JP2025049003A - system - Google Patents

Abstract

[Problem] A system according to an embodiment aims to effectively utilize earth observation data. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects earth observation data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates information based on the analysis results obtained by the analysis unit. The provision unit provides the information generated by the generation unit. [Selected Figure] Figure 1

Description

The technology disclosed herein relates to a system.

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

JP 2022-180282 A

Conventional technology does not fully utilize Earth observation data, and there is room for improvement.

The system according to the embodiment aims to effectively utilize Earth observation data.

The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects Earth observation data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates information based on the analysis results obtained by the analysis unit. The provision unit provides the information generated by the generation unit.

The system according to the embodiment can effectively utilize Earth observation data.

1 is a conceptual diagram showing an example of a 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. FIG. 11 is a conceptual diagram showing an example of a configuration of a data processing system according to a second embodiment. FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. FIG. 13 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.

Below, an example of an embodiment of a system related to the technology disclosed herein is described with reference to the attached drawings.

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

In the following embodiments, the signed processor (hereinafter simply referred to as the "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. The processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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), or a TPU (Tensor Processing Unit).

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

In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and various parameters. 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 code is an interface including a communication processor and an antenna. The communication I/F controls communication between multiple computers. Examples of communication standards applied to the communication I/F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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 only A, only B, or a combination of A and B. In addition, in this specification, the same concept as "A and/or B" is also applied when three or more things are expressed by connecting them with "and/or."

[First embodiment]
FIG. 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 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the 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, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with an indicator (e.g., a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by 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 (see FIG. 2) acquires the data indicating the user input.

The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form of expression that the user can perceive (e.g., voice and/or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs voice according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 transmitting and receiving various types of 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 the 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.

The 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to such examples. Furthermore, the estimation and prediction of emotion also includes, for example, analysis of emotions.

In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also use these models to perform processing similar to that of the specific processing unit 290.

Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device, or a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

(Example 1)
The metaverse Earth system according to the embodiment of the present invention is a system that integrates data obtained from earth observation satellites and uses a generation AI to provide information on the "now" that users want to know. This system collects a wide variety of data obtained from earth observation satellites and uses this as teacher data to construct the metaverse Earth. Next, the generation AI provides information in real time according to the user's request. For example, this includes disaster information prediction and risk assessment information for insurance companies. The generation AI analyzes the earth observation data and generates information according to the user's request. Furthermore, the metaverse Earth provides an interface that users can intuitively operate. Users can select any point on the earth in virtual space and check the observation data. This mechanism is expected to explosively spread the use of earth observation data and contribute to society. For example, by improving the accuracy of disaster information prediction, it is possible to respond quickly and damage can be reduced. In addition, insurance companies can provide appropriate insurance products based on risk assessment information. This improves the safety and security of society as a whole. This allows the metaverse Earth system to efficiently collect, analyze, generate, and provide earth observation data.

The metaverse Earth system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects earth observation data. The earth observation data includes, for example, satellite data, weather data, earthquake data, etc., but is not limited to such examples. The collection unit, for example, acquires satellite data in real time and periodically collects weather data. The collection unit can also acquire earthquake data immediately when an earthquake occurs. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, analyzes data using AI to perform anomaly detection and pattern recognition. The analysis unit, for example, analyzes weather data to generate a weather forecast. The analysis unit can also analyze earthquake data to predict the probability of an earthquake occurring. The generation unit generates information based on the analysis result obtained by the analysis unit. The generation unit, for example, uses generation AI to generate information in real time according to a user's request. The generation unit, for example, generates disaster information and provides it to the user. The generation unit can also generate risk assessment information for insurance companies. The provision unit provides the information generated by the generation unit. The providing unit provides information using AI, for example, and provides feedback to the user. The providing unit provides information through a web application, for example. The providing unit can also provide information through a mobile application. This allows the Metaverse Earth system according to the embodiment to efficiently collect, analyze, generate, and provide Earth observation data.

The collection unit collects earth observation data. Examples of earth observation data include, but are not limited to, satellite data, weather data, and earthquake data. For example, the collection unit acquires satellite data in real time and periodically collects weather data. The collection unit can also immediately acquire earthquake data when an earthquake occurs. Specifically, the collection unit integrates data from multiple satellites to grasp the weather conditions and surface changes of the entire Earth with high accuracy. This includes various satellite data such as optical satellites, radar satellites, and infrared satellites. Optical satellites provide detailed images of the Earth's surface, and radar satellites can detect surface changes regardless of the weather. Infrared satellites help understand the temperature distribution of the Earth's surface. Weather data also includes data from ground weather stations and marine buoys, which allows detailed weather information such as temperature, humidity, wind speed, and precipitation to be collected. Earthquake data is acquired from seismometers and accelerometers in real time to quickly grasp the location and scale of an earthquake. This allows the collection unit to efficiently collect various data from the entire Earth and update it in real time. Furthermore, the collection unit will build a database to centrally manage these data and make them easily accessible to the analysis and generation units. The frequency and accuracy of data collection can be adjusted according to specific situations and needs, allowing the collection unit to collect data flexibly and efficiently and improve the performance of the entire system.

The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses AI to analyze the data and perform anomaly detection and pattern recognition. Specifically, AI uses machine learning algorithms and deep learning technology to extract useful information from large amounts of data. For example, when analyzing weather data to generate a weather forecast, past weather data and current observation data are combined to build a model to predict future weather. This includes time series analysis using multivariate data such as temperature, humidity, wind speed, and precipitation. In addition, when analyzing earthquake data to predict the probability of an earthquake, the risk of an earthquake occurring in a specific area is evaluated based on past earthquake data and geological information. AI can analyze these data in real time and detect abnormal patterns and signs. For example, it detects sudden temperature changes and abnormal precipitation patterns in the weather data, suggesting that this may be a precursor to abnormal weather. It also detects an increase in micro-seismic activity in the earthquake data, and evaluates the possibility that this is a precursor to a large-scale earthquake. Based on these analysis results, the analysis unit provides the generation unit with rapid and accurate information, helping to provide appropriate information to users. Furthermore, the analysis unit can use past data and statistical information to perform long-term risk assessments and trend analysis. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

The generation unit generates information based on the analysis results obtained by the analysis unit. The generation unit generates information in real time according to the user's request, for example, using the generation AI. Specifically, the generation AI generates information in a form that makes the analysis results easy for the user to understand, using natural language processing technology. For example, when generating disaster information, a sentence is generated that describes the current situation and predicted risks in detail based on weather data and earthquake data. The generation AI can quickly provide the necessary information in response to the user's prompts. For example, when a user inputs "Please tell me the current weather forecast," the generation AI describes the current weather and future forecasts in detail based on the latest weather data. In addition, when generating risk assessment information for insurance companies, a report is generated that describes the risk assessment in a specific area or building in detail based on the analysis results. The generation unit generates these pieces of information in real time and provides them to the user, thereby realizing rapid and appropriate information provision. Furthermore, the generation unit can continuously improve the accuracy and content of the generated information based on user feedback. For example, by analyzing feedback from users and updating the model of the generation AI, more accurate and useful information can be provided. This allows the generation unit to provide high-quality information that meets the needs of users, improving the value of the entire system.

The providing unit provides the information generated by the generating unit. For example, the providing unit uses AI to provide information and provide feedback to the user. Specifically, the providing unit provides information through a web application or a mobile application. In the web application, the user can access through a browser and view the latest weather information, earthquake information, risk assessment information, etc. In the mobile application, the user can receive information in real time through a smartphone or tablet. The providing unit provides an interface for visually displaying the information in an easy-to-understand manner, allowing the user to intuitively understand the information. For example, the weather information is displayed on a map, and the current weather and predicted precipitation are displayed in different colors. In addition, earthquake information is displayed on a map, and the location and scale of the earthquake are visually indicated. The providing unit can collect feedback from the user and continuously improve the accuracy and content of the information provided. For example, the user can evaluate and comment on the provided information, and the providing unit can provide information that reflects the user's needs and opinions. In addition, the providing unit can reliably transmit information using multiple communication means. For example, important information is quickly and reliably delivered to the user by using not only web applications and mobile applications, but also email, SMS, push notifications, etc. This allows the provider to provide information to users quickly and reliably, improving the reliability and convenience of the entire system.

The Metaverse Earth system includes an interface unit that provides a user interface. The interface unit provides an interface that can be intuitively operated by the user. For example, the interface unit provides a graphical user interface (GUI) to enable the user to visually check information. The interface unit can also provide a command line interface (CLI) to enable the user to operate by inputting commands. For example, the interface unit provides a map display function for selecting any point on the Earth and checking observation data. The interface unit can also provide a function for filtering and displaying data that is of interest to the user. This allows the user to quickly obtain the information they need through an interface that can be intuitively operated.

The Metaverse Earth system includes an evaluation unit that performs risk assessment for insurance companies. The evaluation unit performs risk assessment for insurance companies. Risk assessment includes, for example, disaster risk, weather risk, earthquake risk, and the like, but is not limited to these examples. The evaluation unit, for example, evaluates disaster risk using AI. The evaluation unit can also evaluate weather risk. Furthermore, the evaluation unit can evaluate earthquake risk. For example, the evaluation unit analyzes disaster data and evaluates disaster risk. Analyzes weather data and evaluates weather risk. Analyzes earthquake data and evaluates earthquake risk. This allows insurance companies to perform appropriate risk assessment. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI.

The collection unit can collect at least one of weather data, earthquake data, and flood data. The collection unit, for example, collects weather data. Weather data includes, for example, temperature, humidity, and wind speed, but is not limited to such examples. The collection unit, for example, collects earthquake data. Earthquake data includes, for example, seismic intensity, epicenter, and time of occurrence, but is not limited to such examples. The collection unit, for example, collects flood data. Flood data includes, for example, water level, flow rate, and flooded area, but is not limited to such examples. By collecting a wide variety of data, it becomes possible to provide more comprehensive information. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

The generation unit can generate information in real time according to a user request. The generation unit generates information in real time according to a user request, for example, using a generation AI. The generation unit generates disaster information, for example, and provides it to the user. The generation unit can also generate risk assessment information for insurance companies. For example, when a user requests disaster information, the generation unit causes the generation AI to analyze disaster data and generate disaster information in real time. When an insurance company requests risk assessment information, the generation AI analyzes risk data and generates risk assessment information in real time. This makes it possible to provide information in real time according to a user request. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

The collection unit can analyze past data collection history and select the optimal collection method. The collection unit, for example, analyzes past data collection history and selects the optimal collection method. For example, the collection unit selects the most effective data collection method for a specific time period based on the past data collection history. The collection unit can also analyze past data collection history and select the most efficient collection method under specific conditions. Furthermore, the collection unit can refer to past data collection history and select the optimal collection method for a specific event. In this way, the optimal collection method can be selected by analyzing past data collection history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

The collection unit can perform filtering based on the user's current field of interest when collecting data. The collection unit, for example, performs filtering based on the user's current field of interest when collecting data. For example, if the user is interested in disaster information, the collection unit can prioritize collecting disaster-related data. Also, if the user is interested in weather information, the collection unit can also prioritize collecting weather data. Furthermore, if the user is interested in earthquake information, the collection unit can also prioritize collecting earthquake data. In this way, by filtering data based on the user's field of interest, more relevant information can be provided. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI.

When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account geographical location information. When collecting data, the collection unit, for example, prioritizes collecting highly relevant data by taking into account geographical location information. For example, if the user is interested in a particular area, the collection unit prioritizes collecting data about that area. Also, if the user is traveling, the collection unit can also prioritize collecting data about the current location. Furthermore, if the user is in a disaster area, the collection unit can also prioritize collecting disaster information about that area. In this way, by taking into account geographical location information, highly relevant data can be prioritized. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

The collection unit can analyze social media activity and collect related data when collecting data. For example, the collection unit analyzes social media activity and collects related data when collecting data. For example, if a user frequently shares disaster information on social media, the collection unit prioritizes collecting disaster-related data. Also, if a user frequently posts weather information, the collection unit can also prioritize collecting weather data. Furthermore, if a user frequently retweets earthquake information, the collection unit can also prioritize collecting earthquake data. In this way, by analyzing social media activity, related data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data of high importance. It can also perform a simplified analysis on data of low importance. It can also perform an analysis with an appropriate level of detail on data of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

The analysis unit can apply different analysis algorithms depending on the category of data during analysis. The analysis unit, for example, applies different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a weather forecasting algorithm to weather data. It can also apply an earthquake forecasting algorithm to earthquake data. It can also apply a flood forecasting algorithm to flood data. In this way, the analysis accuracy is improved by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI.

The analysis unit can determine the analysis priority based on the time of data collection during analysis. The analysis unit, for example, determines the analysis priority based on the time of data collection during analysis. For example, the analysis unit prioritizes analysis of the latest data. The analysis unit can also analyze the latest data while referring to past data. Furthermore, the analysis unit can also prioritize analysis of data collected during a specific period. This enables efficient analysis by determining the analysis priority based on the time of data collection. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. Also, it can analyze less relevant data later. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

The generation unit can adjust the level of detail of the generation based on the importance of the data at the time of generation. The generation unit adjusts the level of detail of the generation based on the importance of the data at the time of generation, for example. For example, the generation unit generates detailed information for data of high importance. Also, the generation unit can generate simplified information for data of low importance. Furthermore, the generation unit can generate information with an appropriate level of detail for data of medium importance. This enables efficient information generation by adjusting the level of detail of the generation based on the importance of the data. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

The generation unit can apply different generation algorithms depending on the category of data at the time of generation. The generation unit, for example, applies different generation algorithms depending on the category of data at the time of generation. For example, the generation unit applies a weather forecasting algorithm to weather data to generate information. Also, the generation unit can apply an earthquake forecasting algorithm to earthquake data to generate information. Furthermore, the generation unit can apply a flood forecasting algorithm to flood data to generate information. In this way, by applying an appropriate generation algorithm depending on the category of data, the generation accuracy is improved. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI.

The generation unit can determine the generation priority based on the time of data collection at the time of generation. The generation unit, for example, determines the generation priority based on the time of data collection at the time of generation. For example, the generation unit generates the latest data preferentially. The generation unit can also generate the latest data while referring to past data. Furthermore, the generation unit can also generate data collected during a specific period preferentially. This enables efficient information generation by determining the generation priority based on the time of data collection. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI.

The generation unit can adjust the order of generation based on the relevance of the data at the time of generation. The generation unit, for example, adjusts the order of generation based on the relevance of the data at the time of generation. For example, the generation unit generates highly relevant data preferentially. Also, data with low relevance can be generated later. Furthermore, the generation unit can dynamically adjust the order of generation according to the relevance of the data. This enables efficient information generation by adjusting the order of generation based on the relevance of the data. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

The providing unit can select the optimal providing method by referring to the user's past usage history at the time of providing. For example, the providing unit selects the optimal providing method by referring to the user's past usage history at the time of providing. For example, the providing unit selects the optimal providing method based on the method used by the user in the past. The providing unit can also select the most effective providing method from the user's past usage history. Furthermore, the providing unit can analyze the user's past usage history and select the optimal providing method under specific conditions. In this way, the optimal providing method can be selected by referring to the user's past usage history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

The providing unit can customize information based on the user's current field of interest at the time of providing. The providing unit customizes information based on the user's current field of interest at the time of providing, for example. For example, if the user is interested in disaster information, the providing unit can provide disaster-related information with priority. Also, if the user is interested in weather information, the providing unit can also provide weather-related information with priority. Furthermore, if the user is interested in earthquake information, the providing unit can also provide earthquake-related information with priority. In this way, by customizing information based on the user's field of interest, more relevant information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

The providing unit can provide optimal information taking into consideration the geographical location information of the user at the time of providing the information. The providing unit, for example, provides optimal information taking into consideration the geographical location information of the user at the time of providing the information. For example, when the user is in a specific area, the providing unit can provide information about that area preferentially. Also, when the user is traveling, the providing unit can also provide information about the area preferentially. Furthermore, when the user is in a disaster area, the providing unit can also provide disaster information about that area preferentially. In this way, optimal information can be provided by taking into consideration the geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

The providing unit can provide related information by analyzing the user's social media activity at the time of providing. The providing unit can provide related information by analyzing the user's social media activity at the time of providing, for example. For example, if the user frequently shares disaster information on social media, the providing unit can provide disaster-related information preferentially. Also, if the user frequently posts weather information, the providing unit can also provide weather-related information preferentially. Furthermore, if the user frequently retweets earthquake information, the providing unit can also provide earthquake-related information preferentially. In this way, by analyzing social media activity, related information can be provided efficiently. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

When displaying the interface, the interface unit can select the optimal display method by referring to the user's past operation history. When displaying the interface, the interface unit, for example, selects the optimal display method by referring to the user's past operation history. For example, the interface unit selects the optimal display method based on the display method used by the user in the past. The interface unit can also select the most effective display method from the user's past operation history. Furthermore, the interface unit can analyze the user's past operation history and select the optimal display method under specific conditions. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above-mentioned processing in the interface unit may be performed, for example, using AI, or may be performed without using AI.

The interface unit can select the optimal display method in consideration of the user's device information when displaying the interface. For example, the interface unit selects the optimal display method in consideration of the user's device information when displaying the interface. For example, if the user is using a smartphone, the interface unit provides a display method that matches the screen size. Also, if the user is using a tablet, the interface unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the interface unit can also provide a simple display method with high visibility. In this way, the optimal display method can be provided by taking into account the user's device information. Some or all of the above-mentioned processing in the interface unit may be performed, for example, using AI, or may be performed without using AI.

The prediction unit can optimize the prediction algorithm by referring to past data when making a prediction. The prediction unit optimizes the prediction algorithm by referring to past data when making a prediction, for example. For example, the prediction unit selects an optimal prediction algorithm based on past data. The prediction unit can also analyze past data and select the most effective prediction algorithm under specific conditions. Furthermore, the prediction unit can dynamically adjust the prediction algorithm by referring to past data. In this way, the prediction algorithm can be optimized by referring to past data. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI.

The prediction unit can apply different prediction methods depending on the category of data when making predictions. The prediction unit, for example, applies different prediction methods depending on the category of data when making predictions. For example, the prediction unit applies a weather forecasting method to weather data. It can also apply an earthquake forecasting method to earthquake data. It can also apply a flood forecasting method to flood data. In this way, prediction accuracy is improved by applying an appropriate forecasting method depending on the category of data. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI.

The prediction unit can weight the prediction based on the time when the data was collected at the time of prediction. The prediction unit, for example, weights the prediction based on the time when the data was collected at the time of prediction. For example, the prediction unit makes a prediction with emphasis on the latest data. The prediction unit can also make a prediction with emphasis on the latest data while referring to past data. Furthermore, the prediction unit can also make a prediction with emphasis on data collected during a specific period. As a result, prediction accuracy is improved by weighting the prediction based on the time when the data was collected. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI.

The prediction unit can improve the accuracy of the prediction by referring to the related market data when making a prediction. The prediction unit can improve the accuracy of the prediction by referring to the related market data when making a prediction, for example. For example, the prediction unit improves the accuracy of the prediction based on the related market data. The prediction unit can also analyze the related market data and improve the accuracy of the prediction under specific conditions. Furthermore, the prediction unit can also dynamically adjust the prediction algorithm by referring to the related market data. In this way, the accuracy of the prediction can be improved by referring to the related market data. Some or all of the above-mentioned processing in the prediction unit can be performed, for example, using AI, or can be performed without using AI.

The evaluation unit can optimize the evaluation algorithm by referring to past data during evaluation. The evaluation unit can optimize the evaluation algorithm by referring to past data during evaluation, for example. For example, the evaluation unit selects an optimal evaluation algorithm based on past data. The evaluation unit can also analyze past data and select an evaluation algorithm that is most effective under specific conditions. Furthermore, the evaluation unit can dynamically adjust the evaluation algorithm by referring to past data. In this way, the evaluation algorithm can be optimized by referring to past data. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI.

The evaluation unit can apply different evaluation methods depending on the category of data during evaluation. The evaluation unit, for example, applies different evaluation methods depending on the category of data during evaluation. For example, the evaluation unit applies a meteorological evaluation method to meteorological data. It can also apply an earthquake evaluation method to earthquake data. It can also apply a flood evaluation method to flood data. In this way, the evaluation accuracy is improved by applying an appropriate evaluation method depending on the category of data. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or may be performed without using AI.

The evaluation unit can weight the evaluation based on the time of data collection during the evaluation. The evaluation unit, for example, weights the evaluation based on the time of data collection during the evaluation. For example, the evaluation unit places emphasis on the latest data during the evaluation. The evaluation unit can also place emphasis on the latest data while referring to past data during the evaluation. Furthermore, the evaluation unit can also place emphasis on data collected during a specific period during the evaluation. In this way, the evaluation accuracy is improved by weighting the evaluation based on the time of data collection. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI.

The evaluation unit can improve the accuracy of the evaluation by referring to the related market data during the evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to the related market data during the evaluation. For example, the evaluation unit improves the accuracy of the evaluation based on the related market data. The evaluation unit can also analyze the related market data and improve the accuracy of the evaluation under specific conditions. Furthermore, the evaluation unit can dynamically adjust the evaluation algorithm by referring to the related market data. In this way, the accuracy of the evaluation can be improved by referring to the related market data. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI or without using AI.

The system according to the embodiment is not limited to the above-mentioned example, and various modifications are possible, for example, as follows:

The Metaverse Earth system can further include a health monitoring unit that monitors the user's health condition. The health monitoring unit collects vital data such as the user's heart rate, blood pressure, and body temperature, and transmits it to the analysis unit. The analysis unit can analyze this data and evaluate the user's health condition. For example, if the heart rate is abnormally high, the analysis unit can generate a notification encouraging the user to rest. Also, if the blood pressure is high, the analysis unit can generate a notification recommending the user to see a doctor. Furthermore, if the body temperature is high, the analysis unit can generate a notification encouraging the user to drink water. This makes it possible to monitor the user's health condition in real time and provide appropriate advice.

The Metaverse Earth system can further include a behavioral analysis unit that analyzes the user's behavioral history. The behavioral analysis unit collects the user's past behavioral data and transmits it to the analysis unit. The analysis unit can analyze this data and identify the user's behavioral patterns. For example, if the user is often in a specific location at a specific time of day, the analysis unit can provide information related to that time period preferentially. Also, if the user frequently participates in a specific event, the analysis unit can provide information related to that event. Furthermore, if the user has specific interests, the analysis unit can provide information related to those interests. This makes it possible to provide more personalized information by analyzing the user's behavioral history.

The Metaverse Earth system can further include a learning analysis unit that analyzes the user's learning history. The learning analysis unit collects the user's past learning data and sends it to the analysis unit. The analysis unit can analyze this data and identify the user's learning patterns. For example, if the user is interested in a particular field, the analysis unit can provide information related to that field preferentially. Also, if the user prefers a particular learning method, the analysis unit can provide learning content based on that method. Furthermore, if the user often studies during a particular time period, the analysis unit can send learning notifications during that time period. This makes it possible to provide more effective learning support by analyzing the user's learning history.

The Metaverse Earth system can further include a purchase analysis unit that analyzes the user's purchasing history. The purchase analysis unit collects the user's past purchasing data and sends it to the analysis unit. The analysis unit can analyze this data and identify the user's purchasing patterns. For example, if the user has a preference for a particular brand, the analysis unit can provide information related to that brand preferentially. Also, if the user frequently purchases products in a particular category, the analysis unit can also provide information related to that category. Furthermore, if the user often purchases a particular product at a particular time, the analysis unit can also provide information related to that time. This makes it possible to provide more personalized information by analyzing the user's purchasing history.

The Metaverse Earth system may further include a social analysis unit that analyzes the user's social network. The social analysis unit collects the user's social network data and transmits it to the analysis unit. The analysis unit may analyze these data and identify the structure of the user's social network. For example, if the user belongs to a particular group, the analysis unit may provide information related to that group preferentially. Also, if the user frequently interacts with a particular person, the analysis unit may provide information related to that person. Furthermore, if the user participates in a particular event, the analysis unit may provide information related to that event. In this way, by analyzing the user's social network, it is possible to provide more relevant information.

The Metaverse Earth system may further include a feedback collection unit that collects user feedback. The feedback collection unit collects user feedback data and transmits it to the analysis unit. The analysis unit may analyze this data and identify improvements to the system. For example, if a user is dissatisfied with a particular function, the analysis unit may generate a proposal to improve that function. Also, if a user gives a high rating to a particular function, the analysis unit may generate a proposal to enhance that function. Furthermore, if a user requests a new function, the analysis unit may generate a proposal to add that function. In this way, by collecting user feedback, the system may be continuously improved.

The processing flow of Example 1 is briefly explained below.

Step 1: The collection unit collects earth observation data. The earth observation data includes, but is not limited to, satellite data, meteorological data, and earthquake data. For example, the collection unit acquires satellite data in real time and periodically collects meteorological data. The collection unit can also immediately acquire earthquake data when an earthquake occurs.
Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses AI to analyze the data and perform anomaly detection and pattern recognition. The analysis unit, for example, analyzes meteorological data to generate a weather forecast. The analysis unit can also analyze earthquake data to predict the probability of an earthquake occurring.
Step 3: The generating unit generates information based on the analysis results obtained by the analyzing unit. The generating unit generates information in real time according to a user request, for example, using a generating AI. The generating unit generates disaster information, for example, and provides it to the user. The generating unit can also generate risk assessment information for insurance companies.
Step 4: The providing unit provides the information generated by the generating unit. The providing unit provides the information using, for example, AI and provides feedback to the user. The providing unit provides the information through, for example, a web application. The providing unit can also provide the information through a mobile application.

(Example 2)
The metaverse Earth system according to the embodiment of the present invention is a system that integrates data obtained from earth observation satellites and uses a generation AI to provide information on the "now" that users want to know. This system collects a wide variety of data obtained from earth observation satellites and uses this as teacher data to construct the metaverse Earth. Next, the generation AI provides information in real time according to the user's request. For example, this includes disaster information prediction and risk assessment information for insurance companies. The generation AI analyzes the earth observation data and generates information according to the user's request. Furthermore, the metaverse Earth provides an interface that users can intuitively operate. Users can select any point on the earth in virtual space and check the observation data. This mechanism is expected to explosively spread the use of earth observation data and contribute to society. For example, by improving the accuracy of disaster information prediction, it is possible to respond quickly and damage can be reduced. In addition, insurance companies can provide appropriate insurance products based on risk assessment information. This improves the safety and security of society as a whole. This allows the metaverse Earth system to efficiently collect, analyze, generate, and provide earth observation data.

The metaverse Earth system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects earth observation data. The earth observation data includes, for example, satellite data, weather data, earthquake data, etc., but is not limited to such examples. The collection unit, for example, acquires satellite data in real time and periodically collects weather data. The collection unit can also acquire earthquake data immediately when an earthquake occurs. The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, analyzes data using AI to perform anomaly detection and pattern recognition. The analysis unit, for example, analyzes weather data to generate a weather forecast. The analysis unit can also analyze earthquake data to predict the probability of an earthquake occurring. The generation unit generates information based on the analysis result obtained by the analysis unit. The generation unit, for example, uses generation AI to generate information in real time according to a user's request. The generation unit, for example, generates disaster information and provides it to the user. The generation unit can also generate risk assessment information for insurance companies. The provision unit provides the information generated by the generation unit. The providing unit provides information using AI, for example, and provides feedback to the user. The providing unit provides information through a web application, for example. The providing unit can also provide information through a mobile application. This allows the Metaverse Earth system according to the embodiment to efficiently collect, analyze, generate, and provide Earth observation data.

The collection unit collects earth observation data. Examples of earth observation data include, but are not limited to, satellite data, weather data, and earthquake data. For example, the collection unit acquires satellite data in real time and periodically collects weather data. The collection unit can also immediately acquire earthquake data when an earthquake occurs. Specifically, the collection unit integrates data from multiple satellites to grasp the weather conditions and surface changes of the entire Earth with high accuracy. This includes various satellite data such as optical satellites, radar satellites, and infrared satellites. Optical satellites provide detailed images of the Earth's surface, and radar satellites can detect surface changes regardless of the weather. Infrared satellites help understand the temperature distribution of the Earth's surface. Weather data also includes data from ground weather stations and marine buoys, which allows detailed weather information such as temperature, humidity, wind speed, and precipitation to be collected. Earthquake data is acquired from seismometers and accelerometers in real time to quickly grasp the location and scale of an earthquake. This allows the collection unit to efficiently collect various data from the entire Earth and update it in real time. Furthermore, the collection unit will build a database to centrally manage these data and make them easily accessible to the analysis and generation units. The frequency and accuracy of data collection can be adjusted according to specific situations and needs, allowing the collection unit to collect data flexibly and efficiently and improve the performance of the entire system.

The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses AI to analyze the data and perform anomaly detection and pattern recognition. Specifically, AI uses machine learning algorithms and deep learning technology to extract useful information from large amounts of data. For example, when analyzing weather data to generate a weather forecast, past weather data and current observation data are combined to build a model to predict future weather. This includes time series analysis using multivariate data such as temperature, humidity, wind speed, and precipitation. In addition, when analyzing earthquake data to predict the probability of an earthquake, the risk of an earthquake occurring in a specific area is evaluated based on past earthquake data and geological information. AI can analyze these data in real time and detect abnormal patterns and signs. For example, it detects sudden temperature changes and abnormal precipitation patterns in the weather data, suggesting that this may be a precursor to abnormal weather. It also detects an increase in micro-seismic activity in the earthquake data, and evaluates the possibility that this is a precursor to a large-scale earthquake. Based on these analysis results, the analysis unit provides the generation unit with rapid and accurate information, helping to provide appropriate information to users. Furthermore, the analysis unit can use past data and statistical information to perform long-term risk assessments and trend analysis. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

The generation unit generates information based on the analysis results obtained by the analysis unit. The generation unit generates information in real time according to the user's request, for example, using the generation AI. Specifically, the generation AI generates information in a form that makes the analysis results easy for the user to understand, using natural language processing technology. For example, when generating disaster information, a sentence is generated that describes the current situation and predicted risks in detail based on weather data and earthquake data. The generation AI can quickly provide the necessary information in response to the user's prompts. For example, when a user inputs "Please tell me the current weather forecast," the generation AI describes the current weather and future forecasts in detail based on the latest weather data. In addition, when generating risk assessment information for insurance companies, a report is generated that describes the risk assessment in a specific area or building in detail based on the analysis results. The generation unit generates these pieces of information in real time and provides them to the user, thereby realizing rapid and appropriate information provision. Furthermore, the generation unit can continuously improve the accuracy and content of the generated information based on user feedback. For example, by analyzing feedback from users and updating the model of the generation AI, more accurate and useful information can be provided. This allows the generation unit to provide high-quality information that meets the needs of users, improving the value of the entire system.

The providing unit provides the information generated by the generating unit. The providing unit provides information using, for example, AI and provides feedback to the user. Specifically, the providing unit provides information through a web application or a mobile application. In the web application, the user can access through a browser and view the latest weather information, earthquake information, risk assessment information, etc. In the mobile application, the user can receive information in real time through a smartphone or tablet. The providing unit provides an interface for visually displaying this information in an easy-to-understand manner, allowing the user to intuitively understand the information. For example, the weather information is displayed on a map, and the current weather and predicted precipitation are displayed in different colors. In addition, earthquake information is displayed on a map, and the location and scale of the earthquake are visually indicated. The providing unit can collect feedback from the user and continuously improve the accuracy and content of the information provided. For example, the user can evaluate and comment on the provided information, and the providing unit can provide information that reflects the user's needs and opinions. In addition, the providing unit can reliably transmit information using multiple communication means. For example, important information is quickly and reliably delivered to the user by using not only web applications and mobile applications, but also email, SMS, push notifications, etc. This allows the provider to provide information to users quickly and reliably, improving the reliability and convenience of the entire system.

The Metaverse Earth system includes an interface unit that provides a user interface. The interface unit provides an interface that can be intuitively operated by the user. For example, the interface unit provides a graphical user interface (GUI) to enable the user to visually check information. The interface unit can also provide a command line interface (CLI) to enable the user to operate by inputting commands. For example, the interface unit provides a map display function for selecting any point on the Earth and checking observation data. The interface unit can also provide a function for filtering and displaying data that is of interest to the user. This allows the user to quickly obtain the information they need through an interface that can be intuitively operated.

The Metaverse Earth system includes a prediction unit that predicts disaster information. The prediction unit predicts disaster information. Disaster information includes, for example, earthquakes, floods, typhoons, etc., but is not limited to these examples. The prediction unit predicts the probability of earthquakes, for example, using AI. The prediction unit can also predict the risk of floods. Furthermore, the prediction unit can predict the path of typhoons. For example, the prediction unit analyzes earthquake data to predict the probability of earthquakes. Analyzes flood data to predict the risk of floods. Analyzes typhoon data to predict the path of typhoons. This improves the accuracy of disaster information prediction and enables rapid response. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to these examples.

The Metaverse Earth system includes an evaluation unit that performs risk assessment for insurance companies. The evaluation unit performs risk assessment for insurance companies. Risk assessment includes, for example, disaster risk, weather risk, earthquake risk, and the like, but is not limited to these examples. The evaluation unit, for example, evaluates disaster risk using AI. The evaluation unit can also evaluate weather risk. Furthermore, the evaluation unit can evaluate earthquake risk. For example, the evaluation unit analyzes disaster data and evaluates disaster risk. Analyzes weather data and evaluates weather risk. Analyzes earthquake data and evaluates earthquake risk. This allows insurance companies to perform appropriate risk assessment. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI.

The collection unit can collect at least one of weather data, earthquake data, and flood data. The collection unit, for example, collects weather data. Weather data includes, for example, temperature, humidity, and wind speed, but is not limited to such examples. The collection unit, for example, collects earthquake data. Earthquake data includes, for example, seismic intensity, epicenter, and time of occurrence, but is not limited to such examples. The collection unit, for example, collects flood data. Flood data includes, for example, water level, flow rate, and flooded area, but is not limited to such examples. By collecting a wide variety of data, it becomes possible to provide more comprehensive information. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

The generation unit can generate information in real time according to a user request. The generation unit generates information in real time according to a user request, for example, using a generation AI. The generation unit generates disaster information, for example, and provides it to the user. The generation unit can also generate risk assessment information for insurance companies. For example, when a user requests disaster information, the generation unit causes the generation AI to analyze disaster data and generate disaster information in real time. When an insurance company requests risk assessment information, the generation AI analyzes risk data and generates risk assessment information in real time. This makes it possible to provide information in real time according to a user request. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of data collection based on the estimated user's emotions. For example, when the user is nervous, the collection unit increases the frequency of data collection to enhance real-time information provision. Also, when the user is relaxed, the collection unit can reduce the frequency of data collection to provide the minimum necessary information. Furthermore, when the user is excited, the collection unit can also preferentially collect data related to a specific field of interest. This makes it possible to provide more appropriate information by adjusting the timing of data collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

The collection unit can analyze past data collection history and select the optimal collection method. The collection unit, for example, analyzes past data collection history and selects the optimal collection method. For example, the collection unit selects the most effective data collection method for a specific time period based on the past data collection history. The collection unit can also analyze past data collection history and select the most efficient collection method under specific conditions. Furthermore, the collection unit can refer to past data collection history and select the optimal collection method for a specific event. In this way, the optimal collection method can be selected by analyzing past data collection history. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

The collection unit can perform filtering based on the user's current field of interest when collecting data. The collection unit, for example, performs filtering based on the user's current field of interest when collecting data. For example, if the user is interested in disaster information, the collection unit can prioritize collecting disaster-related data. Also, if the user is interested in weather information, the collection unit can also prioritize collecting weather data. Furthermore, if the user is interested in earthquake information, the collection unit can also prioritize collecting earthquake data. In this way, by filtering data based on the user's field of interest, more relevant information can be provided. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI.

The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions and determines the priority of data to be collected based on the estimated user's emotions. For example, when the user is nervous, the collection unit prioritizes collecting data with high urgency. Also, when the user is relaxed, the collection unit can also prioritize collecting general information. Furthermore, when the user is excited, the collection unit can also prioritize collecting data related to a specific field of interest. This makes it possible to provide more appropriate information by determining the priority of data according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account geographical location information. When collecting data, the collection unit, for example, prioritizes collecting highly relevant data by taking into account geographical location information. For example, if a user is interested in a particular area, the collection unit prioritizes collecting data about that area. Also, if the user is traveling, the collection unit can also prioritize collecting data about the current location. Furthermore, if the user is in a disaster area, the collection unit can also prioritize collecting disaster information about that area. In this way, by taking into account geographical location information, highly relevant data can be prioritized. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

The collection unit can analyze social media activity and collect related data when collecting data. For example, the collection unit analyzes social media activity and collects related data when collecting data. For example, if a user frequently shares disaster information on social media, the collection unit prioritizes collecting disaster-related data. Also, if a user frequently posts weather information, the collection unit can also prioritize collecting weather data. Furthermore, if a user frequently retweets earthquake information, the collection unit can also prioritize collecting earthquake data. In this way, by analyzing social media activity, related data can be collected efficiently. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

The analysis unit can estimate the user's emotions and adjust the method of expressing the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the method of expressing the analysis based on the estimated user's emotions. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. When the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, when the user is excited, the analysis unit can also provide a visually stimulating analysis result. In this way, by adjusting the method of expressing the analysis according to the user's emotions, a more appropriate analysis result can be provided. The estimation of emotions is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data of high importance. It can also perform a simplified analysis on data of low importance. It can also perform an analysis with an appropriate level of detail on data of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

The analysis unit can apply different analysis algorithms depending on the category of data during analysis. The analysis unit, for example, applies different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a weather forecasting algorithm to weather data. It can also apply an earthquake forecasting algorithm to earthquake data. It can also apply a flood forecasting algorithm to flood data. In this way, by applying an appropriate analysis algorithm depending on the category of data, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit provides a short and to the point analysis result. Also, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. In this way, by adjusting the length of the analysis according to the user's emotions, a more appropriate analysis result can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

The analysis unit can determine the analysis priority based on the time of data collection during analysis. The analysis unit, for example, determines the analysis priority based on the time of data collection during analysis. For example, the analysis unit prioritizes analysis of the latest data. The analysis unit can also analyze the latest data while referring to past data. Furthermore, the analysis unit can also prioritize analysis of data collected during a specific period. This enables efficient analysis by determining the analysis priority based on the time of data collection. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. Also, it can analyze less relevant data later. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

The generation unit can estimate the user's emotion and adjust the expression method of the generated information based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the expression method of the generated information based on the estimated user's emotion. For example, when the user is nervous, the generation unit generates simple and highly visible information. When the user is relaxed, the generation unit can also generate detailed information. Furthermore, when the user is excited, the generation unit can also generate visually stimulating information. This makes it possible to provide more appropriate information by adjusting the expression method of the information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

The generation unit can adjust the level of detail of the generation based on the importance of the data at the time of generation. The generation unit adjusts the level of detail of the generation based on the importance of the data at the time of generation, for example. For example, the generation unit generates detailed information for data of high importance. Also, the generation unit can generate simplified information for data of low importance. Furthermore, the generation unit can generate information with an appropriate level of detail for data of medium importance. This enables efficient information generation by adjusting the level of detail of the generation based on the importance of the data. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

The generation unit can apply different generation algorithms depending on the category of data at the time of generation. The generation unit, for example, applies different generation algorithms depending on the category of data at the time of generation. For example, the generation unit applies a weather forecasting algorithm to weather data to generate information. Also, the generation unit can apply an earthquake forecasting algorithm to earthquake data to generate information. Furthermore, the generation unit can apply a flood forecasting algorithm to flood data to generate information. In this way, by applying an appropriate generation algorithm depending on the category of data, the generation accuracy is improved. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI.

The generation unit can estimate the user's emotion and adjust the length of the information to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the information to be generated based on the estimated user's emotion. For example, when the user is in a hurry, the generation unit generates short, to-the-point information. When the user is relaxed, the generation unit can also generate detailed information. Furthermore, when the user is excited, the generation unit can also generate visually stimulating information. This makes it possible to provide more appropriate information by adjusting the length of the information according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

The generation unit can determine the generation priority based on the time of data collection at the time of generation. The generation unit, for example, determines the generation priority based on the time of data collection at the time of generation. For example, the generation unit generates the latest data preferentially. The generation unit can also generate the latest data while referring to past data. Furthermore, the generation unit can also generate data collected during a specific period preferentially. This enables efficient information generation by determining the generation priority based on the time of data collection. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI.

The generation unit can adjust the order of generation based on the relevance of the data at the time of generation. The generation unit, for example, adjusts the order of generation based on the relevance of the data at the time of generation. For example, the generation unit generates highly relevant data preferentially. Also, data with low relevance can be generated later. Furthermore, the generation unit can dynamically adjust the order of generation according to the relevance of the data. This enables efficient information generation by adjusting the order of generation based on the relevance of the data. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

The providing unit can estimate the user's emotion and adjust the method of providing information based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the method of providing information based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides information in a simple and highly visible manner. When the user is relaxed, the providing unit can also provide detailed information. Furthermore, when the user is excited, the providing unit can also provide information in a visually stimulating manner. This makes it possible to provide more appropriate information by adjusting the method of providing information according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

The providing unit can select the optimal providing method by referring to the user's past usage history at the time of providing. For example, the providing unit selects the optimal providing method by referring to the user's past usage history at the time of providing. For example, the providing unit selects the optimal providing method based on the method used by the user in the past. The providing unit can also select the most effective providing method from the user's past usage history. Furthermore, the providing unit can analyze the user's past usage history and select the optimal providing method under specific conditions. In this way, the optimal providing method can be selected by referring to the user's past usage history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI.

The providing unit can customize information based on the user's current field of interest at the time of providing. The providing unit customizes information based on the user's current field of interest at the time of providing, for example. For example, if the user is interested in disaster information, the providing unit can provide disaster-related information with priority. Also, if the user is interested in weather information, the providing unit can also provide weather-related information with priority. Furthermore, if the user is interested in earthquake information, the providing unit can also provide earthquake-related information with priority. In this way, by customizing information based on the user's field of interest, more relevant information can be provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions and determines the priority of information provision based on the estimated user's emotions. For example, when the user is nervous, the providing unit can provide information with high urgency with priority. Also, when the user is relaxed, the providing unit can provide general information with priority. Furthermore, when the user is excited, the providing unit can provide information related to a specific field of interest with priority. In this way, by determining the priority of information provision according to the user's emotions, more appropriate information can be provided. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

The providing unit can provide optimal information taking into consideration the geographical location information of the user at the time of providing the information. The providing unit, for example, provides optimal information taking into consideration the geographical location information of the user at the time of providing the information. For example, when the user is in a specific area, the providing unit can provide information about that area preferentially. Also, when the user is traveling, the providing unit can also provide information about the area preferentially. Furthermore, when the user is in a disaster area, the providing unit can also provide disaster information about that area preferentially. In this way, optimal information can be provided by taking into consideration the geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

The providing unit can provide related information by analyzing the user's social media activity at the time of providing. The providing unit can provide related information by analyzing the user's social media activity at the time of providing, for example. For example, if the user frequently shares disaster information on social media, the providing unit can provide disaster-related information preferentially. Also, if the user frequently posts weather information, the providing unit can also provide weather-related information preferentially. Furthermore, if the user frequently retweets earthquake information, the providing unit can also provide earthquake-related information preferentially. In this way, by analyzing social media activity, related information can be provided efficiently. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

The interface unit can estimate the user's emotions and adjust the display method of the interface based on the estimated user's emotions. The interface unit, for example, estimates the user's emotions and adjusts the display method of the interface based on the estimated user's emotions. For example, when the user is nervous, the interface unit provides an interface with calm colors to reduce visual stress. Also, when the user is having fun, the interface unit can provide an interface with bright colors to make input work enjoyable. Furthermore, when the user is tired, the interface unit can provide a simple and highly visible interface to make input work easier. In this way, a more appropriate interface can be provided by adjusting the display method of the interface according to the user's emotions. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

When displaying the interface, the interface unit can select the optimal display method by referring to the user's past operation history. When displaying the interface, the interface unit, for example, selects the optimal display method by referring to the user's past operation history. For example, the interface unit selects the optimal display method based on the display method used by the user in the past. The interface unit can also select the most effective display method from the user's past operation history. Furthermore, the interface unit can analyze the user's past operation history and select the optimal display method under specific conditions. In this way, the optimal display method can be selected by referring to the user's past operation history. Some or all of the above-mentioned processing in the interface unit may be performed, for example, using AI, or may be performed without using AI.

The interface unit can estimate the user's emotions and adjust the interface operation procedure based on the estimated user's emotions. The interface unit, for example, estimates the user's emotions and adjusts the interface operation procedure based on the estimated user's emotions. For example, when the user is nervous, the interface unit provides a simple and intuitive operation procedure. Also, when the user is relaxed, the interface unit can provide detailed operation procedures. Furthermore, when the user is excited, the interface unit can provide visually stimulating operation procedures. In this way, by adjusting the interface operation procedure according to the user's emotions, a more appropriate operation procedure can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

The interface unit can select the optimal display method in consideration of the user's device information when displaying the interface. For example, the interface unit selects the optimal display method in consideration of the user's device information when displaying the interface. For example, if the user is using a smartphone, the interface unit provides a display method that matches the screen size. Also, if the user is using a tablet, the interface unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the interface unit can also provide a simple display method with high visibility. In this way, the optimal display method can be provided by taking into account the user's device information. Some or all of the above-mentioned processing in the interface unit may be performed, for example, using AI, or may be performed without using AI.

The prediction unit can estimate the user's emotion and adjust the prediction method based on the estimated user's emotion. The prediction unit, for example, estimates the user's emotion and adjusts the prediction method based on the estimated user's emotion. For example, when the user is nervous, the prediction unit provides a simple and highly visible prediction result. When the user is relaxed, the prediction unit can also provide a detailed prediction result. Furthermore, when the user is excited, the prediction unit can also provide a visually stimulating prediction result. In this way, by adjusting the prediction method according to the user's emotion, a more appropriate prediction result can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

The prediction unit can optimize the prediction algorithm by referring to past data when making a prediction. The prediction unit optimizes the prediction algorithm by referring to past data when making a prediction, for example. For example, the prediction unit selects an optimal prediction algorithm based on past data. The prediction unit can also analyze past data and select the most effective prediction algorithm under specific conditions. Furthermore, the prediction unit can dynamically adjust the prediction algorithm by referring to past data. In this way, the prediction algorithm can be optimized by referring to past data. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI.

The prediction unit can apply different prediction methods depending on the category of data when making predictions. The prediction unit, for example, applies different prediction methods depending on the category of data when making predictions. For example, the prediction unit applies a weather forecasting method to weather data. It can also apply an earthquake forecasting method to earthquake data. It can also apply a flood forecasting method to flood data. In this way, prediction accuracy is improved by applying an appropriate forecasting method depending on the category of data. Some or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI or may be performed without using AI.

The prediction unit can estimate the user's emotions and determine the priority of predictions based on the estimated user's emotions. The prediction unit, for example, estimates the user's emotions and determines the priority of predictions based on the estimated user's emotions. For example, if the user is nervous, the prediction unit can provide a prediction with high urgency as a priority. In addition, if the user is relaxed, the prediction unit can also provide a general prediction as a priority. Furthermore, if the user is excited, the prediction unit can also provide a prediction related to a specific field of interest as a priority. In this way, by determining the priority of predictions according to the user's emotions, more appropriate prediction results can be provided. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

The prediction unit can weight the prediction based on the time when the data was collected at the time of prediction. The prediction unit, for example, weights the prediction based on the time when the data was collected at the time of prediction. For example, the prediction unit makes a prediction with emphasis on the latest data. The prediction unit can also make a prediction with emphasis on the latest data while referring to past data. Furthermore, the prediction unit can also make a prediction with emphasis on data collected during a specific period. As a result, prediction accuracy is improved by weighting the prediction based on the time when the data was collected. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI.

The prediction unit can improve the accuracy of the prediction by referring to the related market data when making a prediction. The prediction unit can improve the accuracy of the prediction by referring to the related market data when making a prediction, for example. For example, the prediction unit improves the accuracy of the prediction based on the related market data. The prediction unit can also analyze the related market data and improve the accuracy of the prediction under specific conditions. Furthermore, the prediction unit can also dynamically adjust the prediction algorithm by referring to the related market data. In this way, the accuracy of the prediction can be improved by referring to the related market data. Some or all of the above-mentioned processing in the prediction unit can be performed, for example, using AI, or can be performed without using AI.

The evaluation unit can estimate the user's emotions and adjust the evaluation method based on the estimated user's emotions. The evaluation unit, for example, estimates the user's emotions and adjusts the evaluation method based on the estimated user's emotions. For example, when the user is nervous, the evaluation unit provides a simple and highly visible evaluation result. When the user is relaxed, the evaluation unit can also provide a detailed evaluation result. Furthermore, when the user is excited, the evaluation unit can also provide a visually stimulating evaluation result. In this way, by adjusting the evaluation method according to the user's emotions, a more appropriate evaluation result can be provided. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

The evaluation unit can optimize the evaluation algorithm by referring to past data during evaluation. The evaluation unit can optimize the evaluation algorithm by referring to past data during evaluation, for example. For example, the evaluation unit selects an optimal evaluation algorithm based on past data. The evaluation unit can also analyze past data and select an evaluation algorithm that is most effective under specific conditions. Furthermore, the evaluation unit can dynamically adjust the evaluation algorithm by referring to past data. In this way, the evaluation algorithm can be optimized by referring to past data. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI.

The evaluation unit can apply different evaluation methods depending on the category of data during evaluation. The evaluation unit, for example, applies different evaluation methods depending on the category of data during evaluation. For example, the evaluation unit applies a meteorological evaluation method to meteorological data. It can also apply an earthquake evaluation method to earthquake data. It can also apply a flood evaluation method to flood data. In this way, the evaluation accuracy is improved by applying an appropriate evaluation method depending on the category of data. Some or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI or may be performed without using AI.

The evaluation unit can estimate the user's emotions and determine the priority of evaluations based on the estimated user's emotions. The evaluation unit, for example, estimates the user's emotions and determines the priority of evaluations based on the estimated user's emotions. For example, if the user is nervous, the evaluation unit can provide a high priority evaluation of urgency. Also, if the user is relaxed, the evaluation unit can provide a general evaluation with priority. Furthermore, if the user is excited, the evaluation unit can provide an evaluation related to a specific field of interest with priority. In this way, by determining the priority of evaluations according to the user's emotions, a more appropriate evaluation result can be provided. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

The evaluation unit can weight the evaluation based on the time of data collection during the evaluation. The evaluation unit, for example, weights the evaluation based on the time of data collection during the evaluation. For example, the evaluation unit places emphasis on the latest data during the evaluation. The evaluation unit can also place emphasis on the latest data while referring to past data during the evaluation. Furthermore, the evaluation unit can also place emphasis on data collected during a specific period during the evaluation. In this way, the evaluation accuracy is improved by weighting the evaluation based on the time of data collection. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI.

The evaluation unit can improve the accuracy of the evaluation by referring to the related market data during the evaluation. The evaluation unit can improve the accuracy of the evaluation by referring to the related market data during the evaluation. For example, the evaluation unit improves the accuracy of the evaluation based on the related market data. The evaluation unit can also analyze the related market data and improve the accuracy of the evaluation under specific conditions. Furthermore, the evaluation unit can dynamically adjust the evaluation algorithm by referring to the related market data. In this way, the accuracy of the evaluation can be improved by referring to the related market data. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI or without using AI.

The system according to the embodiment is not limited to the above-mentioned example, and various modifications are possible, for example, as follows:

The Metaverse Earth system can further include a health monitoring unit that monitors the user's health condition. The health monitoring unit collects vital data such as the user's heart rate, blood pressure, and body temperature, and transmits it to the analysis unit. The analysis unit can analyze this data and evaluate the user's health condition. For example, if the heart rate is abnormally high, the analysis unit can generate a notification encouraging the user to rest. Also, if the blood pressure is high, the analysis unit can generate a notification recommending the user to see a doctor. Furthermore, if the body temperature is high, the analysis unit can generate a notification encouraging the user to drink water. This makes it possible to monitor the user's health condition in real time and provide appropriate advice.

The Metaverse Earth system can further include a behavioral analysis unit that analyzes the user's behavioral history. The behavioral analysis unit collects the user's past behavioral data and transmits it to the analysis unit. The analysis unit can analyze this data and identify the user's behavioral patterns. For example, if the user is often in a specific location at a specific time of day, the analysis unit can provide information related to that time period preferentially. Also, if the user frequently participates in a specific event, the analysis unit can provide information related to that event. Furthermore, if the user has specific interests, the analysis unit can provide information related to those interests. This makes it possible to provide more personalized information by analyzing the user's behavioral history.

The Metaverse Earth system may further include an emotional interaction unit that estimates the user's emotions and adjusts interactions based on the estimated emotions. The emotional interaction unit estimates emotions from the user's facial expressions and voice and transmits them to the analysis unit. The analysis unit may analyze these data and evaluate the user's emotional state. For example, if the user is feeling stressed, the analysis unit may provide relaxing content. Also, if the user is excited, the analysis unit may provide energetic content. Furthermore, if the user is sad, the analysis unit may provide comforting content. This may provide interactions according to the user's emotions, thereby achieving a better user experience.

The Metaverse Earth system can further include a learning analysis unit that analyzes the user's learning history. The learning analysis unit collects the user's past learning data and sends it to the analysis unit. The analysis unit can analyze this data and identify the user's learning patterns. For example, if the user is interested in a particular field, the analysis unit can provide information related to that field preferentially. Also, if the user prefers a particular learning method, the analysis unit can provide learning content based on that method. Furthermore, if the user often studies during a particular time period, the analysis unit can send learning notifications during that time period. This makes it possible to provide more effective learning support by analyzing the user's learning history.

The Metaverse Earth system can further include a notification adjustment unit that estimates the user's emotions and adjusts the content of the notification based on the estimated emotions. The notification adjustment unit estimates the user's emotions and sends them to the analysis unit. The analysis unit can analyze these data and generate notification content according to the user's emotional state. For example, if the user is nervous, the analysis unit generates a simple, highly visible notification. Also, if the user is relaxed, the analysis unit can generate a detailed notification. Furthermore, if the user is excited, the analysis unit can generate a visually stimulating notification. This makes it possible to provide more appropriate information by providing notification content according to the user's emotions.

The Metaverse Earth system can further include a purchase analysis unit that analyzes the user's purchasing history. The purchase analysis unit collects the user's past purchasing data and sends it to the analysis unit. The analysis unit can analyze this data and identify the user's purchasing patterns. For example, if the user has a preference for a particular brand, the analysis unit can provide information related to that brand preferentially. Also, if the user frequently purchases products in a particular category, the analysis unit can also provide information related to that category. Furthermore, if the user often purchases a particular product at a particular time, the analysis unit can also provide information related to that time. This makes it possible to provide more personalized information by analyzing the user's purchasing history.

The Metaverse Earth system can further include a display adjustment unit that estimates the user's emotions and adjusts the display method of data based on the estimated emotions. The display adjustment unit estimates the user's emotions and transmits them to the analysis unit. The analysis unit can analyze these data and generate a display method according to the user's emotional state. For example, if the user is nervous, the analysis unit provides a display method with calm colors. Also, if the user is relaxed, the analysis unit can provide a display method with bright colors. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating display method. In this way, by providing a display method according to the user's emotions, more appropriate information can be provided.

The Metaverse Earth system may further include a social analysis unit that analyzes the user's social network. The social analysis unit collects the user's social network data and transmits it to the analysis unit. The analysis unit may analyze these data and identify the structure of the user's social network. For example, if the user belongs to a particular group, the analysis unit may provide information related to that group preferentially. Also, if the user frequently interacts with a particular person, the analysis unit may provide information related to that person. Furthermore, if the user participates in a particular event, the analysis unit may provide information related to that event. In this way, by analyzing the user's social network, it is possible to provide more relevant information.

The Metaverse Earth system may further include a priority determination unit that estimates the user's emotions and determines the priority of data based on the estimated emotions. The priority determination unit estimates the user's emotions and transmits them to the analysis unit. The analysis unit analyzes these data and can determine the priority of data according to the user's emotional state. For example, if the user is nervous, the analysis unit can provide data with high urgency with priority. Also, if the user is relaxed, the analysis unit can provide general data with priority. Furthermore, if the user is excited, the analysis unit can provide data related to a specific field of interest with priority. In this way, more appropriate information can be provided by determining the priority of data according to the user's emotions.

The Metaverse Earth system may further include a feedback collection unit that collects user feedback. The feedback collection unit collects user feedback data and transmits it to the analysis unit. The analysis unit may analyze this data and identify improvements to the system. For example, if a user is dissatisfied with a particular function, the analysis unit may generate a proposal to improve that function. Also, if a user gives a high rating to a particular function, the analysis unit may generate a proposal to enhance that function. Furthermore, if a user requests a new function, the analysis unit may generate a proposal to add that function. In this way, by collecting user feedback, the system may be continuously improved.

The process flow for Example 2 is briefly explained below.

Step 1: The collection unit collects earth observation data. The earth observation data includes, but is not limited to, satellite data, meteorological data, and earthquake data. For example, the collection unit acquires satellite data in real time and periodically collects meteorological data. The collection unit can also immediately acquire earthquake data when an earthquake occurs.
Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses AI to analyze the data and perform anomaly detection and pattern recognition. The analysis unit, for example, analyzes meteorological data to generate a weather forecast. The analysis unit can also analyze earthquake data to predict the probability of an earthquake occurring.
Step 3: The generating unit generates information based on the analysis results obtained by the analyzing unit. The generating unit generates information in real time according to a user request, for example, using a generating AI. The generating unit generates disaster information, for example, and provides it to the user. The generating unit can also generate risk assessment information for insurance companies.
Step 4: The providing unit provides the information generated by the generating unit. The providing unit provides the information using, for example, AI and provides feedback to the user. The providing unit provides the information through, for example, a web application. The providing unit can also provide the information through a mobile application.

The specific processing unit 290 transmits the result 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 result of the specific processing. The microphone 38B acquires audio indicating a user input for the result 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.

The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is generative AI such as ChatGPT (registered trademark) (Internet search <URL: https://openai.com/blog/chatgpt>). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats such as voice data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model to output an inference result from a prompt that does not include an instruction, in which case the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12, etc., the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI other than the generation AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to such examples. The AI may also be an AI agent. In addition, when the processing of each part described above is performed by AI, the processing is performed in part or in whole by AI, but is not limited to such examples. In addition, the processing performed by AI, including the generating AI, may be replaced with rule-based processing, and the rule-based processing may be replaced with processing performed by AI, including the generating AI.

The processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. The specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, provision unit, interface unit, prediction unit, and evaluation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects earth observation data using the camera 42 and sensors of the smart device 14, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates information based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the generated information to the user. The interface unit is realized, for example, by the control unit 46A of the smart device 14, and provides an interface that the user can intuitively operate. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts disaster information. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs risk assessment for insurance companies. The correspondence between each part and the device or control unit is not limited to the above example, and various modifications are possible.

[Second embodiment]
FIG. 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 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the 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 and/or a LAN.

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, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to 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 the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical able-bodied 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 performed in a secure state.

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

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.

The 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to such examples. Furthermore, the estimation and prediction of emotion also includes, for example, analysis of emotions.

In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores the specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can use these models to perform processing similar to that of the specific processing unit 290.

Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device owned by a user (for example, a mobile phone, a robot, a home appliance, etc.).

The specific processing unit 290 transmits the result 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 result of the specific processing. The microphone 238 acquires audio indicating a user input for the result 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.

The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model to output an inference result from a prompt that does not include an instruction, in which case the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12, etc., the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI other than the generation AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to such examples. The AI may also be an AI agent. In addition, when the processing of each part described above is performed by AI, the processing is performed in part or in whole by AI, but is not limited to such examples. In addition, processing performed by AI, including the generating AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including the generating AI.

The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, provision unit, interface unit, prediction unit, and evaluation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects earth observation data using the camera 42 and sensor of the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates information based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the generated information to the user. The interface unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides an interface that the user can intuitively operate. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts disaster information. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs risk evaluation for the insurance company. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[Third embodiment]
FIG. 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 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the 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 and/or a LAN.

The headset type 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, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the 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 by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to 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 the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical able-bodied 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 performed in a secure state.

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

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.

The 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to such examples. Furthermore, the estimation and prediction of emotion also includes, for example, analysis of emotions.

In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores the specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device owned by a user (for example, a mobile phone, a robot, a home appliance, etc.).

The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input for the result 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.

The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model to output an inference result from a prompt that does not include an instruction, in which case the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12, etc., the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI other than the generation AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to such examples. The AI may also be an AI agent. In addition, when the processing of each part described above is performed by AI, the processing is performed in part or in whole by AI, but is not limited to such examples. In addition, the processing performed by AI, including the generating AI, may be replaced with rule-based processing, and the rule-based processing may be replaced with processing performed by AI, including the generating AI.

The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, provision unit, interface unit, prediction unit, and evaluation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects earth observation data using the camera 42 and sensors of the headset type terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates information based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the generated information to the user. The interface unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides an interface that the user can intuitively operate. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts disaster information. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs risk evaluation for the insurance company. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

[Fourth embodiment]
FIG. 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 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the 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 and/or a LAN.

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, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to 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 image sensor or a CCD image sensor, and captures the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical able-bodied 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 performed in a secure state.

The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and legs. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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.

The 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. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to such examples. Furthermore, the estimation and prediction of emotion also includes, for example, analysis of emotions.

In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores the specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device owned by a user (for example, a mobile phone, a robot, a home appliance, etc.).

The specific processing unit 290 transmits the result 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 result of the specific processing. The microphone 238 acquires voice indicating the user input for the result of the specific processing. The control unit 46A transmits voice 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 voice data.

The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats such as voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The identification processing unit 290 performs the above-mentioned identification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model to output an inference result from a prompt that does not include an instruction, in which case the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12, etc., the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI other than the generation AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to such examples. The AI may also be an AI agent. In addition, when the processing of each part described above is performed by AI, the processing is performed in part or in whole by AI, but is not limited to such examples. In addition, the processing performed by AI, including the generating AI, may be replaced with rule-based processing, and the rule-based processing may be replaced with processing performed by AI, including the generating AI.

The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, provision unit, interface unit, prediction unit, and evaluation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects earth observation data using the camera 42 and sensors of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates information based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the generated information to the user. The interface unit is realized, for example, by the control unit 46A of the robot 414, and provides an interface that the user can intuitively operate. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts disaster information. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs risk evaluation for insurance companies. The correspondence between each part and the device or control unit is not limited to the above example, and various modifications are possible.

The emotion identification model 59, which serves as an emotion engine, may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. Emotions that represent states and actions arising from a state of mind are arranged on the outer sides of the concentric circles. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

These emotions are distributed in the three o'clock direction of 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 inside 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 emotions become.

Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and remaining battery power, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https://ci.nii.ac.jp/naid/500000375379). The left half of the emotion map is lined with emotions that belong to an area called "reaction" where sensation is dominant. The right half of the emotion map is lined with emotions that belong to an area called "situation" where situation recognition is dominant.

The emotion map defines two emotions that encourage learning. The first 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 has positive feelings such as "I want more" or "I want to know more."

The emotion identification model 59 inputs user input to 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 learning 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 located 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, "peace of mind," "calm," and "reassuring," have similar emotion values.

In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

In the above embodiment, an example has been 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 Universal Serial Bus (USB) 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.

The specific processing program 56 may also 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 all of the 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

The various processors listed below can be used as hardware resources for executing specific processes. Examples of processors include a CPU, which is a general-purpose processor that functions as a hardware resource for executing specific processes by executing software, i.e., a program. 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 to execute specific processes. All of these processors have built-in or connected memory, and all of these processors execute specific processes by using the memory.

The hardware resource that executes the specific process 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 (e.g., a combination of multiple FPGAs, or a combination of a CPU and an FPGA). The hardware resource that executes the specific process 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 by combining one or more CPUs with software, and this processor functions as a hardware resource that executes a specific process. Secondly, there is a configuration in which a processor is used that realizes the functions of the entire system, including multiple hardware resources that execute a specific process, on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the various processors mentioned above as hardware resources.

More specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processing described above is merely an example. It goes without saying that unnecessary steps can be deleted, new steps can be added, and the processing order can be changed without departing from the spirit of the invention.

In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are only examples, and they may be combined with each other, or may be other devices. Also, in the above example, the first and second embodiments have been described separately, but these may be combined.

The above description 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, function, action, and effect is an explanation of an example of the configuration, function, action, and effect 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 replacements may be made to the above description and illustrations, within the scope of the gist of the technology of the present disclosure. Also, in order to avoid confusion and to make it easier to understand the parts related to the technology of the present disclosure, the above description and illustrations omit explanations of technical common sense 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 into this specification to the same extent as if each individual publication, patent application, and technical standard was specifically and individually indicated to be incorporated by reference.

(Appendix 1)
a collection unit for collecting earth observation data;
an analysis unit that analyzes the data collected by the collection unit;
a generation unit that generates information based on the analysis result obtained by the analysis unit;
a providing unit that provides the information generated by the generating unit;
A system comprising:
(Appendix 2)
The system according to claim 1, further comprising an interface unit for providing a user interface.
(Appendix 3)
The system according to claim 1, further comprising a prediction unit for predicting disaster information.
(Appendix 4)
The system according to claim 1, further comprising an assessment unit for performing risk assessment for an insurance company.
(Appendix 5)
The collecting unit includes:
2. The system of claim 1, further comprising: collecting at least one of meteorological data, earthquake data, and flood data.
(Appendix 6)
The generation unit is
2. The system according to claim 1, wherein information is generated in real time in response to a user request.
(Appendix 7)
The collecting unit includes:
2. The system of claim 1, further comprising: estimating a user's emotion; and adjusting timing of data collection based on the estimated user's emotion.
(Appendix 8)
The collecting unit includes:
2. The system according to claim 1, further comprising: analyzing past data collection history and selecting an optimal collection method.
(Appendix 9)
The collecting unit includes:
2. The system of claim 1, further comprising filtering based on a user's current areas of interest during data collection.
(Appendix 10)
The collecting unit includes:
The system according to claim 1, further comprising: estimating a user's emotion; and determining a priority of data to be collected based on the estimated user's emotion.
(Appendix 11)
The collecting unit includes:
The system according to claim 1, characterized in that, when collecting data, highly relevant data is collected preferentially in consideration of geographical location information.
(Appendix 12)
The collecting unit includes:
2. The system of claim 1, wherein during data collection, social media activity is analyzed and associated data is collected.
(Appendix 13)
The analysis unit is
2. The system of claim 1, further comprising: estimating a user's emotion; and adjusting a representation of the analysis based on the estimated user's emotion.
(Appendix 14)
The analysis unit is
The system according to claim 1, further comprising: adjusting a level of detail of the analysis based on the importance of the data during the analysis.
(Appendix 15)
The analysis unit is
2. The system of claim 1, wherein during analysis, different analysis algorithms are applied depending on the category of data.
(Appendix 16)
The analysis unit is
2. The system of claim 1, further comprising: estimating a user's emotion; and adjusting a length of the analysis based on the estimated user's emotion.
(Appendix 17)
The analysis unit is
2. The system of claim 1, further comprising: determining, during analysis, a priority for analysis based on when the data was collected.
(Appendix 18)
The analysis unit is
2. The system of claim 1, wherein during analysis, the order of analysis is adjusted based on data relevance.
(Appendix 19)
The generation unit is
The system according to claim 1, further comprising: estimating a user's emotion; and adjusting a method of expressing generated information based on the estimated user's emotion.
(Appendix 20)
The generation unit is
2. The system of claim 1, further comprising: adjusting, at the time of generation, a level of detail of the generation based on the importance of the data.
(Appendix 21)
The generation unit is
2. The system of claim 1, wherein during generation, different generation algorithms are applied depending on the category of data.
(Appendix 22)
The generation unit is
The system according to claim 1, further comprising: estimating a user's emotion; and adjusting a length of generated information based on the estimated user's emotion.
(Appendix 23)
The generation unit is
The system of claim 1, further comprising: determining, at the time of generation, a generation priority based on when the data was collected.
(Appendix 24)
The generation unit is
2. The system of claim 1, further comprising: adjusting an order of generation based on data relevance during generation.
(Appendix 25)
The providing unit is
The system according to claim 1, further comprising: estimating a user's emotion; and adjusting a method of providing information based on the estimated user's emotion.
(Appendix 26)
The providing unit is
The system according to claim 1, characterized in that, at the time of provision, the optimal provision method is selected by referring to the user's past usage history.
(Appendix 27)
The providing unit is
2. The system of claim 1, wherein the information is customized at the time of provision based on the user's current areas of interest.
(Appendix 28)
The providing unit is
The system according to claim 1, further comprising: estimating a user's emotion; and determining a priority order for providing information based on the estimated user's emotion.
(Appendix 29)
The providing unit is
The system according to claim 1, characterized in that, when providing information, optimal information is provided taking into account the user's geographical location information.
(Appendix 30)
The providing unit is
The system of claim 1, further comprising: analyzing a user's social media activity at the time of provision to provide relevant information.
(Appendix 31)
The interface unit includes:
The system according to claim 2, further comprising: estimating a user's emotion; and adjusting an interface display method based on the estimated user's emotion.
(Appendix 32)
The interface unit includes:
The system according to claim 2, wherein when displaying an interface, the system refers to a user's past operation history to select an optimal display method.
(Appendix 33)
The interface unit includes:
The system according to claim 2, further comprising: estimating a user's emotion; and adjusting an interface operation procedure based on the estimated user's emotion.
(Appendix 34)
The interface unit includes:
The system according to claim 2, wherein when displaying an interface, the system selects an optimal display method taking into consideration device information of the user.
(Appendix 35)
The prediction unit is
The system of claim 3, further comprising: estimating a user's emotion; and adjusting a prediction methodology based on the estimated user's emotion.
(Appendix 36)
The prediction unit is
The system according to claim 3, further comprising: optimizing a prediction algorithm by referring to past data when making predictions.
(Appendix 37)
The prediction unit is
The system of claim 3, further comprising: applying different prediction methods depending on the category of data during prediction.
(Appendix 38)
The prediction unit is
The system of claim 3, further comprising: estimating a user's emotion; and determining a priority of predictions based on the estimated user's emotion.
(Appendix 39)
The prediction unit is
4. The system of claim 3, wherein, at the time of prediction, the prediction is weighted based on when the data was collected.
(Appendix 40)
The prediction unit is
The system of claim 3, further comprising: referencing relevant market data during prediction to improve accuracy of the prediction.
(Appendix 41)
The evaluation unit is
The system according to claim 4, further comprising: estimating a user's emotion; and adjusting a method of evaluation based on the estimated user's emotion.
(Appendix 42)
The evaluation unit is
The system according to claim 4, further comprising: optimizing an evaluation algorithm by referring to past data during evaluation.
(Appendix 43)
The evaluation unit is
5. The system of claim 4, wherein during the evaluation, different evaluation techniques are applied depending on the category of the data.
(Appendix 44)
The evaluation unit is
The system according to claim 4, further comprising: estimating a user's emotion; and determining a priority order of ratings based on the estimated user's emotion.
(Appendix 45)
The evaluation unit is
5. The system of claim 4, wherein, during the evaluation, the evaluation is weighted based on when the data was collected.
(Appendix 46)
The evaluation unit is
The system of claim 4, further comprising: at the time of valuation, referring to relevant market data to improve accuracy of the valuation.

10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset type terminal 414 Robot

Claims (8)

a collection unit for collecting earth observation data;
an analysis unit that analyzes the data collected by the collection unit;
a generation unit that generates information based on the analysis result obtained by the analysis unit;
a providing unit that provides the information generated by the generating unit;
A system comprising: The system according to claim 1, further comprising an interface unit for providing a user interface. The system according to claim 1 , further comprising a prediction unit for predicting disaster information. The system according to claim 1 , further comprising an assessment unit for performing risk assessment for an insurance company. The collecting unit includes:
10. The system of claim 1, further comprising: a data collection unit configured to collect at least one of weather data, earthquake data, and flood data. The generation unit is
2. The system according to claim 1, wherein the system generates information in real time in response to a user request. The collecting unit includes:
The system of claim 1 , further comprising: estimating a user's emotion; and adjusting timing of data collection based on the estimated user's emotion. The collecting unit includes:
The system according to claim 1, further comprising: analyzing a past data collection history and selecting an optimal collection method.

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