JP2024167393A - Information processing system, information processing method, computer program and vehicle - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a robot capable of reducing burden of a user regarding learning of the robot.

SOLUTION: A robot includes: a voice recognition unit for recognizing voice; a learning unit for learning a recognition result of the voice recognition unit; a processing unit for performing corresponding processing of the recognition result of the voice recognition unit using a learning result of the learning unit; a storage unit for storing the learning result in an external storage device; and a learning result acquisition unit for acquiring the learning result from the storage device. A standard program storage unit 23, an artificial intelligence unit 25 and an operation unit 27 are examples of the voice recognition unit, the processing unit and a learning stop unit.

SELECTED DRAWING: Figure 1

COPYRIGHT: (C)2024,JPO&INPIT

Description

REFERENCE TO RELATED APPLICATIONS

This international application claims priority based on Japanese Patent Application No. 2014-262907, filed with the Japan Patent Office on December 25, 2014, the entire contents of which are incorporated herein by reference.

This disclosure relates to robots.

In recent years, robots that can converse with people have been attracting attention. These robots use a microphone to pick up speech produced by people and infer the meaning of the speech through speech recognition. They then respond to the inferred meaning with a response that has been previously associated with the speech (see Patent Document 1).

Patent No. 4015424

It is conceivable that a robot's conversational ability could be improved by learning using artificial intelligence, etc. However, even if the conversational ability of a particular robot is improved through learning, when using another robot in a different location, for example, learning must be carried out from the very basics. One aspect of the present disclosure is to provide a robot that can reduce the burden on users in terms of learning to use a robot.

The robot disclosed herein includes a voice recognition unit that performs voice recognition, a learning unit that learns from the recognition results of the voice recognition unit, a processing unit that performs corresponding processing on the recognition results of the voice recognition unit using the learning results of the learning unit, a storage unit that stores the learning results in an external storage device, and a learning result acquisition unit that acquires the learning results from the storage device.

The robot disclosed herein can learn about the recognition results of the voice recognition unit. The robot disclosed herein can also store the learning results in an external storage device and retrieve the learning results from the storage device.

Therefore, for example, a user can store the learning results resulting from use of a first robot in an external storage device, and when using a second robot, can introduce the stored learning results into the second robot. As a result, the user can utilize the learning results of the first robot, and does not necessarily have to train the second robot from the very basics.

FIG. 2 is a block diagram showing an electrical configuration of the robot according to the embodiment. FIG. 2 is a front view showing the configuration of the robot. 11 is a flowchart showing a program setting process executed by a robot. 1 is a flowchart showing a conversation process executed by a robot. 11 is a flowchart showing a learning stop determination process executed by a robot. FIG. 6A is an explanatory diagram showing an example of an operation for instructing to stop learning, and FIG. 6B is an explanatory diagram showing an example of an operation for instructing to restart learning. FIG. 2 is a block diagram showing the electrical configuration of a computer. FIG. 1 is a perspective view illustrating an external appearance of a computer. FIG. 2 is a block diagram showing an electrical configuration of an in-vehicle device. 4 is a flowchart showing a vehicle control process executed by an in-vehicle device. FIG. 11 is a block diagram showing an electrical configuration of a robot according to another embodiment. 11 is a flowchart showing a program installation process executed by a robot. 1 is a flowchart showing a conversation process executed by a robot. 13 is a flowchart showing the second artificial intelligence unit learning stop determination process executed by the robot. 13 is a flowchart showing a program upload process executed by a robot. FIG. 13 is a block diagram showing an electrical configuration of a robot according to still another embodiment. 1 is a flowchart showing a conversation process executed by a robot. FIG. 2 is an explanatory diagram illustrating a configuration for executing a call process. 10 is a flowchart showing a call pair setting process executed by a host computer. FIG. 2 is an explanatory diagram showing a display showing icons.

1, 301...robot, 3...control unit, 5...microphone, 7...camera, 9...touch panel, 11...sensor group, 13...GPS, 15...speaker, 17...motor group, 19...display, 21...input unit, 23...standard program storage unit, 25...artificial intelligence unit, 26...communication unit, 27...arithmetic unit, 29...output unit, 31...cloud network, 33...terminal, 35...head, 37...torso, 39...right arm, 41...left arm, 43...right leg, 45...left leg, 47, 49...wheels, 51...movement motor, 53...neck joint, 55...shoulder joint, 57...elbow joint, 59...wrist joint, 61...joint motor, 63...keyboard, 65...mouse, 67...casing, 69...terminal, 71...vehicle control unit, 101...computer, 201...vehicle-mounted device

An embodiment of the present disclosure will be described with reference to the drawings.
First Embodiment
1. Configuration of Robot 1 The configuration of the robot 1 will be described with reference to Figures 1 and 2. As shown in Figure 1, the robot 1 includes a control unit 3, a microphone 5, a camera 7, a touch panel 9, a group of sensors 11, a GPS 13, a speaker 15, a group of motors 17, and a display 19.

The control unit 3 includes a microcomputer. Specifically, the control unit includes an input unit 21, a standard program storage unit 23, an artificial intelligence unit 25, a communication unit 26, a calculation unit 27, and an output unit 29.

The input unit 21 acquires information from the microphone 5, camera 7, touch panel 9, sensor group 11, and GPS 13, and outputs the information to the calculation unit 27 and artificial intelligence unit 25.

The standard program storage unit 23 constantly stores standard programs for executing various operations of the robot 1. The standard programs are different from the AI programs described later.
It is a program whose contents do not change.

The artificial intelligence unit 25 can store AI (artificial intelligence) programs for executing various operations of the robot 1. When the artificial intelligence unit 25 stores an AI program, it changes and develops it through learning. The artificial intelligence unit 25 can also install new AI programs and erase AI programs.

The communication unit 26 communicates with an external cloud network 31, a terminal 33, etc. The communication may be wireless communication or wired communication.
The arithmetic unit 27 uses a standard program or an AI program to execute the calculations necessary to realize various operations of the robot 1.

The output unit 29 outputs the calculation results of the calculation unit 27 to the speaker 15, the motor group 17, and the display 19. The detailed functions of each unit belonging to the control unit 3 will be described later.

As shown in FIG. 2, the robot 1 has a humanoid external shape and includes a head 35 , a torso 37 , a right arm 39 , a left arm 41 , a right leg 43 , and a left leg 45 .
The microphone 5, the camera 7, and the speaker 15 are provided in the head 35. The display 19 and the touch panel 9 are provided on the front side of the body 37.

The right leg 43 and the left leg 45 each have wheels 47, 49 for movement. Two wheels 47, 49 are provided in the front-to-rear direction. Therefore, the robot 1 comes into contact with the ground with a total of four wheels. The wheels 47, 49 are driven by a movement motor 51 belonging to the motor group 17. The robot 1 can move forward and backward by rotating the wheels 47, 49 with the driving force of the movement motor 51. In addition, by making the rotation speed of the wheel 47 different from the rotation speed of the wheel 49, the robot 1 can turn right or left.

The robot 1 has a neck joint 53, a shoulder joint 55, an elbow joint 57, and a wrist joint 59. The degree of freedom of each joint can be set appropriately from 1 to 3. Each joint is operated by the driving force of a joint motor 61 belonging to the motor group 17. By driving an appropriately selected one of the above joints with the joint motor 61, the robot 1 performs a predetermined operation and expresses a predetermined posture.

The multiple sensors belonging to the sensor group 11 detect the position, speed, acceleration, inclination, joint angles, etc. of each part of the robot 1. The detection results are fed back to the arithmetic unit 27 via the input unit 21 and are used to control the movement of the robot 1.

The standard program storage unit 23, the artificial intelligence unit 25, and the calculation unit 27 are examples of a voice recognition unit, a processing unit, and a learning stop unit. The artificial intelligence unit 25 is an example of a learning unit. The communication unit 26 is an example of a storage unit and a learning result acquisition unit. The input unit 21 and the communication unit 26 are examples of an identification information acquisition unit. The cloud network 31 is an example of an external storage device.

2. Processing Executed by Robot 1 (2-1) Program Setting Processing In order to determine which of the standard program and the AI program to use, the control unit 3 executes the program setting processing shown in Fig. 3. This program setting processing is executed repeatedly at predetermined time intervals when the power supply of the robot 1 is on.

In step 1, it is determined whether or not an AI program is being used at that time. If an AI program is not being used (i.e., the standard program is being used), proceed to step 2; if an AI program is being used, proceed to step 5.

In step 2, it is determined whether or not the user's identification information has been input. The identification information can be input, for example, in the following manner.
- The identification information is transmitted from the terminal 33 to the communication unit 26.

- Electromagnetic waves (e.g., radio waves, infrared rays, etc.) representing the identification information are transmitted to the communication unit 26. The electromagnetic waves representing the identification information may be transmitted from the terminal 33 or from a fixed device (e.g., a beacon, a wireless LAN access point, etc.). The electromagnetic waves representing the identification information may be transmitted periodically, in response to a user's instruction, or when the terminal 33 or a fixed device detects the robot 1.

The user speaks out the content of the identification information. The microphone 5 picks up the voice and identifies the identification information through voice recognition.
A one-dimensional or two-dimensional barcode representing identification information is photographed by the camera 7.

Use the touch panel 9 to input identification information.
The identification information may be composed of numbers or letters, a one-dimensional or two-dimensional image (e.g., a one-dimensional or two-dimensional barcode), biometric information of the user (e.g., a fingerprint, a vein pattern in any part of the body, iris, face, etc.), or may be composed of voice.

If the identification information has been input, the process proceeds to step 3, and if the identification information has not been input, the process ends.
In step 3, the AI program and data set corresponding to the identification information determined to have been input in step 2 are installed from the cloud network 31. The installed AI program and data set are stored in the artificial intelligence unit 25.

In addition, the cloud network 31 stores identification information in association with AI programs and data sets. The data sets are used for using and learning AI programs, and include dictionary data used in voice recognition and voice synthesis.

In step 4, the program used by robot 1 is changed from the standard program to the AI program installed in step 3. From this point on, robot 1 uses the AI program.

On the other hand, if the result of step 1 is positive, then in step 5 it is determined whether the conditions for ending use of the robot 1 have been met. The conditions for ending use can be, for example, as follows:

The user does not operate the robot 1 for a predetermined period of time or longer.
A state in which the user cannot be recognized in images from the camera 7 or in audio picked up by the microphone 5 continues for a predetermined period of time or longer.

The user makes an input to the robot 1 indicating that use has ended (for example, the user says out loud "end of use" or inputs information indicating that use has ended on the touch panel 9).

・Robot 1 is powered off.
・The preset time arrives.
If the usage end condition is satisfied, the process proceeds to step 6, and if the usage end condition is not satisfied, the process ends.

In step 6, the AI program and dataset are uploaded to the cloud network 31. At this time, they are uploaded in association with the user's identification information. Note that if learning, which will be described later, has been performed, the AI program and dataset uploaded will be those after learning.

In step 7, the AI programs and data sets are erased from the artificial intelligence unit 25.
In step 8, the program to be used is changed from the AI program to the standard program. From this point on, the robot 1 uses the standard program.

(2-2) Conversation Processing The control unit 3 executes the conversation processing shown in Fig. 4. This processing is executed when the microphone 5 detects a volume equal to or greater than a predetermined threshold.

In step 11, the microphone 5 is used to capture voice.
In step 12, the contents of the voice acquired in step 11 are recognized by a well-known voice recognition technology. At this time, if the standard program is used, the contents of the voice are recognized using standard dictionary data that is pre-installed in the robot 1. In addition, if the AI program is used, the contents of the voice are recognized using dictionary data that is installed from the cloud network 31 together with the AI program and that has been reinforced by past learning.

In step 13, data of the voice that responds to the content of the voice recognized in step 12 (hereinafter referred to as response voice data) is created. At this time, if the standard program is being used, the response voice data is created using standard dictionary data that is pre-installed in the robot 1. In addition, if the AI program is being used, the response voice data is created using dictionary data that is installed from the cloud network 31 together with the AI program and that has been strengthened by past learning.

The content of the response voice data can be created, for example, by detecting keywords from the content of the voice recognized in step 12, and searching dictionary data for items that are previously targeted by the keywords. The content of the response voice data can also be inferred from the content of the voice recognized in step 12 using artificial intelligence.

In step 14, the speaker 15 is used to pronounce the answer voice data created in step 13. In other words, the answer to the voice acquired in step 11 is pronounced.

In step 15, it is determined whether or not the AI program is being used at that time. If the AI program is being used, the process proceeds to step 16, and if the standard program is being used, the process ends.

In step 16, it is determined whether learning is stopped at that time. Note that whether learning is stopped is set by a learning stop determination process described later. If learning is not stopped, the process proceeds to step 17, and if learning is stopped, the process ends.

In step 17, the learning restriction contents set at that time are obtained. The learning restriction contents include, for example, information about the user's (the user corresponding to the identification information determined to have been entered in step 2) family and acquaintances (names, addresses, telephone numbers, email addresses, career history, images including faces), etc.

In step 18, learning is performed on the content of the voice recognized in step 12. An example of learning is machine learning. Machine learning may be supervised learning or unsupervised learning. Furthermore, learning may be learning by artificial intelligence. In this case, keywords may be extracted from the voice recognized in step 12, and the keywords may be added to a data set (e.g., dictionary data used for voice recognition). The keywords may be used, for example, in the process of creating answer voice data (step 13).

However, even if the contents of the voice recognized in step 12 are subject to the learning restriction contents acquired in step 17, they are not to be learned.
In step 19, the AI program and the dataset are updated to reflect the learning results in step 18. The AI program and the dataset updated to reflect the learning results are an example of the learning results.

(2-3) Learning stop determination process The control unit 3 repeatedly executes the learning stop determination process shown in Fig. 5 at predetermined time intervals. In step 21 of Fig. 5, it is determined whether or not the AI program is being used. If the AI program is being used, the process proceeds to step 22, and if the standard program is being used, the process ends.

In step 22 , the position information of the robot 1 is acquired using the GPS 13 .
In step 23, an image of the surroundings of the robot 1 is captured using the camera 7.
In step 24, the microphone 5 is used to capture voice.

In step 25, it is determined whether learning is currently stopped. The state of learning being stopped begins in step 28, which will be described later, and ends when learning is resumed in step 30, which will be described later. If learning is not currently stopped, proceed to step 26; if learning is currently stopped, proceed to step 29.

In step 26, it is determined whether there is anything in the image acquired in step 23 or the audio acquired in step 24 that would trigger learning to stop. Examples of learning stop triggers include the following:

- A preset action is recognized in the image acquired in step 23 as an action instructing the user to stop learning. One example of such an action is placing the index finger upright in front of the mouth, as shown in FIG. 6A. Another example of such an action is a wink.

In the voice acquired in step 24, a voice that is preset as a keyword instructing the user to stop learning is recognized. Examples of such keywords include "secret,""off the record," and "private." Other examples of such keywords include the names of the user's family members or acquaintances. The user can register the names of family members or acquaintances in the robot 1 as keywords in advance. The robot 1 may also infer which words are the names of the user's family members or acquaintances based on previously recognized voice data, and register the words that it infers are the names of the user's family members or acquaintances as keywords.

- The face of a person registered in advance is recognized in the image acquired in step 23. This person may be, for example, the user's family or acquaintances. The user can register in advance a facial image of a person that will trigger the learning to stop.

If there is a trigger to stop learning, the process proceeds to step 28; if there is no trigger to stop learning, the process proceeds to step 27.
In step 27, it is determined whether the location information acquired in step 22 corresponds to a location registered in advance as a location where learning should be stopped. Examples of the location where learning should be stopped include the user's home, a conference room, etc.

The user can register in advance the location where learning should be stopped. Also, the robot 1 can infer the location where learning should be stopped based on past data, and register the inferred location. For example, the robot 1 can infer that the location where learning should be stopped is the location where learning was previously recognized as a trigger for stopping learning.

A learning stop state is initiated in step 28. From this point onwards, a positive determination is made in step 16 in the conversation process, and learning in step 18 is not performed.
On the other hand, if the answer is YES in step 25, then in step 29 it is determined whether or not there is anything in the image acquired in step 23 or the audio acquired in step 24 that may be a trigger for restarting learning. Examples of triggers for restarting learning include the following:

- A preset action is recognized in the image acquired in step 23 as an action instructing the user to resume learning. For example, as shown in FIG. 6B, one such action is making a circle with the thumb and index finger (indicating "OK").

- In the voice acquired in step 24, a voice that has been set in advance as a keyword instructing the user to resume learning is recognized. Examples of such keywords include "OK," "resume," and "learn."

If there is a trigger to restart learning, the process proceeds to step 30, and if there is no trigger to restart learning, the process ends.
Learning is resumed in step 30. From this point on, a negative determination is made in step 16 in the conversation process, and learning in step 18 is performed.

(2-4) Schedule Management Process The robot 1 can execute the schedule management process shown below. The user inputs his/her own schedule information to the robot 1 in advance. The schedule information may be input, for example, using the touch panel 9 or by voice input. In addition, the schedule information may be transmitted from the terminal 33 to the communication unit 26.

The schedule information includes at least a due date and items to be performed by that due date. The robot 1 determines whether or not the user has performed the items included in the schedule information based on the voice captured by the microphone 5, the image captured by the camera 7, the information captured from the terminal 33, etc., at a time point a predetermined time (e.g., one day, three hours) before the due date. If the items have not been performed yet, the robot 1 warns the user by a voice from the speaker 15 or an image displayed on the display 19.

3. Effects of the Robot 1 (1A) The robot 1 can change and develop the AI program through learning. The robot 1 can also strengthen the contents of a data set (e.g., dictionary data) through learning. The robot 1 can then upload the AI program and data set after learning to the cloud network 31. The robot 1 can also install the AI program and data set uploaded to the cloud network 31.

The robot 1 on which the learned AI program and data set are installed may be the same as the robot 1 that previously performed learning, or it may be different. Furthermore, the robot 1 on which the learned AI program and data set are installed may be located in the same place where learning was previously performed, or it may be located in a different place.

Therefore, the user can install the AI program and data set that have been learned from the user's past use from the cloud network 31 onto the robot 1 at the location where the user is at the time (e.g., various locations such as the workplace, a store, or home), and use the robot 1.

(1B) The robot 1 allows the installation of an AI program and a data set corresponding to a user's identification information, provided that the user's identification information is input. This prevents a user's AI program and data set from being used by another person without permission.

(1C) Robot 1 can recognize human voices and produce a speech response to them. In other words, robot 1 can converse with people. Furthermore, when using an AI program, robot 1 learns based on the results of voice recognition and uses the learning results to create a speech response, so the more it learns, the more advanced the conversations it can have.

(1D) The robot 1 stops learning depending on the actions of people around it, the results of identifying people, etc. This prevents the robot 1 from learning things that are undesirable for the user and then telling others about them later.

(1E) The robot 1 stops learning depending on the location where it is located. This prevents the robot 1 from telling others about things it learned in a location where it is not desired to learn.

(1F) The robot 1 resumes learning in response to the actions of people around it, etc. This can promote the learning of the robot 1.
(1G) The robot 1 does not learn any matter that falls under the learning restriction contents. This prevents the robot 1 from learning any matter that is undesirable for the user and then telling others about it later.

(1H) The robot 1 can manage the user's schedule.
(1I) When the conditions for ending the use of the robot 1 are satisfied, the robot 1 erases the AI program and data set. This allows the user to prevent others from using the user's AI program and data set later.
Second Embodiment
1. Configuration of the Computer 101 The configuration of the computer 101 will be described with reference to Figures 7 and 8. The electrical configuration of the computer 101 is basically the same as that of the robot 1 in the first embodiment. However, the input unit 21 in the computer 101 is connected to an external microphone 5, camera 7, keyboard 63, mouse 65, and touch panel 9. In addition, the output unit 29 is connected to an external speaker 15 and display 19.

As shown in FIG. 8, the computer 101 has a box-shaped housing 67 that houses each component. The computer 101 also has multiple terminals 69 to which external devices (e.g., microphone 5, camera 7, keyboard 63, mouse 65, touch panel 9, speaker 15, display 19, etc.) can be connected. The computer 101 is a robot in the broad sense of the word.

2. Processing Executed by the Computer 101 The computer 101 executes a program setting process, a conversation process, a learning stop determination process, and a schedule management process, similar to the robot 1 of the first embodiment. The computer 101 also has the same functions as a well-known computer.

3. Effects of the Computer 101 The computer 101 has the effects (1A) to (1I) described above.
Third Embodiment
1. Configuration of the on-vehicle device 201 The configuration of the on-vehicle device 201 will be described with reference to Fig. 9. The on-vehicle device 201 is mounted on a vehicle. The electrical configuration of the on-vehicle device 201 is basically the same as that of the computer 101 in the second embodiment. However, the input unit 21 in the on-vehicle device 201 is connected to the microphone 5, camera 7, touch panel 9, sensor group 11, and GPS 13 provided in the vehicle.

The microphone 5 is installed inside the vehicle's cabin and detects the voices of the vehicle occupants (driver or other passengers). The camera 7 photographs the occupants. The touch panel 9 is installed inside the vehicle's cabin and is operated by the occupants. The sensor group 11 detects the driver's driving operations (steering angle, accelerator depression amount, brake depression amount, shift position, etc.) and the vehicle's state (speed, acceleration, yaw rate, state of the power unit (internal combustion engine, motor, etc.), remaining fuel, remaining battery, etc.).

The output unit 29 is also connected to the speaker 15, the display 19, and the vehicle control unit 71. The speaker 15 and the display 19 are provided inside the vehicle cabin. The vehicle control unit 71 performs various controls related to the vehicle (e.g., steering, acceleration, deceleration, shift changes, etc.). The vehicle-mounted device 201 is a robot in the broad sense.

2. Processing Executed by the Vehicle-Mounted Unit 201 (2-1) Processing Similar to That of the Robot 1 The vehicle-mounted unit 201 executes program setting processing, conversation processing, learning stop determination processing, and schedule management processing, similar to the robot 1 of the first embodiment.

(2-2) Vehicle Control Processing The control unit 3 of the vehicle-mounted device 201 repeatedly executes the vehicle control processing shown in Fig. 10 at predetermined time intervals. This processing is executed when the microphone 5 detects a volume equal to or greater than a predetermined threshold.

In step 31, voice is acquired using the microphone 5. In step 32, instructions to the vehicle (e.g., starting, stopping, decelerating, accelerating, turning right or left, lane changing, shifting, etc.) are recognized from the voice acquired in step 31 using a well-known voice recognition technology. At this time, if a standard program is being used, the above instructions are recognized using standard dictionary data that is pre-installed in the in-vehicle device 201. Also, if an AI program is being used, the above instructions are recognized using dictionary data that is installed from the cloud network 31 together with the AI program and that has been strengthened by past learning.

In step 33, the vehicle control details are determined according to the instruction for the vehicle recognized in step 32. For example, if the instruction for the vehicle is to start, the timing for releasing the brakes, the amount of increase in engine speed and the rate of increase, etc. are determined. At this time, if a standard program is being used, the vehicle control details are determined using the standard program. Also, if an AI program is being used, the control details are determined using an AI program that has evolved through past learning.

In step 34, the control contents determined in step 33 are output to the vehicle control unit 71. The vehicle control unit 71 controls the vehicle in accordance with the control contents.
In step 35, it is determined whether or not the AI program is being used at that time. If the AI program is being used, the process proceeds to step 36, and if the standard program is being used, the process ends.

In step 36, detection results relating to the state of the vehicle are obtained from the sensor group 11.
In step 37, learning is performed based on the control content output in step 34 and the sensor output obtained in step 36. This learning is to modify the control content so that the sensor output obtained in step 36 falls within a preset optimum range.

For example, if the control content output in step 34 was starting, it is confirmed whether the sensor output during the starting process (e.g., sensor output of speed, acceleration, etc.) was within the optimal range, and if it was outside the optimal range, the control content for starting is modified so that the sensor output for the next and subsequent starts approaches the optimal range.

In step 38, the AI program is updated to reflect the learning results in step 37.
3. Effects of the Vehicle-Mounted Device 201 The vehicle-mounted device 201 provides the effects (1A) to (1I) described above. In addition, the vehicle-mounted device 201 also provides the following effects.

(3A) The vehicle-mounted device 201 can recognize human voices and perform corresponding vehicle control. In addition, when the vehicle-mounted device 201 is using an AI program, the vehicle-mounted device 201 learns based on the vehicle control content output to the vehicle control unit 71 and the sensor output indicating the vehicle state, so that the more the learning progresses, the more advanced the vehicle control can be performed.

(3B) The vehicle-mounted device 201 can change and develop the AI program for vehicle control processing through learning. The vehicle-mounted device 201 can also install the AI program uploaded to the cloud network 31.

The vehicle-mounted device 201 into which the learned AI program is installed may be the same as the vehicle-mounted device 201 that performed learning in the past, or it may be different.
Therefore, a user can install and use an AI program that has been learned through his or her own past use in the onboard device 201 of any vehicle from the cloud network 31.
Fourth Embodiment
1. Configuration of Robot 301 The configuration of the robot 301 of this embodiment is basically the same as that of the robot 1 of the first embodiment. The following mainly describes the differences from the first embodiment. As shown in FIG. 11, the robot 301 includes a first artificial intelligence unit 73 and a second artificial intelligence unit 75.

The first artificial intelligence unit 73 stores an AI program and a data set for executing various operations of the robot 301. Hereinafter, this program will be referred to as the first AI program, and this data set will be referred to as the first data set.

The first artificial intelligence unit 73 changes and develops the first AI program and the first data set through learning. The learning performed by the first artificial intelligence unit 73 is not affected by the learning stop and learning restrictions described below. In addition, the first AI program and the first data set are not uploaded to the cloud network 31.

The second artificial intelligence unit 75 is capable of storing an AI program and a dataset. Hereinafter, this program will be referred to as the second AI program, and this dataset will be referred to as the second dataset.

The second AI program is basically the same as the first AI program, but is not used to execute various operations of the robot 301. The second artificial intelligence unit 75 changes and develops the second AI program and the second data set through learning. The second AI program and the second data set accumulate the learning results and are uploaded to the cloud network 31. This allows the learning results to be stored in the cloud network 31.

However, the learning performed by the second artificial intelligence unit 75 is limited by the learning stop and learning limit described below. Therefore, the learning results stored in the cloud network 31 by uploading the second AI program and the second data set are limited.

The second AI program and the second data set can be downloaded from the cloud network 31 to the second artificial intelligence unit 75. Then, based on the downloaded second AI program and the second data set, learning of the first AI program and the first data set can be performed. Details will be described later.

2. Processing Executed by the Robot 301 (2-1) Program Installation Processing When the power supply of the robot 301 is turned on, the control unit 3 executes a program installation processing shown in FIG.

In step 41, it is determined whether or not the user's identification information has been input. The identification information may be the same as that in the first embodiment. If the identification information has been input, the process proceeds to step 42; if the identification information has not been input, the process ends.

In step 42, the second AI program and the second data set corresponding to the identification information determined to have been input in step 41 are installed from the cloud network 31. The installed second AI program and the second data set are stored in the second artificial intelligence unit 75. If the second AI program and the second data set are already stored in the second artificial intelligence unit 75, they are overwritten.

In step 43, the content that is included in the second AI program and the second data set installed in step 42 but is not stored in the first AI program and the first data set is learned.

In step 44, the first AI program and the first data set are updated by adding the contents learned in step 43. In other words, the first AI program and the first data set are learned based on the downloaded second AI program and second data set.

(2-2) Conversation Processing The control unit 3 executes the conversation processing shown in Fig. 13. This processing is executed when the microphone 5 detects a volume equal to or greater than a predetermined threshold. The conversation processing is performed using a first AI program and a first data set stored in the first artificial intelligence unit 73.

The processing in steps 51 to 54 is similar to the processing in steps 11 to 14 in the first embodiment.
In step 55, the first artificial intelligence unit 73 learns about the contents of the voice recognized in step 52. The contents of the learning are the same as those in the first embodiment.

In step 56, the first AI program and the first data set are updated to reflect the learning results in step 55.
In step 57, it is determined whether or not the second artificial intelligence unit 75 is currently stopping learning. The stopping of learning of the second artificial intelligence unit 75 is set by a second artificial intelligence unit learning stop determination process, which will be described later. If the second artificial intelligence unit 75 is not currently stopping learning, the process proceeds to step 58, and if learning is currently stopping, the process ends.

In step 58, the learning restriction contents set at that time are obtained. The learning restriction contents include, for example, information about the family and acquaintances of the user (the user corresponding to the identification information determined to have been entered in step 41) (names, addresses, telephone numbers, email addresses, career history, images including faces), etc.

In step 59, the second artificial intelligence unit 75 learns about the contents of the voice recognized in step 52. The contents of the learning are the same as those in the first embodiment. However, even if the contents of the voice recognized in step 52 are subject to the learning restriction contents acquired in step 58, they are not learned.

In step 60, the second AI program and the second data set are updated to reflect the learning results in step 59.
(2-3) Processing for Determining Whether to Stop Learning of the Second Artificial Intelligence Unit The control unit 3 repeatedly executes the processing for determining whether to stop learning of the second artificial intelligence unit shown in FIG. 14 at predetermined time intervals.

In step 71 , the position information of the robot 301 is acquired using the GPS 13 .
In step 72, the camera 7 is used to capture an image of the surroundings of the robot 301.

In step 73, the microphone 5 is used to obtain voice.
In step 74, it is determined whether or not the second artificial intelligence unit 75 is currently halted from learning. The halted learning state is started in step 77, which will be described later, and ends when learning is resumed in step 79, which will be described later. If learning is not currently halted, the process proceeds to step 75. If learning is currently halted, the process proceeds to step 78.

In step 75, it is determined whether or not there is a trigger for stopping learning in the image acquired in step 72 or the audio acquired in step 73. The trigger for stopping learning is the same as in the first embodiment. If there is a trigger for stopping learning, proceed to step 77, and if there is no trigger for stopping learning, proceed to step 76.

In step 76, it is determined whether the location information acquired in step 71 corresponds to a location that has been registered in advance as a location where learning should be stopped. The location where learning should be stopped is the same as in the first embodiment.

In step 77, the second artificial intelligence unit 75 enters a learning stop state. From this point onwards, a positive judgment is made in step 57 in the conversation process, and learning is not performed in step 59.

On the other hand, if the determination in step 74 is affirmative, then in step 78, it is determined whether or not there is a trigger for restarting learning in the image acquired in step 72 or the audio acquired in step 73. The trigger for restarting learning is the same as in the first embodiment. If there is a trigger for restarting learning, the process proceeds to step 79, and if there is no trigger for restarting learning, the process ends.

In step 79, learning in the second artificial intelligence unit 75 is resumed. From this point onwards, a negative judgment is made in step 57 in the conversation process, and learning in step 59 is carried out.

(2-4) Program Upload Processing The control unit 3 repeatedly executes the program upload processing shown in FIG. 15 at predetermined time intervals.

In step 81, it is determined whether the usage end condition of the robot 301 has been satisfied. The usage end condition is the same as that determined in step 5 of the first embodiment. If the usage end condition has been satisfied, the process proceeds to step 82, and if the usage end condition has not been satisfied, the process ends.

In step 82, the second AI program and the second data set are uploaded to the cloud network 31. At this time, they are uploaded in association with the user's identification information. Note that if the above-mentioned learning has been performed, the second AI program and the second data set to be uploaded are those after learning.

3. Effects of the Robot 301 The robot 301 has the effects (1B), (1C), (1F), and (1H) described above. In addition, the robot 301 also has the following effects.

(4A) The robot 301 can change and develop the first AI program and the first data set through learning. Furthermore, the learning in the first AI program and the first data set is not affected by the learning stop process and the learning limit process.

Furthermore, since the first AI program and the first data set are not uploaded to the cloud network 31, their contents can be prevented from being known to others.
(4B) The robot 301 can upload the second AI program and the second data set after learning to the cloud network 31. The robot 301 can also install the second AI program and the second data set from the cloud network 31.

The robot 301 on which the second AI program and the second data set are installed after learning may be the same as the robot 301 that has previously learned, or it may be different. Furthermore, the robot 301 on which the second AI program and the second data set are installed after learning may be located in the same place where learning was previously performed, or it may be located in a different place.

Therefore, the user can install the second AI program and the second data set, which have been learned through the user's own past use, from the cloud network 31 to the robot 301 in the location where the user is at the time (e.g., various locations such as the workplace, store, home, etc.). The second AI program and the second data set can then be used to learn, and the first AI program and the first data set can be changed and developed.

(4C) The robot 301 restricts learning of the second AI program and the second data set uploaded to the cloud network 31. That is, the robot 301 stops learning of the second AI program and the second data set depending on the actions of people around it, the results of identifying people, etc. The robot 301 also stops learning of the second AI program and the second data set depending on the location where it is located. The robot 301 also does not include any items that fall under the learning restriction content in the second AI program and the second data set.

Therefore, it is possible to prevent the second AI program and the second data set, which include information that the user does not want others to know, from being uploaded to the cloud network 31. <Fifth embodiment>
1. Configuration of the Robot 401 The configuration of the robot 401 of this embodiment is basically the same as that of the robot 1 of the first embodiment. The following mainly describes the differences from the first embodiment. As shown in FIG. 16, the robot 401 can connect to the Internet 77 using the communication unit 26. The Internet 77 is an example of a network. The robot 401 can be placed inside a vehicle. In this case, the robot 401 can converse with a vehicle occupant by a conversation process, which will be described later.

2. Processing Executed by Robot 401 (2-1) Processing Similar to Robot 1 The robot 401 executes a program setting process, a learning stop determination process, and a schedule management process, similar to the robot 1 of the first embodiment.

(2-2) Conversation, etc. Processing The control unit 3 of the robot 401 repeatedly executes the conversation, etc. processing shown in Fig. 17 at predetermined time intervals. This processing is executed when the microphone 5 detects a volume equal to or greater than a predetermined threshold.

The processes in steps 91 and 92 are similar to the processes in steps 11 and 12 in the first embodiment, respectively.
In step 93, information related to the content of the voice recognized in step 92 is searched and acquired from the Internet 77 using the communication unit 26. For example, a keyword is extracted from the content of the voice recognized in step 92, and information including items previously associated with the keyword is searched for. Examples of search targets include SNS (social networking services), blogs, and electronic bulletin boards. Furthermore, if the content of the voice recognized in step 92 is a question, an answer to the question is searched for. As a search method, for example, a known search engine can be used.

In step 94, response voice data is created for the content of the voice recognized in step 92, essentially in the same manner as in step 13 in the first embodiment. However, in this step, the information acquired in step 93 is also used to create the response voice data. Using the information acquired in step 93 means, for example, including a voiced version of that information in the response voice data.

In step 95, the type of voice to be pronounced in step 96 (described later) is determined based on the content of the answer created in step 94. Examples of the type of voice include a male voice, a female voice, an adult voice, a child's voice, etc.

Specifically, the type of voice is determined as follows: First, the control unit 3 extracts features (e.g., features unique to men, features unique to women, features unique to adults, features unique to children, etc.) from the content of the answer created in step 94.

The control unit 3 is provided with a map that associates features in the content of the answer with the type of voice. The control unit 3 inputs the features extracted as described above into the map, thereby determining the type of voice that corresponds to the extracted features.

For example, if features specific to men and features specific to adults are extracted from the content of the answer created in step 94, the control unit 3 determines the voice of an adult male.
In step 96, a voice is generated from the speaker 15 based on the answer voice data created in step 94. At this time, the voice type set in step 95 is used for the voice.

In step 97, first, the emotion (e.g., joy, anger, sadness, calm, etc.) corresponding to the content of the voice recognized in step 92 or the content of the answer created in step 94 is obtained. This emotion is obtained as follows.

The control unit 3 is provided with a map that associates emotions with features that appear in the content of the voice or the content of the answer. Examples of features that appear in the content of the voice include volume, intonation, and high and low pitch. Also, examples of features that appear in the content of the answer include words and phrases that reflect emotions that are included in the content of the answer.

The control unit 3 obtains the corresponding emotion by inputting the content of the voice recognized in step 92 or the features extracted from the answer created in step 94 into the map.

Next, the control unit 3 creates a facial image of a person or character expressing the acquired emotion and displays it on the display 19. For example, if the acquired emotion is joy, the control unit 3 displays a facial image of a smiling person or character on the display 19. If the acquired emotion is sadness, the control unit 3 displays a facial image of a crying person or character on the display 19.

The processes in steps 98 to 102 are similar to the processes in steps 15 to 19 in the first embodiment, respectively.
3. Effects of the Robot 401 The robot 401 has the effects (1A) to (1I) described above. In addition, the robot 401 also has the following effects.

(5A) The robot 401 can search and acquire information related to the content of the voice on the Internet 77. The robot 401 then uses the information acquired in this way to create response voice data. This allows the robot 401 to create more appropriate response voice data.

(5B) The robot 401 can determine the type of voice depending on the content of the answer. Therefore, the user is less likely to feel a discord between the content of the answer and the type of voice that pronounces it.

(5C) The robot 401 acquires an emotion corresponding to the content of the recognized voice or the content of the reply, and displays a facial image of a person or character expressing the acquired emotion on the display 19. This allows the user to feel as if the robot 401 is a human being.
Sixth Embodiment
1. Configuration of Robot 501 The configuration of the robot 501 of this embodiment is basically the same as that of the robot 1 of the first embodiment.

2. Processing Executed by Robot 501 (2-1) Processing Similar to Robot 1 The robot 501 executes program setting processing, conversation processing, learning stop determination processing, and schedule management processing, similar to the robot 1 of the first embodiment.

18, a plurality of robots 501 enable a call between a user 79 and a user 81. For example, the robot 501 on the user 79's side (hereinafter referred to as robot 501A) acquires the voice uttered by the user 79 through the microphone 5. In addition, the robot 501A receives an input from the user 79 specifying the robot 501 (hereinafter referred to as robot 501B) with which to make a call, through the touch panel 9.

The robot 501A transmits to the host computer 83, using the communication unit 26, a signal obtained by converting the voice uttered by the user 79 (hereinafter referred to as a converted voice signal), an identification signal for the robot 501A, and an identification signal for the robot 501B, the other party designated by the user 79.

The host computer 83 determines whether the identification signal of the received robot 501A and the identification signal of the call partner robot 501B are set as a call pair. If they are set as a call pair, the host computer 83 transfers the voice conversion signal to the call partner robot 501B. The setting of the call pair will be described later.

The robot 501B receives the transferred voice conversion signal using the communication unit 26, and produces a sound using the speaker 15 based on the voice conversion signal. Through the above process, the voice produced by the user 79 is transmitted to the user 81 on the robot 501B side. In addition to producing a sound, the robot 501B also converts the voice into characters and displays the characters on the display 19.

In addition, by having robot 501B perform the processing of robot 501A described above, and robot 501A perform the processing of robot 501B described above, it is possible to transmit the voice spoken by user 81 to user 79.

On the other hand, if the identification signal of the received robot 501A and the identification signal of the other party's robot 501B are not set as a call pair, the host computer 83 will not transfer the above voice conversion signal.

(2-3) Call Pair Setting Process Next, the process in which the host computer 83 sets up the above call pair will be described with reference to FIG.

In step 111, it is determined whether a new call pair setting request has been received. A call pair setting request is a request that robot 501A sends to host computer 83. The call pair setting request includes an identification signal of robot 501A that sent the request, characteristics of user 79 on the robot 501A side (e.g., hobbies, preferences in food, clothing, and shelter, interests, etc.), and image data of a facial photograph of user 79.

The call pair setting request may be sent by the robot 501A in response to a user instruction, or may be sent automatically by the robot 501A. If the host computer 83 receives a new call pair setting request, the process proceeds to step 112; if not, the process ends.

In step 112, the characteristics of user 79 included in the call pair setting request (hereinafter referred to as a new setting request) determined to have been received in step 111 is searched for in the setting waiting list. Here, the setting waiting list is a list of call pair setting requests (hereinafter referred to as past setting requests) previously received from any robot 501.

In step 113, it is determined whether or not a past setting request is found that has characteristics matching those of user 79 included in the new setting request as a result of the search in step 112. If such a past setting request is found, the process proceeds to step 114; if not, the process proceeds to step 116.

In step 114, robot 501B, which was found in step 113 and was the sender of the previous setting request, and robot 501A, which is the sender of the new setting request, are set as a call pair.

In step 115, each of robots 501A and 501B set as a call pair is notified of information about the other robot 501. That is, robot 501A is notified of information about robot 501B, and robot 501B is notified of information about robot 501A. The notified information includes an identification signal of the other robot 501, image data of a face photo of the corresponding user, etc.

On the other hand, if the determination in step 113 is negative, the new configuration request is added to the configuration waiting list in step 116. From this point on, the new configuration request becomes part of the list of past configuration requests.

The robot 501, which has been notified of the information about the other robot 501, uses the information to create an icon 85 representing the other user in the call pair, and displays it on the display 19 as shown in Fig. 20. The icon 85 includes a facial photograph of the other user in the call pair. The robot 501 can display multiple icons 85 on the display 19.

When a specific icon 85 is touched by a user, the robot 501 recognizes the user corresponding to that icon 85 as the communication partner. Then, when the robot 501 transmits the voice conversion signal to the host computer 83 as described above, it transmits an identification signal of the robot 501 corresponding to the touched icon 85 to the host computer 83.

(2-4) Other Processing The host computer 83 analyzes the contents of the converted voice signal sent by the robot 501A during the call processing described in (2-2) above. If the analysis result corresponds to a prohibited matter (e.g., matter related to a crime) that has been set in advance, the host computer 83 will not start the call processing or will end the call processing midway. Therefore, the host computer 83 can prevent the call processing from being used for crimes, etc.

In addition, during the call processing in (2-2) above, the robot 501B also uses the voice transmitted from the other computer 501A to learn. This allows learning to be carried out even more efficiently.

In addition, in the call processing of (2-2) above, when the user 79 stops speaking for a predetermined period of time or when the user 79 gives an instruction, the robot 501A transmits the voice conversion signal that it created to the host computer 83. In this case, the robot 501A and the user 81 will be in conversation. The computer 501A can create the voice conversion signal using the learning results obtained from past call processing. The robot 501A may also store sounds (e.g., backchannels, etc.) that the robot 501A or the robot 501B made in past calls and create a voice conversion signal corresponding to the sounds.

3. Effects of the Robot 501 The robot 501 has the effects (1A) to (1I) described above. In addition, the robot 501 also has the following effects.

(6A) A user 79 of the robot 501A and a user 81 of the robot 501B can communicate with each other.
(6B) A call between user 79 and user 81 is premised on the premise that robot 501A and robot 501B are set as a call pair. A call pair is set when the characteristics of user 79 and user 81 match. Thus, robot 501 selectively enables a call between users with matching characteristics.

(6C) The robot 501 creates an icon 85 including a facial photograph of the user who is the other party to the call, and displays it on the display 19. The user can easily select the other party to the call by touching the icon 85. In addition, since the icon 85 includes a facial photograph of the other party to the call, the user can easily understand which icon 85 corresponds to which other party to the call.
<Other embodiments>
(1) In the first to sixth embodiments, the robots 1, 301, 401, 501, the computer 101, and the vehicle-mounted device 201 may increase or decrease the learning restriction content depending on the situation. For example, when it is recognized that an item that was originally included in the learning restriction content was spoken by multiple people, the item may be excluded from the learning restriction content.

(2) In the first to sixth embodiments, when the robot 1, 301, 401, 501, the computer 101, and the in-vehicle unit 201 acquire voice through the microphone 5, they may judge the tone of the voice and decide whether or not to learn the content of the voice depending on the result of the judgment.

(3) In the first to sixth embodiments, the process performed in response to the voice recognition result may be something other than creating answer voice data (step 13) or determining the vehicle control content (step 33). For example, the robot 1, 301, 401, 501 may be moved or an external device connected to the computer 101 may be operated in response to the voice recognition result. In addition, a predetermined action (e.g. opening and closing a box, opening and closing a window, locking or unlocking a door, operating a home appliance, etc.) may be performed in response to the voice recognition result.

(4) In the first to sixth embodiments, the AI program and data set may be stored in something other than the cloud network 31. For example, they may be stored in a well-known server, storage medium, etc.

(5) In the third embodiment, the on-board device 201 may be mounted on a moving object other than a vehicle (for example, a railroad car, an aircraft, a ship, etc.) and may control the moving object.
(6) In the first, fourth to sixth embodiments, the form of the robot 1, 301, 401, 501 does not have to be a human form. For example, the form may be an animal, a fish, an imaginary character, or the like.

(7) In the first to third, fifth and sixth embodiments, the robot 1, 401, 501, computer 101 and in-vehicle device 201 may use the standard program and the AI program simultaneously. In this case, basic processing is performed by the standard program, and additional processing obtained as a result of learning can be performed by the AI program.

(8) In the first to sixth embodiments, the robot 1, 301, 401, 501, computer 101, and in-vehicle device 201 may take the form of a home appliance (e.g., a television, a refrigerator, an air conditioner, a vacuum cleaner, a washing machine, etc.), a mobile terminal (e.g., a mobile phone (including a smartphone), a glasses-type terminal, a wristwatch-type terminal), etc.

(9) In the first to sixth embodiments, the robot 1, 301, 401, 501, computer 101, and in-vehicle device 201 may have a function of automatically acquiring images using the camera 7 and transmitting the acquired images over a network. Examples of destinations include a server, a terminal, a vehicle, another robot, and the like. The image to be transmitted may be the image itself acquired using the camera 7, or may be an image of a portion extracted from the image acquired using the camera 7 (e.g., a person's face, a person's entire body, a vehicle, a vehicle license plate, etc.).

The robots 1, 301, 401, 501, computers 101, and vehicle-mounted devices 201 having the above functions can be used for crime prevention purposes, monitoring other robots, and the like. The robots 1, 301, 401, 501, computers 101, and vehicle-mounted devices 201 having the above functions can be installed, for example, indoors, on the street, at the entrance to a specific facility (e.g., a house, an apartment building, an office, a parking lot, etc.), near other robots, and the like.

Furthermore, the robot 1, 301, 401, 501, computer 101, and vehicle-mounted device 201 having the above functions can have the function of flying in the air or can be mounted on an aircraft. In this case, images of the ground taken from a viewpoint in the sky can be transmitted over a network. In this case, the robot 1, 301, 401, 501, computer 101, and vehicle-mounted device 201 can recognize white lines on the road in the captured images and move along the white lines. Furthermore, the robot 1, 301, 401, 501, computer 101, and vehicle-mounted device 201 can recognize traffic lights in images of the ground taken from a viewpoint in the sky and transmit the display contents (red light, green light, etc.) to a vehicle on the ground.

(10) In the first to sixth embodiments, the robot 1, 301, 401, 501, computer 101, and vehicle-mounted device 201 may recognize human behavior using the camera 7 and output a sound that is pre-associated with the recognition result.

For example, if the system recognizes that a person leaves the kitchen with the gas still on, it can output a voice warning to that person. Also, if the system recognizes that a person is searching for a specific object, it can search for the location of that object and notify the user of its location by voice. Also, if the system recognizes that a person enters a house through the entrance, it can output a voice saying "Welcome home." Human actions and the voices associated with them can be increased through learning.

When sound is emitted as described above, the type of sound can be associated with the situation at the time and the content of the sound. For example, if a father is recognized in an image, the type of sound "Welcome home" can be determined to be the voice of his child.

(11) In the first to sixth embodiments, the control unit includes a microcomputer. However, the control unit may be a combination of individual electronic circuits, an AISIC (Application Specified Integrated Circuit), a programmable logic device such as an FPGA (Field Programmable Gate Array), or a combination of these.
(12) A part or all of the configurations of the first to sixth embodiments may be combined as appropriate. For example, the configurations of the fourth to sixth embodiments may be applied to the second and third embodiments.

The present disclosure relates to an information processing system, an information processing method, and a computer program .

It is conceivable that the conversation ability of a robot can be improved by learning using artificial intelligence. One aspect of the present disclosure is to provide a new technology related to conversation using artificial intelligence.

According to one aspect of the present disclosure, there is provided an information processing system, the information processing system including a voice acquisition unit, an output unit, and a learning control unit.
The voice capturing unit is configured to capture the voice of the user.
The output unit is configured to create a response voice to the user's voice acquired by the voice acquisition unit using artificial intelligence having a learning function of learning the conversation with the user, and to output the response voice to the user.
The learning control unit is configured to limit the learning function based on the user's voice.

According to another aspect of the present disclosure, there may be provided an information processing system including a voice acquisition unit, a read-out unit, an output unit, a learning unit, and a learning control unit.
The voice capturing unit is configured to capture the voice of the user.
The reading unit is configured to read from the storage device a dataset relating to the artificial intelligence that has been trained through past conversations with the user.
The output unit is configured to use the retrieved data set to generate a response word as a word responsive to the voice acquired by the voice acquisition unit through artificial intelligence, and to output the response word to a user.
The learning unit is configured to perform learning operations based on the user's voice and record the learning results in the dataset by updating the dataset stored by the storage device.
The learning control unit is configured to limit a learning operation by the learning unit based on the user's voice.

According to another aspect of the present disclosure, there may be provided an information processing system including a voice acquisition unit, a search unit, and an output unit.
The voice capturing unit is configured to capture the voice of the user.
The search unit is configured to search for relevant information related to the question contained in the user's voice acquired by the voice acquisition unit on the Internet based on the content of the question.
The output unit is adapted to generate an answer to the question based on the relevant information retrieved from the Internet by the search unit, and to output the answer to the user at least in the form of voice.

Claims (3)

A voice recognition unit (23, 25, 27) for performing voice recognition;
a learning unit (25) for learning about the recognition result of the speech recognition unit;
a processing unit (23, 25, 27) for performing corresponding processing on the recognition result of the speech recognition unit using the learning result of the learning unit;
a storage unit (26) for storing the learning result in an external storage device;
a learning result acquisition unit (26) for acquiring the learning result from the storage device;
A robot (1, 301) comprising: An identification information acquisition unit (21, 26) for acquiring user identification information,
The robot according to claim 1 , wherein the learning result acquisition unit acquires the learning result associated with the identification information from the external storage device. The robot according to claim 1 or 2, further comprising a memory limiting unit (23, 25, 27, 75) that limits the learning results stored in the external memory device according to the position of the robot or a user instruction.

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