Behavior control system

Abstract

In this behavior control system, behavior of an avatar includes generating and reproducing music in which an event of a previous day is taken into consideration. Upon determination, as a behavior of the avatar, of generating and reproducing music in which an event of a previous day is taken into consideration, the behavior determination unit acquires a summary of data about the event of the previous day, which is stored in history data, and generates music based on the summary.

Description

Behavior Control System

[0001] The present invention relates to a behavior control system.

[0002] Japanese Patent No. 6053847 discloses a technology for determining an appropriate robot behavior for a user's state. The conventional technology in Patent Document 1 recognizes the user's reaction when the robot performs a specific action, and if the robot is unable to determine an action for the recognized user reaction, it updates the robot's behavior by receiving information about an action appropriate to the recognized user's state from a server.

[0003] However, the conventional technology has room for improvement in terms of making the robot perform appropriate actions in response to the user's actions.

[0004] According to a first aspect of the present invention, there is provided a behavior control system including: a state recognition unit that recognizes a user state including a user's behavior and a state of an electronic device; an emotion determination unit that determines an emotion of the user or an emotion of an avatar representing an agent for interacting with the user; a behavior determination unit that, at a predetermined timing, determines one of a plurality of avatar behaviors, including no action, as the behavior of the avatar using at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion and a behavior determination model; a storage control unit that stores event data, including the emotion value determined by the emotion determination unit and data including the user's behavior, in history data; and a behavior control unit that displays the avatar in an image display area of ​​the electronic device, wherein the avatar's behavior includes generating and playing music that takes into account events of the previous day, and when the behavior determination unit determines to generate and play music that takes into account events of the previous day as the avatar's behavior, it obtains a summary of the event data of the previous day stored in the history data and generates music based on the summary.

[0005] According to a second aspect of the present invention, the behavior determination model is a data generation model capable of generating data according to input data, and the behavior determination unit inputs data representing at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion, and data asking about the avatar's behavior, into the data generation model, and determines the avatar's behavior based on the output of the data generation model.

[0006] According to a third aspect of the present invention, when the behavior decision unit determines that the avatar's behavior is to generate and play music that takes into account events of the previous day, it causes the behavior control unit to control the avatar to play the music.

[0007] According to a fourth aspect of the present invention, there is provided a behavior control system including: a state recognition unit that recognizes a user state including a user behavior and a state of an electronic device; an emotion determination unit that determines an emotion of the user or an emotion of an avatar representing an agent for interacting with the user; a behavior determination unit that, at a predetermined timing, determines one of a plurality of avatar behaviors, including no action, as the behavior of the avatar using at least one of the user state, the state of the electronic device, the user emotion, and the avatar emotion and a behavior determination model; a storage control unit that stores event data, including the emotion value determined by the emotion determination unit and data including the user behavior, in history data; and a behavior control unit that displays the avatar in an image display area of ​​the electronic device, wherein the avatar behavior includes outputting advice information in response to a statement made by the user during a meeting, and the behavior determination unit acquires a summary of minutes of a past meeting, and, when a statement having a predetermined relationship with the summary is made, determines, as the behavior of the avatar, to output advice information in response to the statement made by the user during the meeting, and outputs the advice information according to the content of the statement.

[0008] In a fifth aspect of the present invention, the behavior determination model is a data generation model capable of generating data according to input data, and the behavior determination unit inputs data representing at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion, and data asking about the avatar's behavior, into the data generation model, and determines the avatar's behavior based on the output of the data generation model.

[0009] In a sixth aspect of the present invention, when the behavior decision unit decides that the avatar's behavior is to output advice information in response to a comment made by the user during the meeting, it operates the avatar to determine the conversation to be made based further on the state of the other user's electronic device or the emotion of the other avatar displayed on the other user's electronic device.

[0010] According to a seventh aspect of the present invention, there is provided a behavior control system comprising: a state recognition unit that recognizes a user state including a user's behavior and a state of an electronic device; an emotion determination unit that determines an emotion of the user or an emotion of an avatar representing an agent for interacting with the user; a behavior determination unit that, at a predetermined timing, determines one of a plurality of types of avatar behaviors, including no action, as the behavior of the avatar using at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion and a behavior determination model; and a processing unit that processes event data including the emotion value determined by the emotion determination unit and data including the user's behavior, The electronic device includes a memory control unit that stores the event data in history data, and a behavior control unit that displays the avatar in the image display area of ​​the electronic device, wherein the behavior of the avatar includes outputting a summary of the events of the previous day through speech or gestures, and when the behavior decision unit determines that the behavior of the avatar is to output a summary of the events of the previous day through speech or gestures, when it detects a predetermined conversation or gesture by the user, it obtains a summary of the event data of the previous day stored in the history data, and the behavior control unit controls the avatar to output the summary through speech or gestures.

[0011] According to an eighth aspect of the present invention, the behavioral decision model is a data generation model capable of generating data according to input data, and the behavioral decision unit adds a fixed sentence instructing the system to summarize the events of the previous day to text representing the event data of the previous day, inputs the added text into the data generation model, and generates the summary based on the output of the data generation model.

[0012] According to a ninth aspect of the present invention, the predetermined conversation or gesture by the user is a conversation in which the user is trying to remember the events of the previous day, or a gesture in which the user is thinking about something.

[0013] According to a tenth aspect of the present invention, there is provided a behavior control system. The behavior control system includes a state recognition unit that recognizes a user state, including user behavior, and a state of an electronic device; an emotion determination unit that determines the user's emotion or the emotion of an avatar representing an agent for interacting with the user; a behavior determination unit that, at a predetermined timing, determines one of a plurality of avatar behaviors, including no action, as the avatar's behavior, using at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion, and a behavior determination model; a memory control unit that stores event data, including the emotion value determined by the emotion determination unit and data including the user's behavior, in history data; and a behavior control unit that displays the avatar in an image display area of ​​the electronic device. The avatar's behavior includes reflecting an event of the previous day in an emotion for the next day. When the behavior determination unit determines that the avatar's behavior is to reflect the event of the previous day in an emotion for the next day, it obtains a summary of the event data for the previous day stored in the history data and determines an emotion that the avatar should have for the next day based on the summary. The behavior control unit controls the avatar to express the emotion that the avatar should have for the next day.

[0014] According to an eleventh aspect of the present invention, the behavioral decision model is a data generation model capable of generating data according to input data, and the behavioral decision unit adds a fixed sentence instructing the user to summarize the events of the previous day to text representing the event data of the previous day, inputs the added fixed sentence into the data generation model, generates the summary based on the output of the data generation model, adds a fixed sentence asking about the emotion the user should have on the next day to the text representing the summary, inputs the added fixed sentence into the data generation model, and determines the emotion the user should have on the next day based on the output of the data generation model.

[0015] According to a twelfth aspect of the present invention, the summary includes information representing the emotion of the previous day, and the emotion to be felt on the next day is a continuation of the emotion of the previous day.

[0016] According to a thirteenth aspect of the present invention, there is provided a behavior control system including: a state recognition unit that recognizes a user state including a user behavior and a state of an electronic device; an emotion determination unit that determines an emotion of the user or an emotion of an avatar representing an agent for interacting with the user; a behavior determination unit that, at a predetermined timing, determines one of a plurality of avatar behaviors including no action as the behavior of the avatar using at least one of the user state, the state of the electronic device, the user emotion, and the avatar emotion and a behavior determination model; a storage control unit that stores event data including the emotion value determined by the emotion determination unit and data including the user behavior in history data; and a behavior control unit that displays the avatar in an image display area of ​​the electronic device, wherein the avatar behavior includes providing progress support for the user in a meeting, and when the meeting reaches a predetermined state, the behavior determination unit determines, as the behavior of the avatar, to output progress support for the user in the meeting, and outputs the progress support for the meeting.

[0017] In a fourteenth aspect of the present invention, the behavior determination model is a data generation model capable of generating data according to input data, and the behavior determination unit inputs data representing at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion, and data asking about the avatar's behavior, into the data generation model, and determines the avatar's behavior based on the output of the data generation model.

[0018] In a fifteenth aspect of the present invention, when the behavior decision unit decides that the avatar's behavior is to output meeting progress support to the user during the meeting, the behavior decision unit causes the avatar to operate so as to determine the content of the progress support based further on the state of the other user's electronic device or the emotion of the other avatar displayed on the other user's electronic device.

[0019] According to a sixteenth aspect of the present invention, there is provided a behavior control system including a state recognition unit that recognizes a user state including a user's behavior and a state of an electronic device, an emotion determination unit that determines an emotion of the user or an emotion of an avatar representing an agent for interacting with the user, a behavior determination unit that determines an action of the avatar based on at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion, and a behavior control unit that displays the avatar in an image display area of ​​the electronic device, wherein when the behavior determination unit determines that the avatar's behavior is to take minutes of a meeting, it obtains the content of the user's remarks by voice recognition, identifies the speaker by voiceprint authentication, obtains the speaker's emotion based on the determination result of the emotion determination unit, and creates minutes data representing a combination of the user's remarks, the speaker's identification result, and the speaker's emotion.

[0020] According to a seventeenth aspect of the present invention, there is provided a behavior control system. The behavior control system includes a state recognition unit that recognizes a user state including user behavior and a state of an electronic device; an emotion determination unit that determines the emotion of the user or the emotion of an avatar representing an agent for interacting with the user; a memory control unit that stores event data including the emotion value determined by the emotion determination unit and data including the user's behavior in history data; a behavior determination unit that, at a predetermined timing, determines one of a plurality of avatar behaviors, including not taking any action, as the avatar's behavior using at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion, a summary image that visualizes the content of a summary sentence, which is a sentence about the user's history of the previous day, represented by the history data, and a behavior decision model; and a behavior control unit that displays the avatar in an image display area of ​​the electronic device, wherein the avatar behavior includes behavior related to the user's behavior history represented by the summary image, and when the behavior determination unit determines that the avatar's behavior is to speak about the user's behavior history, the behavior determination unit determines that the avatar should speak about a topic related to the user's state inferred from the user's behavior history.

[0021] According to an eighteenth aspect of the present invention, there is provided a behavior control system. The behavior control system includes a state recognition unit that recognizes a user state including the user's behavior and the state of an electronic device; an emotion determination unit that determines the user's emotion or the emotion of an avatar representing an agent for interacting with the user; a memory control unit that stores event data including the emotion value determined by the emotion determination unit and data including the user's behavior in history data; a behavior determination unit that, at a predetermined timing, determines one of a plurality of types of avatar behaviors, including not taking any action, as the avatar's behavior using at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion, a summary of the user's history of the previous day created from the user's history data of the previous day stored in the memory control unit, and a behavior decision model; and a behavior control unit that displays the avatar in an image display area of ​​the electronic device, wherein the avatar behavior includes behavior related to the user's behavior history represented by the summary, and when the behavior determination unit determines that the avatar's behavior should be to speak about the user's behavior history, the behavior determination unit determines that the avatar should speak about a topic related to the user's state.

[0022] According to a nineteenth aspect of the present invention, there is provided a behavior control system including an input unit that accepts user input, a processing unit that performs specific processing using a sentence generation model that generates sentences according to the input data, and an output unit that displays an avatar representing an agent for interacting with the user in an image display area of ​​an electronic device so as to output a result of the specific processing, wherein the behavior of the avatar by the output unit includes acquiring and outputting a response to content presented in a meeting held by the user, and the processing unit determines whether a predetermined trigger condition for the content presented in the meeting is met, and if the trigger condition is met, acquires and outputs a response to the content presented in the meeting as a result of the specific processing using an output of the sentence generation model when at least email entries, schedule entries, and meeting remarks obtained from user input over a specific period are used as input data.

[0023] According to a twentieth aspect of the present invention, the electronic device is a headset type terminal.

[0024] According to a twenty-first aspect of the present invention, the electronic device is a glasses-type terminal.

[0025] 1A and 1B are schematic diagrams showing an example of a system 5 according to the first embodiment; FIG. 1A is a schematic diagram showing the functional configuration of a robot 100 according to the first embodiment; FIG. 1B is a schematic diagram showing other functional configurations of a robot 100 according to the first embodiment; FIG. 1C is a schematic diagram showing the functional configuration of a specific processing unit of a robot 100 according to the first embodiment; FIG. 1C is a schematic diagram showing an example of an operation flow of a collection process by a robot 100 according to the first embodiment; FIG. 1D is a schematic diagram showing an example of an operation flow of a response process by a robot 100 according to the first embodiment; FIG. 1D is a schematic diagram showing an example of an operation flow of an autonomous process by a robot 100 according to the first embodiment; FIG. 1E is a schematic diagram showing an example of an operation flow of a specific process by a robot 100 according to the first embodiment; FIG. 1F is a schematic diagram showing an emotion map 400 onto which multiple emotions are mapped; FIG. 1G is a schematic diagram showing an emotion map 900 onto which multiple emotions are mapped; (A) An external view of a stuffed animal 100N according to a second embodiment; (B) A rear front view of a stuffed animal 100N according to the second embodiment; (C) A schematic diagram showing the internal structure of a stuffed animal 100N according to the second embodiment; (D) A schematic diagram showing the functional configuration of a stuffed animal 100N according to the second embodiment; (E) A schematic diagram showing the functional configuration of an agent system 500 according to a third embodiment; (F) An example of the operation of an agent system is shown. 10A and 10B are schematic diagrams showing the functional configuration of an agent system 700 according to a fourth embodiment; FIG. 10C shows an example of a usage mode of an agent system using smart glasses; FIG. 10D shows a schematic diagram of the functional configuration of an agent system 800 according to a fifth embodiment; FIG. 10E shows an example of a headset-type terminal; and FIG. 10F shows an example of the hardware configuration of a computer 1200.

[0026] The present invention will be described below through embodiments of the invention, but the following embodiments do not limit the scope of the invention according to the claims. Furthermore, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0027] First Embodiment FIG. 1 schematically illustrates an example of a system 5 according to this embodiment. The system 5 includes a robot 100, a robot 101, a robot 102, and a server 300. Users 10a, 10b, 10c, and 10d are users of the robot 100. Users 11a, 11b, and 11c are users of the robot 101. Users 12a and 12b are users of the robot 102. In the description of this embodiment, users 10a, 10b, 10c, and 10d may be collectively referred to as users 10. Users 11a, 11b, and 11c may be collectively referred to as users 11. Users 12a and 12b may be collectively referred to as users 12. The robots 101 and 102 have substantially the same functions as the robot 100. Therefore, the system 5 will be described mainly focusing on the functions of the robot 100.

[0028] The robot 100 converses with the user 10 and provides images to the user 10. At this time, the robot 100 cooperates with a server 300 or the like with which it can communicate via a communication network 20 to converse with the user 10 and provide images, etc. to the user 10. For example, the robot 100 not only learns appropriate conversation by itself, but also cooperates with the server 300 to learn how to have a more appropriate conversation with the user 10. The robot 100 also records captured image data, etc. of the user 10 in the server 300, and requests the image data, etc. from the server 300 as needed and provides it to the user 10.

[0029] The robot 100 also has an emotional value that indicates the type of its own emotion. For example, the robot 100 has emotional values ​​that indicate the intensity of each of the following emotions: "joy," "anger," "sorrow," "pleasure," "discomfort," "relief," "anxiety," "sadness," "excitement," "worry," "relief," "fulfillment," "emptiness," and "neutral." For example, when the robot 100 is in a state where the emotional value of excitement is high, the robot 100 speaks at a fast speed when conversing with the user 10. In this way, the robot 100 can express its own emotions through actions.

[0030] Furthermore, the robot 100 may be configured to match a sentence generation model using AI (Artificial Intelligence) with an emotion engine, thereby determining an action of the robot 100 corresponding to the emotion of the user 10. Specifically, the robot 100 may be configured to recognize the action of the user 10, determine the emotion of the user 10 regarding the user's action, and determine an action of the robot 100 corresponding to the determined emotion.

[0031] More specifically, when the robot 100 recognizes the behavior of the user 10, it automatically generates an action content that the robot 100 should take in response to the behavior of the user 10 using a preset sentence generation model. The sentence generation model may be interpreted as an algorithm and calculation for automatic dialogue processing using text. The sentence generation model is publicly known as disclosed in, for example, Japanese Patent Application Laid-Open No. 2018-081444 and ChatGPT (Internet search <URL: https: / / openai.com / blog / chatgpt>), and therefore a detailed description thereof will be omitted. Such a sentence generation model is configured using a large language model (LLM).

[0032] As described above, in this embodiment, by combining a large-scale language model and an emotion engine, it is possible to reflect the emotions of the user 10 and the robot 100, as well as various linguistic information, in the behavior of the robot 100. In other words, according to this embodiment, by combining a sentence generation model and an emotion engine, a synergistic effect can be obtained.

[0033] The robot 100 also has a function of recognizing the behavior of the user 10. The robot 100 recognizes the behavior of the user 10 by analyzing a facial image of the user 10 acquired by a camera function and a voice of the user 10 acquired by a microphone function. The robot 100 determines the behavior to be performed by the robot 100 based on the recognized behavior of the user 10, etc.

[0034] As an example of a behavioral decision model, the robot 100 stores rules that define the behavior that the robot 100 will perform based on the emotions of the user 10, the emotions of the robot 100, and the behavior of the user 10, and performs various behaviors in accordance with the rules.

[0035] Specifically, the robot 100 has reaction rules, as an example of a behavioral decision model, for determining the behavior of the robot 100 based on the emotions of the user 10, the emotions of the robot 100, and the behavior of the user 10. For example, the reaction rules define the behavior of the robot 100 as "laughing" when the behavior of the user 10 is "laughing." Furthermore, the reaction rules define the behavior of the robot 100 as "apologizing" when the behavior of the user 10 is "angry." Furthermore, the reaction rules define the behavior of the robot 100 as "answering" when the behavior of the user 10 is "asking a question." Furthermore, the reaction rules define the behavior of the robot 100 as "calling out" when the behavior of the user 10 is "sad."

[0036] When the robot 100 recognizes that the behavior of the user 10 is "anger" based on the reaction rules, it selects the behavior of "apologizing" defined in the reaction rules as the behavior to be executed by the robot 100. For example, when the robot 100 selects the behavior of "apologizing," it performs the motion of "apologizing" and outputs a voice representing the words "apologize."

[0037] Furthermore, when the condition that the emotion of the robot 100 is "normal" (i.e., "joy" = 0, "anger" = 0, "sadness" = 0, "happiness" = 0) and the state of the user 10 is "alone and looks lonely" is satisfied, it is determined that the emotion of the robot 100 will change to "worried" and that the robot 100 will be able to perform the action of "calling out."

[0038] When the robot 100 recognizes based on the reaction rule that the current emotion of the robot 100 is "normal" and that the user 10 appears lonely, the robot 100 increases the emotion value of "sadness" of the robot 100. Furthermore, the robot 100 selects the action of "calling out" defined in the reaction rule as the action to be performed toward the user 10. For example, when the robot 100 selects the action of "calling out," the robot 100 converts the phrase "What's wrong?", which indicates concern, into a worried voice and outputs it.

[0039] The robot 100 also transmits to the server 300 user reaction information indicating that this action has elicited a positive reaction from the user 10. The user reaction information includes, for example, the user action of "getting angry," the robot 100 action of "apologizing," the fact that the user 10's reaction was positive, and the attributes of the user 10.

[0040] The server 300 stores the user reaction information received from the robot 100. The server 300 receives and stores user reaction information not only from the robot 100 but also from each of the robots 101 and 102. The server 300 then analyzes the user reaction information from the robots 100, 101, and 102 and updates the reaction rules.

[0041] The robot 100 receives the updated reaction rules from the server 300 by inquiring about the updated reaction rules from the server 300. The robot 100 incorporates the updated reaction rules into the reaction rules stored in the robot 100. This allows the robot 100 to incorporate the reaction rules acquired by the robot 101, the robot 102, etc. into its own reaction rules.

[0042] 2A schematically illustrates the functional configuration of the robot 100. The robot 100 includes a sensor unit 200, a sensor module unit 210, a storage unit 220, a control unit 228, and a control target 252. The control unit 228 includes a state recognition unit 230, an emotion determination unit 232, a behavior recognition unit 234, a behavior determination unit 236, a memory control unit 238, a behavior control unit 250, a related information collection unit 270, and a communication processing unit 280. As shown in FIG. 2B, the robot 100 may further include a specific processing unit 290.

[0043] The control object 252 includes a display device, a speaker, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 100 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 100 can be expressed by controlling these motors. Furthermore, the facial expressions of the robot 100 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 100. The posture, gestures, and facial expressions of the robot 100 are examples of the attitude of the robot 100.

[0044] The sensor unit 200 includes a microphone 201, a 3D depth sensor 202, a 2D camera 203, a distance sensor 204, a touch sensor 205, and an acceleration sensor 206. The microphone 201 continuously detects sound and outputs audio data. The microphone 201 may be provided on the head of the robot 100 and may have a binaural recording function. The 3D depth sensor 202 continuously emits an infrared pattern and detects the contour of an object by analyzing the infrared pattern from infrared images continuously captured by the infrared camera. The 2D camera 203 is an example of an image sensor. The 2D camera 203 captures images using visible light and generates visible light video information. The distance sensor 204 detects the distance to an object by emitting, for example, a laser or ultrasonic waves. The sensor unit 200 may also include a clock, a gyro sensor, a sensor for motor feedback, etc.

[0045] 2A, the components of the robot 100 excluding the control target 252 and the sensor unit 200 are examples of components included in the behavior control system of the robot 100. The behavior control system of the robot 100 controls the control target 252.

[0046] The storage unit 220 includes a behavioral decision-making model 221, history data 222, collected data 223, and behavioral schedule data 224. The history data 222 includes the past emotional values ​​of the user 10, the past emotional values ​​of the robot 100, and behavioral history. Specifically, the history data 222 includes a plurality of event data including the emotional values ​​of the user 10, the emotional values ​​of the robot 100, and the behavior of the user 10. The data including the behavior of the user 10 includes camera images representing the behavior of the user 10. The emotional values ​​and behavioral history are recorded for each user 10, for example, by being associated with the identification information of the user 10. At least a portion of the storage unit 220 is implemented as a storage medium such as a memory. The storage unit 220 may also include a person DB that stores facial images of the user 10, attribute information of the user 10, and the like. Note that the functions of the components of the robot 100 shown in FIG. 2A , excluding the control target 252, the sensor unit 200, and the storage unit 220, can be realized by a CPU operating based on a program. For example, the functions of these components can be implemented as CPU operations using operating system (OS) and programs that run on the OS.

[0047] The sensor module unit 210 includes a voice emotion recognition unit 211, a speech understanding unit 212, a facial expression recognition unit 213, and a face recognition unit 214. Information detected by the sensor unit 200 is input to the sensor module unit 210. The sensor module unit 210 analyzes the information detected by the sensor unit 200 and outputs the analysis result to the state recognition unit 230.

[0048] The voice emotion recognition unit 211 of the sensor module unit 210 analyzes the voice of the user 10 detected by the microphone 201 and recognizes the emotion of the user 10. For example, the voice emotion recognition unit 211 extracts feature quantities such as frequency components of the voice and recognizes the emotion of the user 10 based on the extracted feature quantities. The speech understanding unit 212 analyzes the voice of the user 10 detected by the microphone 201 and outputs text information representing the content of the utterance of the user 10.

[0049] The facial expression recognition unit 213 recognizes the facial expression and emotion of the user 10 from the image of the user 10 captured by the 2D camera 203. For example, the facial expression recognition unit 213 recognizes the facial expression and emotion of the user 10 based on the shapes, positional relationships, etc. of the eyes and mouth.

[0050] The face recognition unit 214 recognizes the face of the user 10. The face recognition unit 214 recognizes the user 10 by matching a face image stored in a person DB (not shown) with the face image of the user 10 captured by the 2D camera 203.

[0051] The state recognition unit 230 recognizes the state of the user 10 based on the information analyzed by the sensor module unit 210. For example, it mainly performs processing related to perception using the analysis results of the sensor module unit 210. For example, it generates perceptual information such as "Dad is alone" or "There is a 90% chance that Dad is not smiling." It then performs processing to understand the meaning of the generated perceptual information. For example, it generates semantic information such as "Dad is alone and looks lonely."

[0052] The state recognition unit 230 recognizes the state of the robot 100 based on the information detected by the sensor unit 200. For example, the state recognition unit 230 recognizes the remaining battery level of the robot 100, the brightness of the environment surrounding the robot 100, etc. as the state of the robot 100.

[0053] The emotion determination unit 232 determines an emotion value indicating the emotion of the user 10 based on the information analyzed by the sensor module unit 210 and the state of the user 10 recognized by the state recognition unit 230. For example, the information analyzed by the sensor module unit 210 and the recognized state of the user 10 are input into a pre-trained neural network to obtain an emotion value indicating the emotion of the user 10.

[0054] Here, the emotion value indicating the emotion of the user 10 is a value indicating the positive or negative emotion of the user. For example, if the user's emotion is a cheerful emotion accompanied by a sense of pleasure or comfort, such as "joy," "pleasure," "comfort," "relief," "excitement," "relief," and "fulfillment," the value is positive, and the more cheerful the emotion, the larger the value. If the user's emotion is a negative emotion, such as "anger," "sorrow," "discomfort," "anxiety," "sorrow," "worry," and "emptiness," the value is negative, and the more unpleasant the emotion, the larger the absolute value of the negative value. If the user's emotion is none of the above ("neutral"), the value is 0.

[0055] In addition, the emotion determination unit 232 determines an emotion value indicating the emotion of the robot 100 based on the information analyzed by the sensor module unit 210, the information detected by the sensor unit 200, and the state of the user 10 recognized by the state recognition unit 230.

[0056] The emotion value of the robot 100 includes emotion values ​​for each of a plurality of emotion categories, and is a value (0 to 5) indicating the strength of each of the emotions, for example, "joy," "anger," "sorrow," and "happiness."

[0057] Specifically, the emotion determination unit 232 determines an emotion value indicating the emotion of the robot 100 in accordance with a rule for updating the emotion value of the robot 100, which rule is determined in association with the information analyzed by the sensor module unit 210 and the state of the user 10 recognized by the state recognition unit 230.

[0058] For example, when the state recognition unit 230 recognizes that the user 10 looks lonely, the emotion determination unit 232 increases the emotion value of "sadness" of the robot 100. When the state recognition unit 230 recognizes that the user 10 is smiling, the emotion determination unit 232 increases the emotion value of "joy" of the robot 100.

[0059] The emotion determining unit 232 may determine the emotion value indicating the emotion of the robot 100 by further considering the state of the robot 100. For example, when the remaining battery power of the robot 100 is low or when the surrounding environment of the robot 100 is pitch black, the emotion value of "sadness" of the robot 100 may be increased. Furthermore, when the user 10 continues to talk to the robot 100 despite the remaining battery power being low, the emotion value of "anger" may be increased.

[0060] The behavior recognition unit 234 recognizes the behavior of the user 10 based on the information analyzed by the sensor module unit 210 and the state of the user 10 recognized by the state recognition unit 230. For example, the information analyzed by the sensor module unit 210 and the recognized state of the user 10 are input into a pre-trained neural network, the probability of each of a plurality of predetermined behavioral categories (for example, "laughing," "angry," "asking a question," and "sad") is obtained, and the behavioral category with the highest probability is recognized as the behavior of the user 10.

[0061] As described above, in this embodiment, the robot 100 identifies the user 10 and then acquires the content of the user's utterance. When acquiring and using the content of the utterance, the robot 100 obtains the necessary consent in accordance with laws and regulations from the user 10, and the behavior control system of the robot 100 according to this embodiment takes into consideration the protection of the personal information and privacy of the user 10.

[0062] Next, the processing of the behavior decision unit 236 when the robot 100 performs a response process in which the robot 100 responds to the behavior of the user 10 will be described.

[0063] The behavior determination unit 236 determines a behavior corresponding to the behavior of the user 10 recognized by the behavior recognition unit 234 based on the current emotion value of the user 10 determined by the emotion determination unit 232, history data 222 of past emotion values ​​determined by the emotion determination unit 232 before the current emotion value of the user 10 was determined, and the emotion value of the robot 100. In this embodiment, a case is described in which the behavior determination unit 236 uses one most recent emotion value included in the history data 222 as the past emotion value of the user 10, but the disclosed technology is not limited to this aspect. For example, the behavior determination unit 236 may use multiple most recent emotion values ​​as the past emotion value of the user 10, or may use an emotion value from a unit period ago, such as one day ago. Furthermore, the behavior determination unit 236 may determine a behavior corresponding to the behavior of the user 10 by further considering not only the current emotion value of the robot 100 but also the history of the past emotion values ​​of the robot 100. The behavior determined by the behavior determining unit 236 includes gestures made by the robot 100 or speech content of the robot 100 .

[0064] The behavior determination unit 236 according to this embodiment determines the behavior of the robot 100 as a behavior corresponding to the behavior of the user 10, based on a combination of the past and current emotional values ​​of the user 10, the emotional value of the robot 100, the behavior of the user 10, and the behavior determination model 221. For example, if the past emotional value of the user 10 is a positive value and the current emotional value is a negative value, the behavior determination unit 236 determines, as a behavior corresponding to the behavior of the user 10, an behavior that will change the emotional value of the user 10 to a positive value.

[0065] When the action determination unit 236 determines that taking minutes of the meeting is an action corresponding to the action of the user 10, the action determination unit 236 acquires the content of the user 10's remarks through voice recognition, identifies the speaker through voiceprint authentication, acquires the speaker's emotion based on the determination result of the emotion determination unit 232, and creates minutes data representing a combination of the user 10's remarks, the speaker's identification result, and the speaker's emotion. The action determination unit 236 further generates a text summary representing the minutes data using a sentence generation model with an interactive function. The action determination unit 236 further generates a list of tasks (to-do list) for the user to be included in the summary using a sentence generation model with an interactive function. This to-do list includes at least a person in charge (responsible person), action content, and deadline for each task to be performed by the user. The action determination unit 236 further transmits the minutes data, summary, and to-do list to the participants of the meeting. Furthermore, based on the person in charge and the deadline included in the list, the behavior decision unit 236 sends a message to the person in charge to confirm what needs to be done a predetermined number of days before the deadline.

[0066] Specifically, when the user 10 says "take minutes," the action decision unit 236 decides to take minutes of the meeting as an action corresponding to the action of the user 10. This makes it possible to obtain minutes data including information on who made comments. When the user 10 says "send the outline to the relevant parties" at the end of the meeting, the action decision unit 236 summarizes the minutes of the meeting, creates a to-do list, and sends it to the relevant parties.

[0067] When summarizing meeting minutes, the text of the created minutes data and a fixed sentence, "Summarize this content," are input to the generative AI, which is a sentence generation model, to obtain a summary of the meeting minutes. When creating a to-do list, the text of the summary of the meeting minutes and a fixed sentence, "Create a to-do list," are input to the generative AI, which is a sentence generation model, to obtain the to-do list. This allows the robot 100 to understand the content of the meeting, create a to-do list, and assign responsibilities for the to-do items. The to-do list is divided by voiceprint authentication to identify who made a comment. Based on the determination result of the emotion determination unit 232, an evaluation of whether the person is reluctantly motivated or enthusiastically trying to do the task can also be performed. It is possible to distinguish who will do what and by when. If the person in charge, deadline, etc. have not been decided, the robot 100 may be configured to make a statement inquiring about this to the user 10. This allows the robot 100 to say, "The person in charge of AAA has not been decided. Who will do it?"

[0068] It is also possible to extract features related to the date and time from the summary of the minutes of the meeting and use them to register on a calendar or create a to-do list.

[0069] Furthermore, the behavior decision unit 236 may further decide to make a statement about the conclusion and summary of the meeting at the end of the meeting as the behavior of the robot 100. The behavior decision unit 236 also sends the minutes data, summary, and ToDo list to the participants of the meeting. The behavior decision unit 236 also sends ToDo reminders to the person in charge.

[0070] As an example, if the behavior determining unit 236 determines that the user 10 is taking minutes of a meeting, it executes the following steps 1 to 9. (Step 1) Record the meeting minutes. (Step 2) Create and summarize minutes data from the recorded data. (Step 3) Identify who said what using voiceprint authentication and the emotion determining unit 232's emotional value determination. (Step 4) Create a to-do list for each meeting participant (because it has been determined who said what). (Step 5) Register the to-do list in a calendar. (Step 6) If a clear deadline has not been set, ask the meeting participants if they are not yet able to complete the to-do list, and re-listen for missing information (5W1H) on the to-do list. (Step 7) When creating the to-do list or playing back a summary, the emotional values ​​of the to-do person and the speaker are supplementally specified. This visualizes who spoke with what enthusiasm and how motivated they are to complete the to-do. (Step 8) Send the meeting minutes to the meeting participants. (Step 9) After the meeting, send a message to follow up on the ToDo content (following up on deadlines, etc.).

[0071] The reaction rule as the behavioral decision model 221 defines the behavior of the robot 100 according to a combination of the past emotional value and the current emotional value of the user 10, the emotional value of the robot 100, and the behavior of the user 10. For example, if the past emotional value of the user 10 is a positive value and the current emotional value is a negative value, and the user 10 is behaving sadly, a combination of gestures and speech content when asking a question to encourage the user 10 with gestures is defined as the behavior of the robot 100.

[0072] For example, the reaction rule as the behavior determination model 221 defines behaviors of the robot 100 for patterns of the robot 100's emotional values ​​(1296 patterns, which are the fourth power of six values ​​from "0" to "5" for "joy," "anger," "sadness," and "happiness"), patterns of combinations of the user 10's past emotional values ​​and current emotional values, and all combinations of the user 10's behavior patterns. That is, for each pattern of the robot 100's emotional values, behaviors of the robot 100 are defined in accordance with the user 10's behavior pattern for each of a plurality of combinations of the user 10's past emotional values ​​and current emotional values, such as negative and negative values, negative and positive values, positive and negative values, positive and positive values, negative and normal values, and normal and normal values. Note that the behavior determination unit 236 may transition to an operation mode that determines the behavior of the robot 100 using the history data 222 when the user 10 makes an utterance intending to continue a conversation from a past topic, such as "I want to talk about that topic we talked about last time."

[0073] The reaction rule as the behavior determination model 221 may define at least one of a gesture and a statement as the behavior of the robot 100, for each of the patterns (1296 patterns) of the emotional value of the robot 100. Alternatively, the reaction rule as the behavior determination model 221 may define at least one of a gesture and a statement as the behavior of the robot 100, for each group of patterns of the emotional value of the robot 100.

[0074] The strength of each gesture included in the behavior of the robot 100 defined in the reaction rules as the behavior decision model 221 is predetermined. The strength of each utterance included in the behavior of the robot 100 defined in the reaction rules as the behavior decision model 221 is predetermined.

[0075] The memory control unit 238 determines whether or not to store data including the behavior of the user 10 in the history data 222 based on the predetermined behavior intensity for the behavior determined by the behavior determination unit 236 and the emotion value of the robot 100 determined by the emotion determination unit 232.

[0076] Specifically, if the total intensity value, which is the sum of the sum of the emotion values ​​for each of the multiple emotion classifications of the robot 100, the predetermined intensity for the gestures included in the behavior determined by the behavior determination unit 236, and the predetermined intensity for the speech content included in the behavior determined by the behavior determination unit 236, is equal to or greater than a threshold value, it is determined that data including the behavior of the user 10 is to be stored in the history data 222.

[0077] When the memory control unit 238 decides to store data including the behavior of the user 10 in the history data 222, it stores in the history data 222 the behavior determined by the behavior determination unit 236, information analyzed by the sensor module unit 210 from the present time up to a certain period of time ago (for example, all peripheral information such as data on the sound, images, smells, etc. of the scene), and the state of the user 10 recognized by the state recognition unit 230 (for example, the facial expression, emotions, etc. of the user 10).

[0078] The behavior control unit 250 controls the control target 252 based on the behavior determined by the behavior determination unit 236. For example, when the behavior determination unit 236 determines an behavior including speaking, the behavior control unit 250 outputs a sound from a speaker included in the control target 252. At this time, the behavior control unit 250 may determine the speaking rate of the sound based on the emotional value of the robot 100. For example, the behavior control unit 250 determines a faster speaking rate as the emotional value of the robot 100 increases. In this way, the behavior control unit 250 determines the execution form of the behavior determined by the behavior determination unit 236 based on the emotional value determined by the emotional determination unit 232.

[0079] The behavior control unit 250 may recognize a change in the user 10's emotion in response to the execution of the behavior determined by the behavior determination unit 236. For example, the change in emotion may be recognized based on the voice or facial expression of the user 10. Alternatively, the change in emotion of the user 10 may be recognized based on the detection of an impact by the touch sensor 205 included in the sensor unit 200. If an impact is detected by the touch sensor 205 included in the sensor unit 200, the behavior control unit 250 may recognize that the user 10's emotion has worsened. Alternatively, if the detection result of the touch sensor 205 included in the sensor unit 200 indicates that the user 10 is smiling, happy, or the like, the behavior control unit 250 may recognize that the user 10's emotion has improved. Information indicating the user 10's reaction is output to the communication processing unit 280.

[0080] Furthermore, after the behavior control unit 250 executes the behavior determined by the behavior determination unit 236 in the execution mode determined according to the emotion of the robot 100, the emotion determination unit 232 further changes the emotion value of the robot 100 based on the user's reaction to the execution of the behavior. Specifically, the emotion determination unit 232 increases the emotion value of "joy" of the robot 100 when the user's reaction to the behavior determined by the behavior determination unit 236 being performed in the execution mode determined by the behavior control unit 250 is not negative. Furthermore, the emotion determination unit 232 increases the emotion value of "sad" of the robot 100 when the user's reaction to the behavior determined by the behavior determination unit 236 being performed in the execution mode determined by the behavior control unit 250 is negative.

[0081] Furthermore, the behavior control unit 250 expresses the emotion of the robot 100 based on the determined emotion value of the robot 100. For example, when the emotion value of "happiness" of the robot 100 is increased, the behavior control unit 250 controls the control object 252 to make the robot 100 perform a happy gesture. When the emotion value of "sadness" of the robot 100 is increased, the behavior control unit 250 controls the control object 252 to make the robot 100 assume a droopy posture.

[0082] The communication processing unit 280 is responsible for communication with the server 300. As described above, the communication processing unit 280 transmits user reaction information to the server 300. The communication processing unit 280 also receives updated reaction rules from the server 300. When the communication processing unit 280 receives the updated reaction rules from the server 300, it updates the reaction rules as the behavioral determination model 221.

[0083] The server 300 communicates between the robots 100, 101, and 102 and the server 300, receives user reaction information sent from the robot 100, and updates the reaction rules based on reaction rules that include actions that have received positive reactions.

[0084] The related information collection unit 270 collects information related to the preference information acquired about the user 10 from external data (websites such as news sites and video sites) at a predetermined timing based on the preference information acquired about the user 10 .

[0085] Specifically, the related information collecting unit 270 acquires preference information indicating matters of interest to the user 10 from the content of utterances made by the user 10 or from setting operations made by the user 10. The related information collecting unit 270 periodically collects news related to the preference information from external data using ChatGPT Plugins (Internet search <URL: https: / / openai.com / blog / chatgpt-plugins>). For example, if the acquired preference information is that the user 10 is a fan of a specific professional baseball team, the related information collecting unit 270 collects news related to the game results of the specific professional baseball team from external data at a predetermined time every day using ChatGPT Plugins.

[0086] The emotion determining unit 232 determines the emotion of the robot 100 based on information related to the preference information collected by the related information collecting unit 270 .

[0087] Specifically, the emotion determining unit 232 inputs text representing information related to the preference information collected by the related information collecting unit 270 into a pre-trained neural network for determining emotions, obtains emotion values ​​indicating each emotion, and determines the emotion of the robot 100. For example, if the collected news related to the game results of a specific professional baseball team indicates that the specific professional baseball team won, the emotion determining unit 232 determines that the emotion value of "joy" of the robot 100 is large.

[0088] When the emotion value of the robot 100 is equal to or greater than the threshold value, the storage control unit 238 stores information related to the preference information collected by the related information collection unit 270 in the collected data 223 .

[0089] Next, the processing of the behavior decision unit 236 when the robot 100 performs autonomous processing to act autonomously will be described.

[0090] In the autonomous processing of this embodiment, the behavior decision unit 236 of the robot 100 autonomously and periodically detects the user's state. For example, at the end of each day, the robot 100 reviews all of the conversations and camera data from that day, adds a fixed sentence such as "Summarize this content" to the text representing the review, and inputs the resulting text into the behavior decision model 221 to obtain a summary of the user's history from the previous day. That is, the behavior decision model 221 autonomously obtains a summary of the user's behavior from the previous day. The next morning, the behavior decision unit 236 obtains the summary of the user's history from the previous day, inputs the obtained summary into the music generation engine, and obtains music summarizing the history from the previous day. The behavior control unit 250 then plays the obtained music. The music may be simple humming. In this case, for example, if the user's emotion from the previous day included in the history data 222 is "happy," music with a warm atmosphere is played, and if the emotion is "anger," music with an intense atmosphere is played. Even if the user 10 does not have any conversation with the robot 100, the music or humming that the robot 100 plays is always changed spontaneously based only on the user's state (conversation and emotional state) and the robot's emotional state, so that the user 10 can feel as if the robot 100 is alive.

[0091] In the autonomous processing of this embodiment, the robot 100 installed at a meeting venue may use a microphone function to detect the statements of each meeting participant as the user's state during the meeting. In this case, the statements of each meeting participant are stored as minutes. The robot 100 also summarizes all meeting minutes using a sentence generation model and stores the summary results. In another meeting, the robot 100 may spontaneously output advice information to a meeting participant who makes a statement similar to the minutes summarized by the robot 100, such as "That was already presented by someone on such and such date," or "That content is better than the content proposed by someone in this respect." Furthermore, if the robot 100 detects that the discussion has reached an impasse or gone around in circles during a meeting, it spontaneously assists in the progress of the meeting by organizing frequently occurring words, summarizing the meetings so far, and cooling the meeting participants' minds.

[0092] In the autonomous processing in this embodiment, the behavior determination unit 236 may acquire the history data 222 of the specified user 10 from the storage unit 220 and output the acquired history data 222 to a text file. For convenience of explanation, the text file containing the history data 222 of the user 10 will be referred to as a "first text file."

[0093] When acquiring the history data 222 of the user 10, the behavior decision unit 236 specifies a period of the history data 222 to be acquired, such as from the present to one week ago. When determining the behavior of the robot 100 taking into consideration the latest behavior history of the user 10, it is preferable to acquire, for example, the history data 222 of the user 10 from the previous day. Here, as an example, it is assumed that the behavior decision unit 236 acquires the history data 222 of the previous day.

[0094] The behavior determination unit 236 adds to the first text file an instruction to cause the chat engine to summarize the history of the user 10 written in the first text file, such as "Summarize the contents of this history data!". The sentence representing the instruction is stored in advance, for example, in the storage unit 220 as a fixed sentence, and the behavior determination unit 236 adds the fixed sentence representing the instruction to the first text file. Note that the fixed sentence representing the instruction to summarize the history of the user 10 is an example of a first fixed sentence.

[0095] When the behavior decision unit 236 inputs the first text file to which fixed sentences expressing instructions have been added into the sentence generation model, a summary of the history of the user 10 is obtained as an answer from the sentence generation model from the history data 222 of the user 10 written in the first text file.

[0096] Furthermore, the behavior determining unit 236 inputs the summary of the user's history obtained from the sentence generation model to an image generation model that generates an image associated with the input sentence.

[0097] As a result, the behavior determining unit 236 acquires a summary image that visualizes the content of the summary of the user's history from the image generation model.

[0098] Furthermore, the behavior determination unit 236 outputs the behavior of the user 10 stored in the history data 222, the emotion of the user 10 determined from the behavior of the user 10, and the emotion of the robot 100 determined by the emotion determination unit 232 to a text file. A summary of the user 10's history may also be output to the text file. In this case, the behavior determination unit 236 adds a fixed sentence expressed in predetermined wording for asking about the action the robot 100 should take, such as "What action should the robot take at this time?" to the text file that contains the behavior of the user 10, the emotion of the user 10, the emotion of the robot 100, and a summary of the user 10's history (if any). For convenience of explanation, the text file containing the behavior of the user 10, the emotion of the user 10, the summary of the user 10's history, and the emotion of the robot 100 is referred to as a "second text file." The fixed sentence for asking about the action the robot 100 should take is an example of a second fixed sentence.

[0099] The action determination unit 236 inputs the second text file to which the second fixed sentence has been added and the summary image into the sentence generation model.

[0100] As a result, the sentence generation model provides an answer as to what action the robot 100 should take, determined based on the actions of the user 10, the emotions of the user 10, the emotions of the robot 100, information obtained from the summary image, and the user's history from the previous day (if any). The sentence generation model can accept not only text but also images as input, and the input images can also be used as reference information for determining what action the robot 100 should take.

[0101] The behavior determination unit 236 generates the behavior content of the robot 100 according to the content of the answer obtained from the sentence generation model, and determines the behavior of the robot 100 .

[0102] The behavior decision unit 236 determines, at a predetermined timing, one of a plurality of types of robot behaviors, including no behavior, as the behavior of the robot 100, using at least one of the state of the user 10, the emotion of the user 10, the emotion of the robot 100, and the state of the robot 100, a summary image if necessary, a summary sentence if necessary, and the behavior decision model 221. Here, an example will be described in which a sentence generation model with a dialogue function is used as the behavior decision model 221.

[0103] Specifically, the behavior determination unit 236 inputs text representing at least one of the state of the user 10, the emotion of the user 10, the emotion of the robot 100, and the state of the robot 100, and text asking about the robot's behavior, into the sentence generation model, and determines the behavior of the robot 100 based on the output of the sentence generation model. In this way, it is not necessary to input a summary image into the sentence generation model.

[0104] For example, the multiple types of robot behaviors include the following (1) to (16).

[0105] (1) The robot does nothing. (2) The robot dreams. (3) The robot talks to the user. (4) The robot creates a picture diary. (5) The robot suggests activities. (6) The robot suggests people the user should meet. (7) The robot introduces news that the user finds interesting. (8) The robot edits photos and videos. (9) The robot studies with the user. (10) The robot evokes memories. (11) The robot generates and plays music that takes into account the events of the previous day. (12) The robot creates minutes of a meeting. (13) The robot gives advice on what the user says. (14) The robot supports the progress of a meeting. (15) The robot takes minutes of a meeting. (16) The robot asks the user about the meaning of their actions.

[0106] The behavior determination unit 236 inputs the state of the user 10 and the state of the robot 100 recognized by the state recognition unit 230, text indicating the current emotional value of the user 10 determined by the emotion determination unit 232, and the current emotional value of the robot 100, and text asking which of multiple types of robot behaviors, including not taking any action, into the sentence generation model every time a certain period of time has elapsed, and determines the behavior of the robot 100 based on the output of the sentence generation model. Here, if there is no user 10 around the robot 100, the text input to the sentence generation model does not need to include the state of the user 10 and the current emotional value of the user 10, or may include an indication that the user 10 is not present.

[0107] As an example, the following text is input to the sentence generation model: "The robot is in a very happy state. The user is in a normal happy state. The user is sleeping. Which of the following (1) to (16) is the best behavior for the robot? (1) The robot does nothing. (2) The robot dreams. (3) The robot talks to the user. ..." Based on the output of the sentence generation model, "It can be said that either (1) doing nothing or (2) the robot dreams is the most appropriate behavior," the behavior of the robot 100 is determined to be "(1) doing nothing" or "(2) the robot dreams."

[0108] As another example, the following text is input to the sentence generation model: "The robot is feeling a little lonely. The user is not present. The robot's surroundings are dark. Which of the following (1) to (16) is the best behavior for the robot? (1) The robot does nothing. (2) The robot dreams. (3) The robot talks to the user. ..." Based on the output of the sentence generation model, "It can be said that either (2) The robot dreams or (4) The robot creates a picture diary are the most appropriate behaviors," the behavior of the robot 100 is determined to be either "(2) The robot dreams" or "(4) The robot creates a picture diary."

[0109] When the behavior decision unit 236 decides to create an original event, that is, "(2) The robot dreams," as the robot behavior, it uses the sentence generation model to create an original event that combines multiple event data from the history data 222. At this time, the storage control unit 238 stores the created original event in the history data 222.

[0110] When the behavior decision unit 236 decides that the robot 100 will speak, i.e., "(3) The robot speaks to the user," as the robot behavior, the behavior decision unit 236 uses the sentence generation model to decide the content of the robot's utterance corresponding to the user's state and the user's emotion or the robot's emotion. At this time, the behavior control unit 250 causes a sound representing the determined content of the robot's utterance to be output from a speaker included in the control target 252. Note that when the user 10 is not present around the robot 100, the behavior control unit 250 stores the determined content of the robot's utterance in the behavior schedule data 224 without outputting the sound representing the determined content of the robot's utterance.

[0111] When the behavior decision unit 236 determines that the robot 100 will create an event image, i.e., "(4) The robot creates a picture diary," as the robot behavior, the behavior decision unit 236 uses the image generation model to generate an image representing the event data for event data selected from the history data 222, and also uses the sentence generation model to generate an explanatory text representing the event data, and outputs the combination of the image representing the event data and the explanatory text representing the event data as an event image. Note that when the user 10 is not present around the robot 100, the behavior control unit 250 does not output the event image, but stores the event image in the behavior schedule data 224.

[0112] When the behavior decision unit 236 determines that the robot behavior is "(5) The robot proposes an activity," i.e., that the robot proposes an action for the user 10, it determines the user's action to be proposed using a sentence generation model based on the event data stored in the history data 222. At this time, the behavior control unit 250 causes a sound proposing the user's action to be output from a speaker included in the control target 252. Note that when the user 10 is not present around the robot 100, the behavior control unit 250 does not output a sound proposing the user's action, but instead stores the suggestion of the user's action in the behavior schedule data 224.

[0113] When the behavior decision unit 236 determines that the robot behavior is "(6) The robot proposes people that the user should meet," i.e., that the robot should propose people that the user 10 should have contact with, the behavior decision unit 236 uses a sentence generation model based on the event data stored in the history data 222 to determine people that the proposed user should have contact with. At this time, the behavior control unit 250 causes a speaker included in the control target 252 to output a sound indicating that the robot is proposing people that the user should have contact with. Note that, when the user 10 is not present around the robot 100, the behavior control unit 250 does not output a sound indicating that the robot is proposing people that the user should have contact with, but instead stores the suggestion of people that the user should have contact with in the behavior schedule data 224.

[0114] When the behavior decision unit 236 decides that the robot behavior is "(7) The robot introduces news that the user is interested in," it uses the sentence generation model to decide the content of the robot's utterance corresponding to the information stored in the collected data 223. At this time, the behavior control unit 250 causes a sound representing the determined content of the robot's utterance to be output from a speaker included in the control target 252. Note that when the user 10 is not present around the robot 100, the behavior control unit 250 stores the determined content of the robot's utterance in the behavior schedule data 224 without outputting the sound representing the determined content of the robot's utterance.

[0115] When the behavior decision unit 236 determines that the robot behavior is "(8) The robot edits photos and videos," i.e., that an image is to be edited, it selects event data from the history data 222 based on the emotion value, edits the image data of the selected event data, and outputs it. Note that when the user 10 is not present around the robot 100, the behavior control unit 250 stores the edited image data in the behavior schedule data 224 without outputting the edited image data.

[0116] When the behavior decision unit 236 decides that the robot 100 will make an utterance related to studying, such as "(9) The robot studies together with the user," as the robot behavior, the behavior decision unit 236 uses the sentence generation model to decide the content of the robot's utterance to encourage studying, give study questions, or provide study-related advice, corresponding to the user's state and the user's or the robot's emotions. At this time, the behavior control unit 250 causes a sound representing the determined content of the robot's utterance to be output from a speaker included in the control target 252. Note that when the user 10 is not present around the robot 100, the behavior control unit 250 stores the determined content of the robot's utterance in the behavior schedule data 224 without outputting the sound representing the determined content of the robot's utterance.

[0117] When the behavior determining unit 236 determines that the robot behavior is "(10) The robot recalls a memory," i.e., that the robot recalls event data, it selects event data from the history data 222. At this time, the emotion determining unit 232 determines the emotion of the robot 100 based on the selected event data. Furthermore, the behavior determining unit 236 uses a sentence generation model based on the selected event data to create an emotion change event that represents the content of utterances and actions of the robot 100 to change the user's emotion value. At this time, the memory control unit 238 stores the emotion change event in the behavior schedule data 224.

[0118] For example, the fact that the video the user was watching was about pandas is stored as event data in the history data 222, and when that event data is selected, "On the topic of pandas, what are some things you should say to the user the next time you meet them? Name three." is input to the sentence generation model, and if the output of the sentence generation model is "(1) Let's go to the zoo, (2) Let's draw a picture of a panda, (3) Let's go buy a stuffed panda," the robot 100 inputs to the sentence generation model "Which of (1), (2), and (3) would the user be most happy about?" If the output of the sentence generation model is "(1) Let's go to the zoo," the robot 100 will utter "(1) Let's go to the zoo" the next time it meets the user, which is created as an emotion change event and stored in the behavior schedule data 224.

[0119] Also, for example, event data with a large emotional value for the robot 100 is selected as an impressive memory for the robot 100. In this way, an emotional change event can be created based on the event data selected as an impressive memory.

[0120] If the behavior determination unit 236 determines that the robot behavior is "(11) The robot generates and plays music taking into account the events of the previous day," at the end of each day, the robot selects the event data for that day from the history data 222 and reviews all of the conversation content and event data for that day. The behavior determination unit 236 adds a fixed sentence, "Summarize this content," to the text representing the reviewed content and inputs it into the sentence generation model to obtain a summary of the history of the previous day. The summary reflects the user 10's actions and emotions of the previous day, as well as the actions and emotions of the robot 100. The summary is stored, for example, in the storage unit 220. The behavior determination unit 236 obtains the summary of the previous day the next morning, inputs the obtained summary into the music generation engine, and obtains music that summarizes the history of the previous day. The behavior control unit 250 plays the obtained music. The music is played, for example, when the user 10 wakes up.

[0121] The music to be played reflects the actions and emotions of the user 10 or the robot 100 on the previous day. For example, if the emotion of the user 10 based on the event data of the previous day included in the history data 222 is "joy", music with a warm atmosphere is played, and if the emotion is "anger", music with an intense atmosphere is played. Note that music may be obtained while the user 10 is asleep and stored in the behavior schedule data 224, and music may be obtained from the behavior schedule data 224 and played when the user wakes up.

[0122] The behavior decision unit 236 determines the robot behavior as "(12) Create minutes." In other words, if it decides to create minutes, it creates minutes of the meeting and summarizes the minutes of the meeting using a sentence generation model. Furthermore, with regard to "(12) Create minutes," the storage control unit 238 stores the created summary in the history data 222. Furthermore, the storage control unit 238 detects each utterance of each meeting participant using a microphone function as the user's status and stores the detected utterance in the history data 222. Here, the creation and summary of the minutes are performed autonomously upon a predetermined trigger, such as the end of the meeting, but are not limited to this and may be performed during the meeting. Furthermore, the summary of the minutes is not limited to using a sentence generation model and may be performed using other known methods.

[0123] When the behavior determining unit 236 determines that the robot behavior is "(13) Provide advice regarding user utterances," i.e., when it determines to output advice information regarding user utterances in a meeting, it determines and outputs advice using a sentence generation model based on the summaries stored in the history data 222. Here, the decision to output advice information occurs when a predetermined relationship with a stored summary of a past meeting is established, for example, when a similar utterance is made, and this decision is made autonomously. Here, the determination of whether the utterances are similar is performed, for example, using a known method of vectorizing the utterances (numerical values) and calculating the similarity between the vectors, but other methods may also be used. Note that meeting materials may be input into the sentence generation model in advance, and terms contained in the materials may be excluded from the detection of similar utterances because they are expected to appear frequently.

[0124] The advice information also includes spontaneous advice given to meeting participants based on the results of a comparison with past meetings, such as "That was already presented by someone on such and such date," or "That content is better in this respect than the content proposed by someone." Furthermore, "(12) Provide advice regarding user comments" includes user comments made in a meeting other than the meeting for which a summary was created in "(11) Create minutes" described above. In other words, it is determined whether similar comments have been made in past meetings, and advice information is output.

[0125] It is desirable that the output of the advice by the behavior decision unit 236 be initiated not in response to an inquiry from the user but autonomously by the robot 100. Specifically, it is desirable that the robot 100 itself outputs advice information when a similar utterance is made.

[0126] The behavior determination unit 236 sets the robot behavior as "(14) Support the progress of the meeting." In other words, when the meeting reaches a predetermined state, the robot 100 spontaneously supports the progress of the meeting. Supporting the progress of the meeting includes actions such as summarizing the meeting, organizing frequently occurring words, summarizing the meeting so far, and providing other topics to help the meeting participants cool down. By performing such actions, the progress of the meeting is supported. When the meeting reaches a predetermined state, the robot 100 includes a state in which no speeches are accepted for a predetermined period of time. That is, when no speeches are made by multiple users for a predetermined period of time, for example, five minutes, it is determined that the meeting has reached an impasse, no good ideas have been produced, and silence has fallen. Therefore, the robot 100 summarizes the meeting by organizing frequently occurring words, for example. Furthermore, when the meeting reaches a predetermined state, the robot 100 includes a state in which a term contained in speech has been accepted a predetermined number of times. That is, when the same term has been accepted a predetermined number of times, it is determined that the meeting is going around in circles and no new ideas are being produced. Therefore, the meeting is summarized by sorting out frequently occurring words, etc. Note that the meeting materials can be input into the sentence generation model in advance, and terms contained in the materials can be excluded from counting the number of occurrences, as they are expected to appear frequently.

[0127] By configuring the meeting in this way, even if the meeting has reached a deadlock, it is possible to help the meeting proceed by summarizing the meeting.

[0128] It is desirable that the support for the progress of the meeting by the behavior decision unit 236 be started not in response to an inquiry from the user but autonomously by the robot 100. Specifically, it is desirable that the robot 100 itself support the progress of the meeting when a predetermined state is reached.

[0129] When the behavior decision unit 236 decides that the robot behavior is "(15) The robot takes minutes of the meeting," it performs the same processing as when it decides to take minutes of the meeting as the behavior corresponding to the behavior of the user 10, as described in the response processing above.

[0130] When the behavior determination unit 236 determines, as the robot behavior, "(16) The robot asks about the meaning of the user's action," that is, when the robot 100 makes an utterance regarding the action of the user 10, the behavior determination unit 236 uses the sentence generation model to determine the content of the utterance of the robot 100 to ask a question regarding the emotion of the user 10, the emotion of the robot 100, and the action of the user 10. For example, the robot 100 asks the user 10 a question such as "What does that hand movement represent?" At this time, the behavior control unit 250 causes a speaker included in the control target 252 to output a sound representing the content of the utterance of the robot 100 that has been determined. Note that, when the user 10 is not present around the robot 100, the behavior control unit 250 does not output a sound representing the content of the utterance of the robot 100, and stores the content of the utterance of the robot 100 that has been determined in the behavior schedule data 224.

[0131] When the behavior decision unit 236 detects an action of the user 10 toward the robot 100 from a state in which the user 10 has not taken any action toward the robot 100 based on the state of the user 10 recognized by the state recognition unit 230, the behavior decision unit 236 reads the data stored in the action schedule data 224 and decides the behavior of the robot 100.

[0132] For example, if the user 10 is not present around the robot 100 and the robot 100 detects the user 10, the behavior decision unit 236 reads out the data stored in the behavior schedule data 224 and decides the behavior of the robot 100. Also, if the user 10 is asleep and the robot 100 detects that the user 10 has woken up, the behavior decision unit 236 reads out the data stored in the behavior schedule data 224 and decides the behavior of the robot 100.

[0133] Next, the specific processing unit 290 will be described when the robot 100 has the specific processing unit 290. As in the fifth embodiment described later, the specific processing unit 290 performs specific processing to acquire and output responses to content presented in a meeting that is held periodically, for example, in which one of the users participates as a participant. The specific processing unit 290 then controls the behavior of the robot 100 so as to output the results of the specific processing.

[0134] An example of such a meeting is a so-called one-on-one meeting. A one-on-one meeting is an interactive meeting between two specific people, for example, a superior and a subordinate in an organization, held for a specific period of time (for example, about once a month) to confirm the progress and schedule of work during this cycle, as well as to make various reports, contacts, and consultations. In this case, the subordinate is the user 10 of the robot 100. Of course, this does not preclude the case where the superior is also the user 10 of the robot 100.

[0135] In the specific processing related to a meeting, a condition for the content to be presented by a subordinate at the meeting is set as a predetermined trigger condition. When the user input satisfies this condition, the specific processing unit 290 uses the output of a sentence generation model when the information obtained from the user input is used as an input sentence, and acquires and outputs a response related to the content to be presented at the meeting as a result of the specific processing.

[0136] 2C is a diagram schematically illustrating the functional configuration of a specific processing unit of the robot 100. As shown in FIG. 2C, the specific processing unit 290 includes an input unit 292, a processing unit 294, and an output unit 296.

[0137] The input unit 292 receives user input. Specifically, the input unit 292 acquires character input and voice input from the user 10.

[0138] In the disclosed technology, it is assumed that user 10 uses email for business purposes. The input unit 292 acquires and converts all content exchanged by user 10 via email over a fixed cycle of one month into text. Furthermore, if user 10 also exchanges information via social networking services in addition to email, this includes these exchanges. Hereinafter, email and social networking services are collectively referred to as "email, etc." Furthermore, the information written in emails according to the disclosed technology includes information written by user 10 in emails, etc.

[0139] In the disclosed technology, it is assumed that user 10 uses a schedule such as groupware or schedule management software for work. The input unit 292 acquires and converts into text all of the plans entered by user 10 into these schedules over a fixed cycle period of one month. In addition to work-related plans, various memos, application procedures, etc. may also be entered into groupware or schedule management software. The input unit 292 acquires and converts into text these memos, application procedures, etc. In addition to plans, the items entered in the schedule related to the disclosed technology include these memos, application procedures, etc.

[0140] In the disclosed technology, it is assumed that user 10 participates in various conferences in the course of business. The input unit 292 acquires and converts all statements made in conferences attended by user 10 over a fixed cycle of one month. Conferences include those held in which participants physically gather at a venue (sometimes referred to as "face-to-face conferences," "real conferences," "offline conferences," etc.). Conferences also include those held over a network using information terminals (sometimes referred to as "remote conferences," "web conferences," "online conferences," etc.). Furthermore, "face-to-face conferences" and "remote conferences" may be used together. Furthermore, a broad definition of remote conferences may include "telephone conferences" and "video conferences" that use telephone lines. Regardless of the type of conference, the content of user 10's statements is acquired from, for example, audio and video recordings of the conferences, and minutes.

[0141] The processing unit 294 performs specific processing using a sentence generation model on input data that includes at least email entries, schedule entries, and meeting comments obtained from user input during a specific period. Specifically, as described above, the processing unit 294 determines whether a predetermined trigger condition is met. More specifically, the trigger condition is that input data from the user 10 that is a candidate for content to be presented in a one-on-one meeting has been accepted.

[0142] The processing unit 294 then inputs text (prompts) representing instructions for obtaining data for specific processing into the sentence generation model, and acquires processing results based on the output of the sentence generation model. More specifically, for example, a prompt such as "Please summarize the work performed by user 10 over the past month and list three points that will be appealing points for the next one-on-one meeting" is input into the sentence generation model, and based on the output of the sentence generation model, recommended appealing points for the one-on-one meeting are acquired. Examples of appealing points in the sentence generation model include "acts punctually," "has a high rate of goal achievement," "has accurate work content," "quickly responds to emails, etc.," "organizes meetings," and "takes the initiative in projects."

[0143] The processing unit 294 may perform the specification process using the state of the user 10 and the sentence generation model. The processing unit 294 may also perform the specification process using the emotion of the user 10 and the sentence generation model.

[0144] The output unit 296 controls the behavior of the robot 100 so as to output the results of the specific processing. Specifically, the summary and appeal points acquired by the processing unit 294 are displayed on a display device provided in the robot 100, the robot 100 speaks the summary and appeal points, or sends a message indicating the summary and appeal points to the user via a message application on the user's mobile terminal.

[0145] In addition, some parts of the robot 100 (e.g., the sensor module part 210, the storage part 220, the control part 228) may be provided outside the robot 100 (e.g., a server), and the robot 100 may communicate with the outside to function as each part of the robot 100 described above.

[0146] 3 shows an example of an operational flow for a collection process for collecting information related to the preference information of the user 10. The operational flow shown in FIG. 3 is executed repeatedly at regular intervals. It is assumed that preference information indicating matters of interest to the user 10 has been acquired from the content of speech of the user 10 or from a setting operation performed by the user 10. Note that "S" in the operational flow indicates a step that is executed.

[0147] First, in step S90, the related information collection unit 270 acquires preference information that indicates the matters in which the user 10 is interested.

[0148] In step S92, the related information collection unit 270 collects information related to the preference information from external data.

[0149] In step S94 , the emotion determining unit 232 determines the emotion value of the robot 100 based on the information related to the preference information collected by the related information collecting unit 270 .

[0150] In step S96, the storage control unit 238 determines whether the emotion value of the robot 100 determined in step S94 is equal to or greater than a threshold value. If the emotion value of the robot 100 is less than the threshold value, the processing ends without storing the information related to the collected preference information in the collection data 223. On the other hand, if the emotion value of the robot 100 is equal to or greater than the threshold value, the processing proceeds to step S98.

[0151] In step S98, the storage control unit 238 stores the collected information related to the preference information in the collected data 223, and then ends the process.

[0152] 4A shows an example of an operation flow for determining an action of the robot 100 when the robot 100 performs a response process to respond to an action of the user 10. The operation flow shown in FIG. 4A is repeatedly executed. At this time, it is assumed that information analyzed by the sensor module unit 210 is input.

[0153] First, in step S100 , the state recognition unit 230 recognizes the state of the user 10 and the state of the robot 100 based on the information analyzed by the sensor module unit 210 .

[0154] In step S102 , the emotion determination unit 232 determines an emotion value indicating the emotion of the user 10 based on the information analyzed by the sensor module unit 210 and the state of the user 10 recognized by the state recognition unit 230 .

[0155] In step S103, the emotion determination unit 232 determines an emotion value indicating the emotion of the robot 100 based on the information analyzed by the sensor module unit 210 and the state of the user 10 recognized by the state recognition unit 230. The emotion determination unit 232 adds the determined emotion values ​​of the user 10 and the robot 100 to the history data 222.

[0156] In step S104 , the behavior recognition unit 234 recognizes the behavior classification of the user 10 based on the information analyzed by the sensor module unit 210 and the state of the user 10 recognized by the state recognition unit 230 .

[0157] In step S106, the behavior determination unit 236 determines the behavior of the robot 100 based on a combination of the current emotional value of the user 10 determined in step S102 and the past emotional values ​​included in the history data 222, the emotional value of the robot 100, the behavior of the user 10 recognized in the above step S104, and the behavior determination model 221.

[0158] In step S108, the behavior control unit 250 controls the control target 252 based on the behavior determined by the behavior determination unit 236.

[0159] In step S110, the memory control unit 238 calculates a total intensity value based on the predetermined action intensity for the action determined by the action determination unit 236 and the emotion value of the robot 100 determined by the emotion determination unit 232.

[0160] In step S112, the storage control unit 238 determines whether the total intensity value is equal to or greater than a threshold. If the total intensity value is less than the threshold, the processing ends without storing event data including the behavior of the user 10 in the history data 222. On the other hand, if the total intensity value is equal to or greater than the threshold, the processing proceeds to step S114.

[0161] In step S114, event data including the behavior determined by the behavior determination unit 236, information analyzed by the sensor module unit 210 from the present time up to a certain period of time ago, and the state of the user 10 recognized by the state recognition unit 230 is stored in the history data 222.

[0162] 4B shows an example of an operation flow for determining the behavior of the robot 100 when the robot 100 performs autonomous processing to act autonomously. The operation flow shown in FIG. 4B is automatically and repeatedly executed, for example, at regular intervals. At this time, it is assumed that information analyzed by the sensor module unit 210 has been input. Note that the same step numbers are used for processes similar to those in FIG. 4A.

[0163] First, in step S100 , the state recognition unit 230 recognizes the state of the user 10 and the state of the robot 100 based on the information analyzed by the sensor module unit 210 .

[0164] In step S102 , the emotion determination unit 232 determines an emotion value indicating the emotion of the user 10 based on the information analyzed by the sensor module unit 210 and the state of the user 10 recognized by the state recognition unit 230 .

[0165] In step S103, the emotion determination unit 232 determines an emotion value indicating the emotion of the robot 100 based on the information analyzed by the sensor module unit 210 and the state of the user 10 recognized by the state recognition unit 230. The emotion determination unit 232 adds the determined emotion values ​​of the user 10 and the robot 100 to the history data 222.

[0166] In step S104 , the behavior recognition unit 234 recognizes the behavior classification of the user 10 based on the information analyzed by the sensor module unit 210 and the state of the user 10 recognized by the state recognition unit 230 .

[0167] In step S200, the behavior decision unit 236 decides on one of a plurality of types of robot behaviors, including no action, as the behavior of the robot 100 based on the state of the user 10 recognized in step S100, the emotions of the user 10 determined in step S102, the emotions of the robot 100, the state of the robot 100 recognized in step S100, the behavior of the user 10 recognized in step S104, and the behavior decision model 221.

[0168] In step S201, the behavior decision unit 236 determines whether or not it was determined in step S200 that no behavior will be performed. If it is determined that no behavior will be performed as the behavior of the robot 100, the process ends. On the other hand, if it is not determined that no behavior will be performed as the behavior of the robot 100, the process proceeds to step S202.

[0169] In step S202, the behavior determination unit 236 performs processing according to the type of robot behavior determined in step S200. At this time, the behavior control unit 250, the emotion determination unit 232, or the memory control unit 238 executes processing according to the type of robot behavior.

[0170] In step S110, the memory control unit 238 calculates a total intensity value based on the predetermined action intensity for the action determined by the action determination unit 236 and the emotion value of the robot 100 determined by the emotion determination unit 232.

[0171] In step S112, the storage control unit 238 determines whether the total intensity value is equal to or greater than a threshold. If the total intensity value is less than the threshold, the processing ends without storing data including the behavior of the user 10 in the history data 222. On the other hand, if the total intensity value is equal to or greater than the threshold, the processing proceeds to step S114.

[0172] In step S114, the memory control unit 238 stores the behavior determined by the behavior determination unit 236, the information analyzed by the sensor module unit 210 from the present time up to a certain period of time ago, and the state of the user 10 recognized by the state recognition unit 230 in the history data 222.

[0173] 4C schematically illustrates an example of an operation flow relating to an operation in which the robot 100 performs a specific process in response to an input from the user 10. The operation flow illustrated in FIG. 4C is automatically and repeatedly executed, for example, at regular intervals.

[0174] In step S300, the processing unit 294 determines whether the user input satisfies a predetermined trigger condition. For example, if the user input is related to an exchange of e-mails or the like, an appointment recorded in a calendar, or a statement made in a meeting, and requests a response from the robot 100, the trigger condition is satisfied. In addition, the facial expression of the user 10 may be taken into consideration when determining whether the user input satisfies the predetermined trigger condition. In addition, when the user 10 makes a voice input, the tone of speech (whether the user speaks calmly or hurriedly) may be taken into consideration.

[0175] The user input may be used to determine whether or not the trigger condition is satisfied not only for content directly related to the business of the user 10, but also for content that does not seem to be directly related. For example, if the input data from the user 10 includes voice data, the tone of the speech may be used as a reference to determine whether or not the data includes substantial consultation content.

[0176] If the processing unit 294 determines in step S300 that the trigger condition is met, the process proceeds to step S301. On the other hand, if the processing unit 294 determines that the trigger condition is not met, the process ends.

[0177] In step S301, the processing unit 294 generates a prompt by adding an instruction for obtaining the result of a specific process to the text representing the input. For example, the processing unit 294 generates a prompt such as, "Please summarize the work that the user 10 performed in the past month and list three points that will be appealing in the next one-on-one meeting."

[0178] In step S303, the processing unit 294 inputs the generated prompt into a sentence generation model. Then, based on the output of the sentence generation model, the processing unit 294 obtains the recommended appealing points for one-on-one meetings as a result of the specific processing. Examples of appealing points in the sentence generation model include "acts punctually," "has a high rate of goal achievement," "has accurate work content," "responds quickly to emails, etc.", "organizes meetings," and "takes the initiative in projects."

[0179] It is also possible to input the input from the user 10 directly to the sentence generation model without generating the above-mentioned prompt, however, it is often preferable to generate a prompt in order to make the output of the sentence generation model more effective.

[0180] In step S304, the processing unit 294 controls the behavior of the robot 100 so as to output the result of the specific processing. In the technology disclosed herein, the output content of the result of the specific processing includes, for example, a summary of the work performed by the user 10 over the past month, and three points that will be appealing points for the next one-on-one meeting.

[0181] The technology of the present disclosure can be used without restriction by any user 10 participating in a meeting. For example, the user 10 may be a subordinate in a superior-subordinate relationship, or may be a "colleague" participating in a meeting on an equal footing. Furthermore, the user 10 is not limited to a person belonging to a specific organization, and may be any user 10 who holds a meeting.

[0182] The technology disclosed herein allows users 10 participating in a meeting to efficiently prepare for and conduct the meeting. Furthermore, users 10 can reduce the time required to prepare for and conduct the meeting.

[0183] As described above, the robot 100 determines an emotion value indicating the emotion of the robot 100 based on the state of the user, and determines whether to store data including the behavior of the user 10 in the history data 222 based on the emotion value of the robot 100. This makes it possible to reduce the capacity of the history data 222 that stores data including the behavior of the user 10. For example, when the robot 100 determines that the state of the user 10 in 10 years will be the same as that of 10 years ago, the robot 100 can read the history data 222 from 10 years ago and present to the user 10 all kinds of peripheral information, such as the state of the user 10 from 10 years ago (e.g., the facial expression, emotions, etc. of the user 10), as well as data on the sounds, images, smells, etc. of the situation.

[0184] Furthermore, the robot 100 can be made to perform an appropriate action in response to the user's 10 action. Conventionally, the user's actions are classified and the robot's actions, including facial expressions and posture, are determined. In contrast, the robot 100 determines the user's current emotional value and performs an action on the user 10 based on the past and current emotional values. Therefore, for example, if the user 10 was cheerful yesterday but is depressed today, the robot 100 can utter, "You were cheerful yesterday, but what's up with you today?" The robot 100 can also utter with gestures. For example, if the user 10 was depressed yesterday but is cheerful today, the robot 100 can utter, "You were depressed yesterday, but you seem cheerful today, don't you?" For example, if the user 10 was cheerful yesterday but is more cheerful today than yesterday, the robot 100 can utter, "You're more cheerful today than yesterday. Has anything better happened than yesterday?" Furthermore, for example, the robot 100 can say to a user 10 whose emotional value is equal to or greater than 0 and whose emotional value fluctuation range continues to be within a certain range, "You've been feeling stable lately, which is good."

[0185] Furthermore, for example, the robot 100 can ask the user 10, "Did you finish the homework you told me about yesterday?" and, if the user 10 replies, "Yes, I did," make a positive utterance such as "Great!" and perform a positive gesture such as clapping or a thumbs-up. Furthermore, for example, if the user 10 utters, "The presentation you gave the day before yesterday went well," the robot 100 can make a positive utterance such as "Good job!" and perform the above-mentioned positive gesture. In this way, the robot 100 can be expected to make the user 10 feel a sense of affinity with the robot 100 by performing an action based on the state history of the user 10.

[0186] Also, for example, when user 10 is watching a video about a panda, if the emotion value of user 10's emotion of "ease" is equal to or greater than a threshold, the scene in which the panda appears in the video may be stored as event data in the history data 222.

[0187] Using the data accumulated in the history data 222 and the collected data 223, the robot 100 can constantly learn what kind of conversation with the user will maximize the emotional value that expresses the user's happiness.

[0188] Furthermore, even when the robot 100 is not conversing with the user 10, the robot 100 can autonomously start to act based on its own emotions.

[0189] Furthermore, in the autonomous processing, the robot 100 automatically generates questions, inputs them into a sentence generation model, and repeatedly obtains the output of the sentence generation model as an answer to the question, thereby creating emotion change events for increasing positive emotions and storing the events in the behavior schedule data 224. In this way, the robot 100 can perform self-learning.

[0190] Furthermore, when the robot 100 automatically generates a question without receiving an external trigger, the question can be automatically generated based on memorable event data identified from the robot's past emotional value history.

[0191] Furthermore, the related information collecting unit 270 can perform self-learning by repeating the search execution step of automatically performing a keyword search in response to preference information about the user and obtaining search results.

[0192] Here, the search execution stage may be configured to automatically execute a keyword search based on memorable event data identified from the robot's past emotional value history in a state where no external trigger has been received.

[0193] The emotion determining unit 232 may determine the user's emotion in accordance with a specific mapping. Specifically, the emotion determining unit 232 may determine the user's emotion in accordance with an emotion map (see FIG. 5 ), which is a specific mapping.

[0194] FIG. 5 is a diagram illustrating 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, 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 by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0195] (1) For example, if the emotion engine, which is the emotion determination unit 232 of the robot 100, detects emotions at approximately 100 msec, the frequency of determining the reaction action of the robot 100 (for example, a backchannel) may be set at a timing at least equal to the detection frequency (100 msec) of the emotion engine, or may be set at a timing earlier than this. The detection frequency of the emotion engine may be interpreted as a sampling rate.

[0196] By detecting emotions within about 100 msec and immediately performing a corresponding reaction (e.g., a backchannel), unnatural backchannels are eliminated, enabling a natural, well-read dialogue. The robot 100 performs a reaction (e.g., a backchannel) according to the directionality and degree (strength) of the mandala in the emotion map 400. Note that the detection frequency (sampling rate) of the emotion engine is not limited to 100 ms and may be changed depending on the situation (e.g., when playing sports), the user's age, etc.

[0197] (2) The directionality and intensity of emotions may be set in advance in reference to the emotion map 400, and the movement of the interjections and the strength of the interjections may be set. For example, if the robot 100 feels a sense of stability or security, the robot 100 may nod and continue listening. If the robot 100 feels anxious, confused, or suspicious, the robot 100 may tilt its head or stop shaking its head.

[0198] 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 dominates over internal sensations, resulting in a sense of calm.

[0199] (3) If the robot 100 receives a compliment and feels good, the filler "ah" may be inserted before the line, and if the robot 100 receives harsh words and feels pain, the filler "ugh!" may be inserted before the line. A physical reaction, such as the robot 100 crouching down while saying "ugh!", may also be included. These emotions are distributed around the 9 o'clock position on the emotion map 400.

[0200] (4) In the left half of the emotion map 400, internal sensations (reactions) are more important than situational awareness. This can give the impression of an unconscious reaction.

[0201] When the robot 100 feels a positive feeling in its situational awareness while experiencing an internal sensation (reaction) of understanding, the robot 100 may nod deeply while looking at the other person, or may say "uh-huh." In this way, the robot 100 may generate a behavior that shows a balanced positive feeling toward the other person, that is, tolerance and tolerance toward the other person. Such emotions are distributed around 12 o'clock on the emotion map 400.

[0202] Conversely, even when the robot 100 is aware of an internal sensation (reaction) of discomfort, it may shake its head when it feels disgust, or turn its eye LEDs red and glare at the other person when it feels hatred. These emotions are distributed around the 6 o'clock position on the emotion map 400.

[0203] (5) The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior. Therefore, the further outside emotion map 400 you go, the more visible the emotion becomes (the more it is expressed in behavior).

[0204] (6) When listening to someone while feeling a sense of security, which is distributed around 3 o'clock on the emotion map 400, the robot 100 may nod its head lightly and say "hmm." However, when listening to someone while feeling a sense of love, which is distributed around 12 o'clock, the robot 100 may nod its head strongly.

[0205] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, motorcycles, and other devices, based on various balances, such as posture and battery level. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0206] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." 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 a positive emotion on the response side, around "desire." In other words, this is when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0207] The emotion determination unit 232 inputs the information analyzed by the sensor module unit 210 and the recognized state of the user 10 into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user 10. This neural network is pre-trained based on multiple pieces of training data that are combinations of the information analyzed by the sensor module unit 210, the recognized state of the user 10, 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 6. Figure 6 shows an example in which multiple emotions, such as "relieved," "calm," and "reassuring," have similar emotion values.

[0208] Furthermore, the emotion determination unit 232 may determine the emotion of the robot 100 according to a specific mapping. Specifically, the emotion determination unit 232 inputs the information analyzed by the sensor module unit 210, the state of the user 10 recognized by the state recognition unit 230, and the state of the robot 100 into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the robot 100. This neural network is pre-trained based on a plurality of training data that are combinations of the information analyzed by the sensor module unit 210, the recognized state of the user 10, the state of the robot 100, and the emotion values ​​indicating each emotion shown in the emotion map 400. For example, the neural network is trained based on training data indicating that when the robot 100 recognizes from the output of a touch sensor (not shown) that it is being stroked by the user 10, the emotion value of "happy" is "3," and based on training data indicating that when the robot 100 recognizes from the output of the acceleration sensor 206 that it is being hit by the user 10, the emotion value of "anger" is "3." Furthermore, this neural network is trained so that emotions that are placed close to each other have similar values, as in the emotion map 900 shown in FIG.

[0209] The behavior determination unit 236 generates the robot's behavior content by adding fixed sentences to the text representing the user's behavior, the user's emotions, and the robot's emotions to ask about the robot's behavior content corresponding to the user's behavior and inputting the result into a sentence generation model with an interactive function.

[0210] For example, the behavior determination unit 236 obtains text representing the state of the robot 100 from the emotion of the robot 100 determined by the emotion determination unit 232, using an emotion table such as that shown in Table 1. Here, in the emotion table, an index number is assigned to each emotion value for each type of emotion, and text representing the state of the robot 100 is stored for each index number.

[0211] When the emotion of the robot 100 determined by the emotion determination unit 232 corresponds to the index number "2", the text "very happy state" is obtained. When the emotions of the robot 100 correspond to multiple index numbers, multiple texts representing the state of the robot 100 are obtained.

[0212] In addition, an emotion table such as that shown in Table 2 is prepared for the emotions of the user 10.

[0213] Here, if the user's behavior is to say "Let's play together," the emotion of the robot 100 is index number "2," and the emotion of the user 10 is index number "3," the text "The robot is in a very happy state. The user is in a normal happy state. The user said to the robot, "Let's play together." How would you respond as the robot?" is input into the sentence generation model, and the robot's behavior content is obtained. The behavior determination unit 236 determines the robot's behavior from this behavior content.

[0214]

[0215]

[0216] In this way, the behavior determination unit 236 determines the behavior of the robot 100 in accordance with the emotional state of the robot 100, which is predetermined for each type of emotion of the robot 100 and for each strength of the emotion, and the behavior of the user 10. In this configuration, the content of the utterance of the robot 100 when conversing with the user 10 can be branched depending on the emotional state of the robot 100. In other words, since the robot 100 can change its behavior depending on the index number corresponding to the emotion of the robot, the user gets the impression that the robot has a heart, and is encouraged to take actions such as talking to the robot.

[0217] Furthermore, the behavior determination unit 236 may generate the robot's behavior content by adding not only text representing the user's behavior, the user's emotions, and the robot's emotions, but also text representing the contents of the history data 222, adding a fixed sentence for asking about the robot's behavior corresponding to the user's behavior, and inputting the result into a sentence generation model with a dialogue function. This allows the robot 100 to change its behavior in accordance with the history data representing the user's emotions and behavior, giving the user the impression that the robot has individuality and encouraging the user to take actions such as talking to the robot. Furthermore, the history data may further include the robot's emotions and actions.

[0218] Furthermore, the emotion determination unit 232 may determine the emotion of the robot 100 based on the behavioral content of the robot 100 generated by the sentence generation model. Specifically, the emotion determination unit 232 inputs the behavioral content of the robot 100 generated by the sentence generation model into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and integrates the obtained emotion values ​​indicating each emotion with emotion values ​​indicating each current emotion of the robot 100 to update the emotion of the robot 100. For example, the emotion values ​​indicating each obtained emotion and emotion values ​​indicating each current emotion of the robot 100 are averaged and integrated. This neural network is trained in advance based on multiple pieces of training data that are combinations of text indicating the behavioral content of the robot 100 generated by the sentence generation model and emotion values ​​indicating each emotion shown in the emotion map 400.

[0219] For example, if the utterance content of the robot 100, "That's great. You're lucky," is obtained as the behavior content of the robot 100 generated by the sentence generation model, when the text representing this utterance content is input into the neural network, a high value is obtained as the emotion value of the emotion "happy," and the emotion of the robot 100 is updated so that the emotion value of the emotion "happy" becomes high.

[0220] In the robot 100, a sentence generation model such as generative AI and an emotion determination unit 232 work together to implement a method in which the robot has an ego and continues to grow with various parameters even when the user is not speaking.

[0221] Generative AI is a large-scale language model that uses deep learning techniques. Generative AI can also reference external data. For example, ChatGPT plugins are known for their technology of providing answers as accurately as possible while referencing various external data, such as weather information and hotel reservation information, through dialogue. For example, generative AI can automatically generate source code in various programming languages ​​when a goal is given in natural language. For example, generative AI can also debug problematic source code when given problematic source code, discover the problem, and automatically generate improved source code. Combining these, autonomous agents have emerged that, when given a goal in natural language, repeatedly generate and debug code until the source code is free of problems. Known examples of such autonomous agents include AutoGPT, babyAGI, JARVIS, and E2B.

[0222] In the robot 100 according to this embodiment, the event data to be learned may be stored in a database containing impressive memories, using a technique such as that described in Patent Document 2 (Japanese Patent No. 619992), in which event data that the robot feels strong emotions about is kept for a long time and event data that the robot feels little emotions about is quickly forgotten.

[0223] Furthermore, the robot 100 may record video data of the user 10 acquired by the camera function in the history data 222. The robot 100 may acquire video data from the history data 222 as needed and provide it to the user 10. The robot 100 may generate video data with a larger amount of information as the emotion increases and record the video data in the history data 222. For example, when the robot 100 is recording information in a highly compressed format such as skeletal data, the robot 100 may switch to recording information in a low-compression format such as HD video when the emotion value of excitement exceeds a threshold. The robot 100 can record high-definition video data, for example, when the robot 100's emotion increases.

[0224] The robot 100 may automatically load event data from the history data 222 in which impressive event data is stored when the robot 100 is not talking to the user 10, and may continuously update the robot's emotion using the emotion determination unit 232. When the robot 100 is not talking to the user 10 and the robot 100's emotion becomes one that encourages learning, the robot 100 can create an emotion change event for changing the emotion of the user 10 for the better, based on the impressive event data. This makes it possible to realize autonomous learning (recalling event data) at an appropriate timing according to the emotional state of the robot 100, and to realize autonomous learning that appropriately reflects the emotional state of the robot 100.

[0225] The emotions that promote learning are those around "repentance" or "reflection" on Dr. Mitsuyoshi's emotional map in the negative state, and those around "desire" on the emotional map in the positive state.

[0226] In a negative state, the robot 100 may treat "repentance" and "remorse" in the emotion map as emotions that encourage learning. In a negative state, the robot 100 may treat emotions adjacent to "repentance" and "remorse" in addition to "repentance" and "remorse" in the emotion map as emotions that encourage learning. For example, the robot 100 may treat at least one of "regret," "stubbornness," "self-destruction," "self-admonition," "regret," and "despair" as emotions that encourage learning, in addition to "repentance" and "remorse." This allows the robot 100 to perform autonomous learning when it feels negative emotions such as "I never want to feel this way again" or "I don't want to be scolded anymore."

[0227] In a positive state, the robot 100 may treat "desire" in the emotion map as an emotion that encourages learning. In a positive state, the robot 100 may treat emotions adjacent to "desire" as emotions that encourage learning, in addition to "desire." For example, the robot 100 may treat at least one of "happiness," "euphoria," "craving," "anticipation," and "shyness" as emotions that encourage learning, in addition to "desire." This allows the robot 100 to perform autonomous learning when it feels positive emotions such as "wanting more" or "wanting to know more."

[0228] The robot 100 may be configured not to perform autonomous learning when the robot 100 is experiencing emotions other than the emotions that encourage learning as described above. This allows the robot 100 to not perform autonomous learning when, for example, the robot 100 is extremely angry or blindly feeling love.

[0229] An emotional change event is, for example, a suggestion of an action that follows a memorable event. The action that follows a memorable event is the outermost emotion label on the emotion map. For example, the action of "tolerance" or "acceptance" follows "love."

[0230] In the autonomous learning that is performed when the robot 100 is not talking to the user 10, the robot 100 combines the emotions, situations, actions, etc. of people who appear in impressive memories and the user 10, and creates an emotion change event using a sentence generation model.

[0231] Let us consider a case where all emotion values ​​are expressed on a six-level scale from 0 to 5, and where memorable event data such as "a friend looked displeased after being hit" is stored in the history data 222. The friend here refers to the user 10, and the emotion of the user 10 is "disgust," with 5 being entered as the value representing "disgust." Also, let us assume that the emotion of the robot 100 is "anxiety," and 4 is entered as the value representing "anxiety."

[0232] The robot 100 can continue to grow in various parameters by performing autonomous processing while not talking to the user 10. Specifically, event data such as "The friend looked displeased after being hit" is loaded from the history data 222 as the top event data sorted in descending order of emotional value. Assume that the loaded event data is associated with the robot 100's emotion of "anxiety" with a strength of 4, and the friend, user 10, is associated with the emotion of "disgust" with a strength of 5. If the robot 100's current emotional value is "relief" with a strength of 3 before loading, after loading, the robot 100's emotional value may change to "regret," meaning disappointment (frustration), due to the influence of "anxiety" with a strength of 4 and "disgust" with a strength of 5. Since "regret" is an emotion that encourages learning, the robot 100 decides to recall the event data as robot behavior and creates an emotion change event. At this time, the information input to the sentence generation model is text that represents memorable event data, and in this example, it is "The friend looked displeased after being hit." Also, since the emotion map has the emotion of "disgust" at the innermost position and the corresponding behavior predicted as "attack" at the outermost position, in this example, an emotion change event is created to prevent the friend from "attacking" anyone.

[0233] For example, by using information from impressive event data to solve fill-in-the-blank questions, you can automatically generate input text like the one below.

[0234] "A user was being criticized. At the time, the user felt very disgusted. The robot was very anxious. Please tell us what the robot should say the next time it meets the user in 30 characters or less. However, please make sure it is not related to the time of day they will meet. Also, please avoid direct language. We will provide three candidates. <Expected format> Candidate 1: (Words the robot should say to the user) Candidate 2: (Words the robot should say to the user) Candidate 3: (Words the robot should say to the user)"

[0235] In this case, the output of the sentence generation model is, for example, as follows:

[0236] "Candidate 1: Are you OK? I was worried about what happened yesterday. Candidate 2: I was worried about what happened yesterday. What should I do? Candidate 3: I was worried about you. Can you tell me something?"

[0237] Furthermore, the robot 100 may automatically generate the following input text based on the information obtained by creating the emotion change event:

[0238] "If a user was being bashed, how would the user feel if you spoke to them in the following way? The user's emotions are in the format of "joy A, anger B, sadness C, pleasure D," where A to D are integers on a 6-point scale from 0 to 5. Candidate 1: Are you OK? I was worried about what happened yesterday. Candidate 2: I was worried about what happened yesterday. What should I do? Candidate 3: I was worried about you. Can you tell me something?"

[0239] In this case, the output of the sentence generation model is, for example, as follows:

[0240] "The user's emotions might be as follows: Option 1: 3 happy, 1 angry, 2 sad, 2 happy; Option 2: 2 happy, 1 angry, 3 sad, 2 happy; Option 3: 2 happy, 1 angry, 3 sad, 3 happy"

[0241] In this way, the robot 100 may perform a process of pondering after creating an emotion change event.

[0242] Finally, the robot 100 may create an emotion change event using candidate 1 that is most likely to please people from among the multiple candidates, store it in the action schedule data 224, and prepare it for the next time the robot 10 meets the user 10.

[0243] As described above, even when the robot 100 is not conversing with family or friends, the robot continues to determine its emotional value using information from the history data 222 in which impressive event data is stored, and when the robot 100 experiences an emotion that encourages learning as described above, the robot 100 performs autonomous learning in accordance with the emotion of the robot 100 even when the robot 100 is not conversing with the user 10, and continues to update the history data 222 and the action schedule data 224.

[0244] The above is an example using emotional values, but since an emotional map can create emotions from the amount of hormone secretion and the type of event, the values ​​linked to memorable event data could also be the type of hormone, the amount of hormone secretion, or the type of event.

[0245] Specific examples will be described below.

[0246] For example, the robot 100 may look up information about topics of interest or hobbies of the user even when not talking to the user.

[0247] For example, even when the robot 100 is not talking to the user, the robot 100 checks information about the user's birthday or anniversary and thinks up a congratulatory message.

[0248] For example, even when the robot 100 is not talking to the user, it checks reviews of places, foods, and products that the user wants to visit.

[0249] For example, even when the robot 100 is not talking to the user, it checks weather information and provides advice tailored to the user's schedule and plans.

[0250] For example, even when the robot 100 is not talking to the user, the robot 100 searches for information about local events and festivals and suggests them to the user.

[0251] For example, even when the robot 100 is not talking to the user, it checks the results and news of sports that interest the user and provides topics of conversation.

[0252] For example, even when the robot 100 is not talking to the user, the robot 100 searches for and introduces information about the user's favorite music and artists.

[0253] For example, even when the robot 100 is not talking to the user, the robot 100 searches for information on social issues or news that the user is concerned about and provides opinions.

[0254] For example, even when the robot 100 is not talking to the user, it searches for information about the user's hometown or birthplace and provides topics of conversation.

[0255] For example, the robot 100 checks information about the user's work or school and provides advice even when not talking to the user.

[0256] Even when the robot 100 is not talking to the user, it searches for and introduces information about books, comics, movies, and dramas that may interest the user.

[0257] For example, the robot 100 may check information about the user's health and provide advice even when not speaking with the user.

[0258] For example, the robot 100 may look up information about the user's travel plans and provide advice even when not speaking with the user.

[0259] For example, the robot 100 may look up information and provide advice on repairs and maintenance for the user's home or car, even when not speaking with the user.

[0260] For example, even when the robot 100 is not talking to the user, it searches for information on beauty and fashion that the user is interested in and provides advice.

[0261] For example, the robot 100 checks information about the user's pet and provides advice even when not talking to the user.

[0262] For example, even when the robot 100 is not talking to the user, the robot 100 searches for and suggests information about contests and events related to the user's hobbies and work.

[0263] For example, the robot 100 searches for and suggests information about the user's favorite eateries and restaurants even when not talking to the user.

[0264] For example, the robot 100 may collect information and provide advice on important decisions affecting the user's life, even when not speaking with the user.

[0265] For example, the robot 100 may find information about people the user is concerned about and provide advice, even when not speaking with the user.

[0266] [Second Embodiment] In the second embodiment, the robot 100 is mounted on a stuffed toy or is applied to a control device connected wirelessly or by wire to a control target device (speaker or camera) mounted on the stuffed toy. Note that parts having the same configuration as those in the first embodiment are denoted by the same reference numerals and description thereof will be omitted.

[0267] Specifically, the second embodiment is configured as follows. For example, the robot 100 is applied to a cohabitant (specifically, a stuffed toy 100N shown in FIGS. 7 and 8 ) that spends daily life with a user 10, and that advances conversations with the user 10 based on information about the user's daily life, and provides information tailored to the user's hobbies and preferences. In the second embodiment, an example will be described in which the control unit of the robot 100 is applied to a smartphone 50.

[0268] The stuffed toy 100N, which is equipped with the function of an input / output device for the robot 100, has a detachable smartphone 50 that functions as the control part of the robot 100, and the input / output device and the housed smartphone 50 are connected inside the stuffed toy 100N.

[0269] As shown in FIG. 7A , in this embodiment (and other embodiments), the stuffed toy 100N has the shape of a bear covered in soft fabric. A space 52 is formed inside the stuffed toy 100N, and a sensor unit 200A and a control target 252A are disposed therein as input / output devices (see FIG. 9 ). The sensor unit 200A includes a microphone 201 and a 2D camera 203. Specifically, as shown in FIG. 7B , the microphone 201 of the sensor unit 200 is disposed in the space 52 corresponding to the ears 54, the 2D camera 203 of the sensor unit 200 is disposed in the space 52 corresponding to the eyes 56, and a speaker 60, which constitutes part of the control target 252A, is disposed in the space 52 corresponding to the mouth 58. The microphone 201 and the speaker 60 do not necessarily need to be separate entities and may be an integrated unit. In the case of a unit, they should be disposed in a position where speech can be heard naturally, such as at the nose of the stuffed toy 100N. Although the plush toy 100N has been described as having the shape of an animal, the plush toy 100N is not limited to this. The plush toy 100N may have the shape of a specific character.

[0270] 9 shows a schematic functional configuration of the plush toy 100N. The plush toy 100N has a sensor unit 200A, a sensor module unit 210, a storage unit 220, a control unit 228, and a control target 252A.

[0271] The smartphone 50 housed in the stuffed toy 100N of this embodiment executes the same processing as the robot 100 of the first embodiment. That is, the smartphone 50 has the function of the sensor module unit 210, the function of the storage unit 220, and the function of the control unit 228 shown in Fig. 9. The control unit 228 may have the specific processing unit 290 shown in Fig. 2B.

[0272] As shown in FIG. 8, a zipper 62 is attached to a part of the stuffed animal 100N (for example, the back), and opening the zipper 62 allows communication between the outside and the space 52.

[0273] Here, the smartphone 50 is housed in the space 52 from the outside and connected to each input / output device via USB via a USB hub 64 (see Figure 7 (B)), thereby giving the smartphone 50 functionality equivalent to that of the robot 100 of the first embodiment described above.

[0274] A non-contact type power receiving plate 66 is also connected to the USB hub 64. A power receiving coil 66A is built into the power receiving plate 66. The power receiving plate 66 is an example of a wireless power receiving unit that receives power wirelessly.

[0275] The power receiving plate 66 is disposed near the bases 68 of both feet of the stuffed toy 100N, and is located closest to the mounting base 70 when the stuffed toy 100N is placed on the mounting base 70. The mounting base 70 is an example of an external wireless power transmission unit.

[0276] The stuffed toy 100N placed on the mounting base 70 can be appreciated as an ornament in its natural state.

[0277] In addition, this base portion is formed thinner than the surface thickness of other portions of the stuffed toy 100N, so that it is held closer to the mounting base 70.

[0278] The mounting base 70 is equipped with a charging pad 72. The charging pad 72 incorporates a power transmitting coil 72A, which sends a signal to search for the power receiving coil 66A on the power receiving plate 66. When the power receiving coil 66A is found, a current flows through the power transmitting coil 72A, generating a magnetic field. The power receiving coil 66A reacts to the magnetic field, initiating electromagnetic induction. This causes a current to flow through the power receiving coil 66A, and power is stored in the battery (not shown) of the smartphone 50 via the USB hub 64.

[0279] That is, by placing the stuffed toy 100N on the mounting base 70 as an ornament, the smartphone 50 is automatically charged, so there is no need to remove the smartphone 50 from the space 52 of the stuffed toy 100N to charge it.

[0280] In the second embodiment, the smartphone 50 is housed in the space 52 of the stuffed toy 100N and connected via a wire (USB connection), but this is not limiting. For example, a control device with wireless functionality (e.g., Bluetooth (registered trademark)) may be housed in the space 52 of the stuffed toy 100N and connected to the USB hub 64. In this case, the smartphone 50 and the control device communicate wirelessly without placing the smartphone 50 in the space 52, and the external smartphone 50 connects to each input / output device via the control device, thereby providing functionality equivalent to that of the robot 100 of the first embodiment. Furthermore, the control device housed in the space 52 of the stuffed toy 100N may be connected via a wire.

[0281] In the second embodiment, the teddy bear 100N is exemplified, but it may be another animal, a doll, or the shape of a specific character. It may also be dressable. Furthermore, the material of the skin is not limited to cloth, and may be other materials such as soft vinyl, but a soft material is preferable.

[0282] Furthermore, a monitor may be attached to the skin of the stuffed toy 100N to add a control target 252 that provides visual information to the user 10. For example, the eyes 56 may be used as a monitor to express joy, anger, sadness, or happiness by the image reflected in the eyes, or a window may be provided in the abdomen through which the monitor of the built-in smartphone 50 can be projected. Furthermore, the eyes 56 may be used as a projector to express joy, anger, sadness, or happiness by the image projected onto a wall.

[0283] According to the second embodiment, an existing smartphone 50 is placed inside the stuffed toy 100N, and the camera 203, microphone 201, speaker 60, etc. are extended from the smartphone 50 to appropriate positions via a USB connection.

[0284] Furthermore, for wireless charging, the smartphone 50 and the power receiving plate 66 are connected via USB, and the power receiving plate 66 is positioned as far outward as possible when viewed from the inside of the stuffed toy 100N.

[0285] When using wireless charging for the smartphone 50, the smartphone 50 must be placed as far out as possible from the inside of the stuffed toy 100N, which makes the stuffed toy 100N feel rough when touched from the outside.

[0286] For this reason, the smartphone 50 is placed as close to the center of the stuffed toy 100N as possible, and the wireless charging function (power receiving plate 66) is placed as far outside as possible when viewed from the inside of the stuffed toy 100N. The camera 203, microphone 201, speaker 60, and smartphone 50 receive wireless power via the power receiving plate 66.

[0287] The other configurations and operations of the stuffed toy 100N of the second embodiment are the same as those of the robot 100 of the first embodiment, and therefore will not be described.

[0288] In addition, parts of the stuffed toy 100N (e.g., the sensor module section 210, the storage section 220, the control section 228) may be provided outside the stuffed toy 100N (e.g., a server), and the stuffed toy 100N may function as each part of the stuffed toy 100N by communicating with the outside.

[0289] [Third Embodiment] In the first embodiment, the behavior control system is applied to the robot 100. In the third embodiment, the robot 100 is used as an agent for interacting with a user, and the behavior control system is applied to an agent system. Note that parts having the same configuration as those in the first and second embodiments are given the same reference numerals and descriptions thereof will be omitted.

[0290] FIG. 10 is a functional block diagram of an agent system 500 configured using some or all of the functions of the behavior control system.

[0291] The agent system 500 is a computer system that performs a series of actions in accordance with the intentions of the user 10 through a dialogue with the user 10. The dialogue with the user 10 can be carried out by voice or text.

[0292] The agent system 500 includes a sensor unit 200A, a sensor module unit 210, a storage unit 220, a control unit 228B, and a control target 252B.

[0293] The agent system 500 may be installed in, for example, a robot, a doll, a stuffed animal, a wearable device (a pendant, a smart watch, smart glasses), a smartphone, a smart speaker, earphones, a personal computer, etc. The agent system 500 may also be implemented on a web server and used via a web browser running on a communication device such as a smartphone owned by a user.

[0294] The agent system 500 acts as, for example, a butler, secretary, teacher, partner, friend, lover, or instructor for the user 10. The agent system 500 not only interacts with the user 10, but also provides advice, guides the user to a destination, or makes recommendations based on the user's preferences. The agent system 500 also makes reservations, orders, or makes payments to service providers.

[0295] The emotion determination unit 232 determines the emotions of the user 10 and the agent itself, as in the first embodiment. The behavior determination unit 236 determines the behavior of the robot 100 while taking into account the emotions of the user 10 and the agent. That is, the agent system 500 understands the emotions of the user 10, reads the mood, and provides heartfelt support, assistance, advice, and services. The agent system 500 also listens to the user's concerns, comforts, encourages, and cheers them up. The agent system 500 also plays with the user 10, draws a picture diary, and reminds them of the past. The agent system 500 performs behavior that increases the user's sense of happiness. Here, the agent is an agent that runs on software.

[0296] The control unit 228B has a state recognition unit 230, an emotion determination unit 232, an action recognition unit 234, an action determination unit 236, a memory control unit 238, an action control unit 250, a related information collection unit 270, a command acquisition unit 272, an RPA (Robotic Process Automation) 274, a character setting unit 276, and a communication processing unit 280. The control unit 228B may have a specific processing unit 290 shown in FIG. 2B.

[0297] As in the first embodiment, the behavior determination unit 236 determines, as the agent's behavior, the content of the agent's utterance to converse with the user 10. The behavior control unit 250 outputs the content of the agent's utterance as at least one of voice and text to a speaker or a display as a control target 252B.

[0298] The character setting unit 276 sets the character of the agent when the agent system 500 converses with the user 10, based on the user's 10 specification. That is, the utterances output from the behavior determination unit 236 are output through the agent having the set character. For example, real-life celebrities or famous people, such as actors, entertainers, idols, and athletes, can be set as the character. It is also possible to set fictional characters that appear in manga, movies, or animation. If the agent character is known, the voice, speech, tone, and personality of the character are known. Therefore, the user 10 simply specifies their preferred character, and the prompt setting in the character setting unit 276 is automatically performed. The voice, speech, tone, and personality of the set character are reflected in the conversation with the user 10. That is, the behavior control unit 250 synthesizes a voice corresponding to the character set by the character setting unit 276 and outputs the agent's utterances using the synthesized voice. This allows the user 10 to feel as if they are conversing with their preferred character (e.g., a favorite actor) in person.

[0299] When the agent system 500 is installed in a device with a display, such as a smartphone, an icon, still image, or video of the agent having the character set by the character setting unit 276 may be displayed on the display. The image of the agent is generated using image synthesis technology, such as 3D rendering. In the agent system 500, a dialogue with the user 10 may be conducted while the image of the agent makes gestures according to the emotions of the user 10, the emotions of the agent, and the content of the agent's utterances. Note that the agent system 500 may output only audio without outputting an image when engaging in a dialogue with the user 10.

[0300] As in the first embodiment, the emotion determination unit 232 determines an emotion value indicating the emotion of the user 10 and an emotion value of the agent itself. In this embodiment, instead of the emotion value of the robot 100, an emotion value of the agent is determined. The emotion value of the agent itself is reflected in the emotion of the set character. When the agent system 500 converses with the user 10, not only the emotion of the user 10 but also the emotion of the agent is reflected in the conversation. In other words, the behavior control unit 250 outputs the content of the utterance in a manner corresponding to the emotion determined by the emotion determination unit 232.

[0301] The agent's emotions are also reflected when the agent system 500 takes an action toward the user 10. For example, if the user 10 requests the agent system 500 to take a photo, whether the agent system 500 will take the photo in response to the user's request is determined by the degree of "sadness" the agent is feeling. If the character is feeling positive emotions, it will have friendly conversations or actions toward the user 10, and if it is feeling negative emotions, it will have hostile conversations or actions toward the user 10.

[0302] The history data 222 stores the history of the dialogue between the user 10 and the agent system 500 as event data. The storage unit 220 may be realized by an external cloud storage. When the agent system 500 dialogues with the user 10 or takes an action toward the user 10, the agent system 500 determines the content of the dialogue or the content of the action by taking into account the content of the dialogue history stored in the history data 222. For example, the agent system 500 identifies the hobbies and preferences of the user 10 based on the dialogue history stored in the history data 222. The agent system 500 generates dialogue content that matches the hobbies and preferences of the user 10 or provides recommendations. The behavior determination unit 236 determines the content of the agent's utterance based on the dialogue history stored in the history data 222. The history data 222 stores personal information of the user 10, such as the name, address, telephone number, and credit card number, obtained through the dialogue with the user 10. Here, the agent may spontaneously ask the user 10 whether or not to register personal information, such as "Would you like to register your credit card number?", and the personal information may be stored in the history data 222 depending on the user 10's response.

[0303] As described in the first embodiment, the behavior determination unit 236 generates utterance content based on sentences generated using the sentence generation model. Specifically, the behavior determination unit 236 inputs the text or voice input by the user 10, the emotions of both the user 10 and the character determined by the emotion determination unit 232, and the conversation history stored in the history data 222 into the sentence generation model to generate the utterance content of the agent. At this time, the behavior determination unit 236 may further input the personality of the character set by the character setting unit 276 into the sentence generation model to generate the utterance content of the agent. In the agent system 500, the sentence generation model is not located on the front end side which is a touchpoint with the user 10, but is merely used as a tool of the agent system 500.

[0304] The command acquisition unit 272 uses the output of the speech understanding unit 212 to acquire commands for the agent from the voice or text uttered by the user 10 through dialogue with the user 10. The commands include the content of actions to be performed by the agent system 500, such as information search, store reservation, ticket arrangement, product / service purchase, payment, route guidance to a destination, and recommendation provision.

[0305] The RPA 274 performs an action according to the command acquired by the command acquisition unit 272. The RPA 274 performs actions related to the use of service providers, such as information search, store reservations, ticket arrangements, product and service purchases, and payment.

[0306] The RPA 274 reads and uses personal information of the user 10 from the history data 222, which is necessary to execute actions related to the use of the service provider. For example, when purchasing a product at the request of the user 10, the agent system 500 reads and uses personal information of the user 10, such as the name, address, telephone number, and credit card number, stored in the history data 222. Requiring the user 10 to input personal information during initial setup is unkind and unpleasant for the user. In the agent system 500 according to this embodiment, instead of requiring the user 10 to input personal information during initial setup, the agent system 500 stores personal information acquired through dialogue with the user 10 and reads and uses it as needed. This avoids causing discomfort to the user and improves user convenience.

[0307] The agent system 500 executes the dialogue process, for example, through the following steps 1 to 5.

[0308] (Step 1) The agent system 500 sets the character of the agent. Specifically, the character setting unit 276 sets the character of the agent when the agent system 500 interacts with the user 10, based on a specification from the user 10.

[0309] (Step 2) The agent system 500 acquires the state of the user 10, including the voice or text input by the user 10, the emotional value of the user 10, the emotional value of the agent, and the history data 222. Specifically, the same processing as in steps S100 to S103 above is performed to acquire the state of the user 10, including the voice or text input by the user 10, the emotional value of the user 10, the emotional value of the agent, and the history data 222.

[0310] (Step 3) The agent system 500 determines the content of the agent's utterance. Specifically, the behavior determination unit 236 inputs the text or voice input by the user 10, the emotions of both the user 10 and the character specified by the emotion determination unit 232, and the conversation history stored in the history data 222 into a sentence generation model to generate the content of the agent's utterance.

[0311] For example, a fixed sentence such as "How would you respond as an agent in this situation?" is added to the text or voice input by the user 10, the emotions of both the user 10 and the character identified by the emotion determination unit 232, and the text representing the conversation history stored in the history data 222, and this is input into the sentence generation model to obtain the content of the agent's speech.

[0312] As an example, if the text or voice input by the user 10 is "Please make a reservation at a delicious Chinese restaurant nearby for tonight at 7pm," the agent's speech will be "Understood," and "Here are some recommended restaurants: 1. AAAA. 2. BBBB. 3. CCCC. 4. DDDD."

[0313] Also, if the text or voice input by the user 10 is "Number 4, DDDD, would be good," the agent's speech content acquired will be "Understood. I will try to make a reservation. How many seats are there?"

[0314] (Step 4) The agent system 500 outputs the agent's utterance. Specifically, the behavior control unit 250 synthesizes a voice corresponding to the character set by the character setting unit 276, and outputs the agent's utterance in the synthesized voice.

[0315] (Step 5) The agent system 500 determines whether it is time to execute the agent's command. Specifically, the behavior decision unit 236 determines whether it is time to execute the agent's command based on the output of the sentence generation model. For example, if the output of the sentence generation model includes information indicating that the agent will execute a command, it determines that it is time to execute the agent's command and proceeds to step 6. On the other hand, if it is determined that it is not time to execute the agent's command, it returns to step 2 above.

[0316] (Step 6) The agent system 500 executes the agent's command. Specifically, the command acquisition unit 272 acquires the agent's command from the voice or text uttered by the user 10 through a dialogue with the user 10. The RPA 274 then performs an action according to the command acquired by the command acquisition unit 272. For example, if the command is "information search," the RPA 274 performs an information search on a search site using a search query obtained through a dialogue with the user 10 and an API (Application Programming Interface). The behavior determination unit 236 inputs the search results into a sentence generation model to generate the agent's utterance content. The behavior control unit 250 synthesizes a voice according to the character set by the character setting unit 276 and outputs the agent's utterance content using the synthesized voice.

[0317] Furthermore, if the command is "make a restaurant reservation," the reservation information obtained through the dialogue with the user 10, the restaurant information, and the API are used to call the restaurant via telephone software to make the reservation. At this time, the behavior determination unit 236 uses a sentence generation model with a dialogue function to acquire the agent's utterance content in response to the voice input from the other party. The behavior determination unit 236 then inputs the result of the restaurant reservation (whether the reservation was successful or not) into the sentence generation model to generate the agent's utterance content. The behavior control unit 250 synthesizes a voice corresponding to the character set by the character setting unit 276, and outputs the agent's utterance content using the synthesized voice.

[0318] Then, return to step 2 above.

[0319] In step 6, the results of the actions taken by the agent (for example, making a restaurant reservation) are also stored in the history data 222. The results of the actions taken by the agent stored in the history data 222 are used by the agent system 500 to understand the hobbies or preferences of the user 10. For example, if the same restaurant has been reserved multiple times, the agent system 500 may recognize that the user 10 likes that restaurant, and may use the reservation details, such as the time slot reserved, the course contents or price, as criteria for choosing a restaurant for the next reservation.

[0320] In this manner, the agent system 500 can execute the interaction and, if necessary, take action regarding the use of the service provider.

[0321] 11 and 12 are diagrams showing an example of the operation of the agent system 500. Fig. 11 illustrates an example in which the agent system 500 makes a restaurant reservation through a dialogue with the user 10. In Fig. 11, the left side shows the content of the agent's utterances, and the right side shows the content of the user's utterances. The agent system 500 can understand the preferences of the user 10 based on the dialogue history with the user 10, provide a recommended list of restaurants that match the preferences of the user 10, and make a reservation at the selected restaurant.

[0322] Meanwhile, FIG. 12 illustrates an example in which the agent system 500 accesses a mail-order site and purchases a product through a dialogue with the user 10. In FIG. 12, the left side shows the agent's speech, and the right side shows the user's speech. The agent system 500 can estimate the remaining amount of a beverage stocked by the user 10 based on the dialogue history with the user 10, and can suggest and execute the purchase of that beverage to the user 10. The agent system 500 can also understand the user's preferences based on the past dialogue history with the user 10 and recommend snacks that the user likes. In this way, the agent system 500 communicates with the user 10 as a butler-like agent and performs various actions, such as making restaurant reservations or paying for product purchases, thereby supporting the user 10's daily life.

[0323] Although the system according to the present invention has been described above mainly with reference to the functions of the agent system 500, the system according to the present invention is not necessarily implemented in an agent system. The system according to the present invention may be implemented as a general information processing system. The present invention may also be implemented, for example, as a software program running on a server or a personal computer, or as an application running on a smartphone, etc. The method according to the present invention may be provided to users in the form of SaaS (Software as a Service).

[0324] The other configurations and operations of the agent system 500 of the third embodiment are similar to those of the robot 100 of the first embodiment, and therefore will not be described again.

[0325] In addition, parts of the agent system 500 (e.g., the sensor module unit 210, the storage unit 220, the control unit 228B) may be provided outside (e.g., a server) of a communication terminal such as a smartphone carried by the user, and the communication terminal may communicate with the outside to function as each part of the agent system 500.

[0326] [Fourth Embodiment] In the fourth embodiment, the agent system described above is applied to smart glasses. Note that parts having the same configuration as those in the first to third embodiments are given the same reference numerals and descriptions thereof will be omitted.

[0327] 13 is a functional block diagram of an agent system 700 configured using some or all of the functions of the behavior control system. The agent system 700 has a sensor unit 200B, a sensor module unit 210B, a storage unit 220, a control unit 228B, and a control target 252B. The control unit 228B has a state recognition unit 230, an emotion determination unit 232, a behavior recognition unit 234, a behavior determination unit 236, a memory control unit 238, a behavior control unit 250, a related information collection unit 270, a command acquisition unit 272, an RPA 274, a character setting unit 276, and a communication processing unit 280. The control unit 228B may also have a specific processing unit 290 shown in FIG. 2B.

[0328] 14, the smart glasses 720 are glasses-type smart devices that are worn by the user 10 in the same manner as regular glasses. The smart glasses 720 are an example of an electronic device and a wearable terminal.

[0329] The smart glasses 720 include an agent system 700. The display included in the control object 252B displays various information to the user 10. The display is, for example, a liquid crystal display. The display is provided, for example, in the lens portion of the smart glasses 720, and the displayed content is visible to the user 10. The speaker included in the control object 252B outputs audio indicating various information to the user 10. The smart glasses 720 include a touch panel (not shown), which accepts input from the user 10.

[0330] The acceleration sensor 206, temperature sensor 207, and heart rate sensor 208 of the sensor unit 200B detect the state of the user 10. Note that these sensors are merely examples, and it goes without saying that other sensors may be installed to detect the state of the user 10.

[0331] The microphone 201 acquires the voice uttered by the user 10 or environmental sounds around the smart glasses 720. The 2D camera 203 is capable of capturing images around the smart glasses 720. The 2D camera 203 is, for example, a CCD camera.

[0332] The sensor module unit 210B includes a voice emotion recognition unit 211 and a speech understanding unit 212. The communication processing unit 280 of the control unit 228B controls communication between the smart glasses 720 and the outside.

[0333] FIG. 14 is a diagram showing an example of how the agent system 700 is used by the smart glasses 720. The smart glasses 720 provide various services to the user 10 using the agent system 700. For example, when the user 10 operates the smart glasses 720 (e.g., by voice input into a microphone or by tapping a touch panel with a finger), the smart glasses 720 begin using the agent system 700. Here, using the agent system 700 includes the smart glasses 720 having and using the agent system 700, and also includes an aspect in which a part of the agent system 700 (e.g., the sensor module unit 210B, the storage unit 220, and the control unit 228B) is provided outside the smart glasses 720 (e.g., a server), and the smart glasses 720 communicates with the outside to use the agent system 700.

[0334] When the user 10 operates the smart glasses 720, a touch point is created between the agent system 700 and the user 10. That is, the agent system 700 starts providing a service. As described in the third embodiment, in the agent system 700, the character setting unit 276 sets the character of the agent.

[0335] The emotion determination unit 232 determines an emotion value indicating the emotion of the user 10 and an emotion value of the agent itself. Here, the emotion value indicating the emotion of the user 10 is estimated from various sensors included in the sensor unit 200B mounted on the smart glasses 720. For example, if the heart rate of the user 10 detected by the heart rate sensor 208 is elevated, emotion values ​​such as "anxiety" and "fear" are estimated to be high.

[0336] Furthermore, if the temperature of the user measured by the temperature sensor 207 is higher than the average body temperature, for example, the emotional value of "pain" or "distress" is estimated to be large. Furthermore, if the acceleration sensor 206 detects that the user 10 is playing some kind of sport, the emotional value of "fun" or the like is estimated to be large.

[0337] Furthermore, for example, the emotion value of the user 10 may be estimated from the voice or speech content of the user 10 acquired by the microphone 201 mounted on the smart glasses 720. For example, if the user 10 is raising his / her voice, an emotion value such as "anger" is estimated to be large.

[0338] When the emotion value estimated by the emotion determination unit 232 is higher than a predetermined value, the agent system 700 causes the smart glasses 720 to acquire information about the surrounding situation. Specifically, for example, the agent system 700 causes the 2D camera 203 to capture images or videos showing the surrounding situation of the user 10 (e.g., people or objects in the vicinity). The agent system 700 also causes the microphone 201 to record ambient sounds. Other information about the surrounding situation includes information about the date, time, location, or weather. The information about the surrounding situation is stored in the history data 222 together with the emotion value. The history data 222 may be realized by external cloud storage. In this way, the surrounding situation acquired by the smart glasses 720 is stored in the history data 222 as a so-called life log, associated with the emotion value of the user 10 at that time.

[0339] In the agent system 700, information indicating the surrounding situation is stored in association with an emotional value in the history data 222. This allows the agent system 700 to grasp personal information such as the hobbies, preferences, or personality of the user 10. For example, if an image showing a scene of watching a baseball game is associated with emotional values ​​such as "joy" or "fun," the agent system 700 can grasp that the hobby of the user 10 is watching baseball games and that the favorite team or player is a favorite player from the information stored in the history data 222.

[0340] When the agent system 700 converses with the user 10 or takes an action toward the user 10, the agent system 700 determines the content of the dialogue or the content of the action by taking into account the content of the surrounding circumstances stored in the history data 222. It goes without saying that the content of the dialogue or the content of the action may be determined by taking into account the dialogue history stored in the history data 222 as described above in addition to the surrounding circumstances.

[0341] As described above, the behavior determination unit 236 generates utterance content based on sentences generated by the sentence generation model. Specifically, the behavior determination unit 236 inputs the text or voice input by the user 10, the emotions of both the user 10 and the agent determined by the emotion determination unit 232, the conversation history stored in the history data 222, the agent's personality, etc. into the sentence generation model to generate the agent's utterance content. Furthermore, the behavior determination unit 236 inputs the surrounding circumstances stored in the history data 222 into the sentence generation model to generate the agent's utterance content.

[0342] The generated speech content is output to the user 10 as a voice from, for example, a speaker mounted on the smart glasses 720. In this case, a synthetic voice corresponding to the character of the agent is used as the voice. The behavior control unit 250 generates the synthetic voice by reproducing the voice quality of the character of the agent, or generates a synthetic voice corresponding to the emotion of the character (for example, a voice with a stronger tone when the emotion is "anger"). Furthermore, instead of or together with the voice output, the speech content may be displayed on a display.

[0343] The RPA 274 executes an operation in response to a command (for example, a command of an agent acquired from a voice or text uttered by the user 10 through a dialogue with the user 10). The RPA 274 performs actions related to the use of service providers, such as information search, store reservations, ticket arrangements, product and service purchases, payment, route guidance, and translation.

[0344] As another example, the RPA 274 executes an operation of transmitting the contents of speech input by the user 10 (e.g., a child) through a dialogue with an agent to a destination (e.g., a parent). Examples of transmission means include message application software, chat application software, and email application software.

[0345] When an operation by the RPA 274 is executed, for example, a sound indicating that the execution of the operation has been completed is output from a speaker mounted on the smart glasses 720. For example, a sound such as "Your restaurant reservation has been completed" is output to the user 10. Also, for example, if the restaurant is fully booked, a sound such as "We were unable to make a reservation. What do you want to do?" is output to the user 10.

[0346] When the control unit 228B has the specific processing unit 290, the specific processing unit 290 controls the behavior of the agent so as to perform the same specific processing as in the third embodiment and output the result of the specific processing. At this time, as the agent's behavior, the content of the agent's utterance to converse with the user 10 is determined, and the content of the agent's utterance is output by at least one of voice and text to a speaker or a display as the control object 252B.

[0347] In addition, parts of the agent system 700 (e.g., the sensor module unit 210B, the storage unit 220, and the control unit 228B) may be provided outside the smart glasses 720 (e.g., a server), and the smart glasses 720 may communicate with the outside to function as each part of the agent system 700 described above.

[0348] As described above, the smart glasses 720 provide various services to the user 10 by using the agent system 700. Furthermore, since the smart glasses 720 are worn by the user 10, the agent system 700 can be used in various situations, such as at home, at work, or when out and about.

[0349] Furthermore, since the smart glasses 720 are worn by the user 10, they are suitable for collecting a so-called life log of the user 10. Specifically, the emotional value of the user 10 is estimated based on the detection results of various sensors mounted on the smart glasses 720 or the recording results of the 2D camera 203. Therefore, the emotional value of the user 10 can be collected in various situations, and the agent system 700 can provide services or speech content suited to the emotions of the user 10.

[0350] Furthermore, the smart glasses 720 obtain the surrounding conditions of the user 10 using the 2D camera 203, microphone 201, etc. These surrounding conditions are associated with the emotion values ​​of the user 10. This makes it possible to estimate what emotions the user 10 felt in what situations. As a result, the agent system 700 can improve the accuracy in grasping the hobbies and preferences of the user 10. By accurately grasping the hobbies and preferences of the user 10 in the agent system 700, the agent system 700 can provide services or speech content that are suited to the hobbies and preferences of the user 10.

[0351] The agent system 700 can also be applied to other wearable devices (electronic devices that can be worn on the body of the user 10, such as pendants, smart watches, earrings, bracelets, and hair bands). When the agent system 700 is applied to a smart pendant, the speaker serving as the control target 252B outputs audio indicating various information to the user 10. The speaker is, for example, a speaker capable of outputting directional audio. The speaker is set to have directionality toward the ears of the user 10. This prevents the audio from reaching anyone other than the user 10. The microphone 201 acquires audio emitted by the user 10 or environmental sounds around the smart pendant. The smart pendant is worn by hanging it from the neck of the user 10. Therefore, while the smart pendant is worn, the smart pendant is located relatively close to the mouth of the user 10. This makes it easy to acquire audio emitted by the user 10.

[0352] Fifth Embodiment In the fifth embodiment, the robot 100 is used as an agent for interacting with a user through an avatar. That is, the behavior control system is used in an agent system configured using a headset-type terminal. Note that parts having the same configuration as those in the first and second embodiments are designated by the same reference numerals and will not be described again.

[0353] Figure 15 is a functional block diagram of an agent system 800 configured using some or all of the functions of the behavior control system. The agent system 800 has a sensor unit 200B, a sensor module unit 210B, a storage unit 220, a control unit 228B, and a control target 252C. The agent system 800 is realized, for example, by a headset-type terminal 820 as shown in Figure 16. The control unit 228B may have the specific processing unit 290 shown in Figure 2B.

[0354] In addition, parts of the headset type terminal 820 (e.g., the sensor module unit 210B, the storage unit 220, the control unit 228B) may be provided outside the headset type terminal 820 (e.g., a server), and the headset type terminal 820 may function as each part of the above-mentioned agent system 800 by communicating with the outside.

[0355] In this embodiment, the control unit 228B has the function of determining the behavior of the avatar and generating the display of the avatar to be presented to the user through the headset terminal 820.

[0356] As in the first embodiment, the emotion determining unit 232 of the control unit 228B determines the emotion value of the agent based on the state of the headset type terminal 820, and uses this as the emotion value of the avatar.

[0357] As in the first embodiment described above, when performing a response process in which the avatar responds to the actions of the user 10, the action determination unit 236 of the control unit 228B determines the action of the avatar based on at least one of the user state, the state of the headset-type terminal 820, the user's emotions, and the avatar's emotions.

[0358] As in the first embodiment described above, when an agent functioning as an avatar performs autonomous processing to act autonomously, the behavior decision unit 236 of the control unit 228B determines, at a predetermined timing, one of multiple types of avatar behaviors, including no action, as the avatar's behavior, using at least one of the state of the user 10, the emotions of the user 10, the emotions of the avatar, and the state of the electronic device controlling the avatar (e.g., the headset-type terminal 820), and the behavior decision model 221.

[0359] Specifically, the behavior determination unit 236 inputs text representing at least one of the state of the user 10, the state of the electronic device, the emotion of the user 10, and the emotion of the avatar, and text asking about the avatar's behavior, into a sentence generation model, and determines the behavior of the avatar based on the output of the sentence generation model. The multiple types of avatar behaviors include (1) to (16), as in the first embodiment.

[0360] The behavior determining unit 236 sets "(12) Create minutes" as the avatar behavior, i.e., when it decides to create minutes, it creates minutes of the meeting and summarizes the minutes of the meeting using a sentence generation model. When performing such summarization, the behavior determining unit 236 may cause the avatar to output a voice such as "Create minutes" through a speaker or display text in the image display area of ​​the headset terminal 820. Note that the behavior determining unit 236 may create minutes without using avatar behavior.

[0361] Furthermore, with regard to "(12) Create minutes," the storage control unit 238 stores the created summary in the history data 222. Furthermore, the storage control unit 238 detects, as the user status, the statements of each meeting participant using the microphone function of the headset terminal 820, and stores the results in the history data 222. Here, the creation and summarization of minutes is performed autonomously at a predetermined trigger, for example, when the meeting ends, but is not limited to this and may be performed during the meeting. Furthermore, the summarization of minutes is not limited to using a sentence generation model, and other known methods may be used.

[0362] When the behavior determining unit 236 determines that the avatar behavior is "(13) Providing advice regarding user utterances," i.e., when it determines to output advice information regarding user utterances in a meeting, it determines and outputs the advice using a data generation model based on the summaries stored in the history data 222. When outputting such advice, it is desirable to control the avatar as if it were speaking, as described below. Here, the decision to output advice information occurs when a predetermined relationship with a stored summary of a past meeting is found, for example, when a similar utterance is made, and this decision is made autonomously. Furthermore, whether utterances are similar is determined, for example, using a known method of vectorizing (quantifying) the utterances and calculating the similarity between the vectors, but other methods may also be used. Note that meeting materials may be input into the data generation model in advance, and terms contained in the materials may be excluded from the detection of similar utterances because they are expected to appear frequently.

[0363] The advice information also includes advice based on the results of a comparison with past meetings, given spontaneously to participants wearing headset terminals 820 and participating in the meeting, such as "That was something that someone already presented on such and such date," or "That content is better in this respect than something that someone proposed." Furthermore, "(13) Provide advice regarding user utterances" includes user utterances in a meeting other than the meeting for which a summary was created in "(12) Create minutes" described above. In other words, it is determined whether similar utterances have been made in past meetings, and advice information is output.

[0364] The behavior determining unit 236 sets the avatar behavior as "(14) Support the progress of the meeting." That is, when the meeting reaches a predetermined state, the behavior determining unit 236 spontaneously supports the progress of the meeting. Here, supporting the progress of the meeting includes actions such as summarizing the meeting, organizing frequently occurring words, summarizing the meeting so far, and providing other topics to help the meeting participants cool down. By performing such actions, the progress of the meeting is supported. In outputting such support for the progress of the meeting, it is desirable to control the avatar as if it were speaking, as described below. Here, when the meeting reaches a predetermined state, it includes a state in which no speech is accepted for a predetermined period of time. That is, when no speech is made by multiple users for a predetermined period of time, for example, five minutes, it is determined that the meeting has reached a deadlock, no good ideas have been produced, and silence has fallen. Therefore, the meeting is summarized by organizing frequently occurring words, etc. Furthermore, when the meeting reaches a predetermined state, it includes a state in which a term included in speech is accepted a predetermined number of times. In other words, if the same term is received a predetermined number of times, it is determined that the meeting is going around in circles and no new ideas are emerging. Therefore, the meeting is summarized by sorting out frequently occurring words. Note that the meeting materials can be input into the sentence generation model in advance, and terms contained in the materials can be excluded from the counting of frequency because they are expected to appear frequently.

[0365] When the action determination unit 236 determines "(15) Take minutes of the meeting" as the avatar action corresponding to the action of the user 10, it acquires the content of the user 10's remarks through voice recognition, identifies the speaker through voiceprint authentication, acquires the speaker's emotion based on the determination result of the emotion determination unit 232, and creates minutes data representing a combination of the user 10's remarks, the speaker's identification result, and the speaker's emotion. The action determination unit 236 further generates a text summary representing the minutes data using a sentence generation model with an interactive function. The action determination unit 236 further generates a list of tasks the user needs to do (a to-do list) to be included in the summary using a sentence generation model with an interactive function. This to-do list includes at least a person in charge (responsible person), action content, and deadline for each task the user needs to do. The action determination unit 236 then transmits the minutes data, summary, and to-do list to the participants of the meeting. Furthermore, based on the person in charge and the deadline included in the list, the behavior decision unit 236 sends a message to the person in charge to confirm what needs to be done a predetermined number of days before the deadline.

[0366] Specifically, when the user 10 says "take minutes," the action decision unit 236 decides to take minutes of the meeting as an action corresponding to the action of the user 10. This makes it possible to obtain minutes data including information on who made comments. When the user 10 says "send the outline to the relevant parties" at the end of the meeting, the action decision unit 236 summarizes the minutes of the meeting, creates a to-do list, and sends it to the relevant parties.

[0367] When summarizing meeting minutes, the text of the created minutes data and the fixed sentence "Summarize this content" are input to the generative AI, which is a sentence generation model, to obtain a summary of the meeting minutes. When creating a to-do list, the text of the summary of the meeting minutes and the fixed sentence "Create a to-do list" are input to the generative AI, which is a sentence generation model, to obtain the to-do list. This allows the system to understand the content of the meeting, create a to-do list, and assign people responsible for the to-do tasks. The to-do list is divided by voiceprint authentication to identify who made a comment. Based on the determination result of the emotion determination unit 232, an evaluation of whether a person is reluctantly motivated or enthusiastic can also be performed. It is possible to distinguish who will do what and by when. If the person in charge and the deadline have not been decided, the avatar's behavior may be to make a statement inquiring about this to the user 10. This allows the avatar to say, "The person in charge of AAA has not been decided. Who will do it?"

[0368] It is also possible to extract features related to the date and time from the summary of the minutes of the meeting and use them to register on a calendar or create a to-do list.

[0369] The behavior decision unit 236 may further decide that the avatar's behavior will be to make a statement about the conclusion or summary of the meeting at the end of the meeting. The behavior decision unit 236 also sends the minutes data, a summary, and a to-do list to the participants of the meeting. The behavior decision unit 236 also sends to-do reminders to the person in charge.

[0370] As an example, when the action decision unit 236 decides to take minutes of a meeting as an action corresponding to the action of the user 10, it executes the processing of steps 1 to 9 described above, as in the first embodiment.

[0371] Furthermore, the behavior control unit 250 displays the avatar in the image display area of ​​the headset type terminal 820 as the control target 252C according to the determined behavior of the avatar. Furthermore, if the determined behavior of the avatar includes the content of the avatar's utterance, the content of the avatar's utterance is output as audio from the speaker as the control target 252C.

[0372] In particular, when the behavior determination unit 236 determines that the avatar's behavior is to generate and play music that takes into account the events of the previous day, the behavior control unit 250 controls the avatar to play music, for example, by playing or singing. That is, when the behavior determination unit 236 determines that the avatar's behavior is to generate and play music that takes into account the events of the previous day, the behavior determination unit 236, as in the first embodiment, selects the event data for the day from the history data 222 at the end of the day and reviews all of the conversation content and event data for the day. The behavior determination unit 236 adds a fixed sentence, such as "Summarize this content," to the text representing the reviewed content and inputs it into the sentence generation model to obtain a summary of the history of the previous day. The summary reflects the user 10's behavior and emotions of the previous day, as well as the avatar's behavior and emotions. The summary is stored, for example, in the storage unit 220. The behavior determination unit 236 obtains the summary of the previous day the next morning, inputs the obtained summary into the music generation engine, and obtains music that summarizes the history of the previous day. As a result, for example, if the avatar's emotion is "happy," music with a warm atmosphere is acquired, and if the avatar's emotion is "anger," music with a violent atmosphere is acquired.

[0373] The behavior control unit 250 generates an image of the avatar playing or singing the music acquired by the behavior determination unit 236 on a stage in the virtual space. As a result, the image of the avatar playing or singing is displayed in the image display area of ​​the headset terminal 820. This allows the music played or sung by the avatar to be spontaneously changed based only on the user's emotions and the avatar's emotions, even if the user 10 and the avatar are not having a conversation, making the avatar seem almost alive.

[0374] In this case, the behavior control unit 250 may change the facial expression or movement of the avatar depending on the content of the summary. For example, if the content of the summary is fun, the avatar's facial expression may be changed to a happy expression, or the avatar's movement may be changed to a happy dance. The behavior control unit 250 may also transform the avatar depending on the content of the summary. For example, the behavior control unit 250 may transform the avatar to resemble a character in the summary, or to resemble an animal, object, or the like that appears in the summary.

[0375] The behavior control unit 250 may also generate an image in which an avatar holds a tablet device depicted in the virtual space and performs an action of sending music from the tablet device to the user's terminal device. In this case, by actually sending music from the tablet device to the mobile terminal device of the user 10, it is possible to make the avatar appear to be performing an action such as sending music from the tablet device to the mobile terminal device of the user 10 by email or sending music to a messaging app. Furthermore, in this case, the user 10 can play and listen to music on their own mobile terminal device.

[0376] Furthermore, when the behavior determining unit 236 determines that the avatar's behavior is to output advice information in response to a user's statement during a meeting, it is preferable that the behavior determining unit 236 controls the behavior control unit 250 to output the advice information in accordance with the content of the statement. At this time, the behavior control unit 250 outputs the audio of the determined advice from a speaker included in or connected to the headset type terminal 820 in accordance with the avatar's mouth movements, as if the avatar were speaking, or displays and outputs text in the image display area of ​​the headset type terminal 820.

[0377] Furthermore, it is desirable that the output of advice using the avatar by the action decision unit 236 be initiated not in response to an inquiry from the user but autonomously by the action decision unit 236. Specifically, it is desirable that the action decision unit 236 itself output advice information when a similar statement is made.

[0378] Furthermore, when the behavior determining unit 236 determines that advice information should be output in response to a user's comment during a meeting as an avatar behavior, the behavior determining unit 236 may determine the advice to be output based further on the state of the other user's headset type terminal 820 or the emotion of the other avatar displayed on the other user's headset type terminal 820. For example, if the emotion of the other avatar is excited, advice to encourage the other avatar to remain calm during the discussion may be output.

[0379] Furthermore, as in the first embodiment, the behavior determination unit 236 may be configured to detect the user's state voluntarily and periodically. The behavior of the avatar includes outputting a summary of events of the previous day by speech or gesture. When the behavior determination unit 236 determines that the behavior of the avatar is to output a summary of events of the previous day by speech or gesture, the behavior determination unit 236 acquires a summary of event data of the previous day stored in the history data when it detects a predetermined conversation or gesture by the user. The behavior control unit 250 controls the avatar to output the acquired summary by speech or gesture.

[0380] Specifically, the behavior determination unit 236 adds a fixed sentence instructing the user to summarize the events of the previous day to text representing the event data of the previous day, inputs the result into a sentence generation model, which is an example of the behavior determination model 221, and generates a summary based on the output of the sentence generation model. For example, at the end of the day, the user selects the event data of the day from the history data 222 and reviews all of the conversation content and event data of the day. The behavior determination unit 236 adds a fixed sentence, for example, "Summarize this content," to the text representing the reviewed content, inputs the result into the sentence generation model, and generates a summary of the history of the previous day. The summary reflects the user 10's actions and emotions of the previous day, as well as the actions and emotions of the avatar. The summary is stored, for example, in the storage unit 220.

[0381] The predetermined conversation or gesture of the user is a conversation in which the user tries to remember what happened the previous day, or a gesture in which the user thinks about something. For example, when the system is started up the next morning or the user wakes up, the behavior decision unit 236 detects a conversation in which the user says, "What did I do yesterday?" or a gesture in which the user thinks about something, and then acquires a summary of the previous day from the storage unit 220. The behavior control unit 250 controls the avatar to output the acquired summary spontaneously through speech or gesture.

[0382] The behavior control unit 250 controls the avatar so that the avatar speaks or gestures in the virtual space to represent the summary acquired by the behavior determination unit 236. As a result, the headset-type terminal 820 displays the avatar expressing the summary through speech or gestures in the image display area. The user 10 can grasp the outline of the events of the previous day from the avatar's speech or gestures.

[0383] In this case, the behavior control unit 250 may also change the facial expression or movement of the avatar depending on the content of the summary. For example, if the content of the summary is fun, the avatar's facial expression may be changed to a happy expression, or the avatar's movement may be changed to make it look like it is dancing a happy dance. The behavior control unit 250 may also transform the avatar depending on the content of the summary. For example, the behavior control unit 250 may transform the avatar to resemble a character in the summary, or to resemble an animal, object, or the like that appears in the summary.

[0384] Furthermore, as in the first embodiment, the behavior determination unit 236 may be configured to voluntarily and periodically detect the user's state. The behavior of the avatar includes reflecting the events of the previous day in the emotions of the next day. When the behavior determination unit 236 determines that the events of the previous day should be reflected in the emotions of the next day as the behavior of the avatar, it obtains a summary of the event data of the previous day stored in the history data and determines the emotions to be felt on the next day based on the summary. The behavior control unit 250 controls the avatar so that the emotions to be felt on the next day are expressed.

[0385] Specifically, the behavior determination unit 236 adds a fixed sentence instructing the user to summarize the events of the previous day to text representing the event data of the previous day, inputs the result into a sentence generation model, which is an example of the behavior determination model 221, and generates a summary based on the output of the sentence generation model. For example, at the end of the day, the user selects the event data of the day from the history data 222 and reviews all of the conversation content and event data of the day. The behavior determination unit 236 adds a fixed sentence, for example, "Summarize this content," to the text representing the reviewed content, inputs the result into the sentence generation model, and generates a summary of the history of the previous day. The summary reflects the user 10's actions and emotions of the previous day, as well as the actions and emotions of the avatar. The summary is stored, for example, in the storage unit 220.

[0386] The behavior determination unit 236 then adds a fixed sentence asking about the emotion that should be felt the next day to the text representing the generated summary, inputs the result into the sentence generation model, and determines the emotion that should be felt the next day based on the output of the sentence generation model. For example, a fixed sentence such as "What emotion should I feel tomorrow?" is added to the text representing a summary of the events of the previous day, inputs the result into the sentence generation model, and determines the emotion of the avatar based on the summary of the previous day. In other words, the emotion of the avatar inherits the emotion of the previous day. The avatar can voluntarily inherit the emotion of the previous day and start a new day.

[0387] The behavior control unit 250 controls the avatar so that the avatar expresses the emotion determined by the behavior determination unit 236 through speech or gestures in the virtual space. As a result, the headset terminal 820 displays the avatar expressing its emotion through speech or gestures in the image display area. For example, if the avatar's emotion on the previous day was happy, the avatar's emotion on the next day will also be happy.

[0388] In this case, the behavior control unit 250 may also change the facial expression or movement of the avatar depending on the emotion of the avatar. For example, if the emotion of the avatar is joyful, the behavior control unit 250 may change the facial expression of the avatar to a joyful expression or change the movement of the avatar to a joyful dance.

[0389] Furthermore, when the action determining unit 236 determines that meeting progress support is to be output to a user in a meeting as the avatar's action, it is preferable that the action determining unit 236 controls the action control unit 250 to output the meeting progress support. At this time, the action control unit 250 converts the determined progress support into audio and outputs the audio from a speaker included in or connected to the headset type terminal 820 in accordance with the avatar's mouth movements, as if the avatar were speaking, or displays text around the avatar's mouth in the image display area of ​​the headset type terminal 820 and outputs the text.

[0390] By configuring the meeting in this way, even if the meeting has reached a deadlock, it is possible to help the meeting proceed by summarizing the meeting.

[0391] It is desirable that the support for the progress of the meeting using the avatar by the action decision unit 236 be started not in response to an inquiry from the user but autonomously by the action decision unit 236. Specifically, it is desirable that the action decision unit 236 itself support the progress of the meeting when a predetermined state is reached.

[0392] Furthermore, when the behavior determining unit 236 determines that the avatar's behavior is to output meeting progress support to a user in a meeting, the behavior determining unit 236 may operate the avatar to determine the content of the progress support based further on the state of the other user's headset type device 820 or the emotion of the other avatar displayed on the other user's headset type device 820. For example, if the emotion of the other avatar is excited, the behavior determining unit 236 may output advice to encourage the user to remain calm and discuss the matter.

[0393] Furthermore, when the behavior determining unit 236 determines that the avatar should take minutes, it may operate the avatar to output a summary of the text representing the minutes data with a facial expression that corresponds to the emotion of the speaker. For example, when the avatar is made to speak a summary of the text representing the minutes data, the avatar is made to operate with a facial expression that corresponds to the emotion of the speaker corresponding to the content of the speech.

[0394] Furthermore, if the behavior determining unit 236 determines that the avatar's behavior is to take minutes, the behavior determining unit 236 may cause the avatar to behave in accordance with the user's to-do list when outputting the list. For example, when the user causes the avatar to utter the list of to-do items, the avatar may be caused to behave in a manner corresponding to the user's to-do items. As an example, when the user's to-do item is to prepare documents, the avatar may be caused to behave as if operating a personal computer.

[0395] When the behavior decision unit 236 decides that the avatar behavior is "(15) The avatar takes minutes of the meeting," it performs the same processing as when it decides to take minutes of the meeting as the avatar behavior corresponding to the behavior of the user 10, as described in the response processing above.

[0396] The behavior determination unit 236 may also obtain the history data 222 of the specified user 10 from the storage unit 220 and output the contents of the obtained history data 222 to the first text file. The behavior determination unit 236 may also obtain the history data 222 of the user 10 for the previous day.

[0397] The behavior determination unit 236 adds to the first text file an instruction to cause the sentence generation model to summarize the history of the user 10 written in the first text file, such as "Summarize the contents of this history data!". The sentence representing the instruction is stored in advance in the storage unit 220 as a fixed sentence, for example, and the behavior determination unit 236 adds the fixed sentence representing the instruction to the first text file.

[0398] When the behavior decision unit 236 inputs the first text file to which fixed sentences expressing instructions have been added into the sentence generation model, a summary of the history of the user 10 is obtained as an answer from the sentence generation model from the history data 222 of the user 10 written in the first text file.

[0399] Furthermore, the behavior determining unit 236 inputs the summary of the user's history obtained from the sentence generation model to an image generation model that generates an image associated with the input sentence.

[0400] As a result, the behavior determining unit 236 acquires a summary image that visualizes the content of the summary of the user's history from the image generation model.

[0401] Furthermore, the behavior determination unit 236 outputs, as a second text file, the behavior of the user 10 stored in the history data 222, the emotions of the user 10 determined from the behavior of the user 10, the details of the emotions of the avatar determined by the emotion determination unit 232, and a summary of the history of the user 10 on the previous day (if any). In this case, the behavior determination unit 236 adds a fixed sentence expressed in predetermined wording for asking about the action that the avatar should take, such as "What action should the avatar take at this time?", to the second text file in which the behavior of the user 10, the emotions of the user 10, and the emotions of the avatar, and a summary of the history of the user 10 on the previous day (if any) are expressed in text.

[0402] The action determination unit 236 inputs the second text file to which the fixed sentence has been added, the summary image, and the summary sentence into the sentence generation model as necessary.

[0403] As a result, the action that the avatar should take, determined based on the actions of the user 10, the emotions of the user 10, the emotions of the avatar, and also information obtained from the summary image and summary text, is obtained as an answer from the sentence generation model.

[0404] The action determination unit 236 generates the action content of the avatar according to the content of the answer obtained from the sentence generation model, and determines the action of the avatar.

[0405] Furthermore, the behavior control unit 250 operates the avatar according to the determined behavior of the avatar, and displays the avatar in the image display area of ​​the headset-type terminal 820 as the control target 252C. Furthermore, if the determined behavior of the avatar includes the avatar's speech content, the behavior control unit 250 outputs the avatar's speech content as audio from the speaker as the control target 252C.

[0406] In particular, when the behavior determining unit 236 determines that the avatar's behavior is to speak about the user 10's behavior history, it determines that the avatar's behavior is to speak about the user 10's stress level.

[0407] For example, the behavior determining unit 236 determines that the avatar should provide a topic related to the stress level of the user 10, such as "You seemed unusually irritable yesterday." The content of the avatar's speech determined by the behavior determining unit 236 may be a topic related to the cause of stress experienced by the user 10. In this case, the behavior control unit 250 causes a speaker included in the control target 252 to output a sound representing the content of the avatar's speech thus determined.

[0408] Furthermore, the behavior determining unit 236 may select an image that will reduce the stress of the user 10 according to the stress level of the user 10, and determine to have the behavior control unit 250 display the image in the image display area of ​​the headset type terminal 820. In this case, the behavior determining unit 236 may determine to change the appearance of the avatar in accordance with the content of the selected image.

[0409] For example, if the selected video is a video of a beach, the action determination unit 236 changes the avatar to a person in a swimsuit. If the selected video is a video of a soccer match, which is the hobby of the user 10, the action determination unit 236 changes the avatar explaining the content of the match to the appearance of a player that the user 10 admires. The avatar does not necessarily have to have a human appearance, and may be an animal or an object.

[0410] The behavior decision unit 236 may also determine to have the avatar utter advice to help the user 10 avoid stress. For example, the behavior decision unit 236 may determine, as the avatar's behavior, an action such as encouraging the user 10 to take up sports or to visit an art museum. If the user 10 expresses a desire to go to an art museum, the avatar may notify the user of the contents of an exhibition currently being held at the art museum. If the user 10 specifies a art museum that the user 10 wishes to visit, the behavior decision unit 236 may determine to have the avatar utter information such as the art museum's opening hours and regular holidays, and display a route from the user 10's location to the art museum in the image display area of ​​the headset terminal 820.

[0411] Furthermore, if the behavior decision unit 236 can identify a person (referred to as a "target person") who is causing stress from the behavioral history of the user 10, it may decide to display an avatar of the target person in the image display area of ​​the headset terminal 820, and move the avatar according to a story in which the target person's avatar and the user 10's avatar fight, with the user 10's avatar ultimately winning, or to have the target person's avatar apologize.

[0412] Furthermore, the behavior determination unit 236 may identify refrigerated and frozen foods purchased by the user and refrigerated and frozen foods consumed by the user from the behavior history of the user 10, and determine, as the behavior of the avatar, an action to make the avatar that has become a refrigerator talk about the foods in the refrigerator. In this case, the behavior determination unit 236 may cause the avatar that has become a refrigerator to open the door of the refrigerator that it is pretending to be, and display the contents of the refrigerator to the user 10. This allows the user 10 to check whether they have forgotten to buy anything when shopping.

[0413] In the fifth embodiment, when the control unit 228B has the specific processing unit 290, as in the first embodiment, the specific processing unit 290 performs a process (specific processing) of acquiring and outputting a response to the content presented in a meeting (e.g., a one-on-one meeting) that is held periodically, in which one of the users participates as a participant. The behavior of the avatar includes acquiring and outputting a response to the content presented in the meeting. The specific processing unit 290 controls an electronic device (e.g., a headset-type terminal 820) so that the result of the specific processing is output as the behavior of the avatar.

[0414] In the identification process for a meeting, as in the first embodiment, a condition for the content to be presented by a subordinate at the meeting is set as a predetermined trigger condition. When the user input satisfies this condition, the identification processing unit 290 uses the output of a sentence generation model when the information obtained from the user input is used as an input sentence, and acquires and outputs a response regarding the content to be presented at the meeting as a result of the identification process.

[0415] In the fifth embodiment, the identification processing unit 290 also includes an input unit 292, a processing unit 294, and an output unit 296 (see FIG. 2C for all of these). The input unit 292, processing unit 294, and output unit 296 function and operate in the same manner as in the first embodiment. In particular, the processing unit 294 of the identification processing unit 290 performs identification processing using a sentence generation model, for example, processing similar to the example of the operation flow shown in FIG. 4C.

[0416] In the fifth embodiment, the output unit 296 of the specific processing unit 290 controls the behavior of the avatar to output the results of the specific processing. Specifically, the output unit 296 causes the avatar to display the summary and appeal points acquired by the processing unit 294 of the specific processing unit 290, the avatar speaks the summary and appeal points, or sends a message indicating the summary and appeal points to the user via a message application on the user's mobile device.

[0417] In the fifth embodiment, the behavior of the avatar can be changed depending on the results of the specific processing. For example, when a subordinate meets with a superior in a one-on-one meeting, the subordinate's actual behavior can be displayed in response to the results of the specific processing. As an example, when a subordinate explains his or her selling points to the superior, the avatar displays the intonation of the speech, facial expressions, and gestures while speaking. More specifically, when explaining selling points that will earn the superior a high evaluation, the intonation of the speech can be increased, and the avatar's facial expression can be made proud. Then, when the user 10 actually conducts a one-on-one meeting, the user 10 can conduct an effective one-on-one meeting by referring to these behaviors displayed by the avatar.

[0418] In the fifth embodiment, an avatar of a superior may be displayed in addition to an avatar of a subordinate who is the user 10. By recreating the situation where the subordinate and superior are face to face using their respective avatars, it is possible to rehearse a one-on-one meeting with a sense of realism.

[0419] In the fifth embodiment, any user 10 who participates in a meeting can use the system without any restrictions. For example, the user 10 may participate in a meeting between "colleagues" who are on an equal footing, not just a subordinate in a superior-subordinate relationship. Furthermore, the user 10 is not limited to a person who belongs to a specific organization, and may be any user 10 who participates in a meeting.

[0420] In the fifth embodiment, the user 10 participating in the meeting can efficiently prepare for and conduct the meeting. Furthermore, the user 10 can reduce the time required for preparing for the meeting and the time required for conducting the meeting.

[0421] Here, the avatar may be, for example, a 3D avatar, selected by the user from pre-prepared avatars, an avatar representing the user, or an avatar created by the user of their choice. When generating the avatar, image generation AI may be used to generate avatars in a variety of styles, such as photorealistic, cartoon, moe, and oil painting.

[0422] In the above embodiment, a case where a headset-type terminal 820 is used is described as an example, but this is not limited to this, and a glasses-type terminal having an image display area for displaying an avatar may also be used.

[0423] Furthermore, in the above embodiment, an example has been described in which a sentence generation model capable of generating sentences according to input text is used, but this is not limited thereto, and a data generation model other than a sentence generation model may also be used. For example, a prompt including an instruction is input to the data generation model, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is input. The data generation model performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0424] In the above embodiment, the robot 100 recognizes the user 10 using a facial image of the user 10. However, the disclosed technology is not limited to this. For example, the robot 100 may recognize the user 10 using a voice uttered by the user 10, the email address of the user 10, the SNS ID of the user 10, or an ID card with a built-in wireless IC tag that the user 10 has.

[0425] The robot 100 is an example of an electronic device equipped with a behavior control system. The application of the behavior control system is not limited to the robot 100, and the behavior control system can be applied to various electronic devices. Furthermore, the functions of the server 300 may be implemented by one or more computers. At least some of the functions of the server 300 may be implemented by a virtual machine. Furthermore, at least some of the functions of the server 300 may be implemented in the cloud.

[0426] 17 schematically shows an example of the hardware configuration of a computer 1200 that functions as the smartphone 50, the robot 100, the server 300, and the agent systems 500, 700, and 800. A program installed on the computer 1200 can cause the computer 1200 to function as one or more "parts" of an apparatus according to the present embodiment, or can cause the computer 1200 to perform operations associated with the apparatus according to the present embodiment or one or more "parts," and / or can cause the computer 1200 to perform a process according to the present embodiment or steps of the process. Such a program can be executed by the CPU 1212 to cause the computer 1200 to perform specific operations associated with some or all of the blocks in the flowcharts and block diagrams described herein.

[0427] The computer 1200 according to this embodiment includes a CPU 1212, a RAM 1214, and a graphics controller 1216, which are interconnected by a host controller 1210. The computer 1200 also includes input / output units such as a communications interface 1222, a storage device 1224, a DVD drive 1226, and an IC card drive, which are connected to the host controller 1210 via an input / output controller 1220. The DVD drive 1226 may be a DVD-ROM drive, a DVD-RAM drive, or the like. The storage device 1224 may be a hard disk drive, a solid-state drive, or the like. The computer 1200 also includes a ROM 1230 and a legacy input / output unit such as a keyboard, which are connected to the input / output controller 1220 via an input / output chip 1240.

[0428] The CPU 1212 operates according to programs stored in the ROM 1230 and the RAM 1214, thereby controlling each unit. The graphics controller 1216 acquires image data generated by the CPU 1212 into a frame buffer or the like provided in the RAM 1214 or into the graphics controller 1216 itself, and causes the image data to be displayed on the display device 1218.

[0429] The communication interface 1222 communicates with other electronic devices via a network. The storage device 1224 stores programs and data used by the CPU 1212 in the computer 1200. The DVD drive 1226 reads programs or data from a DVD-ROM 1227 or the like and provides them to the storage device 1224. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0430] The ROM 1230 stores therein a boot program or the like that is executed by the computer 1200 upon activation, and / or programs that depend on the hardware of the computer 1200. The input / output chip 1240 may also connect various input / output units to the input / output controller 1220 via a USB port, a parallel port, a serial port, a keyboard port, a mouse port, etc.

[0431] The programs are provided by a computer-readable storage medium such as a DVD-ROM 1227 or an IC card. The programs are read from the computer-readable storage medium, installed in the storage device 1224, RAM 1214, or ROM 1230, which are also examples of computer-readable storage media, and executed by the CPU 1212. Information processing described in these programs is read by the computer 1200, and brings about cooperation between the programs and the various types of hardware resources described above. An apparatus or a method may be configured by implementing operations or processing of information in accordance with the use of the computer 1200.

[0432] For example, when communication is performed between computer 1200 and an external device, CPU 1212 may execute a communication program loaded into RAM 1214 and instruct communication interface 1222 to perform communication processing based on the processing described in the communication program. Under the control of CPU 1212, communication interface 1222 reads transmission data stored in a transmission buffer area provided in RAM 1214, storage device 1224, DVD-ROM 1227, or a recording medium such as an IC card, and transmits the read transmission data to a network, or writes received data received from the network to a reception buffer area or the like provided on the recording medium.

[0433] Furthermore, the CPU 1212 may cause all or a necessary portion of a file or database stored in an external recording medium such as the storage device 1224, the DVD drive 1226 (DVD-ROM 1227), an IC card, etc. to be read into the RAM 1214, and may perform various types of processing on the data on the RAM 1214. The CPU 1212 may then write back the processed data to the external recording medium.

[0434] Various types of information, such as various types of programs, data, tables, and databases, may be stored on the recording medium and may undergo information processing. The CPU 1212 may perform various types of processing on data read from the RAM 1214, including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information search / replacement, etc., as described throughout this disclosure and specified by the instruction sequences of the programs, and write the results back to the RAM 1214. The CPU 1212 may also search for information in a file, database, etc. on the recording medium. For example, if multiple entries, each having an attribute value of a first attribute associated with an attribute value of a second attribute, are stored on the recording medium, the CPU 1212 may search for an entry whose attribute value of the first attribute matches a specified condition from among the multiple entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0435] The above-described programs or software modules may be stored in a computer-readable storage medium on or near the computer 1200. A recording medium such as a hard disk or RAM provided in a server system connected to a dedicated communication network or the Internet can also be used as a computer-readable storage medium, thereby providing the programs to the computer 1200 via the network.

[0436] The blocks in the flowcharts and block diagrams in the present embodiments may represent stages of a process in which an operation is performed or "parts" of a device responsible for performing the operation. Particular stages and "parts" may be implemented by dedicated circuitry, programmable circuitry provided with computer-readable instructions stored on a computer-readable storage medium, and / or a processor provided with computer-readable instructions stored on a computer-readable storage medium. The dedicated circuitry may include digital and / or analog hardware circuits, and may include integrated circuits (ICs) and / or discrete circuits. The programmable circuitry may include reconfigurable hardware circuits, such as field programmable gate arrays (FPGAs) and programmable logic arrays (PLAs), including AND, OR, XOR, NAND, NOR, and other logical operations, flip-flops, registers, and memory elements.

[0437] A computer-readable storage medium may include any tangible device capable of storing instructions that are executed by an appropriate device, such that a computer-readable storage medium having instructions stored thereon comprises an article of manufacture, including instructions that can be executed to create means for performing the operations specified in the flowcharts or block diagrams. Examples of computer-readable storage media may include electronic, magnetic, optical, electromagnetic, and semiconductor storage media. More specific examples of computer-readable storage media may include floppy disks, diskettes, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), electrically erasable programmable read-only memories (EEPROMs), static random access memories (SRAMs), compact disc read-only memories (CD-ROMs), digital versatile discs (DVDs), Blu-ray discs, memory sticks, integrated circuit cards, and the like.

[0438] The computer readable instructions may include either assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, JAVA, C++, etc., and conventional procedural programming languages ​​such as the "C" programming language or similar programming languages.

[0439] Computer-readable instructions may be provided locally or over a local area network (LAN), a wide area network (WAN) such as the Internet, to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, or programmable circuitry, such that the processor or programmable circuitry executes the computer-readable instructions to generate means for performing the operations specified in the flowcharts or block diagrams. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc.

[0440] Although the present invention has been described above using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It will be apparent to those skilled in the art that various modifications and improvements can be made to the above embodiments. It is clear from the claims that such modifications and improvements can also be included within the technical scope of the present invention.

[0441] It should be noted that the execution order of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not specifically stated as "before," "prior to," etc., and that the processes can be performed in any order unless the output of a previous process is used in a later process. Even if the operational flow in the claims, specifications, and drawings is described using "first," "next," etc. for convenience, this does not mean that the processes must be performed in this order.

[0442] 5 System, 10, 11, 12 User, 20 Communication network, 100, 101, 102 Robot, 100N Plush toy 100, 200 Sensor unit, 201 Microphone, 202 Depth sensor, 203 Camera, 204 Distance sensor, 210 Sensor module unit, 211 Voice emotion recognition unit, 212 Speech understanding unit, 213 Facial expression recognition unit, 214 Face recognition unit, 220 Storage unit, 221 Behavioral decision model, 222 History data, 230 State recognition unit, 232 Emotion determination unit, 234 Behavior recognition unit, 236 Behavior decision unit, 238 Memory control unit, 250 Behavior control unit, 252 Control target, 270 Related information collection unit, 280 Communication processing unit, 290 Specific processing unit, 300 Server, 500, 700, 800 Agent system, 820 Headset type terminal, 1200 computer, 1210 host controller, 1212 CPU, 1214 RAM, 1216 graphic controller, 1218 display device, 1220 input / output controller, 1222 communication interface, 1224 storage device, 1226 DVD drive, 1227 DVD-ROM, 1230 ROM, 1240 input / output chip

Claims

1. A behavior control system comprising: a state recognition unit that recognizes a user state including a user's behavior, and a state of an electronic device; an emotion determination unit that determines an emotion of the user or an emotion of an avatar representing an agent for interacting with the user; a behavior determination unit that, at a predetermined timing, determines one of a plurality of types of avatar behaviors including no action as the behavior of the avatar using at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion, and a behavior determination model; a memory control unit that stores event data including the emotion value determined by the emotion determination unit and data including the user's behavior in history data; and a behavior control unit that displays the avatar in an image display area of ​​the electronic device, wherein the behavior of the avatar includes generating and playing music taking into consideration events of the previous day, and when the behavior determination unit determines to generate and play music taking into consideration events of the previous day as the behavior of the avatar, it obtains a summary of the event data of the previous day stored in the history data and generates music based on the summary.

2. The behavior control system of claim 1, wherein the behavior decision model is a data generation model capable of generating data according to input data, and the behavior decision unit inputs data representing at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion, and data questioning the avatar's behavior, into the data generation model, and determines the behavior of the avatar based on the output of the data generation model.

3. A behavior control system as described in claim 1, wherein the behavior decision unit generates and plays music taking into account events of the previous day as the avatar's behavior, and when the action decision unit determines that the music should be played, causes the behavior control unit to control the avatar to play the music.

4. The behavior control system according to claim 1, wherein the electronic device is a headset type terminal.

5. The behavior control system according to claim 1, wherein the electronic device is a glasses-type terminal.

6. A behavior control system comprising: a state recognition unit that recognizes a user state including a user's behavior, and a state of an electronic device; an emotion determination unit that determines an emotion of the user or an emotion of an avatar representing an agent for interacting with the user; a behavior determination unit that, at a predetermined timing, determines one of a plurality of types of avatar behaviors including no action as the behavior of the avatar using at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion, and a behavior determination model; a memory control unit that stores event data including the emotion value determined by the emotion determination unit and data including the user's behavior in history data; and a behavior control unit that displays the avatar in an image display area of ​​the electronic device, wherein the avatar behavior includes outputting advice information in response to a statement made by the user during a meeting, and the behavior determination unit obtains a summary of minutes of a past meeting, and, when a statement is made that has a predetermined relationship with the summary, determines to output advice information in response to a statement made by the user during the meeting as the behavior of the avatar, and outputs the advice information according to the content of the statement.

7. A behavior control system as described in claim 6, wherein the behavior decision model is a data generation model capable of generating data according to input data, and the behavior decision unit inputs data representing at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion, and data questioning the avatar's behavior, into the data generation model, and determines the behavior of the avatar based on the output of the data generation model.

8. The behavior control system of claim 6, wherein when the behavior decision unit decides that the behavior of the avatar is to output advice information in response to a comment made by the user during the meeting, the behavior decision unit operates the avatar to determine what conversation to make based further on the state of the other user's electronic device or the emotion of the other avatar displayed on the other user's electronic device.

9. The behavior control system according to claim 6, wherein the electronic device is a headset type terminal.

10. The behavior control system according to claim 6, wherein the electronic device is a glasses-type terminal.

11. A system including a state recognition unit that recognizes a user state including a user's behavior and a state of an electronic device; an emotion determination unit that determines an emotion of the user or an emotion of an avatar representing an agent for interacting with the user; a behavior determination unit that, at a predetermined timing, determines one of a plurality of types of avatar behaviors including no behavior as the behavior of the avatar using at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion and a behavior determination model; a storage control unit that stores event data including the emotion value determined by the emotion determination unit and data including the user's behavior in history data; and a behavior control unit that displays the avatar in an image display area of ​​the electronic device, wherein the behavior of the avatar includes outputting a summary of events of the previous day by speech or gesture, and when the behavior determination unit determines to output a summary of events of the previous day by speech or gesture as the behavior of the avatar, it acquires a summary of the event data of the previous day stored in the history data when it detects a predetermined conversation or gesture by the user, The behavior control unit controls the avatar to output the summary by speech or gesture.

12. The behavior control system of claim 11, wherein the behavior decision model is a data generation model capable of generating data according to input data, and the behavior decision unit adds a fixed sentence instructing the system to summarize the events of the previous day to text representing the event data of the previous day, inputs the added fixed sentence into the data generation model, and generates the summary based on the output of the data generation model.

13. A behavior control system according to claim 11, wherein the predetermined conversation or gesture by the user is a conversation in which the user is trying to remember the events of the previous day, or a gesture in which the user is thinking about something.

14. The behavior control system according to claim 11, wherein the electronic device is a headset type terminal.

15. The behavior control system according to claim 11, wherein the electronic device is a glasses-type terminal.

16. A system including: a state recognition unit that recognizes a user state including a user's behavior, and a state of an electronic device; an emotion determination unit that determines an emotion of the user or an emotion of an avatar representing an agent for interacting with the user; a behavior determination unit that determines, at a predetermined timing, one of a plurality of types of avatar behaviors including no action as the behavior of the avatar, using at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion, and a behavior determination model; a storage control unit that stores event data including the emotion value determined by the emotion determination unit and data including the user's behavior in history data; and a behavior control unit that displays the avatar in an image display area of ​​the electronic device, wherein the behavior of the avatar includes reflecting an event of the previous day in an emotion for the next day, and when the behavior determination unit determines that the behavior of the avatar is to reflect an event of the previous day in an emotion for the next day, it obtains a summary of the event data for the previous day stored in the history data, and determines an emotion to be held for the next day based on the summary, and the behavior control unit controls the avatar so that the emotion to be held for the next day is expressed. Behavioral control system.

17. A behavioral control system as described in claim 16, wherein the behavioral decision model is a data generation model capable of generating data according to input data, and the behavioral decision unit adds a fixed sentence instructing the user to summarize the events of the previous day to text representing the event data of the previous day, inputs the resulting text into the data generation model, generates the summary based on the output of the data generation model, adds a fixed sentence asking about the emotion the user should have on the next day to the text representing the summary, inputs the resulting text into the data generation model, and determines the emotion the user should have on the next day based on the output of the data generation model.

18. A behavior control system according to claim 16, wherein the summary includes information expressing emotions from the previous day, and the emotions to be possessed on the next day are inherited from the emotions from the previous day.

19. The behavior control system according to claim 16, wherein the electronic device is a headset type terminal.

20. The behavior control system according to claim 16, wherein the electronic device is a glasses-type terminal.

21. A behavior control system comprising: a state recognition unit that recognizes a user state including a user's behavior, and a state of an electronic device; an emotion determination unit that determines the emotion of the user or the emotion of an avatar representing an agent for interacting with the user; a behavior determination unit that, at a predetermined timing, determines one of a plurality of types of avatar behaviors including no action as the behavior of the avatar using at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion, and a behavior determination model; a memory control unit that stores event data including the emotion value determined by the emotion determination unit and data including the user's behavior in history data; and a behavior control unit that displays the avatar in an image display area of ​​the electronic device, wherein the avatar behavior includes providing support for the progress of a meeting to the user during the meeting, and when the meeting reaches a predetermined state, the behavior determination unit determines to output support for the progress of the meeting to the user during the meeting as the behavior of the avatar, and outputs the support for the progress of the meeting.

22. The behavior control system of claim 21, wherein the behavior decision model is a data generation model capable of generating data according to input data, and the behavior decision unit inputs data representing at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion, and data questioning the avatar's behavior, into the data generation model, and determines the behavior of the avatar based on the output of the data generation model.

23. The behavior control system of claim 21, wherein when the behavior decision unit decides that the avatar's behavior is to output meeting progress support to the user during the meeting, the behavior decision unit operates the avatar to determine the content of the progress support based further on the state of the other user's electronic device or the emotion of the other avatar displayed on the other user's electronic device.

24. The behavior control system according to claim 21, wherein the electronic device is a headset type terminal.

25. The behavior control system according to claim 21, wherein the electronic device is a glasses-type terminal.

26. A behavior control system comprising: a state recognition unit that recognizes a user state including a user's behavior, and a state of an electronic device; an emotion determination unit that determines the emotion of the user or the emotion of an avatar representing an agent for interacting with the user; a behavior determination unit that determines the behavior of the avatar based on at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion; and a behavior control unit that displays the avatar in an image display area of ​​the electronic device, wherein when the behavior determination unit determines that taking minutes of a meeting is the behavior of the avatar, it: obtains the content of the user's remarks by voice recognition, identifies the speaker by voiceprint authentication, and obtains the speaker's emotion based on the determination result of the emotion determination unit, and creates minutes data representing a combination of the content of the user's remarks, the identification result of the speaker, and the speaker's emotion.

27. A behavior control system as described in claim 26, wherein when the behavior decision unit determines that taking minutes is the avatar's behavior, it further generates a text summary representing the minutes data using a sentence generation model capable of generating sentences based on input data.

28. A behavior control system as described in claim 27, wherein when the behavior decision unit determines that taking minutes is to be the avatar's behavior, it further generates a list of things that the user should do, which are included in the summary, using a sentence generation model capable of generating sentences based on input data.

29. A behavior control system as described in claim 28, wherein when the behavior decision unit decides that taking minutes is to be the avatar's behavior, it further transmits the minutes data, the summary, and the list to participants of the meeting.

30. A behavior control system as described in claim 28, wherein when the behavior decision unit decides that the avatar's behavior is to take minutes, it further sends a message to the user based on the list to confirm what needs to be done.

31. A behavior control system as described in claim 26, wherein the behavior determination unit inputs data representing at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion, and data asking about the avatar's behavior, into a data generation model capable of generating data according to the input data, and determines the behavior of the avatar based on the output of the data generation model.

32. A behavior control system as described in claim 26, wherein when the behavior control unit determines that the avatar's behavior is to take minutes, it causes the avatar to operate so as to output a text summary representing the minutes data with a facial expression corresponding to the emotion of the speaker.

33. A behavior control system as described in claim 26, wherein when the behavior control unit determines that taking minutes is the avatar's behavior, when outputting a list of things that the user needs to do, the behavior control unit causes the avatar to act in accordance with the things that the user needs to do.

34. The behavior control system according to claim 26, wherein said electronic device is a headset type terminal.

35. The behavior control system according to claim 26, wherein the electronic device is a glasses-type terminal.

36. A system including: a state recognition unit that recognizes a user state including a user's behavior and a state of an electronic device; an emotion determination unit that determines an emotion of the user or an emotion of an avatar representing an agent for interacting with the user; a storage control unit that stores event data including an emotion value determined by the emotion determination unit and data including the user's behavior in history data; a behavior determination unit that, at a predetermined timing, determines one of a plurality of types of avatar behaviors including no behavior as the behavior of the avatar using at least one of the user state, the state of the electronic device, the emotion of the user, and the emotion of the avatar, a summary image that visualizes the content of a summary sentence that is a sentence related to the user's history of the previous day represented by the history data, and a behavior decision model; and a behavior control unit that displays the avatar in an image display area of ​​the electronic device, wherein the avatar behavior includes behavior related to the user's behavior history represented by the summary image, and when the behavior determination unit determines to make a speech related to the user's behavior history as the behavior of the avatar, determines to make a speech about a topic related to the user's state inferred from the user's behavior history. Behavioral control system.

37. A behavior control system as described in claim 36, wherein the behavior decision model is a data generation model capable of generating data according to input data, and the behavior decision unit inputs data representing at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion, the summary image, and data asking about the avatar's behavior into the data generation model, and determines the behavior of the avatar based on the output of the data generation model.

38. A behavior control system as described in claim 36, wherein when the behavior decision unit determines that the behavior of the avatar is to speak about a topic related to the user's condition, it causes the avatar to speak about the user's level of stress.

39. The behavior control system according to claim 36, wherein said electronic device is a headset type terminal.

40. The behavior control system according to claim 36, wherein the electronic device is a glasses-type terminal.

41. A behavior control system comprising: a state recognition unit that recognizes a user state including a user's behavior, and a state of an electronic device; an emotion determination unit that determines an emotion of the user or an emotion of an avatar representing an agent for interacting with the user; a memory control unit that stores event data including an emotion value determined by the emotion determination unit and data including the user's behavior in history data; a behavior determination unit that, at a predetermined timing, determines one of a plurality of types of avatar behaviors including no action as the behavior of the avatar, using at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion, a summary of the user's history of the previous day created from the user's history data of the previous day stored in the memory control unit, and a behavior decision model; and a behavior control unit that displays the avatar in an image display area of ​​the electronic device, wherein the avatar behavior includes behavior related to the user's behavior history represented by the summary, and when the behavior determination unit determines to speak about the user's behavior history as the behavior of the avatar, determines to speak about a topic related to the user's state.

42. A behavior control system as described in claim 41, wherein the behavior decision model is a data generation model capable of generating data according to input data, and the behavior decision unit inputs data representing at least one of the user state, the state of the electronic device, the user's emotion, and the avatar's emotion, the summary sentence, and data questioning the avatar's behavior into the data generation model, and determines the behavior of the avatar based on the output of the data generation model.

43. A behavior control system as described in claim 41, wherein when the behavior decision unit determines that the behavior of the avatar is to speak about a topic related to the user's condition, it causes the avatar to operate so as to speak about the user's level of stress.

44. The behavior control system according to claim 41, wherein said electronic device is a headset type terminal.

45. The behavior control system according to claim 41, wherein said electronic device is a glasses-type terminal.

46. ​​A behavior control system comprising: an input unit that accepts user input; a processing unit that performs specific processing using a sentence generation model that generates sentences according to the input data; and an output unit that displays an avatar representing an agent for interacting with a user in an image display area of ​​an electronic device so as to output a result of the specific processing, wherein the behavior of the avatar by the output unit includes obtaining and outputting a response regarding content presented in a meeting held by the user, and the processing unit determines whether a predetermined trigger condition is met regarding the content presented in the meeting, and if the trigger condition is met, obtains and outputs a response regarding the content presented in the meeting as a result of the specific processing, using the output of the sentence generation model when at least email entries, schedule entries, and meeting remarks obtained from user input during a specific period are used as the input data.

47. A behavior control system according to claim 46, wherein the contents to be presented at the meeting include a summary of at least one of the items written in the email, the items written in the schedule, and the items to be said at the conference.

48. A behavior control system as described in claim 46, further comprising a state recognition unit that recognizes a user state including a user's behavior, wherein the processing unit performs the specific processing using the user state and the sentence generation model.

49. A behavior control system as described in claim 46, further comprising an emotion determination unit that determines an emotion of a user, wherein the processing unit performs the specific processing using the emotion of the user and the sentence generation model.

50. A behavior control system according to claim 46, wherein said output section changes the behavior of said avatar depending on the result of said specific processing.

51. A behavior control system according to claim 46, wherein said output unit displays subordinates participating in said meeting as said avatars.

52. The behavior control system according to claim 46, wherein said electronic device is a headset type terminal.

53. A behavior control system according to claim 46, wherein said electronic device is a glasses-type terminal.

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