminal that accepts questions the user may have while studying.

The user enters a question on the device and submits it.

The server analyzes the question and provides an appropriate answer instantly.

The device displays the response from the server to the user.

6. Progress Tracking and Comprehension Testing

The server periodically displays an interface on the terminal that conducts comprehension tests.

Users take the test on their device.

The device sends the user's response to the server.

The server scores the answers, stores the results in a database, and provides feedback to the user.

7. Individual feedback and suggestions for next steps

The server analyzes the results of the user's comprehension test and suggests the next learning content and assignments.

The device will display feedback and new challenges to the user.

Users will work on the proposed tasks on their devices.

8. Promoting discussion and communication

The server will provide regular discussion topics.

The device displays discussion topics to the user and provides a form where they can enter their responses and opinions.

Users can enter their opinions on their devices and exchange them with other users.

Specific examples

For example, let's say Mr. C is a second-year junior high school student who wants to study history.

1. User Registration

Mr. C types "Second year junior high school student, interested in history" into his device.

The server stores this information.

2. Cognitive trait assessment test

Mr. C clicks the start test button on his device.

The server will begin the validation test.

Questions such as "Is it easy to remember with an image?" will be displayed on the device.

Mr. C answers the questions.

The server analyzes Mr. C's answers and determines that he is visually dominant.

3. Learning Method Suggestions

The server suggests, "Learn about historical events through videos."

A link to the video material will appear on your device.

4. Providing learning content

Mr. C starts watching the video on his device.

5. Real-time support

Mr. C types the question into his terminal: "I don't understand the background of this war."

The server provides the answer, which is displayed on the device.

6. Progress Tracking and Comprehension Testing

After watching the video, the server will conduct a comprehension test.

Mr. C takes the test on a terminal.

7. Individual feedback and suggestions for next steps

The server analyzes the test results and suggests, "Next, let's learn more about the causes of this war."

The next learning content will be displayed to Mr. C on his device.

8. Promoting discussion and communication

The server suggests that next week's theme is 'Important Figures in Modern History' and provides a platform for users to exchange opinions.

Mr. C can enter his opinions on the device and discuss them with his classmates.

In this way, the AI system can provide an optimal educational experience tailored to individual learning needs, creating an environment in which Mr. C can learn independently.

The processing flow is explained below.

Step 1:

The server provides a user registration page. Users use their terminal to enter basic information such as name, age, grade, and areas of interest. The server stores the entered basic information in a database.

Step 2:

The server displays the cognitive characteristics assessment test interface on the terminal. The user clicks the Start Test button to begin the test.

Step 3:

The server sequentially sends a series of questions and tasks to the terminal. The terminal displays the questions and tasks to the user and accepts answers. The user answers each question on the terminal.

Step 4:

The server receives the user's responses and stores them in a database in real time. The server analyzes the collected data and determines the user's visual, language, and auditory cognitive characteristics.

Step 5:

Based on the results of the assessment, the server selects the most suitable learning method for the user. The server then presents the selected learning method and related learning materials to the device. The device then displays learning method suggestions and learning material introductions to the user.

Step 6:

The server provides learning content (video, text, audio materials, etc.) appropriate for the user. The user checks the learning content on their device and begins learning. The device records the user's learning progress in real time.

Step 7:

The server provides an interface that accepts questions that users have while studying via their terminal. The user enters and submits a question on the terminal. The server analyzes the question and instantly generates and provides an appropriate answer. The terminal displays the answer from the server to the user.

Step 8:

The server periodically displays an interface on the device that conducts comprehension tests. The user takes the test on the device. The device sends the user's answers to the server. The server scores the answers, stores the results in a database, and provides feedback to the user.

Step 9:

The server analyzes the results of the user's comprehension test and suggests the next learning content and assignments. The device displays feedback and new assignments to the user. The user then works on the suggested assignments.

Step 10:

The server periodically provides discussion topics. The terminal displays the discussion topics to the user and provides a form where they can enter their responses and opinions. Users enter their opinions on the terminal and exchange opinions with other users.

(Example 1)

Next, Example 1 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Modern education requires providing optimal learning methods based on individual cognitive characteristics. However, many current educational systems only offer uniform learning methods, making it difficult to address individual cognitive characteristics. Furthermore, they lack features such as learning progress tracking, comprehension tests, and real-time question resolution, making it impossible to adequately address individual learning needs. Furthermore, there are few systems that automatically suggest learning content or provide feedback, preventing the effectiveness of education from being maximized.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for inputting basic information about the subject, means for determining the cognitive characteristics of the subject, means for proposing the optimal learning method based on the determination results, means for providing learning content, means for tracking learning progress and conducting comprehension tests, means for providing individual feedback to the subject, means for resolving questions during learning in real time, means for saving the subject's input and learning progress in a database, means for collecting the subject's questions and generating answers using a generative AI model, and means for evaluating the subject's answers and dynamically suggesting the next learning content. This makes it possible to provide the optimal learning method according to individual cognitive characteristics, track learning progress, resolve questions in real time, and dynamically suggest learning content.

"Target" refers to individuals who use educational support AI systems to advance their studies.

"Basic information" refers to basic information necessary for education, such as the subject's name, age, grade, and areas of interest.

"Cognitive characteristics" refer to the information processing characteristics of a subject, such as vision, language, and hearing, and indicate individual characteristics in learning.

"Learning methods" are the optimal learning tools and approaches suggested based on cognitive characteristics, including teaching materials and learning formats.

"Learning content" refers to learning videos, texts, audio materials, etc. provided to the target audience, and is an information resource to support learning.

"Learning progress" refers to the progress a subject makes as they progress through their studies.

A "comprehension test" is a test used to measure the subject's level of understanding of the learning content.

"Individualized feedback" refers to specific assessments and guidance provided to students based on their comprehension test results and learning progress.

"Real-time question resolution" means providing immediate and appropriate answers to questions that students have while studying.

A "database" is a system that stores and manages data such as basic information about the subject, their learning progress, and their responses.

A "generative AI model" is an artificial intelligence model used to generate appropriate answers to user questions.

"Dynamic suggestions" means generating and suggesting the next content or assignment to be learned on an ongoing basis based on the student's learning progress and level of understanding.

This invention relates to an educational support AI system that maximizes learning effectiveness by providing optimal learning methods based on individual cognitive characteristics, and addresses educational disparities, school absenteeism, and support for people with disabilities. The system functions between a server, a terminal, and a user.

Overall system overview

This system provides tailored educational support by performing the following processes:

1. Obtain basic information about the subject.

2. Determine the subject's cognitive characteristics.

3. Suggest the optimal learning method based on the assessment results.

4. Provide learning content.

5. Track learning progress and administer comprehension tests.

6. Provide personalized feedback.

7. Resolve questions while studying in real time.

8. Suggest discussion topics and encourage exchange of opinions.

Hardware and software used

The hardware used includes a server and user devices. The server uses a database (e.g., MySQL, PostgreSQL) to manage and analyze user information, an execution environment for machine learning models (e.g., Python, Scikit-learn), a real-time chat system (e.g., Node.js), and a generative AI model (e.g., OpenAI GPT-3).

The user device provides an interface that runs on a web browser and handles user operations and learning progress using HTML, CSS, JavaScript, React, etc.

Program processing overview

User Registration

The server generates a user registration page using HTML and CSS and sends it to the device. The user enters basic information such as name, age, grade, and areas of interest on the device and submits it. The server receives the information in JSON format and stores it in a database.

Cognitive trait assessment test

The server uses JavaScript and React to generate the cognitive trait assessment test interface and display it on the device. The user starts the test and answers a series of questions. The server receives the answers, stores them in a database, and uses a Python machine learning model to analyze the data and determine cognitive traits.

Study method suggestions

The server determines the optimal learning method based on cognitive characteristics and selects relevant learning materials. This is then sent to the device, which then presents it to the user.

Providing learning content

The server provides selected learning content, which users use on their devices to progress through their studies. The devices record learning progress in real time and send it to the server.

Real-time support

The server accepts the user's question through the device and generates an appropriate answer using a generative AI model. This is then sent to the device and displayed.

Progress tracking and comprehension testing

The server periodically conducts comprehension tests and stores the results in a database. It provides feedback to the user and suggests what to study next.

Individual feedback and suggestions for next steps

The server analyzes the next learning content based on the test results and provides feedback and new challenges to the user, thereby presenting the optimal learning path for each individual user.

Fostering discussion and communication

The server periodically generates new discussion topics and presents them to users. Users can then enter their opinions on their devices and exchange them with other users.

Specific examples

For example, let's say Mr. C is a second-year junior high school student who wants to study history.

1. Mr. C types "Second year junior high school student, interested in history" into his device and submits the information. The server stores this information.

2. Mr. C starts the cognitive characteristics assessment test on his device. The server displays the questions, and Mr. C enters the answers. The server analyzes the answers and determines that he is visually dominant.

3. The server suggests, "Let's learn about historical events through videos," and displays a link to the video material on the device.

4. Mr. C begins watching the video on his device. The device records his learning progress and sends it to the server.

5. When Mr. C enters a question such as "I don't understand the background of this war," the server uses a generative AI model to generate and display an answer.

6. After watching the video, the server conducts a comprehension test, and Mr. C takes the test. The results are sent to the server, which then suggests the next learning content.

7. The server will suggest a new task, saying, "Next, let's learn more about the causes of this war," and display this on the terminal.

8. The server suggests that next week's topic is "Important Figures in Modern History" and provides a discussion platform. C can enter his opinions and discuss them with his classmates.

Example prompt

"If you have a second-year junior high school student who wants to study history, how would you use AI to suggest the best learning method? Please explain the specific process."

In this way, AI systems can provide optimal educational experiences tailored to individual learning needs and create an environment in which students can learn independently.

The flow of the identification process in Example 1 will be explained using Figure 11.

Step 1:

User Registration

The server generates a user registration page using HTML and CSS and sends it to the device. The user enters basic information such as name, age, grade, and areas of interest on the device and submits it. The server receives the received information in JSON format and stores it in a database. This registers the user's basic information in the database. The input is the user's basic information, and the output is the user's basic information stored in the database.

Step 2:

Cognitive trait assessment test

The server uses JavaScript and React to generate an interface for the cognitive trait assessment test and displays it on the device. The user clicks a button to start the test and answers a series of questions. The device displays each question and accepts the user's answers. The server stores the received answers in a database and analyzes the data using a Python machine learning model to determine the user's cognitive traits. The input is the user's answers, and the output is the determined cognitive traits. This reveals the user's cognitive traits.

Step 3:

Study method suggestions

The server selects the optimal learning method based on cognitive characteristics and selects related learning materials. The server sends the selected learning method and learning materials to the terminal via a REST API. The terminal displays this to the user. The input is cognitive characteristics and a learning materials database, and the output is a link to the learning method and learning materials presented to the user. This allows the user to be presented with the optimal learning method.

Step 4:

Providing learning content

The server dynamically generates learning content appropriate for the user and sends it to the device. The user checks the learning content on the device and begins learning. The device records learning progress and periodically sends it to the server. The input is the user's cognitive characteristics and progress, and the output is the learning content provided and a record of learning progress. This allows users to study with content that suits them.

Step 5:

Real-time support

The server provides an interface that accepts user questions via the terminal. The user enters the question on the terminal and sends it to the server. The server analyzes the question and generates the optimal answer using a generative AI model. The server sends the answer to the terminal, which displays it to the user. The input is the user's question, and the output is the generated answer. This allows the user's question to be resolved instantly.

Step 6:

Progress tracking and comprehension testing

The server periodically displays an interface on the device that conducts comprehension tests. The user takes the test on the device and sends their answers to the server. The server scores the answers and stores the results in a database. It then generates feedback based on the results and sends it to the device. The input is the comprehension test answers, and the output is the test results and feedback. This evaluates the user's level of understanding and determines the next learning content.

Step 7:

Individual feedback and suggestions for next steps

The server analyzes the test results and determines the next learning content. The server sends feedback and new assignments to the device, which displays them to the user. The user then works on the suggested assignments. The input is the test results and learning goals, and the output is feedback and new suggested assignments. This allows the user to continuously work on appropriate assignments.

Step 8:

Fostering discussion and communication

The server periodically generates discussion topics and sends them to the device. Users input their opinions on their device and send them to the server. The server collects the opinions and displays them to other users, allowing for an exchange of opinions. The input is the user's opinion, and the output is the shared opinion and discussion results. This promotes communication between users and leads to deeper understanding.

(Application Example 1)

Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

With conventional educational systems, it was difficult to efficiently grasp the cognitive characteristics of individual students and propose optimal learning methods based on those characteristics. Furthermore, there was a lack of educational support for industrial product operation methods and control technologies in industrial settings, and automatic and effective measures were needed to improve engineers' skills.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

In this invention, the server includes means for inputting basic information about the subject, means for assessing the cognitive characteristics of the subject, means for proposing the optimal learning method based on the assessment results, means for providing learning content, means for tracking learning progress and conducting comprehension tests, means for providing individual feedback and proposing the next assignment to the subject, means for resolving questions during learning in real time, means for promoting the exchange of opinions between subjects, and means for supporting learning of industrial product operation methods and control technology. This makes it possible to propose the optimal learning method for each subject's cognitive characteristics and to automatically and effectively educate them on industrial product operation methods and control technology.

1. "Basic information of the subject" refers to personal information such as the subject's name, age, years of experience, and area of expertise.

2. "Cognitive characteristics" refers to the individual characteristics of how a subject receives, processes, and understands information. Examples include visual dominance, auditory dominance, and linguistic dominance.

3. "Optimal learning method" refers to the most effective learning method based on the cognitive characteristics of the individual.

4. "Learning Content" refers to the learning materials and information provided to the target audience for learning, including video, text, and audio materials.

5. "Tracking learning progress" refers to recording and managing a student's learning progress in real time.

6. "Comprehension test" refers to a test used to assess the extent to which a subject has understood the learning content.

7. "Individualized feedback" refers to specific advice and next steps provided based on the learner's learning progress and level of understanding.

8. "Resolving questions during learning in real time" refers to providing immediate answers to questions that arise when a student is learning.

9. "Promoting the exchange of opinions" refers to support provided to stimulate information sharing and discussion among participants.

10. "Supporting learning about industrial product operation methods and control technologies" refers to educational support to promote understanding of the correct operation methods and control technologies of industrial products and equipment used in industry.

Overall system configuration

This invention describes a specific form for implementing an educational support system consisting of a server, a terminal, and a user. This system proposes optimal learning methods based on the cognitive characteristics of each individual, and supports education on industrial product operation methods and control technologies.

Hardware and software used

This invention uses the following hardware and software:

Server: High-performance server (e.g., AWS, Google Cloud)

Devices: Smartphones, factory robot HMIs (Human-Machine Interfaces), PCs

Front-end: Angular, HTML5, CSS3, JavaScript

Backend: Node.js, MySQL

AI modeling: Python, TensorFlow, Socket.IO

Program processing

1. User Registration

The server provides a user registration page using Angular on the front end.

Users use the terminal to enter basic information such as their name, age, years of experience, and area of expertise.

The server receives this information via Node.js and stores it in a MySQL database.

2. Assessment of cognitive characteristics

The server uses an Angular front-end to provide an interface for questions and tasks that assess cognitive characteristics.

The user starts the test on their device and answers the questions.

The server receives the responses and analyzes the data using Python and TensorFlow to determine cognitive characteristics.

3. Learning Method Suggestions

Based on the analysis results, the server selects the optimal learning method and suggests corresponding learning materials.

The device will display the suggestions and teaching materials to the user.

4. Providing learning content

The server provides learning content (video, audio, text) that is best suited to the user's cognitive characteristics.

Users can view this content on their devices and progress as they study. Progress is recorded in real time.

5. Real-time support

If users have any questions while studying, they can enter them using their smartphone or tablet.

The server uses Socket.IO to receive questions in real time, processes them in Python, and provides instant answers.

6. Progress Tracking and Comprehension Testing

The server periodically conducts comprehension tests, analyzes the results, stores them in a database, and provides feedback to the user.

7. Individual feedback and suggestions for next steps

The server suggests the next learning task based on the test results and provides related learning content.

8. Promoting discussion and communication

The server will regularly provide discussion topics and an interface to encourage the exchange of opinions between users.

Users can enter their opinions on their devices and discuss with other users.

Examples and prompts

As a concrete example, consider the case where engineer A is learning how to operate a new industrial robot.

If Engineer A enters "Years of experience: 5 years, Specialty: Industrial robot control" and has visually dominant cognitive characteristics, the system will suggest, "Let's learn the basic operation of industrial robots through video materials." Engineer A will begin learning by watching the video materials, and if he has any questions, he can ask, "Please tell me in detail about how the sensors on this robot work." The server will immediately provide an appropriate answer to this question.

Examples of prompt statements include:

"For an engineer with five years of robot operation experience, please suggest a method of operating an industrial robot that is easy for someone with visually dominant cognitive characteristics to learn. Also, please suggest related video teaching materials."

Some examples include:

In this way, it is possible to propose learning methods that are optimal for each individual's cognitive characteristics, and to automatically and effectively educate them on how to operate industrial products and control technologies.

The flow of the specific processing in Application Example 1 will be explained using Figure 12.

Step 1:

User Registration

The server provides a user registration page using Angular on the front end.

Users use the terminal to enter basic information such as their name, age, years of experience, and area of expertise.

The basic information entered is sent from the terminal to the server, which receives this information via Node.js and stores it in a MySQL database.

Enter basic information such as name, age, years of experience, and area of expertise.

Output: Basic user information stored in the database

Step 2:

Assessing cognitive characteristics

The server uses an Angular front-end to provide an interface for questions and tasks that assess cognitive characteristics.

The user starts the test on their device and answers the questions.

The server receives the response data and analyzes it using Python and TensorFlow to determine the user's cognitive characteristics. Categories of cognitive characteristics such as visual, auditory, and language are determined.

Input: User response data

Output: Cognitive characteristics (visual, auditory, language, etc.)

Step 3:

Study method suggestions

Based on the results of the cognitive characteristic analysis, the server selects the optimal learning method and suggests corresponding learning materials.

The device will display the suggestions and teaching materials to the user.

For example, video learning materials will be suggested to visually-oriented users.

Input: Cognitive trait analysis results

Output: Suggested learning methods and materials

Step 4:

Providing learning content

The server provides learning content (video, audio, text) that is best suited to the user's cognitive characteristics.

Users can view this content on their devices and progress through their studies. Progress is recorded in real time from the device to the server.

Input: Suggested learning methods and materials

Output: Providing learning content and recording progress

Step 5:

Real-time support

If users have any questions while studying, they can enter them using their smartphone or tablet.

The server receives questions in real time using Socket.IO, processes them in Python, and provides instant answers.

Input: User question

Output: Answers provided in real time

Step 6:

Progress tracking and comprehension testing

The server periodically conducts comprehension tests, analyzes the results, stores them in a database, and provides feedback to the user.

The terminal displays the test results to the user.

Input: Comprehension test response data

Output: Analysis of test results and feedback

Step 7:

Individual feedback and suggestions for next steps

Based on the results of the comprehension test, the server suggests the next learning task and provides related learning content.

The device will display the next task and related content to the user.

Input: Comprehension test results

Output: Providing the next assignment and related learning content

Step 8:

Fostering discussion and communication

The server will regularly provide discussion topics and an interface to encourage the exchange of opinions between users.

Users can enter their opinions on their devices and discuss with other users.

Input: Discussion topic and user opinions

Output: Facilitated exchange of ideas and shared opinions

In this way, by clarifying the roles of the server, terminal, and user at each step and organizing the specific operations and inputs/outputs, the educational support system is designed to function effectively.

It is also possible to further combine an emotion engine that estimates the user's emotion. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

This invention is an AI system designed to support education. It maximizes learning effectiveness by assessing the cognitive characteristics of each individual and proposing individually appropriate learning methods. Furthermore, by combining it with an emotion engine, it provides an optimal learning experience by taking into account the emotional state of the individual. This system functions among four parties: a server, a terminal, a user, and an emotion engine.

Overall system overview

This system provides educational support tailored to the target individual by performing the following processes:

1. Obtain basic information about the subject.

2. Determine the subject's cognitive characteristics.

3. Suggest the optimal learning method based on the assessment results.

4. Provide learning content.

5. Track learning progress and administer comprehension tests.

6. Provide personalized feedback.

7. Resolve questions while studying in real time.

8. Use an emotion engine to recognize and respond to the subject's emotional state.

9. Suggest discussion topics and encourage exchange of ideas.

Program processing

Below is a natural language explanation of the program processing at each step of this system.

1. User Registration

The server provides a user registration page.

Users use the device to enter basic information such as name, age, grade, and areas of interest.

The server stores the basic information entered in a database.

2. Cognitive trait assessment test

The server displays the cognitive characteristics assessment test interface on the terminal.

The user clicks the Start Test button to begin the test.

3. Conduct cognitive assessment tests

The server sends a series of questions and tasks to the device in sequence.

The device displays questions and tasks to the user and accepts answers.

Users answer each question on their device.

The server receives responses from users and stores them in a database in real time.

The server analyzes the collected data and determines the user's visual, language, and auditory cognitive characteristics.

4. Learning Method Suggestions

Based on the results of the assessment, the server selects the optimal learning method for the user.

The server will present the selected learning method and related learning materials to the device.

The device will display learning method suggestions and introductions to learning materials to the user.

5. Providing learning content

The server provides learning content appropriate to the user (video, text, audio materials, etc.).

Users can check the learning content on their device and begin learning.

The device records the user's learning progress in real time.

6. Real-time support

The server provides an interface via the terminal that accepts questions the user may have while studying.

The user enters a question on the device and submits it.

The server analyzes the question and instantly generates and provides an appropriate answer.

The device displays the response from the server to the user.

7. Progress Tracking and Comprehension Testing

The server periodically displays an interface on the terminal that conducts comprehension tests.

Users take the test on their device.

The device sends the user's response to the server.

The server scores the answers, stores the results in a database, and provides feedback to the user.

8. Individual feedback and suggestions for next steps

The server analyzes the results of the user's comprehension test and suggests the next learning content and assignments.

The device will display feedback and new challenges to the user.

Users will work on the proposed tasks.

9. Emotion Recognition and Response

The server uses an emotion engine to monitor the user's emotional state in real time through the device while they are learning.

The emotion engine identifies emotions from the user's facial expressions and tone of voice, and stores the emotional state in a database.

The server provides optimal feedback and adjusts learning content based on the user's emotional state.

10. Promoting discussion and communication

The server will provide regular discussion topics.

The device displays discussion topics to the user and provides a form where they can enter their responses and opinions.

Users can enter their opinions on their devices and exchange them with other users.

Specific examples

For example, let's say Mr. D is a first-year high school student who wants to study mathematics.

1. User Registration

Mr. D types "First year high school student, interested in mathematics" into his device.

The server stores this information.

2. Cognitive trait assessment test

Mr. D clicks the start test button on his device.

The server will begin the validation test.

Questions such as "Is it easy to remember with an image?" will be displayed on the device.

Mr. D answers the questions.

The server analyzes Mr. D's answers and determines that he is visually dominant.

3. Learning Method Suggestions

The server suggests, "Learn math concepts through videos."

A link to the video material will appear on your device.

4. Providing learning content

Mr. D starts watching the video on his device.

5. Real-time support

Mr. D types a question into his terminal: "I don't know how to use this formula."

The server provides the answer, which is displayed on the device.

6. Progress Tracking and Comprehension Testing

After watching the video, the server will conduct a comprehension test.

Mr. D takes the test on the terminal.

7. Individual feedback and suggestions for next steps

The server analyzes the test results and suggests, "Next, try solving some real problems."

The next learning content will be displayed to Mr. D on his device.

8. Emotion Recognition and Response

The server uses an emotion engine to analyze Mr. D's facial expressions and tone of voice while he is studying.

If it is determined that Mr. D is feeling tired, the server will display a message on his device saying, "Let's take a short break."

Also, if Mr. D is in a state of joy or adaptation, he will be given feedback such as "Good luck," encouraging him to continue learning.

9. Promoting discussion and communication

The server suggests that next week's theme is 'Applications of Functions' and provides a platform where users can exchange opinions.

Mr. D can enter his opinions on the device and discuss them with his classmates.

In this way, the AI system can provide an optimal educational experience tailored to individual learning needs, creating an environment in which D can learn independently. By combining it with an emotion engine, it is possible to respond according to the user's emotional state, further improving learning effectiveness.

The processing flow is explained below.

Step 1:

The server provides a user registration page. Users use their terminal to enter basic information such as name, age, grade, and areas of interest. The server stores the entered basic information in a database.

Step 2:

The server displays the cognitive characteristics assessment test interface on the terminal. The user clicks the Start Test button to begin the test.

Step 3:

The server sequentially sends a series of questions and tasks to the terminal. The terminal displays the questions and tasks to the user and accepts answers. The user answers each question on the terminal.

Step 4:

The server receives the user's responses and stores them in a database in real time. The server analyzes the collected data and determines the user's visual, language, and auditory cognitive characteristics.

Step 5:

Based on the results of the assessment, the server selects the most suitable learning method for the user. The server then presents the selected learning method and related learning materials to the device. The device then displays learning method suggestions and learning material introductions to the user.

Step 6:

The server provides learning content (video, text, audio materials, etc.) appropriate for the user. The user checks the learning content on their device and begins learning. The device records the user's learning progress in real time.

Step 7:

The server provides an interface that accepts questions that users have while studying via their terminal. The user enters and submits a question on the terminal. The server analyzes the question and instantly generates and provides an appropriate answer. The terminal displays the answer from the server to the user.

Step 8:

The server periodically displays an interface on the device that conducts comprehension tests. The user takes the test on the device. The device sends the user's answers to the server. The server scores the answers, stores the results in a database, and provides feedback to the user.

Step 9:

The server analyzes the results of the user's comprehension test and suggests the next learning content and assignments. The device displays feedback and new assignments to the user. The user then works on the suggested assignments.

Step 10:

The server uses an emotion engine to monitor the user's emotional state in real time via the device while they are learning. The emotion engine identifies emotions from the user's facial expressions and tone of voice, and stores the emotional state in a database. The server then provides optimal feedback and adjusts the learning content based on the user's emotional state.

Step 11:

The server periodically provides discussion topics. The terminal displays the discussion topics to the user and provides a form where they can enter their responses and opinions. Users enter their opinions on the terminal and exchange opinions with other users.

(Example 2)

Next, Example 2 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Traditional educational systems have struggled to provide learning methods that fully consider the cognitive characteristics and emotional state of each individual student. They also lacked functionality for resolving questions that arise during learning in real time and for facilitating the exchange of opinions between students. This has made it difficult to provide an optimal learning experience for each student, resulting in the issue of not maximizing learning effectiveness.

The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting basic information about the subject, means for determining the cognitive characteristics of the subject, means for proposing an optimal learning method based on the determination results, means for providing learning content, means for tracking learning progress and conducting comprehension tests, means for providing individual feedback to the subject, means for resolving questions during learning in real time, and means for recognizing and responding to the subject's emotional state using an emotion engine. This makes it possible to provide an optimal learning experience tailored to the subject's individual needs, maximizing learning effectiveness. Furthermore, by encouraging the exchange of opinions between subjects as needed, improvement in the quality of learning can also be expected.

"Target audience" refers to learners who use the educational system.

"Basic information" refers to basic data such as the subject's name, age, grade, and areas of interest.

"Cognitive characteristics" refers to the subject's cognitive style, such as visual, linguistic, and auditory.

"Decision testing" refers to a series of questions or tasks designed to assess a subject's cognitive characteristics.

"Feedback" refers to individual evaluations and advice provided based on the subject's learning results.

"Learning method" refers to the learning process and techniques proposed based on the cognitive characteristics of the target individual.

"Learning content" includes learning materials and resources, such as videos, texts, and audio materials.

An "emotion engine" refers to a system that has the ability to recognize a subject's emotional state and process that data.

"Real-time support" refers to the ability to instantly respond to questions or concerns that students may have while studying.

"Discussion Topics" refer to topics that the server periodically provides for the exchange of opinions among participants.

"Opinion exchange" refers to the activity of sharing opinions and thoughts among participants.

"Server" refers to the central computer that runs the entire system and provides each function.

"Terminal" refers to the computer or mobile device used by a Subject to access the System.

This invention is an AI system designed to support education, maximizing learning effectiveness by determining the cognitive characteristics of each individual and proposing individually appropriate learning methods. Furthermore, by combining it with an emotion engine, it provides an optimal learning experience by taking into account the emotional state of the individual. This system functions among four parties: a server, a terminal, a user, and an emotion engine.

Overall system overview

This system provides educational support tailored to each individual through the following steps:

1. Obtain basic information about the subject.

2. Determine the subject's cognitive characteristics.

3. Suggest the optimal learning method based on the assessment results.

4. Provide learning content.

5. Track learning progress and administer comprehension tests.

6. Provide personalized feedback.

7. Resolve questions while studying in real time.

8. Use an emotion engine to recognize and respond to the subject's emotional state.

9. Suggest discussion topics and encourage exchange of ideas.

Program processing

Processing in this system is carried out through cooperation between the server and the terminal. Details are provided below.

Hardware and Software Use

The server uses a MySQL database to store and manage data.

The server uses Python to implement data analysis and decision algorithms.

The server provides a front end consisting of JavaScript and HTML.

Devices access the server using a browser such as Google Chrome.

The emotion engine uses a camera and microphone to analyze the user's facial expressions and tone of voice.

Specific examples

For example, if Mr. D is a first-year high school student who wants to study mathematics, consider the following:

1. User Registration

D enters "First year high school student, interested in mathematics" into the terminal. The form displayed on the terminal has fields for entering name, age, grade, and areas of interest.

When Mr. D clicks the "Register" button, the server receives the input data and saves it in the MySQL database.

2. Cognitive trait assessment test

Mr. D clicks the start test button on his device. The server sends the assessment test UI, composed of HTML and JavaScript, to the device and asks a series of questions.

Questions such as "Is it easy to remember with an image?" are displayed on the device. Mr. D answers the questions, and the answers are saved in a database in real time.

3. Learning Method Suggestions

Based on Mr. D's cognitive characteristics, the server suggests, "Let's learn mathematical concepts through videos." A link to the video material is displayed on the device.

4. Providing learning content

Mr. D begins watching the video on his device. The server provides the video material via a video streaming service.

5. Real-time support

While learning, Mr. D enters a question into his device, such as "I don't know how to use this formula." The server receives the question, analyzes it using natural language processing (NLP), and instantly generates an appropriate answer, which is displayed on the device.

6. Progress Tracking and Comprehension Testing

After watching the video, the server periodically conducts comprehension tests. Mr. D takes the test on his device and his answers are sent to the server. The server uses an automatic scoring system to score the answers and stores the results in a database.

7. Individual feedback and suggestions for next steps

Based on the test results, the server suggests, "Next, try solving some real problems." The next learning content is displayed to Mr. D on his device.

8. Emotion Recognition and Response

The server uses an emotion recognition algorithm to monitor D's facial expressions and tone of voice in real time while he studies.

If D is identified as feeling tired, the server will display a message on his device saying, "Take a short break." If he is in a state of joy or adaptation, the server will provide feedback saying, "Keep it up," encouraging him to continue learning.

9. Promoting discussion and communication

The server suggests that next week's theme is 'Application of Functions' and provides a platform where users can exchange opinions. Mr. D can enter his opinion on his device and discuss it with his classmates.

Example prompt

"Please explain how to register by entering basic user information."

"Please explain with specific examples what types of questions will be asked during the cognitive assessment test."

"Please explain how you handle real-time support when users have questions while learning."

"Please explain the process of recognizing and responding to user emotions using the emotion engine."

This allows the AI system to provide an optimal educational experience tailored to the user's individual learning needs, improving learning outcomes. By combining it with an emotion engine, it is possible to respond according to the user's emotional state, further improving learning outcomes.

The flow of the identification process in Example 2 will be explained using Figure 13.

Step 1: User Registration

The server provides a user registration page. The user uses their terminal to enter basic information such as name, age, grade, and areas of interest. When the user clicks the "Register" button, the terminal sends the data to the server. The server receives the entered basic information and stores it in a database. The input here is the user's basic information, and the output is the data stored in the database.

Step 2: Cognitive trait assessment test

The server displays the cognitive ability assessment test interface on the terminal. The user clicks the test start button and answers the questions and tasks displayed on the terminal in sequence. The server receives each answer and stores them sequentially in a database. Based on the collected answer data, the server performs data analysis using Python to determine the user's visual, linguistic, and auditory cognitive abilities. The input is the user's test answers, and the output is the assessed cognitive abilities.

Step 3: Suggest a learning method

Based on the assessment results, the server selects the optimal learning method and related learning materials. It sends the selected information to the terminal and displays links to the learning method and learning materials to the user. The input is the assessed cognitive characteristics, and the output is the suggested learning method and links to the learning materials.

Step 4: Provide learning content

The server provides learning content such as videos, texts, and audio materials via a streaming service. Users check the learning content on their device and begin studying. The device records the user's learning progress (e.g., video viewing time and text completion status) in real time and sends it to the server. The input is the learning content, and the output is learning progress data.

Step 5: Real-time support

The server provides an interface through the terminal for users to input questions they have while studying. The user enters the question on the terminal and sends it. The server uses NLP (natural language processing) functions to analyze the question, generates an appropriate answer, and sends it to the terminal. The terminal displays the answer to the user. The input is the user's question, and the output is the generated answer.

Step 6: Progress Tracking and Comprehension Testing

The server displays an interface on the terminal that periodically conducts comprehension tests. The user takes the test on the terminal and submits their answers. The server scores the answers using an automatic scoring system and stores the results in a database. The server then provides feedback on the results to the user. The input is the test answers, and the output is the scoring results and feedback.

Step 7: Individual feedback and suggestions for next steps

The server selects the next learning content and assignments based on the results of the comprehension test. The selected information is sent to the device, and feedback and new assignments are displayed to the user. The user then works on the suggested assignments. The input is the test results, and the output is feedback and suggested assignments.

Step 8: Emotion Recognition and Responding

The server uses an emotion engine to monitor the user's facial expressions and tone of voice in real time via the device while they are studying. It identifies the user's emotional state and stores it in a database. The server then provides optimal feedback and adjusts the learning content based on the user's emotional state. For example, if the user is identified as tired, it will suggest a break, and if they are in an adaptive state, it will display an encouraging message. The input is the user's facial expression and tone of voice data, and the output is the emotion analysis results and feedback based on them.

Step 9: Facilitate discussion and communication

The server periodically provides discussion topics and displays them on the terminal. Users enter their opinions on the topics and exchange them with other users. The server stores the posted opinions in a database and displays them to other participants in real time. The input is the discussion topic and user opinions, and the output is a log of the exchange of opinions.

(Application Example 2)

Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the headset-type terminal 314 will be referred to as the "terminal."

Conventional educational support systems have difficulty providing individual learning methods based on the cognitive characteristics of each student, and in particular, do not provide a learning experience that takes into account their emotional state, resulting in insufficient learning outcomes. Furthermore, they are unable to effectively resolve questions in real time during learning or exchange opinions with other students, resulting in a lack of progress management and feedback provision. It is necessary to solve these issues and provide the optimal learning environment for each student.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes means for inputting basic information about the subject, means for determining the cognitive characteristics of the subject, means for proposing an optimal learning method based on the determination results, means for providing learning content, means for tracking learning progress and administering comprehension tests, means for providing individual feedback to the subject, means for resolving questions during learning in real time, means for recognizing the subject's emotional state and providing feedback accordingly, and means for delivering educational content via a head-mounted display. This makes it possible to provide an optimal learning experience that takes into account the subject's cognitive characteristics and emotional state. Furthermore, by encouraging real-time question resolution and opinion exchange, learning efficiency can be improved.

"Means for entering basic information about the subject" refers to a function that allows basic learning-related information, such as the subject's name, age, grade, and areas of interest, to be entered through a user interface and stored in a database.

"Means for assessing the subject's cognitive characteristics" refers to a function that provides questions and tasks to assess the subject's cognitive features and characteristics, and collects and analyzes the responses.

"Means for suggesting optimal learning methods based on assessment results" is a function that recommends the most suitable learning style and learning materials for each individual based on the assessment results of their cognitive characteristics.

"Means for providing learning content" refers to the function of selecting and delivering learning materials to be provided to the target audience, such as videos, texts, and audio materials.

"Means for tracking learning progress and conducting comprehension tests" refers to a function that monitors the learning progress of participants and periodically conducts tests to assess their comprehension.

"Means for providing individual feedback to the subject" refers to a function for providing appropriate advice and guidance to the subject individually based on their learning outcomes and progress.

"Means to resolve questions during learning in real time" refers to a function that provides immediate answers to questions or queries that students encounter while learning.

"Means for recognizing the subject's emotional state and providing feedback accordingly" refers to a function that monitors the subject's facial expressions, tone of voice, etc. in real time, analyzes their emotional state, and provides appropriate feedback.

"Means for delivering educational content through a head-mounted display" refers to a function that uses a head-mounted display (HMD) to effectively deliver visual and auditory educational content to the target audience.

This invention is an AI system designed to support education, determining the cognitive characteristics and emotional state of the subject and providing optimal learning methods and content based on that. This system functions among four parties: a server, a terminal, a user, and an emotion engine.

First, the user enters basic information. This is done through a user interface provided by the server, and includes information such as name, age, grade, and areas of interest. The entered information is saved using a database management system such as SQLite. This information is used for subsequent cognitive characteristic assessment and learning content customization.

Next, a cognitive trait assessment test is conducted to determine the subject's cognitive traits. The CognitiveTest module is used, and a series of questions and tasks are displayed in the user interface. The user answers these questions, and the responses are collected and analyzed by the server. This identifies visual, linguistic, and auditory cognitive traits.

Based on the assessment results, the server suggests the most appropriate learning method. For example, video materials may be suggested for visually dominant students, and text materials for verbally dominant students. These learning methods and content are provided via devices such as head-mounted displays (HMDs). Using an HMD makes it possible to make the learning experience visual and interactive.

As learning progresses, the server tracks learning progress and periodically administers comprehension tests. The UserTracking module monitors the student's learning progress, and comprehension tests are administered based on that data. Test results are stored in a database and used to adjust subsequent learning plans.

Furthermore, an emotion engine is used to monitor the user's emotional state in real time. The camera and microphone installed in the HMD are used to analyze facial expressions and tone of voice to recognize the user's emotional state. For example, if the server determines that the subject is feeling tired, it will provide feedback such as "Take a short break."

Real-time question resolution is also an important feature. The server accepts questions via the device and provides instant answers generated by AI. This is effective in immediately resolving any difficulties users may have while studying and maintaining continuity in their learning.

Finally, the server periodically provides discussion topics to encourage the exchange of ideas between users. This functionality is realized through the DiscussionModule, allowing users to deepen their understanding by exchanging ideas with other users about what they have learned.

Below is an example of a prompt:

"D is a first-year high school student who is interested in math. Please suggest the best study method for D based on his cognitive characteristics and emotional state. What should we do if D shows no interest while studying?"

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

The server provides a user interface to the terminal for entering basic user information. The user enters basic information such as name, age, grade, and areas of interest. This entered data is sent to the server and stored in a database. The input here is the user's basic information, and the output is the user information stored in the database.

Step 2:

The server displays the cognitive characteristics assessment test interface on the terminal. The user clicks the test start button on the terminal to begin the cognitive characteristics assessment test. The terminal displays the questions received from the server to the user and accepts the user's answers. The user inputs answer data, which the server collects and analyzes in real time. The output is the user's cognitive characteristics assessment results.

Step 3:

The server suggests the optimal learning method based on the cognitive characteristic assessment results. Based on the assessment results, the server selects the most appropriate learning method from various options (video, text, audio materials, etc.). This selected learning method is displayed from the server on the user's device. The input is the cognitive characteristic assessment results, and the output is a suggestion of the optimal learning method.

Step 4:

The server provides the user with the most suitable learning content. The user receives the video and text materials provided by the server via their terminal. The input here is information on the most suitable learning method, and the output is the learning content itself. The learning content is delivered via a head-mounted display (HMD), which the user uses to progress through their studies.

Step 5:

The server tracks learning progress and conducts comprehension tests. Each time a user uses learning content, the server monitors their progress in real time. It also periodically presents tests to assess comprehension, which the user answers. Based on the response data, the server evaluates the level of comprehension and stores this in a database. The input is learning progress data and test answers, and the output is the comprehension assessment results.

Step 6:

The server provides the ability to resolve questions during learning in real time. When a user enters a question into their device, the question is sent to the server. The server analyzes the question and instantly generates an answer, which is sent to the device. The input is the user's question data, and the output is the answer to that question.

Step 7:

The server uses an emotion engine to monitor the user's emotional state in real time. The HMD's built-in camera and microphone collect the user's facial expressions and tone of voice, which are then analyzed by the emotion engine. If the server determines that the user is tired based on this, it provides feedback such as "Take a short break." The input is the user's facial expressions and voice data, and the output is the emotional state analysis results and feedback.

Step 8:

The server periodically provides users with discussion topics and encourages exchange of opinions between users. The server selects the topics and displays them on the user's device. Users use their device to exchange opinions with other users, and the content is sent to the server. The input is the discussion topic and user opinion data, and the output is the result of the exchange of opinions.

The specific processing unit 290 transmits the results of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

Data generation model 58 is what is known as generative AI (artificial intelligence). Examples of data generation model 58 include generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>) and Gemini (Internet search <URL: https://gemini.google.com/?hl=ja>). Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input. Data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

In the above embodiment, an example was given in which the specific processing was performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the headset-type terminal 314.

[Fourth embodiment]

Figure 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

As shown in FIG. 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

The data processing device 12 includes a computer 22, a database 24, and a communication I/F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and communication I/F 26 are also connected to the bus 34. The communication I/F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and/or a LAN (Local Area Network).

The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I/F 44, and a control target 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and control target 443 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user 20's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

The communication I/F 44 is connected to the network 54. The communication I/Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I/Fs 44 and 26 is carried out in a secure manner.

The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32.

The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

Storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

In the robot 414, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

Next, we will explain the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

This invention is an AI system designed to support education. It assesses the cognitive characteristics of each individual and proposes individually appropriate learning methods to maximize learning effectiveness and address educational disparities, school absenteeism, and support for people with disabilities. This system functions between a server, a terminal, and a user.

Overall system overview

This system provides educational support tailored to each individual by performing the following processes:

1. Obtain basic information about the subject.

2. Determine the subject's cognitive characteristics.

3. Suggest the optimal learning method based on the assessment results.

4. Provide learning content.

5. Track learning progress and administer comprehension tests.

6. Provide personalized feedback.

7. Resolve questions while studying in real time.

8. Suggest discussion topics and encourage exchange of opinions.

Program processing

Below is a natural language explanation of the program processing at each step of this system.

1. User Registration

The server provides a user registration page.

Users use the device to enter basic information such as name, age, grade, and areas of interest.

The server stores the user's basic information in a database.

2. Cognitive trait assessment test

The server displays the cognitive characteristics assessment test interface on the terminal.

The user clicks the Start Test button to begin the test.

The server sends a series of questions and tasks to the device in sequence.

The device displays each question or task to the user and accepts their response.

Users answer each question on their device.

The server receives responses from users and stores them in a database in real time.

The server analyzes the collected data and determines the user's visual, language, and auditory cognitive characteristics.

3. Learning Method Suggestions

Based on the results of the assessment, the server selects the optimal learning method for the user.

The server will present the selected learning method and related learning materials to the device.

The device will display learning method suggestions and introductions to learning materials to the user.

4. Providing learning content

The server provides learning content appropriate to the user (video, text, audio materials, etc.).

Users can check the learning content on their device and begin learning.

The device records the user's learning progress in real time.

5. Real-time support

The server provides an interface through the terminal that accepts questions the user may have while studying.

The user enters a question on the device and submits it.

The server analyzes the question and provides an appropriate answer instantly.

The device displays the response from the server to the user.

6. Progress Tracking and Comprehension Testing

The server periodically displays an interface on the terminal that conducts comprehension tests.

Users take the test on their device.

The device sends the user's response to the server.

The server scores the answers, stores the results in a database, and provides feedback to the user.

7. Individual feedback and suggestions for next steps

The server analyzes the results of the user's comprehension test and suggests the next learning content and assignments.

The device will display feedback and new challenges to the user.

Users will work on the proposed tasks on their devices.

8. Promoting discussion and communication

The server will provide regular discussion topics.

The device displays discussion topics to the user and provides a form where they can enter their responses and opinions.

Users can enter their opinions on their devices and exchange them with other users.

Specific examples

For example, let's say Mr. C is a second-year junior high school student who wants to study history.

1. User Registration

Mr. C types "Second year junior high school student, interested in history" into his device.

The server stores this information.

2. Cognitive trait assessment test

Mr. C clicks the start test button on his device.

The server will begin the validation test.

Questions such as "Is it easy to remember with an image?" will be displayed on the device.

Mr. C answers the questions.

The server analyzes Mr. C's answers and determines that he is visually dominant.

3. Learning Method Suggestions

The server suggests, "Learn about historical events through videos."

A link to the video material will appear on your device.

4. Providing learning content

Mr. C starts watching the video on his device.

5. Real-time support

Mr. C types the question into his terminal: "I don't understand the background of this war."

The server provides the answer, which is displayed on the device.

6. Progress Tracking and Comprehension Testing

After watching the video, the server will conduct a comprehension test.

Mr. C takes the test on a terminal.

7. Individual feedback and suggestions for next steps

The server analyzes the test results and suggests, "Next, let's learn more about the causes of this war."

The next learning content will be displayed to Mr. C on his device.

8. Promoting discussion and communication

The server suggests that next week's theme is 'Important Figures in Modern History' and provides a platform for users to exchange opinions.

Mr. C can enter his opinions on the device and discuss them with his classmates.

In this way, the AI system can provide an optimal educational experience tailored to individual learning needs, creating an environment in which Mr. C can learn independently.

The processing flow is explained below.

Step 1:

The server provides a user registration page. Users use their terminal to enter basic information such as name, age, grade, and areas of interest. The server stores the entered basic information in a database.

Step 2:

The server displays the cognitive characteristics assessment test interface on the terminal. The user clicks the Start Test button to begin the test.

Step 3:

The server sequentially sends a series of questions and tasks to the terminal. The terminal displays the questions and tasks to the user and accepts answers. The user answers each question on the terminal.

Step 4:

The server receives the user's responses and stores them in a database in real time. The server analyzes the collected data and determines the user's visual, language, and auditory cognitive characteristics.

Step 5:

Based on the results of the assessment, the server selects the most suitable learning method for the user. The server then presents the selected learning method and related learning materials to the device. The device then displays learning method suggestions and learning material introductions to the user.

Step 6:

The server provides learning content (video, text, audio materials, etc.) appropriate for the user. The user checks the learning content on their device and begins learning. The device records the user's learning progress in real time.

Step 7:

The server provides an interface that accepts questions that users have while studying via their terminal. The user enters and submits a question on the terminal. The server analyzes the question and instantly generates and provides an appropriate answer. The terminal displays the answer from the server to the user.

Step 8:

The server periodically displays an interface on the device that conducts comprehension tests. The user takes the test on the device. The device sends the user's answers to the server. The server scores the answers, stores the results in a database, and provides feedback to the user.

Step 9:

The server analyzes the results of the user's comprehension test and suggests the next learning content and assignments. The device displays feedback and new assignments to the user. The user then works on the suggested assignments.

Step 10:

The server periodically provides discussion topics. The terminal displays the discussion topics to the user and provides a form where they can enter their responses and opinions. Users enter their opinions on the terminal and exchange opinions with other users.

(Example 1)

Next, Example 1 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Modern education requires providing optimal learning methods based on individual cognitive characteristics. However, many current educational systems only offer uniform learning methods, making it difficult to address individual cognitive characteristics. Furthermore, they lack features such as learning progress tracking, comprehension tests, and real-time question resolution, making it impossible to adequately address individual learning needs. Furthermore, there are few systems that automatically suggest learning content or provide feedback, preventing the effectiveness of education from being maximized.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

In this invention, the server includes means for inputting basic information about the subject, means for determining the cognitive characteristics of the subject, means for proposing the optimal learning method based on the determination results, means for providing learning content, means for tracking learning progress and conducting comprehension tests, means for providing individual feedback to the subject, means for resolving questions during learning in real time, means for saving the subject's input and learning progress in a database, means for collecting the subject's questions and generating answers using a generative AI model, and means for evaluating the subject's answers and dynamically suggesting the next learning content. This makes it possible to provide the optimal learning method according to individual cognitive characteristics, track learning progress, resolve questions in real time, and dynamically suggest learning content.

"Target" refers to individuals who use educational support AI systems to advance their studies.

"Basic information" refers to basic information necessary for education, such as the subject's name, age, grade, and areas of interest.

"Cognitive characteristics" refer to the information processing characteristics of a subject, such as vision, language, and hearing, and indicate individual characteristics in learning.

"Learning methods" are the optimal learning tools and approaches suggested based on cognitive characteristics, including teaching materials and learning formats.

"Learning content" refers to learning videos, texts, audio materials, etc. provided to the target audience, and is an information resource to support learning.

"Learning progress" refers to the progress a subject makes as they progress through their studies.

A "comprehension test" is a test used to measure the subject's level of understanding of the learning content.

"Individualized feedback" refers to specific assessments and guidance provided to students based on the results of comprehension tests and their learning progress.

"Real-time question resolution" means providing immediate and appropriate answers to questions that students have while studying.

A "database" is a system that stores and manages data such as basic information about the subject, their learning progress, and their responses.

A "generative AI model" is an artificial intelligence model used to generate appropriate answers to user questions.

"Dynamic suggestions" means generating and suggesting the next content or assignment to be learned on an ongoing basis based on the student's learning progress and level of understanding.

This invention relates to an educational support AI system that maximizes learning effectiveness by providing optimal learning methods based on individual cognitive characteristics, and addresses educational disparities, school absenteeism, and support for people with disabilities. The system functions between a server, a terminal, and a user.

Overall system overview

This system provides tailored educational support by performing the following processes:

1. Obtain basic information about the subject.

2. Determine the subject's cognitive characteristics.

3. Suggest the optimal learning method based on the assessment results.

4. Provide learning content.

5. Track learning progress and administer comprehension tests.

6. Provide personalized feedback.

7. Resolve questions while studying in real time.

8. Suggest discussion topics and encourage exchange of opinions.

Hardware and software used

The hardware used includes a server and user devices. The server uses a database (e.g., MySQL, PostgreSQL) to manage and analyze user information, an execution environment for machine learning models (e.g., Python, Scikit-learn), a real-time chat system (e.g., Node.js), and a generative AI model (e.g., OpenAI GPT-3).

The user device provides an interface that runs on a web browser and handles user operations and learning progress using HTML, CSS, JavaScript, React, etc.

Program processing overview

User Registration

The server generates a user registration page using HTML and CSS and sends it to the device. The user enters basic information such as name, age, grade, and areas of interest on the device and submits it. The server receives the information in JSON format and stores it in a database.

Cognitive trait assessment test

The server uses JavaScript and React to generate the cognitive trait assessment test interface and display it on the device. The user starts the test and answers a series of questions. The server receives the answers, stores them in a database, and uses a Python machine learning model to analyze the data and determine cognitive traits.

Suggested learning methods

The server determines the optimal learning method based on cognitive characteristics and selects relevant learning materials. This is then sent to the device, which then presents it to the user.

Providing learning content

The server provides selected learning content, which users use on their devices to progress through their studies. The devices record learning progress in real time and send it to the server.

Real-time support

The server accepts the user's question through the device and generates an appropriate answer using a generative AI model. This is then sent to the device and displayed.

Progress tracking and comprehension testing

The server periodically conducts comprehension tests and stores the results in a database. It provides feedback to the user and suggests what to study next.

Individual feedback and suggestions for next steps

The server analyzes the next learning content based on the test results and provides feedback and new challenges to the user, thereby presenting the optimal learning path for each individual user.

Fostering discussion and communication

The server periodically generates new discussion topics and presents them to users. Users can then enter their opinions on their devices and exchange them with other users.

Specific examples

For example, let's say Mr. C is a second-year junior high school student who wants to study history.

1. Mr. C types "Second year junior high school student, interested in history" into his device and submits the information. The server stores this information.

2. Mr. C starts the cognitive characteristics assessment test on his device. The server displays the questions, and Mr. C enters the answers. The server analyzes the answers and determines that he is visually dominant.

3. The server suggests, "Let's learn about historical events through videos," and displays a link to the video material on the device.

4. Mr. C begins watching the video on his device. The device records his learning progress and sends it to the server.

5. When Mr. C enters a question such as "I don't understand the background of this war," the server uses a generative AI model to generate and display an answer.

6. After watching the video, the server conducts a comprehension test, and Mr. C takes the test. The results are sent to the server, which then suggests the next learning content.

7. The server will suggest a new task, saying, "Next, let's learn more about the causes of this war," and display this on the terminal.

8. The server suggests that next week's topic is "Important Figures in Modern History" and provides a discussion platform. C can enter his opinions and discuss them with his classmates.

Example prompt

"If you have a second-year junior high school student who wants to study history, how would you use AI to suggest the best learning method? Please explain the specific process."

In this way, AI systems can provide optimal educational experiences tailored to individual learning needs and create an environment in which students can learn independently.

The flow of the identification process in Example 1 will be explained using Figure 11.

Step 1:

User Registration

The server generates a user registration page using HTML and CSS and sends it to the device. The user enters basic information such as name, age, grade, and areas of interest on the device and submits it. The server receives the received information in JSON format and stores it in a database. This registers the user's basic information in the database. The input is the user's basic information, and the output is the user's basic information stored in the database.

Step 2:

Cognitive trait assessment test

The server uses JavaScript and React to generate an interface for the cognitive trait assessment test and displays it on the device. The user clicks a button to start the test and answers a series of questions. The device displays each question and accepts the user's answers. The server stores the received answers in a database and analyzes the data using a Python machine learning model to determine the user's cognitive traits. The input is the user's answers, and the output is the determined cognitive traits. This reveals the user's cognitive traits.

Step 3:

Study method suggestions

The server selects the optimal learning method based on cognitive characteristics and selects related learning materials. The server sends the selected learning method and learning materials to the terminal via a REST API. The terminal displays this to the user. The input is cognitive characteristics and a learning materials database, and the output is a link to the learning method and learning materials presented to the user. This allows the user to be presented with the optimal learning method.

Step 4:

Providing learning content

The server dynamically generates learning content appropriate for the user and sends it to the device. The user checks the learning content on the device and begins learning. The device records learning progress and periodically sends it to the server. The input is the user's cognitive characteristics and progress, and the output is the learning content provided and a record of learning progress. This allows users to study with content that suits them.

Step 5:

Real-time support

The server provides an interface that accepts user questions via the terminal. The user enters the question on the terminal and sends it to the server. The server analyzes the question and generates the optimal answer using a generative AI model. The server sends the answer to the terminal, which displays it to the user. The input is the user's question, and the output is the generated answer. This allows the user's question to be resolved instantly.

Step 6:

Progress tracking and comprehension testing

The server periodically displays an interface on the device that conducts comprehension tests. The user takes the test on the device and sends their answers to the server. The server scores the answers and stores the results in a database. It then generates feedback based on the results and sends it to the device. The input is the comprehension test answers, and the output is the test results and feedback. This evaluates the user's level of understanding and determines the next learning content.

Step 7:

Individual feedback and suggestions for next steps

The server analyzes the test results and determines the next learning content. The server sends feedback and new assignments to the device, which displays them to the user. The user then works on the suggested assignments. The input is the test results and learning goals, and the output is feedback and new suggested assignments. This allows the user to continuously work on appropriate assignments.

Step 8:

Fostering discussion and communication

The server periodically generates discussion topics and sends them to the device. Users input their opinions on their device and send them to the server. The server collects the opinions and displays them to other users, allowing for an exchange of opinions. The input is the user's opinion, and the output is the shared opinion and discussion results. This promotes communication between users and leads to deeper understanding.

(Application Example 1)

Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

With conventional educational systems, it was difficult to efficiently grasp the cognitive characteristics of individual students and propose optimal learning methods based on those characteristics. Furthermore, there was a lack of educational support for industrial product operation methods and control technologies in industrial settings, and automatic and effective measures were needed to improve engineers' skills.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

In this invention, the server includes means for inputting basic information about the subject, means for assessing the cognitive characteristics of the subject, means for proposing the optimal learning method based on the assessment results, means for providing learning content, means for tracking learning progress and conducting comprehension tests, means for providing individual feedback and proposing the next assignment to the subject, means for resolving questions during learning in real time, means for promoting the exchange of opinions between subjects, and means for supporting learning of industrial product operation methods and control technology. This makes it possible to propose the optimal learning method for each subject's cognitive characteristics and to automatically and effectively educate them on industrial product operation methods and control technology.

1. "Basic information of the subject" refers to personal information such as the subject's name, age, years of experience, and area of expertise.

2. "Cognitive characteristics" refers to the individual characteristics of how a subject receives, processes, and understands information. Examples include visual dominance, auditory dominance, and linguistic dominance.

3. "Optimal learning method" refers to the most effective learning method based on the cognitive characteristics of the individual.

4. "Learning Content" refers to the learning materials and information provided to the target audience for learning, including video, text, and audio materials.

5. "Tracking learning progress" refers to recording and managing a student's learning progress in real time.

6. "Comprehension test" refers to a test used to assess the extent to which a subject has understood the learning content.

7. "Individualized feedback" refers to specific advice and next steps provided based on the learner's learning progress and level of understanding.

8. "Resolving questions during learning in real time" refers to providing immediate answers to questions that arise when a student is learning.

9. "Promoting the exchange of opinions" refers to support provided to stimulate information sharing and discussion among participants.

10. "Supporting learning about industrial product operation methods and control technologies" refers to educational support to promote understanding of the correct operation methods and control technologies of industrial products and equipment used in industry.

Overall system configuration

This invention describes a specific form for implementing an educational support system consisting of a server, a terminal, and a user. This system proposes optimal learning methods based on the cognitive characteristics of each individual, and supports education on industrial product operation methods and control technologies.

Hardware and software used

This invention uses the following hardware and software:

Server: High-performance server (e.g., AWS, Google Cloud)

Devices: Smartphones, factory robot HMIs (Human-Machine Interfaces), PCs

Front-end: Angular, HTML5, CSS3, JavaScript

Backend: Node.js, MySQL

AI modeling: Python, TensorFlow, Socket.IO

Program processing

1. User Registration

The server provides a user registration page using Angular on the front end.

Users use the terminal to enter basic information such as their name, age, years of experience, and area of expertise.

The server receives this information via Node.js and stores it in a MySQL database.

2. Assessment of cognitive characteristics

The server uses an Angular front-end to provide an interface for questions and tasks that assess cognitive characteristics.

The user starts the test on their device and answers the questions.

The server receives the responses and analyzes the data using Python and TensorFlow to determine cognitive characteristics.

3. Learning Method Suggestions

Based on the analysis results, the server selects the optimal learning method and suggests corresponding learning materials.

The device will display the suggestions and teaching materials to the user.

4. Providing learning content

The server provides learning content (video, audio, text) that is best suited to the user's cognitive characteristics.

Users can view this content on their devices and progress through their studies, with their progress recorded in real time.

5. Real-time support

If users have any questions while studying, they can enter them using their smartphone or tablet.

The server uses Socket.IO to receive questions in real time, processes them in Python, and provides instant answers.

6. Progress Tracking and Comprehension Testing

The server periodically conducts comprehension tests, analyzes the results, stores them in a database, and provides feedback to the user.

7. Individual feedback and suggestions for next steps

The server suggests the next learning task based on the test results and provides related learning content.

8. Promoting discussion and communication

The server will regularly provide discussion topics and an interface to encourage the exchange of opinions between users.

Users can enter their opinions on their devices and discuss with other users.

Examples and prompts

As a concrete example, consider the case where engineer A is learning how to operate a new industrial robot.

If Engineer A enters "Years of experience: 5 years, Specialty: Industrial robot control" and has visually dominant cognitive characteristics, the system will suggest, "Let's learn the basic operation of industrial robots through video materials." Engineer A will begin learning by watching the video materials, and if he has any questions, he can ask, "Please tell me in detail about how the sensors on this robot work." The server will immediately provide an appropriate answer to this question.

Examples of prompt statements include:

"For an engineer with five years of robot operation experience, please suggest a method of operating an industrial robot that is easy for someone with visually dominant cognitive characteristics to learn. Also, please suggest related video teaching materials."

Some examples include:

In this way, it is possible to propose learning methods that are optimal for each individual's cognitive characteristics, and to automatically and effectively educate them on how to operate industrial products and control technologies.

The flow of the specific processing in Application Example 1 will be explained using Figure 12.

Step 1:

User Registration

The server provides a user registration page using Angular on the front end.

Users use the terminal to enter basic information such as their name, age, years of experience, and area of expertise.

The basic information entered is sent from the terminal to the server, which receives this information via Node.js and stores it in a MySQL database.

Enter basic information such as name, age, years of experience, and area of expertise.

Output: Basic user information stored in the database

Step 2:

Assessing cognitive characteristics

The server uses an Angular front-end to provide an interface for questions and tasks that assess cognitive characteristics.

The user starts the test on their device and answers the questions.

The server receives the response data and analyzes it using Python and TensorFlow to determine the user's cognitive characteristics. Categories of cognitive characteristics such as visual, auditory, and language are determined.

Input: User response data

Output: Cognitive characteristics (visual, auditory, language, etc.)

Step 3:

Study method suggestions

Based on the results of the cognitive characteristic analysis, the server selects the optimal learning method and suggests corresponding learning materials.

The device will display the suggestions and teaching materials to the user.

For example, video learning materials are suggested to visually-oriented users.

Input: Cognitive trait analysis results

Output: Suggested learning methods and materials

Step 4:

Providing learning content

The server provides learning content (video, audio, text) that is best suited to the user's cognitive characteristics.

Users can view this content on their devices and progress through their studies. Progress is recorded in real time from the device to the server.

Input: Suggested learning methods and materials

Output: Providing learning content and recording progress

Step 5:

Real-time support

If users have any questions while studying, they can enter them using their smartphone or tablet.

The server uses Socket.IO to receive questions in real time, processes them in Python, and provides instant answers.

Input: User question

Output: Answers provided in real time

Step 6:

Progress tracking and comprehension testing

The server periodically conducts comprehension tests, analyzes the results, stores them in a database, and provides feedback to the user.

The terminal displays the test results to the user.

Input: Comprehension test response data

Output: Analysis of test results and feedback

Step 7:

Individual feedback and suggestions for next steps

Based on the results of the comprehension test, the server suggests the next learning task and provides related learning content.

The device will display the next task and related content to the user.

Input: Comprehension test results

Output: Providing the next assignment and related learning content

Step 8:

Fostering discussion and communication

The server will regularly provide discussion topics and an interface to encourage the exchange of opinions between users.

Users can enter their opinions on their devices and discuss with other users.

Input: Discussion topic and user opinions

Output: Facilitated exchange of ideas and shared opinions

In this way, by clarifying the roles of the server, terminal, and user at each step and organizing the specific operations and inputs/outputs, the educational support system is designed to function effectively.

It is also possible to further combine an emotion engine that estimates the user's emotion. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

This invention is an AI system designed to support education. It maximizes learning effectiveness by assessing the cognitive characteristics of each individual and proposing individually appropriate learning methods. Furthermore, by combining it with an emotion engine, it provides an optimal learning experience by taking into account the emotional state of the individual. This system functions among four parties: a server, a terminal, a user, and an emotion engine.

Overall system overview

This system provides educational support tailored to the target individual by performing the following processes:

1. Obtain basic information about the subject.

2. Determine the subject's cognitive characteristics.

3. Suggest the optimal learning method based on the assessment results.

4. Provide learning content.

5. Track learning progress and administer comprehension tests.

6. Provide personalized feedback.

7. Resolve questions while studying in real time.

8. Use an emotion engine to recognize and respond to the subject's emotional state.

9. Suggest discussion topics and encourage exchange of ideas.

Program processing

Below is a natural language explanation of the program processing at each step of this system.

1. User Registration

The server provides a user registration page.

Users use the device to enter basic information such as name, age, grade, and areas of interest.

The server stores the basic information entered in a database.

2. Cognitive trait assessment test

The server displays the cognitive characteristics assessment test interface on the terminal.

The user clicks the Start Test button to begin the test.

3. Conduct cognitive assessment tests

The server sends a series of questions and tasks to the device in sequence.

The device displays questions and tasks to the user and accepts answers.

Users answer each question on their device.

The server receives responses from users and stores them in a database in real time.

The server analyzes the collected data and determines the user's visual, language, and auditory cognitive characteristics.

4. Learning Method Suggestions

Based on the results of the assessment, the server selects the optimal learning method for the user.

The server will present the selected learning method and related learning materials to the device.

The device will display learning method suggestions and introductions to learning materials to the user.

5. Providing learning content

The server provides learning content appropriate to the user (video, text, audio materials, etc.).

Users can check the learning content on their device and begin learning.

The device records the user's learning progress in real time.

6. Real-time support

The server provides an interface via the terminal that accepts questions the user may have while studying.

The user enters a question on the device and submits it.

The server analyzes the question and instantly generates and provides an appropriate answer.

The device displays the response from the server to the user.

7. Progress Tracking and Comprehension Testing

The server periodically displays an interface on the terminal that conducts comprehension tests.

Users take the test on their device.

The device sends the user's response to the server.

The server scores the answers, stores the results in a database, and provides feedback to the user.

8. Individual feedback and suggestions for next steps

The server analyzes the results of the user's comprehension test and suggests the next learning content and assignments.

The device will display feedback and new challenges to the user.

Users will work on the proposed tasks.

9. Emotion Recognition and Response

The server uses an emotion engine to monitor the user's emotional state in real time through the device while they are learning.

The emotion engine identifies emotions from the user's facial expressions and tone of voice, and stores the emotional state in a database.

The server provides optimal feedback and adjusts learning content based on the user's emotional state.

10. Promoting discussion and communication

The server will provide regular discussion topics.

The device displays discussion topics to the user and provides a form where they can enter their responses and opinions.

Users can enter their opinions on their devices and exchange them with other users.

Specific examples

For example, let's say Mr. D is a first-year high school student who wants to study mathematics.

1. User Registration

Mr. D types "First year high school student, interested in mathematics" into his device.

The server stores this information.

2. Cognitive trait assessment test

Mr. D clicks the start test button on his device.

The server will begin the validation test.

Questions such as "Is it easy to remember with an image?" will be displayed on the device.

Mr. D answers the questions.

The server analyzes Mr. D's answers and determines that he is visually dominant.

3. Learning Method Suggestions

The server suggests, "Learn math concepts through videos."

A link to the video material will appear on your device.

4. Providing learning content

Mr. D starts watching the video on his device.

5. Real-time support

Mr. D types a question into his terminal: "I don't know how to use this formula."

The server provides the answer, which is displayed on the device.

6. Progress Tracking and Comprehension Testing

After watching the video, the server will conduct a comprehension test.

Mr. D takes the test on the terminal.

7. Individual feedback and suggestions for next steps

The server analyzes the test results and suggests, "Next, try solving some real problems."

The next learning content will be displayed to Mr. D on his device.

8. Emotion Recognition and Response

The server uses an emotion engine to analyze Mr. D's facial expressions and tone of voice while he is studying.

If it is determined that Mr. D is feeling tired, the server will display a message on his device saying, "Let's take a short break."

Also, if Mr. D is in a state of joy or adaptation, he will be given feedback such as "Good luck," encouraging him to continue learning.

9. Promoting discussion and communication

The server suggests that next week's theme is 'Applications of Functions' and provides a platform where users can exchange opinions.

Mr. D can enter his opinions on the device and discuss them with his classmates.

In this way, the AI system can provide an optimal educational experience tailored to individual learning needs, creating an environment in which D can learn independently. By combining it with an emotion engine, it is possible to respond according to the user's emotional state, further improving learning effectiveness.

The processing flow is explained below.

Step 1:

The server provides a user registration page. Users use their terminal to enter basic information such as name, age, grade, and areas of interest. The server stores the entered basic information in a database.

Step 2:

The server displays the cognitive characteristics assessment test interface on the terminal. The user clicks the Start Test button to begin the test.

Step 3:

The server sequentially sends a series of questions and tasks to the terminal. The terminal displays the questions and tasks to the user and accepts answers. The user answers each question on the terminal.

Step 4:

The server receives the user's responses and stores them in a database in real time. The server analyzes the collected data and determines the user's visual, language, and auditory cognitive characteristics.

Step 5:

Based on the results of the assessment, the server selects the most suitable learning method for the user. The server then presents the selected learning method and related learning materials to the device. The device then displays learning method suggestions and learning material introductions to the user.

Step 6:

The server provides learning content (video, text, audio materials, etc.) appropriate for the user. The user checks the learning content on their device and begins learning. The device records the user's learning progress in real time.

Step 7:

The server provides an interface that accepts questions that users have while studying via their terminal. The user enters and submits a question on the terminal. The server analyzes the question and instantly generates and provides an appropriate answer. The terminal displays the answer from the server to the user.

Step 8:

The server periodically displays an interface on the device that conducts comprehension tests. The user takes the test on the device. The device sends the user's answers to the server. The server scores the answers, stores the results in a database, and provides feedback to the user.

Step 9:

The server analyzes the results of the user's comprehension test and suggests the next learning content and assignments. The device displays feedback and new assignments to the user. The user then works on the suggested assignments.

Step 10:

The server uses an emotion engine to monitor the user's emotional state in real time via the device while they are learning. The emotion engine identifies emotions from the user's facial expressions and tone of voice, and stores the emotional state in a database. The server then provides optimal feedback and adjusts the learning content based on the user's emotional state.

Step 11:

The server periodically provides discussion topics. The terminal displays the discussion topics to the user and provides a form where they can enter their responses and opinions. Users enter their opinions on the terminal and exchange opinions with other users.

(Example 2)

Next, Example 2 will be described. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

Traditional educational systems have struggled to provide learning methods that fully consider the cognitive characteristics and emotional state of each individual student. They also lacked functionality for resolving questions that arise during learning in real time and for facilitating the exchange of opinions between students. This has made it difficult to provide an optimal learning experience for each student, resulting in the issue of not maximizing learning effectiveness.

The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting basic information about the subject, means for determining the cognitive characteristics of the subject, means for proposing an optimal learning method based on the determination results, means for providing learning content, means for tracking learning progress and conducting comprehension tests, means for providing individual feedback to the subject, means for resolving questions during learning in real time, and means for recognizing and responding to the subject's emotional state using an emotion engine. This makes it possible to provide an optimal learning experience tailored to the subject's individual needs, maximizing learning effectiveness. Furthermore, by encouraging the exchange of opinions between subjects as needed, improvement in the quality of learning can also be expected.

"Target audience" refers to learners who use the educational system.

"Basic information" refers to basic data such as the subject's name, age, grade, and areas of interest.

"Cognitive characteristics" refers to the subject's cognitive style, such as visual, linguistic, and auditory.

"Decision testing" refers to a series of questions or tasks designed to assess a subject's cognitive characteristics.

"Feedback" refers to individual evaluations and advice provided based on the subject's learning results.

"Learning method" refers to the learning process and techniques proposed based on the cognitive characteristics of the target individual.

"Learning content" includes learning materials and resources, such as videos, texts, and audio materials.

An "emotion engine" refers to a system that has the ability to recognize the emotional state of a subject and process that data.

"Real-time support" refers to the ability to instantly respond to questions or concerns that students may have while studying.

"Discussion Topics" refer to topics that the server periodically provides for the exchange of opinions among participants.

"Opinion exchange" refers to the activity of sharing opinions and thoughts among participants.

"Server" refers to the central computer that runs the entire system and provides each function.

"Terminal" refers to the computer or mobile device used by a Subject to access the System.

This invention is an AI system designed to support education, maximizing learning effectiveness by determining the cognitive characteristics of each individual and proposing individually appropriate learning methods. Furthermore, by combining it with an emotion engine, it provides an optimal learning experience by taking into account the emotional state of the individual. This system functions among four parties: a server, a terminal, a user, and an emotion engine.

Overall system overview

This system provides educational support tailored to each individual through the following steps:

1. Obtain basic information about the subject.

2. Determine the subject's cognitive characteristics.

3. Suggest the optimal learning method based on the assessment results.

4. Provide learning content.

5. Track learning progress and administer comprehension tests.

6. Provide personalized feedback.

7. Resolve questions while studying in real time.

8. Use an emotion engine to recognize and respond to the subject's emotional state.

9. Suggest discussion topics and encourage exchange of ideas.

Program processing

Processing in this system is carried out through cooperation between the server and the terminal. Details are provided below.

Hardware and Software Use

The server uses a MySQL database to store and manage data.

The server uses Python to implement data analysis and decision algorithms.

The server provides a front end consisting of JavaScript and HTML.

Devices access the server using a browser such as Google Chrome.

The emotion engine uses a camera and microphone to analyze the user's facial expressions and tone of voice.

Specific examples

For example, if Mr. D is a first-year high school student who wants to study mathematics, consider the following:

1. User Registration

D enters "First year high school student, interested in mathematics" into the terminal. The form displayed on the terminal has fields for entering name, age, grade, and areas of interest.

When Mr. D clicks the "Register" button, the server receives the input data and saves it in the MySQL database.

2. Cognitive trait assessment test

Mr. D clicks the start test button on his device. The server sends the assessment test UI, composed of HTML and JavaScript, to the device and asks a series of questions.

Questions such as "Is it easy to remember with an image?" are displayed on the device. Mr. D answers the questions, and the answers are saved in a database in real time.

3. Learning Method Suggestions

Based on Mr. D's cognitive characteristics, the server suggests, "Let's learn mathematical concepts through videos." A link to the video material is displayed on the device.

4. Providing learning content

Mr. D begins watching the video on his device. The server provides the video material via a video streaming service.

5. Real-time support

While learning, Mr. D enters a question into his device, such as "I don't know how to use this formula." The server receives the question, analyzes it using natural language processing (NLP), and instantly generates an appropriate answer, which is displayed on the device.

6. Progress Tracking and Comprehension Testing

After watching the video, the server periodically conducts comprehension tests. Mr. D takes the test on his device and his answers are sent to the server. The server uses an automatic scoring system to score the answers and stores the results in a database.

7. Individual feedback and suggestions for next steps

Based on the test results, the server suggests, "Next, try solving some real problems." The next learning content is displayed to Mr. D on his device.

8. Emotion Recognition and Response

The server uses an emotion recognition algorithm to monitor D's facial expressions and tone of voice in real time while he studies.

If D is identified as feeling tired, the server will display a message on his device saying, "Take a short break." If he is in a state of joy or adaptation, the server will provide feedback saying, "Keep it up," encouraging him to continue learning.

9. Promoting discussion and communication

The server suggests that next week's theme is 'Application of Functions' and provides a platform where users can exchange opinions. Mr. D can enter his opinion on his device and discuss it with his classmates.

Example prompt

"Please explain how to register by entering basic user information."

"Please explain with specific examples what types of questions will be asked during the cognitive assessment test."

"Please explain how you handle real-time support when users have questions while learning."

"Please explain the process of recognizing and responding to user emotions using the emotion engine."

This allows the AI system to provide an optimal educational experience tailored to the user's individual learning needs, improving learning outcomes. By combining it with an emotion engine, it is possible to respond according to the user's emotional state, further improving learning outcomes.

The flow of the identification process in Example 2 will be explained using Figure 13.

Step 1: User Registration

The server provides a user registration page. The user uses their terminal to enter basic information such as name, age, grade, and areas of interest. When the user clicks the "Register" button, the terminal sends the data to the server. The server receives the entered basic information and stores it in a database. The input here is the user's basic information, and the output is the data stored in the database.

Step 2: Cognitive trait assessment test

The server displays the cognitive ability assessment test interface on the terminal. The user clicks the test start button and answers the questions and tasks displayed on the terminal in sequence. The server receives each answer and stores them sequentially in a database. Based on the collected answer data, the server performs data analysis using Python to determine the user's visual, linguistic, and auditory cognitive abilities. The input is the user's test answers, and the output is the assessed cognitive abilities.

Step 3: Suggest a learning method

Based on the assessment results, the server selects the optimal learning method and related learning materials. It sends the selected information to the terminal and displays links to the learning method and learning materials to the user. The input is the assessed cognitive characteristics, and the output is the suggested learning method and links to the learning materials.

Step 4: Provide learning content

The server provides learning content such as videos, texts, and audio materials via a streaming service. Users check the learning content on their device and begin studying. The device records the user's learning progress (e.g., video viewing time and text completion status) in real time and sends it to the server. The input is the learning content, and the output is learning progress data.

Step 5: Real-time support

The server provides an interface through the terminal for users to input questions they have while studying. The user enters the question on the terminal and sends it. The server uses NLP (natural language processing) functions to analyze the question, generates an appropriate answer, and sends it to the terminal. The terminal displays the answer to the user. The input is the user's question, and the output is the generated answer.

Step 6: Progress Tracking and Comprehension Testing

The server displays an interface on the terminal that periodically conducts comprehension tests. The user takes the test on the terminal and submits their answers. The server scores the answers using an automatic scoring system and stores the results in a database. The server then provides feedback on the results to the user. The input is the test answers, and the output is the scoring results and feedback.

Step 7: Individual feedback and suggestions for next steps

The server selects the next learning content and assignments based on the results of the comprehension test. The selected information is sent to the device, and feedback and new assignments are displayed to the user. The user then works on the suggested assignments. The input is the test results, and the output is feedback and suggested assignments.

Step 8: Emotion Recognition and Responding

The server uses an emotion engine to monitor the user's facial expressions and tone of voice in real time via the device while they are studying. It identifies the user's emotional state and stores it in a database. The server then provides optimal feedback and adjusts the learning content based on the user's emotional state. For example, if the user is identified as tired, it will suggest a break, and if they are in an adaptive state, it will display an encouraging message. The input is the user's facial expression and tone of voice data, and the output is the emotion analysis results and feedback based on them.

Step 9: Facilitate discussion and communication

The server periodically provides discussion topics and displays them on the terminal. Users enter their opinions on the topics and exchange them with other users. The server stores the posted opinions in a database and displays them to other participants in real time. The input is the discussion topic and user opinions, and the output is a log of the exchange of opinions.

(Application Example 2)

Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 will be referred to as the "terminal."

Conventional educational support systems have difficulty providing individual learning methods based on the cognitive characteristics of each student, and in particular, do not provide a learning experience that takes into account their emotional state, resulting in insufficient learning outcomes. Furthermore, they are unable to effectively resolve questions in real time during learning or exchange opinions with other students, resulting in a lack of progress management and feedback provision. It is necessary to solve these issues and provide the optimal learning environment for each student.

The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

In this invention, the server includes means for inputting basic information about the subject, means for determining the cognitive characteristics of the subject, means for proposing an optimal learning method based on the determination results, means for providing learning content, means for tracking learning progress and administering comprehension tests, means for providing individual feedback to the subject, means for resolving questions during learning in real time, means for recognizing the subject's emotional state and providing feedback accordingly, and means for delivering educational content via a head-mounted display. This makes it possible to provide an optimal learning experience that takes into account the subject's cognitive characteristics and emotional state. Furthermore, by encouraging real-time question resolution and opinion exchange, learning efficiency can be improved.

"Means for entering basic information about the subject" refers to a function that allows basic learning-related information, such as the subject's name, age, grade, and areas of interest, to be entered through a user interface and stored in a database.

"Means for assessing the subject's cognitive characteristics" refers to a function that provides questions and tasks to assess the subject's cognitive features and characteristics, and collects and analyzes the responses.

"Means for suggesting optimal learning methods based on assessment results" is a function that recommends the most suitable learning style and learning materials for each individual based on the assessment results of their cognitive characteristics.

"Means for providing learning content" refers to the function of selecting and delivering learning materials to be provided to the target audience, such as videos, texts, and audio materials.

"Means for tracking learning progress and conducting comprehension tests" refers to a function that monitors the learning progress of participants and periodically conducts tests to assess their comprehension.

"Means for providing individual feedback to the subject" refers to a function for providing appropriate advice and guidance to the subject individually based on their learning outcomes and progress.

"Means to resolve questions during learning in real time" refers to a function that provides immediate answers to questions or queries that students encounter while learning.

"Means for recognizing the subject's emotional state and providing feedback accordingly" refers to a function that monitors the subject's facial expressions, tone of voice, etc. in real time, analyzes their emotional state, and provides appropriate feedback.

"Means for delivering educational content through a head-mounted display" refers to a function that uses a head-mounted display (HMD) to effectively deliver visual and auditory educational content to the target audience.

This invention is an AI system designed to support education, determining the cognitive characteristics and emotional state of the subject and providing optimal learning methods and content based on that. This system functions among four parties: a server, a terminal, a user, and an emotion engine.

First, the user enters basic information. This is done through a user interface provided by the server, and includes information such as name, age, grade, and areas of interest. The entered information is saved using a database management system such as SQLite. This information is used for subsequent cognitive characteristic assessment and learning content customization.

Next, a cognitive trait assessment test is conducted to determine the subject's cognitive traits. The CognitiveTest module is used, and a series of questions and tasks are displayed in the user interface. The user answers these questions, and the responses are collected and analyzed by the server. This identifies visual, linguistic, and auditory cognitive traits.

Based on the assessment results, the server suggests the most appropriate learning method. For example, video materials may be suggested for visually dominant students, and text materials for verbally dominant students. These learning methods and content are provided via devices such as head-mounted displays (HMDs). Using an HMD makes it possible to make the learning experience visual and interactive.

As learning progresses, the server tracks learning progress and periodically administers comprehension tests. The UserTracking module monitors the student's learning progress, and comprehension tests are administered based on that data. Test results are stored in a database and used to adjust subsequent learning plans.

Furthermore, an emotion engine is used to monitor the user's emotional state in real time. The camera and microphone installed in the HMD are used to analyze facial expressions and tone of voice to recognize the user's emotional state. For example, if the server determines that the subject is feeling tired, it will provide feedback such as "Take a short break."

Real-time question resolution is also an important feature. The server accepts questions via the device and provides instant answers generated by AI. This is effective in immediately resolving any difficulties users may have while studying and maintaining continuity in their learning.

Finally, the server periodically provides discussion topics to encourage the exchange of ideas between users. This functionality is realized through the DiscussionModule, allowing users to deepen their understanding by exchanging ideas with other users about what they have learned.

Below is an example of a prompt:

"D is a first-year high school student who is interested in math. Please suggest the best study method for D based on his cognitive characteristics and emotional state. What should we do if D shows no interest while studying?"

The flow of the specific processing in Application Example 2 will be explained using Figure 14.

Step 1:

The server provides a user interface to the terminal for entering basic user information. The user enters basic information such as name, age, grade, and areas of interest. This entered data is sent to the server and stored in a database. The input here is the user's basic information, and the output is the user information stored in the database.

Step 2:

The server displays the cognitive characteristics assessment test interface on the terminal. The user clicks the test start button on the terminal to begin the cognitive characteristics assessment test. The terminal displays the questions received from the server to the user and accepts the user's answers. The user inputs answer data, which the server collects and analyzes in real time. The output is the user's cognitive characteristics assessment results.

Step 3:

The server suggests the optimal learning method based on the cognitive characteristic assessment results. Based on the assessment results, the server selects the most appropriate learning method from various options (video, text, audio materials, etc.). This selected learning method is displayed from the server on the user's device. The input is the cognitive characteristic assessment results, and the output is a suggestion of the optimal learning method.

Step 4:

The server provides the user with the most suitable learning content. The user receives the video and text materials provided by the server via their terminal. The input here is information on the most suitable learning method, and the output is the learning content itself. The learning content is delivered via a head-mounted display (HMD), which the user uses to progress through their studies.

Step 5:

The server tracks learning progress and conducts comprehension tests. Each time a user uses learning content, the server monitors their progress in real time. It also periodically presents tests to assess comprehension, which the user answers. Based on the response data, the server evaluates the level of comprehension and stores this in a database. The input is learning progress data and test answers, and the output is the comprehension assessment results.

Step 6:

The server provides the ability to resolve questions during learning in real time. When a user enters a question into their device, the question is sent to the server. The server analyzes the question and instantly generates an answer, which is sent to the device. The input is the user's question data, and the output is the answer to that question.

Step 7:

The server uses an emotion engine to monitor the user's emotional state in real time. The HMD's built-in camera and microphone collect the user's facial expressions and tone of voice, which are then analyzed by the emotion engine. If the server determines that the user is tired based on this, it provides feedback such as "Take a short break." The input is the user's facial expressions and voice data, and the output is the emotional state analysis results and feedback.

Step 8:

The server periodically provides users with discussion topics and encourages exchange of opinions between users. The server selects the topics and displays them on the user's device. Users use their device to exchange opinions with other users, and the content is sent to the server. The input is the discussion topic and user opinion data, and the output is the result of the exchange of opinions.

The specific processing unit 290 transmits the results of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

Data generation model 58 is what is known as generative AI (artificial intelligence). Examples of data generation model 58 include generative AI such as ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>) and Gemini (Internet search <URL: https://gemini.google.com/?hl=ja>). Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input. Data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt, and outputs the inference results in the form of data such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization.

In the above embodiment, an example was given in which the specific processing was performed by the data processing device 12, but the technology disclosed herein is not limited to this, and the specific processing may also be performed by the robot 414.

The emotion identification model 59, which serves as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see Figure 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

Figure 9 shows an emotion map 400 on which multiple emotions are mapped. In emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both emotions and mental states. Emotions that are generally generated from reactions that occur in the brain are arranged on the left side of the concentric circles. Emotions that are generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged above and below the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the top side of the concentric circles, and the emotion of "discomfort" is arranged on the bottom side. In this way, emotion map 400 maps multiple emotions based on the structure by which emotions are generated, with emotions that tend to occur simultaneously being mapped close together.

These emotions are distributed in the 3 o'clock direction on emotion map 400, and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness takes precedence over internal sensations, resulting in a sense of calm.

The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible (expressed in behavior) the emotion becomes.

Here, human emotions are based on various balances such as posture and blood sugar levels, and when these balances deviate from the ideal, it indicates discomfort, and when they approach the ideal, it indicates pleasure. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and remaining battery life, so that when these balances deviate from the ideal, it indicates discomfort, and when they approach the ideal, it indicates pleasure. Emotion maps may be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and emotional brain physiological signal analysis systems, Tokushima University, doctoral dissertation: https://ci.nii.ac.jp/naid/500000375379). The left half of the emotion map is lined with emotions belonging to an area called "reaction," where sensation is dominant. The right half of the emotion map is lined with emotions belonging to an area called "situation," where situational awareness is dominant.

The emotion map defines two emotions that encourage learning. One is the negative emotion around the middle of "repentance" or "reflection" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot experiences positive feelings such as "I want more" or "I want to know more."

The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are close to each other have similar values, as in the emotion map 900 shown in Figure 10. Figure 10 shows an example in which multiple emotions, such as "relieved," "calm," and "reassuring," have similar emotion values.

The system according to the present disclosure has been described above primarily in terms of the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may also be implemented as a general information processing system. The present disclosure may also be implemented, for example, as a software program that runs on a personal computer or an application that runs on a smartphone, etc. The method according to the present disclosure may also be provided to users in the form of SaaS (Software as a Service).

In the above embodiment, an example was given in which a specific process was performed by a single computer 22, but the technology of the present disclosure is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including the computer 22. For example, the data generation model 58 may be provided in a device external to the data processing device 12, and data may be generated in that external device in response to input data.

In the above embodiment, an example was described in which the specific processing program 56 is stored in the storage 32, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

It is not necessary to store the entire specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entire specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

The following types of processors can be used as hardware resources for executing specific processes. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource for executing specific processes by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which are processors with a circuit configuration designed specifically for executing specific processes. All processors have built-in or connected memory, and all use the memory to execute specific processes.

The hardware resource that executes the specific processing may be composed of one of these various processors, or may be composed of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). The hardware resource that executes the specific processing may also be a single processor.

As an example of a configuration using a single processor, first, there is a configuration in which one processor is configured using a combination of one or more CPUs and software, and this processor functions as a hardware resource that executes specific processing. Second, there is a configuration in which a processor is used to realize the functions of an entire system, including multiple hardware resources that execute specific processing, on a single IC chip, as typified by SoC (System-on-a-chip). In this way, specific processing is realized using one or more of the various processors listed above as hardware resources.

More specifically, the hardware structure of these various processors can be an electrical circuit that combines circuit elements such as semiconductor devices. Furthermore, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps can be added, or the processing order can be rearranged, all within the scope of the spirit of the invention.

The above-described written content and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above explanation of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or substitutions may be made to the above-described written content and illustrations, as long as they do not deviate from the spirit of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described written content and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

All publications, patent applications, and technical standards mentioned in this specification are incorporated by reference herein to the same extent as if each individual publication, patent application, and technical standard was specifically and individually indicated to be incorporated by reference.

The following is further disclosed regarding the above embodiments.

(Claim 1)

A means of entering basic information about the target person,

Means for assessing the subject's cognitive characteristics;

A means of proposing the optimal learning method based on the assessment results,

Means for providing learning content,

Methods for tracking learning progress and conducting comprehension tests,

A means of providing individual feedback to the subject,

A way to resolve questions you have while studying in real time,

A system including

(Claim 2)

The system of claim 1 further includes means for suggesting discussion topics to subjects and promoting the exchange of opinions among subjects.

(Claim 3)

The system of claim 1 further includes means for providing questions or tasks to assess the subject's cognitive characteristics and collecting and analyzing the subject's responses.

"Example 1"

(Claim 1)

A means of entering basic information about the target person,

Means for assessing the subject's cognitive characteristics;

A means of proposing the optimal learning method based on the assessment results,

Means for providing learning content,

Methods for tracking learning progress and conducting comprehension tests,

A means of providing individual feedback to the subject,

A way to resolve questions you have while studying in real time,

A means of storing the subject's input and learning progress in a database,

A means of collecting questions from subjects and generating answers using a generative AI model;

A means to evaluate the subject's answers and dynamically suggest the next learning content,

A system including

(Claim 2)

The system of claim 1 further includes means for suggesting discussion topics to subjects and promoting the exchange of opinions among subjects.

(Claim 3)

The system of claim 1 further includes means for providing questions or tasks to assess the subject's cognitive characteristics and collecting and analyzing the subject's responses.

"Application Example 1"

(Claim 1)

A means of entering basic information about the target person,

Means for assessing the subject's cognitive characteristics;

A means of proposing the optimal learning method based on the assessment results,

Means for providing learning content,

Methods for tracking learning progress and conducting comprehension tests,

A means of providing individual feedback and suggesting next steps to participants,

A way to resolve questions you have while studying in real time,

Means to promote exchange of opinions among participants,

A system that includes means to assist in learning how to operate industrial products and control techniques.

(Claim 2)

The system of claim 1 further includes means for suggesting discussion topics to subjects and promoting the exchange of opinions among subjects.

(Claim 3)

The system of claim 1 further includes means for providing questions or tasks to assess the subject's cognitive characteristics and collecting and analyzing the subject's responses.

"Example 2: Combining Emotion Engines"

(Claim 1)

A means of entering basic information about the target person,

Means for assessing the subject's cognitive characteristics;

A means of proposing the optimal learning method based on the assessment results,

Means for providing learning content,

Methods for tracking learning progress and conducting comprehension tests,

A means of providing individual feedback to the subject,

A way to resolve questions you have while studying in real time,

A means of recognizing and responding to the subject's emotional state using an emotion engine;

A system including

(Claim 2)

The system of claim 1 further includes means for suggesting discussion topics to subjects and promoting the exchange of opinions among subjects.

(Claim 3)

The system of claim 1 further includes means for providing questions or tasks to assess the subject's cognitive characteristics and collecting and analyzing the subject's responses.

"Application Example 2: Combining Emotion Engines"

(Claim 1)

A means of entering basic information about the target person,

Means for assessing the subject's cognitive characteristics;

A means of proposing the optimal learning method based on the assessment results,

Means for providing learning content,

Methods for tracking learning progress and conducting comprehension tests,

A means of providing individual feedback to the subject,

A way to resolve questions you have while studying in real time,

Means of recognizing the subject's emotional state and providing feedback accordingly;

A means of delivering educational content through head-mounted displays,

A system including

(Claim 2)

The system of claim 1 further includes means for suggesting discussion topics to subjects and promoting the exchange of opinions among subjects.

(Claim 3)

The system of claim 1 further includes means for providing questions or tasks to assess the subject's cognitive characteristics and collecting and analyzing the subject's responses.

10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset type terminal 414 Robot

Claims (3)

A means for inputting basic information of the subject;
a means for determining cognitive characteristics of a subject;
A means for proposing an optimal learning method based on the judgment results;
a means of delivering learning content;
A means to track learning progress and administer comprehension tests;
a means of providing individualized feedback to subjects;
A way to resolve questions during learning in real time,
A system including: The system of claim 1 further includes means for suggesting discussion topics to subjects and promoting the exchange of opinions among subjects. The system of claim 1 further includes means for providing questions or tasks to assess the subject's cognitive characteristics and collecting and analyzing the subject's responses.