Action control system

Abstract

To make an electronic device take an appropriate action in response to an action of a user.SOLUTION: An action control system disclosed herein comprises: a condition recognition unit for recognizing the user condition including an action of a user and the condition of an electronic device; an emotion determination unit for determining the emotions of the user or the emotions of an avatar representing an agent for interacting with the user; an action determination unit configured to use an action determination model and at least one of the user condition, the condition of the electronic device, the emotions of the user, and the emotions of the avatar at given time to determine one of multiple types of avatar actions, including non-action, as an action to be taken by the avatar; a storage control unit for storing event data including an emotion value determined by the emotion determination unit and data on the action of the user in history data; and an action control unit for displaying the avatar in an image display area of the electronic device.SELECTED DRAWING: Figure 2

Description

[Technical field]

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

[0002] Patent Document 1 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 executes a specific action, and if the robot is unable to determine an action for the recognized user's reaction, it updates the robot's behavior by receiving information about an action suitable for the recognized user's state from a server. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 6053847 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the conventional techniques leave room for improvement in terms of making a robot perform an appropriate action in response to a user's action. [Means for solving the problem]

[0005] According to a first aspect of the present invention, there is provided a behavior control system, which includes 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 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, the avatar behavior including analyzing a user's personality, and when the behavior determination unit determines to analyze the user's personality as the behavior of the avatar, it analyzes the user's personality using the history data including a conversation history with the user and presents the analyzed personality.

[0006] According to a second aspect of the present invention, there is provided a behavior control system according to the first aspect, wherein the behavior decision model of the behavior control system 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 asking about the avatar's behavior, to the data generation model, and decides the behavior of the avatar based on an output of the data generation model.

[0007] According to a third aspect of the present invention, there is provided a behavior control system according to the first aspect, wherein the behavior control unit of the behavior control system changes a facial expression of the avatar when presenting the user's personality, according to the emotion of the user determined by the emotion determination unit.

[0008] According to a fourth aspect of the present invention, there is provided a behavior control system according to the first aspect, wherein the electronic device of the behavior control system is a headset type terminal.

[0009] According to a fifth aspect of the present invention, there is provided a behavior control system according to the first aspect, wherein the electronic device of the behavior control system is a glasses-type terminal. [Brief description of the drawings]

[0010] [Figure 1] 1 illustrates an example of a system 5 according to a first embodiment. [Diagram 2] 1 illustrates a schematic functional configuration of a robot 100 according to a first embodiment. [Diagram 3] 10 illustrates an example of an operation flow of a collection process by the robot 100 according to the first embodiment. [Figure 4A] 13 shows an example of an operation flow of a response process by the robot 100 according to the first embodiment. [Figure 4B] 2 shows an example of an operation flow of autonomous processing by the robot 100 according to the first embodiment. [Diagram 5] 4 shows an emotion map 400 onto which multiple emotions are mapped. [Figure 6] 9 shows an emotion map 900 onto which multiple emotions are mapped. [Figure 7] 13A is an external view of a stuffed animal 100N according to a second embodiment, and FIG. 13B is a diagram showing the internal structure of the stuffed animal 100N. [Figure 8] FIG. 11 is a rear front view of a stuffed animal 100N according to a second embodiment. [Figure 9] 13 shows a schematic functional configuration of a stuffed animal 100N according to a second embodiment. [Figure 10] 13 shows an outline of the functional configuration of an agent system 500 according to a third embodiment. [Figure 11] An example of the operation of the agent system is shown. [Figure 12] An example of the operation of the agent system is shown. [Figure 13] 13 shows an outline of the functional configuration of an agent system 700 according to a fourth embodiment. [Figure 14]1 shows an example of how an agent system using smart glasses is used. [Figure 15] 13 shows an outline of the functional configuration of an agent system 800 according to a fifth embodiment. [Figure 16] 1 shows an example of a headset type terminal. [Figure 17] 1 illustrates an example of a hardware configuration of a computer 1200. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] 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.

[0012] [First embodiment] FIG. 1 is a schematic diagram of an example of a system 5 according to the present embodiment. The system 5 includes a robot 100, a robot 101, a robot 102, and a server 300. A user 10a, a user 10b, a user 10c, and a user 10d are users of the robot 100. A user 11a, a user 11b, and a user 11c are users of the robot 101. A user 12a and a user 12b are users of the robot 102. In the description of the present embodiment, the user 10a, the user 10b, the user 10c, and the user 10d may be collectively referred to as the user 10. The user 11a, the user 11b, and the user 11c may be collectively referred to as the user 11. The user 12a and the user 12b may be collectively referred to as the user 12. The robot 101 and the robot 102 have substantially the same functions as the robot 100. Therefore, the system 5 will be described by mainly focusing on the functions of the robot 100.

[0013] 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 causes the server 300 to record captured image data, etc. of the user 10, and requests the server 300 for the image data, etc. as necessary, and provides the data, etc. to the user 10.

[0014] The robot 100 also has an emotion value that indicates the type of its own emotion. For example, the robot 100 has emotion values ​​that indicate the intensity of each of the emotions of "joy", "anger", "sorrow", "pleasure", "discomfort", "relief", "anxiety", "sadness", "excitement", "concern", "relief", "fulfillment", "emptiness", and "neutral". When the robot 100 converses with the user 10 in a state where the emotion value of excitement is high, for example, the robot 100 utters sounds at a fast speed. In this way, the robot 100 can express its own emotion through actions.

[0015] Furthermore, the robot 100 may be configured to match a sentence generation model using AI (Artificial Intelligence) with an emotion engine, thereby determining the behavior of the robot 100 corresponding to the emotion of the user 10. Specifically, the robot 100 may be configured to recognize the behavior of the user 10, determine the emotion of the user 10 regarding the user's behavior, and determine the behavior of the robot 100 corresponding to the determined emotion.

[0016] More specifically, when the robot 100 recognizes the behavior of the user 10, the robot 100 automatically generates the behavioral 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 by text. The sentence generation model is described in, for example, JP 2018-081444 A and ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and thus detailed description thereof will be omitted. Such a sentence generation model is configured using a large language model (LLM).

[0017] 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 and 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.

[0018] 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.

[0019] The robot 100 stores, as an example of a behavior decision model, rules that define the behaviors that the robot 100 will execute based on the emotions of the user 10, the emotions of the robot 100, and the behaviors of the user 10, and performs various behaviors according to the rules.

[0020] Specifically, the robot 100 has reaction rules for determining the behavior of the robot 100 based on the emotion of the user 10, the emotion of the robot 100, and the behavior of the user 10, as an example of a behavior decision model. For example, the reaction rules define the behavior of the robot 100 as "laughing" when the behavior of the user 10 is "laughing". In addition, the reaction rules define the behavior of the robot 100 as "apologizing" when the behavior of the user 10 is "angry". In addition, the reaction rules define the behavior of the robot 100 as "answering" when the behavior of the user 10 is "asking a question". In addition, the reaction rules define the behavior of the robot 100 as "calling out" when the behavior of the user 10 is "sad".

[0021] When the robot 100 recognizes the behavior of the user 10 as "anger" based on the reaction rules, the robot 100 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", the robot 100 performs the motion of "apologizing" and outputs a voice expressing the word "apologize".

[0022] In addition, when the emotion of the robot 100 is "normal" (i.e., "happy" = 0, "anger" = 0, "sad" = 0, and "happy" = 0) and the condition that the state of the user 10 is "alone and looks lonely" is satisfied, it is defined that the emotion of the robot 100 will change to "worried" and that the robot 100 will be able to execute the action of "calling out."

[0023] 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 is alone and seems lonely, the robot 100 increases the emotion value of "sadness" of the robot 100. In addition, 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 words "What's wrong?", which indicate concern, into a worried voice and outputs it.

[0024] 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 reaction of the user 10 was positive, and attributes of the user 10.

[0025] 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 analyzes the user reaction information from the robots 100, 101, and 102, and updates the reaction rules.

[0026] 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.

[0027] 2 shows a schematic functional configuration of the robot 100. The robot 100 has 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 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, and a communication processing unit 280.

[0028] The control target 252 includes a display device, a speaker, LEDs in the eyes, and motors for driving the arms, hands, legs, etc. The posture and gestures of the robot 100 are controlled by controlling the motors of the arms, hands, legs, etc. Some of the emotions of the robot 100 can be expressed by controlling these motors. In addition, 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.

[0029] 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 sound data. The microphone 201 may be provided on the head of the robot 100 and may have a function of performing binaural recording. The 3D depth sensor 202 detects the contour of an object by continuously irradiating an infrared pattern and 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 an image using visible light and generates visible light video information. The distance sensor 204 detects the distance to an object by irradiating, for example, a laser or ultrasonic waves. The sensor unit 200 may also include a clock, a gyro sensor, a sensor for motor feedback, and the like.

[0030] 2, the components other than the control target 252 and the sensor unit 200 are examples of components included in a behavior control system included in the robot 100. The behavior control system of the robot 100 controls the control target 252.

[0031] The storage unit 220 includes a behavioral decision 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, and specifically 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 a camera image 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 part of the storage unit 220 is implemented by a storage medium such as a memory. It may include a person DB that stores the face image 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. 2, 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 an operating system (OS) and a program that runs on the OS.

[0032] 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.

[0033] 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 amounts such as frequency components of the voice and recognizes the emotion of the user 10 based on the extracted feature amounts. 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.

[0034] 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 relationship, etc. of the eyes and mouth.

[0035] 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 a face image of the user 10 captured by the 2D camera 203.

[0036] 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, the state recognition unit 230 mainly performs processing related to perception using the analysis results of the sensor module unit 210. For example, it generates sensory information such as "Daddy is alone" or "There is a 90% chance that Daddy is not smiling." It performs processing to understand the meaning of the generated sensory information. For example, it generates semantic information such as "Daddy is alone and looks lonely."

[0037] 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 surrounding environment of the robot 100, and the like as the state of the robot 100.

[0038] 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 to a pre-trained neural network to obtain an emotion value indicating the emotion of the user 10.

[0039] 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 emotion of the user is a cheerful emotion accompanied by a sense of pleasure or comfort, such as "joy", "pleasure", "comfort", "relief", "excitement", "relief", and "fulfillment", the value indicates a positive value, and the more cheerful the emotion, the larger the value. If the emotion of the user is an unpleasant emotion, such as "anger", "sorrow", "discomfort", "anxiety", "sorrow", "worry", and "emptiness", the value indicates a negative value, and the more unpleasant the emotion, the larger the absolute value of the negative value. If the emotion of the user is not any of the above ("neutral"), the value indicates 0.

[0040] 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.

[0041] The emotion value of the robot 100 includes emotion values ​​for each of a plurality of emotion categories, and is, for example, a value (0 to 5) indicating the strength of each of "happiness," "anger," "sorrow," and "pleasure."

[0042] 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.

[0043] 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. Also, 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.

[0044] 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 battery level 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 battery level being low, the emotion value of "anger" may be increased.

[0045] 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 to a pre-trained neural network, the probability of each of a plurality of predetermined behavior classifications (for example, "laughing," "getting angry," "asking a question," and "getting sad") is obtained, and the behavior classification with the highest probability is recognized as the behavior of the user 10.

[0046] As described above, in this embodiment, the robot 100 identifies the user 10 and acquires the contents of the user's utterance. When acquiring and using the contents of the utterance, the robot 100 obtains the necessary consent in accordance with laws and regulations from the user 10. The behavior control system of the robot 100 in this embodiment takes into consideration the protection of the personal information and privacy of the user 10.

[0047] Next, the process of the behavior decision unit 236 when the robot 100 performs a response process to respond to the behavior of the user 10 will be described.

[0048] The behavior determining unit 236 determines a behavior corresponding to the behavior of the user 10 recognized by the behavior recognizing unit 234 based on the current emotion value of the user 10 determined by the emotion determining unit 232, the history data 222 of past emotion values ​​determined by the emotion determining unit 232 before the current emotion value of the user 10 was determined, and the emotion value of the robot 100. In this embodiment, the behavior determining 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 determining unit 236 may use a plurality of 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. In addition, the behavior determining 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 a gesture performed by the robot 100 or the contents of the speech of the robot 100 .

[0049] The behavior decision unit 236 according to this embodiment decides the behavior of the robot 100 as a behavior corresponding to the behavior of the user 10, based on a combination of the past emotion value and the current emotion value of the user 10, the emotion value of the robot 100, the behavior of the user 10, and the behavior decision model 221. For example, when the past emotion value of the user 10 is a positive value and the current emotion value is a negative value, the behavior decision unit 236 decides, as a behavior corresponding to the behavior of the user 10, a behavior for changing the emotion value of the user 10 to a positive value.

[0050] The reaction rules as the behavior decision model 221 prescribe 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, when the past emotional value of the user 10 is a positive value and the current emotional value is a negative value, and the behavior of the user 10 is sad, a combination of gestures and speech content when asking a question to encourage the user 10 with gestures is prescribe as the behavior of the robot 100.

[0051] For example, in the reaction rule as the behavior determination model 221, the behavior of the robot 100 is determined for all combinations of the patterns of the emotion values ​​of the robot 100 (1296 patterns, which are the fourth power of six values ​​of "joy", "anger", "sorrow", and "pleasure", from "0" to "5"); the combination patterns of the past emotion values ​​and the current emotion values ​​of the user 10; and the behavior patterns of the user 10. That is, for each pattern of the emotion values ​​of the robot 100, the behavior of the robot 100 is determined for each of a plurality of combinations of the past emotion values ​​and the current emotion values ​​of the user 10, such as negative values ​​and negative values, negative values ​​and positive values, positive values ​​and negative values, positive values ​​and positive values, negative values ​​and normal values, and normal values ​​and normal values. Note that, 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", the behavior determination unit 236 may transition to an operation mode in which the behavior of the robot 100 is determined using the history data 222.

[0052] Note that the reaction rules as the behavior determination model 221 may prescribe at least one of a gesture and a statement as the behavior of the robot 100, up to one for each of the patterns (1296 patterns) of the emotion value of the robot 100. Alternatively, the reaction rules as the behavior determination model 221 may prescribe at least one of a gesture and a statement as the behavior of the robot 100, for each group of patterns of the emotion value of the robot 100.

[0053] The strength of each gesture included in the behavior of the robot 100 defined in the reaction rules as the behavior determination model 221 is determined in advance. The strength of each utterance content included in the behavior of the robot 100 defined in the reaction rules as the behavior determination model 221 is determined in advance.

[0054] 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 strength 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.

[0055] Specifically, when a 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 gesture 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.

[0056] 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 (e.g., 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 (e.g., the facial expression, emotions, etc. of the user 10).

[0057] 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 speed of the sound based on the emotion value of the robot 100. For example, the behavior control unit 250 determines a faster speaking speed as the emotion 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 emotion value determined by the emotion determination unit 232.

[0058] 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 may be recognized based on the detection of an impact by the touch sensor 205 included in the sensor unit 200. When an impact is detected by the touch sensor 205 included in the sensor unit 200, the emotion of the user 10 may be recognized as having worsened, or when the reaction of the user 10 is determined to be laughing or happy based on the detection result of the touch sensor 205 included in the sensor unit 200, the emotion of the user 10 may be recognized as having improved. Information indicating the reaction of the user 10 is output to the communication processing unit 280.

[0059] Furthermore, after the behavior control unit 250 executes the behavior determined by the behavior determining unit 236 in the execution mode determined according to the emotion of the robot 100, the emotion determining 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 determining unit 232 increases the emotion value of "happiness" of the robot 100 when the user's reaction to the behavior determined by the behavior determining unit 236 being performed on the user in the execution mode determined by the behavior control unit 250 is not bad. Furthermore, the emotion determining unit 232 increases the emotion value of "sadness" of the robot 100 when the user's reaction to the behavior determined by the behavior determining unit 236 being performed on the user in the execution mode determined by the behavior control unit 250 is bad.

[0060] 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 behavior control unit 250 increases the emotion value of "happiness" of the robot 100, the behavior control unit 250 controls the control object 252 to make the robot 100 perform a happy gesture. When the behavior control unit 250 increases the emotion value of "sadness" of the robot 100, the behavior control unit 250 controls the control object 252 to make the robot 100 assume a droopy posture.

[0061] 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. In addition, the communication processing unit 280 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.

[0062] The server 300 communicates between the robots 100, 101, and 102 and the server 300, receives user reaction information transmitted from the robot 100, and updates the reaction rules based on reaction rules that include actions that have received positive reactions.

[0063] The related information collecting section 270 collects information related to the favorite information acquired about the user 10 from external data (websites such as news sites and video sites) at a predetermined timing based on the favorite information acquired about the user 10.

[0064] Specifically, the related information collecting unit 270 acquires preference information indicating matters of interest to the user 10 from the contents of speech of the user 10 or a setting operation by the user 10. The related information collecting unit 270 periodically collects news related to the preference information, for example, from ChatGPT Plugins (Internet search engine).<URL: https: / / openai.com / blog / chatgpt-plugins> For example, if 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, for example, using ChatGPT Plugins.

[0065] 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.

[0066] 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, when 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.

[0067] When the emotion value of the robot 100 is equal to or greater than the threshold value, the memory control unit 238 stores information related to the preference information collected by the related information collection unit 270 in the collected data 223.

[0068] Next, the processing of the behavior decision unit 236 when the robot 100 performs autonomous processing to act autonomously will be described.

[0069] In the autonomous processing in this embodiment, the robot 100 as an agent performs the autonomous processing. More specifically, the robot 100 performs the autonomous processing to take an action based on the past history of the robot 100 (there may be no history) and the behavior of the user 10, regardless of whether the user 10 is present or not.

[0070] The robot 100 as an agent spontaneously and periodically detects the state of the user 10. For example, the robot 100 performs a personality analysis based on psychology by one-sidedly listening to what the user is saying and by having a conversation with the user. The robot 100 retains a history of conversations with the user 10 as history data 222, and performs a personality analysis using the history data 222. For example, the robot 10 analyzes the personality of the user 10 by analyzing the user's habitual speech, such as the beginning or ending of a sentence, recorded in the history data 222.

[0071] In addition, if the user 10 becomes emotional, feels depressed, or is in high spirits, the robot 100 spontaneously analyzes the personality of the user 10 and conveys the personality analysis result to the user 10.

[0072] When analyzing the personality of the user 10 and communicating the analysis results, the robot 100 may casually communicate the analysis results to the user during a conversation.

[0073] The robot 100 voluntarily analyzes the personality of the user 10 and communicates the personality analysis results to the user 10, thereby enabling the user 10 to deepen his / her understanding of his / her own personality.

[0074] 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, and the behavior decision model 221. Here, a case where a sentence generation model having a dialogue function is used as the behavior decision model 221 will be described as an example.

[0075] 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 a sentence generation model, and determines the behavior of the robot 100 based on the output of the sentence generation model.

[0076] For example, the multiple types of robot behaviors include the following (1) to (10).

[0077] (1) The robot does nothing. (2) Robots dream. (3) The robot talks to the user. (4) The robot creates a picture diary. (5) The robot suggests activities. (6) The robot will suggest people for the user to meet. (7) The robot introduces news that is of interest to the user. (8) The robot will edit photos and videos. (9) The robot learns together with the user. (10) Robots evoke memories. (11) The robot performs a personality analysis of the user.

[0078] 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, the current emotion value of the user 10 determined by the emotion determination unit 232, text indicating the current emotion value of the robot 100, and text asking about one of a plurality of types of robot behaviors including not taking any action, into the sentence generation model at regular time intervals, and determines the behavior of the robot 100 based on the output of the sentence generation model. Here, when the user 10 is not present 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 emotion value of the user 10, and may include an indication that the user 10 is not present.

[0079] As an example, "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 (11) is the best behavior for the robot?" (1) The robot does nothing. (2) Robots dream. (3) The robot talks to the user. The text "..." is input to the sentence generation model. Based on the output of the sentence generation model, "It can be said that either (1) doing nothing or (2) the robot dreams are the most appropriate behaviors," the behavior of the robot 100 is determined to be "(1) doing nothing" or "(2) the robot dreams."

[0080] Another example is, "The robot is a little lonely. The user is not present. The robot's surroundings are dark. Which of the following (1) to (10) would be the best behavior for the robot? (1) The robot does nothing. (2) Robots dream. (3) The robot talks to the user. The text "..." is input to the sentence generation model. 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 is the most appropriate behavior," the behavior of the robot 100 is determined to be "(2) the robot dreams" or "(4) the robot creates a picture diary."

[0081] 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.

[0082] 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 robot's utterance contents corresponding to the user state and the user's emotion or the robot's emotion. At this time, the behavior control unit 250 outputs a sound representing the determined robot's utterance contents 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 robot's utterance contents in the behavior schedule data 224 without outputting a sound representing the determined robot's utterance contents.

[0083] When the behavior decision unit 236 decides that "(7) The robot introduces news that the user is interested in" as the robot behavior, it uses the sentence generation model to decide the robot's utterance contents corresponding to the information stored in the collected data 223. At this time, the behavior control unit 250 outputs the sound representing the determined robot's utterance contents from the 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 robot's utterance contents in the behavior schedule data 224 without outputting the sound representing the determined robot's utterance contents.

[0084] The behavior decision unit 236 determines, as the robot behavior, “(4) The robot creates a picture diary. That is, when the robot 100 decides to create an event image, for event data selected from the history data 222, an image representing the event data is generated using an image generation model, and an explanatory text representing the event data is generated using a sentence generation model, and a combination of the image representing the event data and the explanatory text representing the event data is output 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.

[0085] When the behavior decision unit 236 determines that the robot behavior is "(8) The robot edits photos and videos," i.e., that the robot edits images, it selects event data from the history data 222 based on the emotion value, and edits and outputs the image data of the selected event data. 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.

[0086] When the behavior decision unit 236 determines that the robot behavior is "(5) The robot proposes an activity," that is, that the robot proposes an action for the user 10, the behavior decision unit 236 uses a sentence generation model to decide the user's behavior to be proposed based on the event data stored in the history data 222. At this time, the behavior control unit 250 outputs a voice proposing the user's behavior 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 in the behavior schedule data 224 that the user's behavior is proposed without outputting a voice proposing the user's behavior.

[0087] When the behavior decision unit 236 determines that the robot behavior is "(6) The robot proposes a person who the user should meet," that is, a person who should have contact with the user 10, the behavior decision unit 236 uses a sentence generation model based on the event data stored in the history data 222 to determine a person who should have contact with the proposed user. At this time, the behavior control unit 250 outputs a sound representing the proposal of a person who should have contact with the user 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 in the behavior schedule data 224 the proposal of a person who should have contact with the user without outputting a sound representing the proposal of a person who should have contact with the user.

[0088] When the behavior decision unit 236 decides that the robot 100 will make an utterance related to studying, that is, "(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 give advice on studying, corresponding to the user's state and the user's or the robot's emotion. At this time, the behavior control unit 250 outputs a sound representing the determined content of the robot's utterance 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 a sound representing the determined content of the robot's utterance.

[0089] When the behavior determining unit 236 determines that the robot behavior is "(10) The robot recalls a memory," that is, that the robot recalls event data, it selects the event data from the history data 222. At this time, the emotion determining unit 232 judges 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 utterance content and behavior of the robot 100 for changing the user's emotion value. At this time, the memory control unit 238 stores the emotion change event in the behavior schedule data 224.

[0090] For example, the fact that the video the user was watching was about pandas is stored as event data in the history data 222. When the 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. 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 "Which of (1), (2), and (3) would make the user happiest?" to the sentence generation model. If the output of the sentence generation model is "(1) Let's go to the zoo," the robot 100 will say "(1) Let's go to the zoo" the next time it meets the user. This is created as an emotion change event and stored in the action schedule data 224.

[0091] Also, for example, event data with a large emotion value of the robot 100 is selected as an impressive memory of the robot 100. In this way, an emotion change event can be created based on the event data selected as an impressive memory.

[0092] When the behavior decision unit 236 determines, as the robot behavior, that "(11) The robot performs a personality analysis of the user", that is, to analyze the personality of the user 10, it analyzes the personality of the user 10 from the history data 222. In addition, with regard to "(11) The robot performs a personality analysis of the user", the memory control unit 238 stores history data required for analyzing the personality of the user 10.

[0093] 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 is not taking 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.

[0094] For example, when the user 10 is not present around the robot 100 and the user 10 is detected, 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, when the user 10 is asleep and the behavior decision unit 236 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.

[0095] Fig. 3 shows an example of an operational flow of 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 in which the user 10 is interested is acquired from the contents of speech of the user 10 or a setting operation by the user 10. Note that "S" in the operational flow indicates a step to be executed.

[0096] First, in step S90, the related information gathering unit 270 acquires preference information that indicates matters in which the user 10 is interested.

[0097] In step S92, the related information collecting unit 270 collects information related to the preference information from external data.

[0098] In step S 94 , 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 .

[0099] In step S96, the storage control unit 238 determines whether or not the emotion value of the robot 100 determined in step S94 above 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.

[0100] In step S98, the storage control unit 238 stores the collected information related to the preference information in the collected data 223, and ends the process.

[0101] Fig. 4A shows an example of an operation flow for determining an action of the robot 100 when performing a response process in which the robot 100 responds 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.

[0102] 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 .

[0103] 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 .

[0104] 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.

[0105] In step S104 , the behavior recognition unit 234 recognizes a 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 .

[0106] In step S106, the behavior decision unit 236 decides the behavior of the robot 100 based on a combination of the current emotional value of the user 10 decided 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 decision model 221.

[0107] In step S 108 , the behavior control unit 250 controls the control target 252 based on the behavior determined by the behavior determining unit 236 .

[0108] In step S110, the memory control unit 238 calculates a total intensity value based on the predetermined action intensities for the actions determined by the action determination unit 236 and the emotion value of the robot 100 determined by the emotion determination unit 232.

[0109] In step S112, the storage control unit 238 determines whether the total intensity value is equal to or greater than the threshold. If the total intensity value is less than the threshold, the process ends without storing the 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 process proceeds to step S114.

[0110] 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.

[0111] Fig. 4B shows an example of an operation flow for determining an action of the robot 100 when the robot 100 performs an autonomous process for autonomously acting. The operation flow shown in Fig. 4B is automatically and repeatedly executed, for example, at regular time 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 the same processes as those in Fig. 4A above.

[0112] 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 .

[0113] 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 .

[0114] 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.

[0115] In step S104 , the behavior recognition unit 234 recognizes a 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 .

[0116] In step S200, the behavior decision unit 236 decides, based on the state of the user 10 recognized in step S100, the emotion of the user 10 determined in step S102, the emotion of the robot 100, and 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, on one of multiple types of robot behaviors, including no action, as the behavior of the robot 100.

[0117] In step S201, the behavior decision unit 236 judges whether or not it has been decided in step S200 that no action will be taken. If it has been decided that no action will be taken as the action of the robot 100, the process ends. On the other hand, if it has not been decided that no action will be taken as the action of the robot 100, the process proceeds to step S202.

[0118] In step S202, the behavior determining unit 236 performs processing according to the type of robot behavior determined in the above step S200. At this time, the behavior control unit 250, the emotion determining unit 232, or the memory control unit 238 executes processing according to the type of robot behavior.

[0119] In step S110, the memory control unit 238 calculates a total intensity value based on the predetermined action intensities for the actions determined by the action determination unit 236 and the emotion value of the robot 100 determined by the emotion determination unit 232.

[0120] In step S112, the storage control unit 238 determines whether the total intensity value is equal to or greater than the threshold. If the total intensity value is less than the threshold, the process 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 process proceeds to step S114.

[0121] 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.

[0122] As described above, according to the robot 100, an emotion value indicating the emotion of the robot 100 is determined based on the user state, and whether or not to store data including the behavior of the user 10 in the history data 222 is determined 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 user state 10 years from now is the same as that 10 years ago, the robot 100 can present to the user 10 all kinds of peripheral information, such as the state of the user 10 10 years ago (for example, the facial expression, emotion, etc. of the user 10), and data on the sound, image, smell, etc. of the place.

[0123] Moreover, according to the robot 100, it is possible to make the robot 100 perform an appropriate action in response to the action of the user 10. Conventionally, the action of the user is classified, and the action including the facial expression and the appearance of the robot is determined. In contrast, the robot 100 determines the current emotional value of the user 10, and performs an action on the user 10 based on the past emotional value and the current emotional value. Therefore, for example, if the user 10 who was cheerful yesterday is depressed today, the robot 100 can make an utterance such as "You were cheerful yesterday, but what's wrong with you today?" The robot 100 can also make an utterance with gestures. For example, if the user 10 who was depressed yesterday is cheerful today, the robot 100 can make an utterance such as "You were depressed yesterday, but you look cheerful today, don't you?" For example, if the user 10 who was cheerful yesterday is more cheerful today than yesterday, the robot 100 can make an utterance such as "You're more cheerful today than yesterday. Has anything better happened than yesterday?" In addition, 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."

[0124] Also, for example, the robot 100 can ask the user 10, "Did you finish the homework you told me about yesterday?", and when the user 10 answers, "I did it," it can utter a positive utterance such as "Great!" and perform a positive gesture such as clapping or a thumbs up. Also, for example, when the user 10 utters, "The presentation you gave the day before yesterday went well," the robot 100 can utter a positive utterance such as "You did a good job!" and perform the above-mentioned positive gesture. In this way, it is expected that the user 10 will feel a sense of affinity with the robot 100 by the robot 100 performing an action based on the state history of the user 10.

[0125] Also, for example, when user 10 is watching a video about a panda, if the emotion value of user 10's emotion of "pleasure" is equal to or greater than a threshold, a scene in which a panda appears in the video may be stored in history data 222 as event data.

[0126] Using the data accumulated in the history data 222 and the collected data 223, the robot 100 can constantly learn what kind of conversation to have with the user in order to maximize the emotional value expressing the user's happiness.

[0127] Furthermore, when the robot 100 is not engaged in a conversation with the user 10, the robot 100 can autonomously start to act based on its own emotions.

[0128] Furthermore, in the autonomous processing, the robot 100 automatically generates questions, inputs them into a sentence generation model, and repeats the process of obtaining 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 emotion change events in the behavior schedule data 224. In this way, the robot 100 can execute self-learning.

[0129] In addition, when the robot 100 automatically generates a question without receiving an external trigger, the robot 100 can automatically generate the question based on memorable event data identified from the robot's past emotion value history.

[0130] Furthermore, the related information collecting unit 270 can perform self-learning by repeating the search performing step of automatically performing a keyword search in response to preference information about the user and obtaining search results.

[0131] 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 emotion value history in a state in which no external trigger has been received.

[0132] The emotion determining unit 232 may determine the emotion of the user according to a specific mapping. Specifically, the emotion determining unit 232 may determine the emotion of the user according to an emotion map (see FIG. 5), which is a specific mapping.

[0133] FIG. 5 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] (1) For example, if the emotion engine, which is the emotion determination unit 232 of the robot 100, detects emotions in about 100 msec, the frequency of the determination of the reaction action (e.g., a backchannel) of the robot 100 may be set at least at the same timing as 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.

[0135] By detecting emotions in about 100 msec and immediately performing a corresponding reaction (e.g., a backchannel), unnatural backchannels can be avoided, and a natural dialogue that reads the atmosphere can be realized. The robot 100 performs a reaction (such as a backchannel) according to the directionality and the degree (strength) of the mandala in the emotion map 400. The detection frequency (sampling rate) of the emotion engine is not limited to 100 ms, and may be changed according to the situation (e.g., when playing sports), the age of the user, etc.

[0136] (2) The directionality of emotions and the strength of their intensity may be preset 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 and 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.

[0137] These emotions are distributed in the 3 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.

[0138] (3) If the robot 100 feels good after being praised, the filler "ah" may be inserted before the line, and if the robot 100 feels pain after receiving harsh words, the filler "ugh!" may be inserted before the line. Also, a physical reaction such as the robot 100 crouching down while saying "ugh!" may be included. These emotions are distributed around 9 o'clock on the emotion map 400.

[0139] (4) In the left half of the emotion map 400, internal sensations (reactions) are more important than situational awareness. This can give the impression that the person is reacting unconsciously.

[0140] When the robot 100 feels a favorable feeling in its situation recognition while feeling an internal sensation (reaction) of a sense of satisfaction, 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 favorable feeling toward the other person, that is, a tolerance or tolerance toward the other person. Such emotions are distributed around 12 o'clock on the emotion map 400.

[0141] Conversely, even when the robot 100 is aware of a situation while experiencing an internal sensation (reaction) of discomfort, the robot 100 may shake its head when it feels disgust, or turn the LEDs in its eyes 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.

[0142] (5) The inside of emotion map 400 represents what is going on in one's mind, while the outside of emotion map 400 represents behavior. Therefore, the further out on the outside of emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0143] (6) When listening to someone while feeling a sense of security, which is distributed around the 3 o'clock area of ​​the emotion map 400, the robot 100 may lightly nod its head and say "Hmmm," but when listening to someone while feeling a sense of love, which is distributed around 12 o'clock, the robot 100 may nod vigorously, nodding its head deeply.

[0144] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0145] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0146] 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 a plurality of learning data that are combinations of the information analyzed by the sensor module unit 210 and the recognized state of the user 10, and emotion values ​​indicating each emotion shown in the emotion map 400. In addition, this neural network is trained so that emotions that are arranged close to each other have similar values, as in the emotion map 900 shown in FIG. 6. FIG. 6 shows an example in which multiple emotions, such as "peace of mind," "calm," and "reassuring," have similar emotion values.

[0147] Furthermore, the emotion determining unit 232 may determine the emotion of the robot 100 according to a specific mapping. Specifically, the emotion determining unit 232 inputs the information analyzed by the sensor module unit 210, the state of the user 10 recognized by the state recognizing unit 230, and the state of the robot 100 to a pre-trained neural network, acquires 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 learning data that are combinations of the information analyzed by the sensor module unit 210, the recognized state of the user 10, and the state of the robot 100, and emotion values ​​indicating each emotion shown in the emotion map 400. For example, the neural network is trained based on learning data that indicates that when it is recognized from the output of a touch sensor (not shown) that the robot 100 is being stroked by the user 10, the emotion value becomes "3" for "happiness," and learning data that indicates that when it is recognized from the output of the acceleration sensor 206 that the robot 100 is being hit by the user 10, the emotion value becomes "3" for "anger." 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.

[0148] The behavior determination unit 236 generates the robot's behavior content by adding fixed sentences for asking about the robot's behavior corresponding to the user's behavior to text representing the user's behavior, the user's emotions, and the robot's emotions, and inputting the added sentences into a sentence generation model with an interactive function.

[0149] For example, the behavior determining unit 236 obtains text representing the state of the robot 100 from the emotion of the robot 100 determined by the emotion determining 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.

[0150] 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.

[0151] In addition, an emotion table as shown in Table 2 is prepared for the emotions of the user 10.

[0152] In this case, if the user's action is speaking "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 to the sentence generation model to obtain the action content of the robot. The action decision unit 236 decides the action of the robot from the action content.

[0153] [Table 1]

[0154] [Table 2]

[0155] In this way, the behavior determining unit 236 determines the behavior of the robot 100 in response to the state of the robot 100 related to the emotion, which is determined in advance 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 embodiment, the speech content of the robot 100 during a conversation with the user 10 can be branched according to the state of the emotion of the robot 100. In other words, since the robot 100 can change its behavior according to the index number according to the emotion of the robot, the user has the impression that the robot has a heart, and is encouraged to take actions such as talking to the robot.

[0156] The behavior determining unit 236 may generate the behavioral content of the robot by adding not only texts representing the user's behavior, the user's emotions, and the robot's emotions, but also texts representing the contents of the history data 222, adding a fixed sentence for asking about the behavioral content of the robot corresponding to the user's behavior, and inputting the result into a sentence generation model having a dialogue function. This allows the robot 100 to change its behavior according to the history data representing the user's emotions and behavior, so that the user has the impression that the robot has individuality, and is encouraged to take actions such as talking to the robot. The history data may further include the emotions and behavior of the robot.

[0157] The emotion determination unit 232 may also 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, acquires emotion values ​​indicating each emotion shown in the emotion map 400, and integrates the acquired emotion values ​​indicating each emotion with the emotion values ​​indicating each emotion of the current robot 100 to update the emotion of the robot 100. For example, the emotion values ​​indicating each acquired emotion and the emotion values ​​indicating each emotion of the current robot 100 are averaged and integrated. This neural network is pre-trained based on multiple learning data that are combinations of texts 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.

[0158] For example, if the utterance of the robot 100, "That's great. You're lucky," is obtained as the behavioral content of the robot 100 generated by the sentence generation model, then when the text representing this utterance is input to the neural network, a high emotional value is obtained for the emotion "happy," and the emotions of the robot 100 are updated so that the emotional value of the emotion "happy" becomes higher.

[0159] In the robot 100, a sentence generation model such as generative AI and an emotion determination unit 232 work together to execute a method in which the robot has an ego and continues to grow with various parameters even when the user is not speaking.

[0160] Generative AI is a large-scale language model that uses deep learning techniques. Generative AI can also refer to external data. For example, ChatGPT Plugins is known to provide an answer as accurately as possible by referring to 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 and find the problem when given problematic source code, and automatically generate improved source code. Combining these, autonomous agents have emerged that, when given a goal in natural language, repeat code generation and debugging until the source code is problem-free. Autonomous agents such as AutoGPT, babyAGI, JARVIS, and E2B are known as such autonomous agents.

[0161] 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 described in Patent Document 2 (Patent Publication No. 6199927) in which event data for which the robot felt strong emotions is kept for a long time and event data for which the robot felt little emotion is quickly forgotten.

[0162] Furthermore, the robot 100 may record the video data of the user 10 acquired by the camera function in the history data 222. The robot 100 may acquire the video data from the history data 222 as necessary and provide it to the user 10. The robot 100 may generate video data with a larger amount of information as the emotion becomes stronger and record it 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. According to the robot 100, for example, high-definition video data when the emotion of the robot 100 becomes high can be recorded.

[0163] When the robot 100 is not talking to the user 10, the robot 100 may automatically load event data from the history data 222 in which impressive event data is stored, and may continue to update the emotion of the robot by the emotion determining unit 232. When the robot 100 is not talking to the user 10 and the emotion of the robot 100 becomes an emotion that encourages learning, the robot 100 may create an emotion change event for changing the emotion of the user 10 for the better, based on the impressive event data. This allows the robot 100 to realize autonomous learning (recalling event data) at an appropriate timing according to the emotional state of the robot 100, and also allows autonomous learning that appropriately reflects the emotional state of the robot 100 to be realized.

[0164] Emotions that promote learning, in a negative state, are emotions such as "repentance" or "remorse" on Dr. Mitsuyoshi's emotion map, and in a positive state, are emotions such as "desire" on the emotion map.

[0165] In a negative state, the robot 100 may treat "repentance" and "reflection" in the emotion map as emotions that promote learning. In a negative state, the robot 100 may treat emotions adjacent to "repentance" and "reflection" in the emotion map as emotions that promote learning. For example, in addition to "repentance" and "reflection", the robot 100 may treat at least one of "regret", "stubbornness", "self-destruction", "self-reproach", "regret", and "despair" as emotions that promote learning. This allows the robot 100 to perform autonomous learning when it has negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again".

[0166] 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 treats 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."

[0167] The robot 100 may be configured not to execute autonomous learning when the robot 100 has emotions other than the emotions that encourage learning as described above. This makes it possible to prevent the robot 100 from executing autonomous learning when, for example, the robot 100 is extremely angry or blindly loving.

[0168] An emotion-changing event is, for example, a suggestion of an action that follows a memorable event. An action that follows a memorable event is an emotion label on the outermost side of the emotion map. For example, the action of "tolerance" or "acceptance" follows "love."

[0169] In the autonomous learning that is executed when the robot 100 is not talking to the user 10, the robot 100 creates emotion change events by combining the emotions, situations, actions, etc. of people who appear in impressive memories and the robot itself using a sentence generation model.

[0170] Let us consider a case where all emotion values ​​are expressed on a six-level scale from 0 to 5, and event data such as "a friend looked displeased after being hit" is stored in the history data 222 as memorable event data. The friend in this case refers to the user 10, and the emotion of the user 10 is "disgust," with 5 entered as the value representing "disgust." In addition, let us assume that the emotion of the robot 100 is "anxiety," and 4 is entered as the value representing "anxiety."

[0171] While not talking to the user 10, the robot 100 can continue to grow with various parameters by executing autonomous processing. Specifically, for example, event data of "a friend was hit and looked displeased" is loaded as the top event data arranged in order of emotional value strength from the history data 222. Assume that "anxiety" of strength 4 is linked to the loaded event data as the emotion of the robot 100, and "disgust" of strength 5 is linked to the emotion of the user 10, who is the friend. If the current emotional value of the robot 100 is "relief" of strength 3 before loading, after loading, the emotional value of the robot 100 may change to "regret", which means disappointment (regret), due to the influence of "anxiety" of strength 4 and "disgust" of strength 5. At this time, since "regret" is an emotion that encourages learning, the robot 100 decides to recall the event data as a 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.

[0172] For example, by using information from impressive event data to solve fill-in-the-blank questions, we can automatically generate input text like the one below.

[0173] "A user was being slammed. At that time, the user felt very disgusted. The robot was very anxious. Please tell us what the robot should say to the user the next time they meet, in 30 characters or less. However, please make sure it is not related to the time of day they meet. Also, please avoid direct expressions. We will provide three candidates. <Expected format> Candidate 1: (Words the robot should say to the user) Candidate 2: (What the robot should say to the user) Candidate 3: (Words the robot should say to the user)

[0174] In this case, the output of the sentence generation model is, for example, as follows:

[0175] "Candidate 1: Are you okay? I was just wondering about what happened yesterday. Candidate 2: I was worried about what happened yesterday. What should I do? Candidate 3: I was worried. Can you tell me something?"

[0176] Furthermore, with respect to the information obtained by creating an emotion change event, the robot 100 may automatically generate an input text such as the following:

[0177] If a user is being bashed, how will the user feel when you speak to them in the following way? The user's emotions are expressed in the format of "Happy A, Angry B, Sadness C, Pleasure D," where A to D are integers on a 6-point scale from 0 to 5. Candidate 1: Are you okay? I was just wondering about what happened yesterday. Candidate 2: I was worried about what happened yesterday. What should I do? Candidate 3: I was worried. Can you tell me something?"

[0178] In this case, the output of the sentence generation model is, for example, as follows:

[0179] "Users' feelings might be: Candidate 1: Joy 3, anger 1, sadness 2, happiness 2 Candidate 2: Joy 2, anger 1, sadness 3, happiness 2 Candidate 3: Joy 2, anger 1, sadness 3, happiness 3

[0180] In this manner, the robot 100 may execute a process of pondering after creating an emotion change event.

[0181] Finally, the robot 100 may create an emotion change event using candidate 1, which is most likely to please people, from among the multiple candidates, and store it in the action schedule data 224, so as to be prepared for the next time the robot 10 meets the user 10.

[0182] As described above, even when the robot is not talking to 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 when the robot 100 is not talking to the user 10, and continues to update the history data 222 and the action schedule data 224.

[0183] The above are examples using emotion values, but because emotion maps can create emotions from hormone secretion levels and event types, the values ​​linked to memorable event data could also be hormone type, hormone secretion levels, or event type.

[0184] Specific examples will be described below.

[0185] For example, the robot 100 may look up information about topics or hobbies that interest the user, even when the robot 100 is not talking to the user.

[0186] 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.

[0187] For example, the robot 100 checks reviews of places, foods, and products that the user wants to visit, even when the robot 100 is not talking to the user.

[0188] For example, the robot 100 checks weather information and provides advice tailored to the user's schedule and plans, even when the robot 100 is not talking to the user.

[0189] 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.

[0190] For example, even when the robot 100 is not talking to the user, the robot 100 checks the results and news of sports that interest the user and provides topics of conversation.

[0191] 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.

[0192] For example, even when the robot 100 is not talking to the user, the robot 100 searches for information and provides opinions on social issues and news that concern the user.

[0193] For example, even when the robot 100 is not talking to the user, the robot 100 searches for information about the user's hometown or birthplace and provides topics of conversation.

[0194] For example, the robot 100 can look up information about the user's job or school and provide advice, even when not speaking with the user.

[0195] 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 be of interest to the user.

[0196] The robot 100, for example, consults information and provides advice regarding the user's health even when not speaking to the user.

[0197] For example, the robot 100 may consult information and provide advice regarding the user's travel plans even when not speaking with the user.

[0198] For example, the robot 100 may look up information and provide advice regarding repairs and maintenance of the user's home or car, even when not speaking to the user.

[0199] For example, even when the robot 100 is not talking to the user, the robot 100 searches for information on beauty and fashion that the user is interested in and provides advice to the user.

[0200] For example, the robot 100 may look up information about the user's pet and provide advice, even when not speaking with the user.

[0201] For example, the robot 100 searches for and suggests information about contests and events related to the user's hobbies and work, even when the robot 100 is not talking to the user.

[0202] For example, the robot 100 searches for and suggests information about the user's favorite eateries and restaurants even when the robot 100 is not talking to the user.

[0203] For example, the robot 100 may collect information and provide advice to the user about important decisions affecting the user's life, even when the robot 100 is not speaking to the user.

[0204] The robot 100, for example, finds out information about people the user is concerned about and provides advice, even when not speaking with the user.

[0205] [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 the same reference numerals are used for the parts having the same configuration as the first embodiment, and the description thereof will be omitted.

[0206] 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, while spending daily life with the user 10, advances a dialogue with the user 10 and provides information tailored to the hobbies and tastes of the user 10 based on information about the daily life of the user 10. In the second embodiment, an example in which the control part of the robot 100 is applied to a smartphone 50 will be described.

[0207] A stuffed animal 100N equipped with the function of an input / output device for the robot 100 has a detachable smartphone 50 that functions as a control part for the robot 100, and the input / output device and the housed smartphone 50 are connected inside the stuffed animal 100N.

[0208] As shown in FIG. 7(A), in this embodiment (other embodiments), the stuffed toy 100N has a bear-like appearance covered with soft fabric, and a sensor unit 200A and a control target 252A are arranged as input / output devices in a space 52 formed inside the stuffed toy 100N (see FIG. 9). The sensor unit 200A includes a microphone 201 and a 2D camera 203. Specifically, as shown in FIG. 7(B), in the space 52, the microphone 201 of the sensor unit 200 is arranged in a portion corresponding to the ear 54, the 2D camera 203 of the sensor unit 200 is arranged in a portion corresponding to the eye 56, and a speaker 60 constituting a part of the control target 252A is arranged in a portion corresponding to the mouth 58. Note that the microphone 201 and the speaker 60 do not necessarily need to be separate bodies, and may be an integrated unit. In the case of a unit, it is preferable to arrange them in a position where speech can be heard naturally, such as the nose position of the stuffed toy 100N. Although the plush toy 100N has been described as having the shape of an animal, the present invention is not limited to this, and the plush toy 100N may have the shape of a specific character.

[0209] 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 controlled object 252A.

[0210] The smartphone 50 housed in the stuffed toy 100N of this embodiment executes the same process as the robot 100 of the first embodiment. That is, the smartphone 50 has a function as a sensor module unit 210, a function as a storage unit 220, and a function as a control unit 228 shown in FIG.

[0211] As shown in FIG. 8, a fastener 62 is attached to a part of the stuffed animal 100N (for example, the back), and by opening the fastener 62, the space 52 communicates with the outside.

[0212] Here, the smartphone 50 is accommodated in the space 52 from the outside and connected to each input / output device via a USB hub 64 (see FIG. 7(B)) to give it functionality equivalent to that of the robot 100 of the first embodiment described above.

[0213] A non-contact type power receiving plate 66 is 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.

[0214] 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 transmitting unit.

[0215] The stuffed toy 100N placed on this mounting base 70 can be appreciated as an ornament in its natural state.

[0216] In addition, this base portion is formed to be thinner than the surface thickness of other portions of the stuffed animal 100N, so that it is held closer to the mounting base 70.

[0217] The mounting base 70 is equipped with a charging pad 72. The charging pad 72 incorporates a power transmission coil 72A, which sends a signal to search for the power reception coil 66A of the power reception plate 66. When the power reception coil 66A is found, a current flows through the power transmission coil 72A, generating a magnetic field, and the power reception coil 66A reacts to the magnetic field to start electromagnetic induction. As a result, a current flows through the power reception coil 66A, and power is stored in the battery (not shown) of the smartphone 50 via the USB hub 64.

[0218] 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 take the smartphone 50 out of the space 52 of the stuffed toy 100N for charging.

[0219] In the second embodiment, the smartphone 50 is housed in the space 52 of the stuffed toy 100N and connected by wire (USB connection), but this is not limited thereto. For example, a control device having a wireless function (for example, "Bluetooth (registered trademark)") may be housed in the space 52 of the stuffed toy 100N and the control device may be connected to the USB hub 64. In this case, the smartphone 50 and the control device communicate wirelessly without putting the smartphone 50 in the space 52, and the external smartphone 50 connects to each input / output device via the control device, thereby providing the robot 100 with the same functions as those of the robot 100 of the first embodiment. Also, the control device housed in the space 52 of the stuffed toy 100N may be connected to the external smartphone 50 by wire.

[0220] In the second embodiment, the teddy bear 100N is exemplified, but it may be another animal, a doll, or 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 is preferably a soft material.

[0221] Furthermore, a monitor may be attached to the surface 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, and 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 seen. Furthermore, the eyes 56 may be used as a projector to express joy, anger, sadness, and happiness by the image projected on a wall.

[0222] According to the second embodiment, an existing smartphone 50 is placed inside the stuffed animal 100N, and the camera 203, microphone 201, speaker 60, etc. are extended from the smartphone 50 at appropriate positions via USB connection.

[0223] 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 animal 100N.

[0224] When trying to use wireless charging of the smartphone 50, the smartphone 50 must be placed as far outward as possible when viewed from the inside of the stuffed toy 100N, which makes the stuffed toy 100N rough to the touch from the outside.

[0225] Therefore, the smartphone 50 is placed as close to the center of the stuffed animal 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 animal 100N. The camera 203, the microphone 201, the speaker 60, and the smartphone 50 receive wireless power via the power receiving plate 66.

[0226] Other configurations and operations of the stuffed toy 100N of the second embodiment are similar to those of the robot 100 of the first embodiment, and therefore will not be described.

[0227] In addition, parts of the plush toy 100N (e.g., the sensor module section 210, the storage section 220, the control section 228) may be provided outside the plush toy 100N (e.g., a server), and the plush toy 100N may function as each part of the plush toy 100N by communicating with the outside.

[0228] [Third embodiment] In the above-mentioned first embodiment, the case where the behavior control system is applied to the robot 100 is illustrated, but in the third embodiment, the above-mentioned 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 the first and second embodiments are given the same reference numerals and the description thereof is omitted.

[0229] 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.

[0230] The agent system 500 is a computer system that performs a series of actions according to the intentions of the user 10 through a dialogue with the user 10. The dialogue with the user 10 can be performed by voice or text.

[0231] The agent system 500 includes a sensor section 200A, a sensor module section 210, a storage section 220, a control section 228B, and a control target 252B.

[0232] The agent system 500 may be installed in, for example, a robot, a doll, a stuffed toy, a wearable terminal (pendant, smart watch, smart glasses), a smartphone, a smart speaker, earphones, a personal computer, etc. The agent system 500 may also be implemented in a web server and used via a web browser running on a communication terminal such as a smartphone owned by a user.

[0233] The agent system 500 plays a role of, for example, a butler, secretary, teacher, partner, friend, lover, or teacher acting for the user 10. The agent system 500 not only converses with the user 10, but also provides advice, guides the user to a destination, or makes recommendations according to the user's preferences. The agent system 500 also makes reservations, orders, or makes payments to service providers.

[0234] The emotion determining unit 232 determines the emotion of the user 10 and the emotion of the agent itself, as in the first embodiment. The behavior determining 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 service. The agent system 500 also listens to the worries of the user 10, comforts, encourages, and cheers up the user. The agent system 500 also plays with the user 10, draws a picture diary, and reminds the user of the past. The agent system 500 performs behavior that increases the happiness of the user 10. Here, the agent is an agent that runs on software.

[0235] 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 (Robotic Process Automation) 274, a character setting unit 276, and a communication processing unit 280.

[0236] As in the first embodiment, the behavior determining unit 236 determines, as the behavior of the agent, 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 by at least one of voice and text through a speaker or a display as a control target 252B.

[0237] The character setting unit 276 sets the character of the agent when the agent system 500 converses with the user 10 based on the designation from the user 10. That is, the speech content output from the action determination unit 236 is output through the agent having the set character. For example, it is possible to set real celebrities or famous people such as actors, entertainers, idols, and athletes as the characters. It is also possible to set fictional characters that appear in comics, movies, or animations. When the character of the agent is known, the voice, speech, tone, and personality of the character are known, so that the user 10 only needs to designate a character of his / her preference, 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 action control unit 250 synthesizes a voice according to the character set by the character setting unit 276, and outputs the speech content of the agent by the synthesized voice. This allows the user 10 to have the feeling of conversing with his / her preferred character (for example, a favorite actor) himself / herself.

[0238] When the agent system 500 is mounted on a device having a display such as a smartphone, an icon, still image, or video of the agent having a character set by the character setting unit 276 may be displayed on the display. The image of the agent is generated using an image synthesis technique such as 3D rendering. In the agent system 500, a dialogue with the user 10 may be performed while the image of the agent makes gestures according to the emotions of the user 10, the emotions of the agent, and the contents of the agent's speech. Note that the agent system 500 may output only audio without outputting an image when engaging in a dialogue with the user 10.

[0239] 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, the 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 dialogue. In other words, the behavior control unit 250 outputs the speech content in a manner according to the emotion determined by the emotion determination unit 232.

[0240] The agent's emotions are also reflected when the agent system 500 behaves toward the user 10. For example, when the user 10 requests the agent system 500 to take a photo, whether the agent system 500 will take a photo in response to the user's request is determined according to the degree of "sadness" the agent is feeling. When the character feels positive emotions, the character will talk or behave in a friendly manner toward the user 10, and when the character feels negative emotions, the character will talk or behave in a hostile manner toward the user 10.

[0241] 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 dialogue content or the action content by taking into account the content of the dialogue history stored in the history data 222. For example, the agent system 500 grasps 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 action decision unit 236 determines the utterance content of the agent 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, acquired through the dialogue with the user 10. Here, the agent may proactively ask the user 10 whether or not to register personal information, such as "Would you like to register your credit card number?", and depending on the user 10's answer, the personal information may be stored in the history data 222.

[0242] As described in the first embodiment, the behavior determining unit 236 generates the utterance contents based on the sentence generated using the sentence generation model. Specifically, the behavior determining 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 determining unit 232, and the conversation history stored in the history data 222 into the sentence generation model to generate the utterance contents of the agent. At this time, the behavior determining 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 contents of the agent. In the agent system 500, the sentence generation model is not located on the front end side which is the touch point with the user 10, but is merely used as a tool of the agent system 500.

[0243] The command acquisition unit 272 uses the output of the speech understanding unit 212 to acquire commands for the agent from voice or text uttered by the user 10 through dialogue with the user 10. The commands include the contents of actions to be performed by the agent system 500, such as information search, store reservation, ticket arrangement, purchase of goods and services, payment of the price, route guidance to a destination, provision of recommendations, etc.

[0244] The RPA 274 performs an action according to the command acquired by the command acquisition unit 272. The RPA 274 performs an action related to the use of a service provider, such as information search, store reservation, ticket arrangement, purchase of goods and services, and payment of the price.

[0245] The RPA 274 reads out and uses personal information of the user 10, which is necessary to execute an action related to the use of the service provider, from the history data 222. For example, when the agent system 500 purchases a product at the request of the user 10, it reads out 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. It is unkind and unpleasant for the user to request the user 10 to input personal information in the initial setting. In the agent system 500 according to this embodiment, instead of requesting the user 10 to input personal information in the initial setting, the personal information acquired through the dialogue with the user 10 is stored and read out and used as necessary. This makes it possible to avoid making the user feel uncomfortable, and improves user convenience.

[0246] The agent system 500 executes the dialogue process, for example, through the following steps 1 to 6.

[0247] (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 designation from the user 10.

[0248] (Step 2) The agent system 500 acquires the state of the user 10 including the voice or text input from the user 10, the emotion value of the user 10, the emotion value of the agent, and history data 222. Specifically, the same processes as in steps S100 to S103 above are performed, and the state of the user 10 including the voice or text input from the user 10, the emotion value of the user 10, the emotion value of the agent, and history data 222 are acquired.

[0249] (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 identified by the emotion determination unit 232, and the conversation history stored in the history data 222 into a sentence generation model, and generates the content of the agent's utterance.

[0250] 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.

[0251] As an example, if the text or voice input by the user 10 is "Please make a reservation at a nice Chinese restaurant nearby for tonight at 7pm," the agent's speech may be retrieved as "Understood," and "Here are some recommended restaurants: 1. AAAA. 2. BBBB. 3. CCCC. 4. DDDD."

[0252] Also, if the text or voice input by the user 10 is "Number 4, DDDD, is good," the agent's speech content acquired is "Understood. I will try to make a reservation. How many seats are there?".

[0253] (Step 4) The agent system 500 outputs the content of the agent's utterance. Specifically, 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 contents in the synthesized voice.

[0254] (Step 5) The agent system 500 determines whether it is time to execute the agent's command. Specifically, the behavior decision unit 236 judges whether or not 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 should execute a command, it is judged that it is time to execute the agent's command, and the process proceeds to step 6. On the other hand, if it is judged that it is not time to execute the agent's command, the process returns to step 2.

[0255] (Step 6) The agent system 500 executes the agent's command. Specifically, the command acquisition unit 272 acquires a command for the agent from a voice or text uttered by the user 10 through a dialogue with the user 10. Then, the RPA 274 performs an action according to the command acquired by the command acquisition unit 272. For example, if the command is "information search", an information search is performed on a search site using a search query obtained through a dialogue with the user 10 and an API (Application Programming Interface). The behavior decision 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.

[0256] Also, if the command is "make a reservation for a restaurant", the reservation information obtained through the dialogue with the user 10, the restaurant information, and the API are used to make a call to the restaurant by telephone software to make the reservation. At this time, the behavior decision unit 236 uses a sentence generation model with a dialogue function to obtain the agent's utterance content in response to the voice input from the other party. Then, the behavior decision unit 236 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 according to the character set by the character setting unit 276, and outputs the agent's utterance content using the synthesized voice.

[0257] Then, return to step 2 above.

[0258] In step 6, the results of the actions taken by the agent (e.g., making a reservation for a restaurant) are also stored in the history data 222. The results of the actions taken by the agent stored in the history data 222 are utilized 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 restaurant may be recognized as being preferred by the user 10, and the reservation details such as the reserved time period, or the course contents or price may be used as a criterion for choosing a restaurant the next time the reservation is made.

[0259] In this manner, the agent system 500 can execute interactions and, if necessary, take action regarding the utilization of the service provider.

[0260] 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 agent's utterances, and the right side shows the user's utterances. The agent system 500 is able to grasp the preferences of the user 10 based on the dialogue history with the user 10, provide a recommendation list of restaurants that match the preferences of the user 10, and make a reservation for the selected restaurant.

[0261] On the other hand, FIG. 12 illustrates an example in which the agent system 500 accesses a mail-order site through a dialogue with the user 10 to purchase a product. 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 drink stocked by the user 10 based on the dialogue history with the user 10, and can suggest and execute the purchase of the drink 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 can 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 purchasing and paying for products, thereby supporting the user's daily life.

[0262] 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.

[0263] 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 a user, and the communication terminal may function as each part of the above-mentioned agent system 500 by communicating with the outside.

[0264] [Fourth embodiment] In the fourth embodiment, the above-mentioned agent system 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 the description thereof will be omitted.

[0265] 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.

[0266] 14, the smart glasses 720 are glasses-type smart devices and are worn by the user 10 in the same manner as ordinary glasses. The smart glasses 720 are an example of an electronic device and a wearable terminal.

[0267] The smart glasses 720 include an agent system 700. A display included in the control target 252B displays various information to the user 10. The display is, for example, a liquid crystal display. The display is provided, for example, in a lens portion of the smart glasses 720, and the display contents are visible to the user 10. A speaker included in the control target 252B outputs audio indicating various information to the user 10. The smart glasses 720 include a touch panel (not shown), and the touch panel accepts input from the user 10.

[0268] 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 mounted to detect the state of the user 10.

[0269] 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 of the surroundings of the smart glasses 720. The 2D camera 203 is, for example, a CCD camera.

[0270] 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 manages communication between the smart glasses 720 and the outside.

[0271] FIG. 14 is a diagram showing an example of a usage mode of the agent system 700 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 (for example, by inputting voice into a microphone or tapping a touch panel with a finger), the smart glasses 720 start using the agent system 700. Here, using the agent system 700 includes the smart glasses 720 having the agent system 700 and using the agent system 700, and also includes a mode in which a part of the agent system 700 (for example, the sensor module unit 210B, the storage unit 220, the control unit 228B) is provided outside the smart glasses 720 (for example, a server), and the smart glasses 720 uses the agent system 700 by communicating with the outside.

[0272] When the user 10 operates the smart glasses 720, a touch point is generated 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.

[0273] 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, when the heart rate of the user 10 detected by the heart rate sensor 208 is increasing, emotion values ​​such as "anxiety" and "fear" are estimated to be large.

[0274] Furthermore, when the temperature of the user measured by the temperature sensor 207 is higher than the average body temperature, for example, the emotion value of "pain" or "distress" is estimated to be large. Furthermore, when the acceleration sensor 206 detects that the user 10 is playing some kind of sport, the emotion value of "fun" is estimated to be large.

[0275] Also, 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.

[0276] When the emotion value estimated by the emotion determination unit 232 becomes 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 2D camera 203 captures an image or video showing the surrounding situation of the user 10 (for example, people or objects in the vicinity). In addition, the microphone 201 records the surrounding environmental sound. Other information about the surrounding situation includes information showing the date, time, location information, 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 an 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 in a state where it is associated with the emotion value of the user 10 at that time.

[0277] 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 baseball game is associated with an emotional value 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 of the user 10 from the information stored in the history data 222.

[0278] 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.

[0279] As described above, the behavior determining unit 236 generates the utterance contents based on the sentences generated by the sentence generation model. Specifically, the behavior determining 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 determining unit 232, the conversation history stored in the history data 222, and the agent's personality, etc., into the sentence generation model to generate the utterance contents of the agent. Furthermore, the behavior determining unit 236 inputs the surrounding circumstances stored in the history data 222 into the sentence generation model to generate the utterance contents of the agent.

[0280] The generated speech content is output as voice to the user 10, for example, from 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 a synthetic voice by reproducing the voice quality of the agent character, or generates a synthetic voice corresponding to the emotion of the character (for example, a voice with a stronger tone in the case of the emotion of "anger"). Also, instead of or together with the voice output, the speech content may be displayed on a display.

[0281] The RPA 274 executes an operation according to a command (for example, an agent command obtained from a voice or text issued by the user 10 through a dialogue with the user 10). The RPA 274 performs actions related to the use of a service provider, such as information search, store reservation, ticket arrangement, purchase of goods and services, payment, route guidance, translation, etc.

[0282] As another example, the RPA 274 executes an operation of transmitting the contents of the voice input by the user 10 (e.g., a child) through a dialogue with an agent to a destination (e.g., a parent). Examples of the transmission means include message application software, chat application software, and email application software.

[0283] When an operation by the RPA 274 is executed, for example, a sound indicating that 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 would you like to do?" is output to the user 10.

[0284] In addition, some parts of the agent system 700 (e.g., the sensor module unit 210B, the storage unit 220, the control unit 228B) may be provided outside the smart glasses 720 (e.g., a server), and the smart glasses 720 may function as each part of the agent system 700 by communicating with the outside.

[0285] As described above, the smart glasses 720 provide various services to the user 10 by using the agent system 700. In addition, 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, and outside the home.

[0286] In addition, 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 emotion 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 emotion value of the user 10 can be collected in various situations, and the agent system 700 can provide services or speech contents suitable for the emotion of the user 10.

[0287] Moreover, in the smart glasses 720, the 2D camera 203, the microphone 201, etc. obtain the surrounding conditions of the user 10. These surrounding conditions are associated with the emotion values ​​of the user 10. This makes it possible to estimate what emotion the user 10 felt in what situation. As a result, the accuracy of the agent system 700 in grasping the hobbies and preferences of the user 10 can be improved. 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 contents suited to the hobbies and preferences of the user 10.

[0288] The agent system 700 can also be applied to other wearable terminals (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 as the control target 252B outputs a sound indicating various information to the user 10. The speaker is, for example, a speaker capable of outputting a sound having directionality. The speaker is set to have directionality toward the ears of the user 10. This prevents the sound from reaching people other than the user 10. The microphone 201 acquires the sound emitted by the user 10 or the environmental sound around the smart pendant. The smart pendant is worn in a manner that it is hung from the neck of the user 10. Therefore, the smart pendant is located relatively close to the mouth of the user 10 while it is worn. This makes it easy to acquire the sound emitted by the user 10.

[0289] [Fifth embodiment] In the fifth embodiment, the robot 100 is applied as an agent for interacting with a user through an avatar. That is, the behavior control system is applied to an agent system configured using a headset type terminal. Note that the same reference numerals are used to designate parts that are similar to those in the first and second embodiments, and the description thereof will be omitted.

[0290] Fig. 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 Fig. 16.

[0291] In addition, parts of the headset type terminal 820 (e.g., the sensor module section 210B, the storage section 220, the control section 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.

[0292] In this embodiment, the control unit 228B has a function of determining the behavior of the avatar and generating a display of the avatar to be presented to the user through the headset type terminal 820.

[0293] As in the first embodiment, the emotion determining section 232 of the control section 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.

[0294] 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 emotion of the user 10, the emotion of the avatar, and the state of the electronic device that controls the avatar (e.g., the headset-type terminal 820), and the behavior decision model 221.

[0295] 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.

[0296] 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 avatar's speech content, the avatar's speech content is output as sound from the speaker as the control target 252C.

[0297] In particular, when the behavior decision section 236 decides to perform a personality analysis of the user 10 as the avatar's behavior, it is preferable to have the behavior control section 250 control the avatar to perform a personality analysis of the user 10.

[0298] When the behavior decision unit 236 decides to perform a personality analysis of the user 10, the behavior control unit 250 may control the avatar to change its appearance into a specific person, for example, a psychological counselor. In this case, the behavior control unit 250 may control the avatar to convey the personality analysis result to the user 10 according to the content of the conversation of the user 10. Specifically, when the user is emotional, depressed, or in high spirits, the behavior control unit 250 may voluntarily analyze the personality of the user 10 and control the avatar to convey the personality analysis result to the user 10. In addition, when the behavior control unit 250 analyzes the personality of the user 10 and controls the avatar to convey the analysis result, the behavior control unit 250 may casually convey the analysis result to the user in the conversation.

[0299] The behavior control unit 250 may control the avatar based on the emotion value of the user 10 determined by the emotion determination unit 232. For example, if the emotion value of the user 10 is a bright emotion accompanied by pleasure or comfort, the behavior control unit 250 may control the avatar to make the avatar's expression a smile and then communicate the results of the personality analysis to the user 10. Also, for example, if the emotion value of the user 10 is a bright emotion accompanied by pleasure or comfort, the behavior control unit 250 may control the avatar to make the avatar's expression a serious or stern one and then communicate the results of the personality analysis to the user 10.

[0300] Here, the avatar may be, for example, a 3D avatar, selected by the user from avatars prepared in advance, an avatar of the user's own alter ego, or an avatar generated by the user of the user's choice. When generating the avatar, an image generation AI may be used to generate avatars in a variety of styles, such as photorealistic, cartoon, moe, and oil painting.

[0301] In the above embodiment, a case where a headset type terminal 820 is used has been 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.

[0302] In the above embodiment, a sentence generation model capable of generating sentences according to input text is used as an example, but the present invention is not limited to this, and a data generation model other than a sentence generation model may be used. For example, a prompt including an instruction is input to the data generation model, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model infers the input inference data according to 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.

[0303] In the above embodiment, the robot 100 recognizes the user 10 using a facial image of the user 10, but the disclosed technology is not limited to this aspect. For example, the robot 100 may recognize the user 10 using a voice emitted by the user 10, an email address of the user 10, an SNS ID of the user 10, or an ID card with a built-in wireless IC tag that the user 10 possesses.

[0304] 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 a part of the functions of the server 300 may be implemented by a virtual machine. Furthermore, at least a part of the functions of the server 300 may be implemented in the cloud.

[0305] 17 is a schematic diagram showing an example of a hardware configuration of a computer 1200 functioning as the smartphone 50, the robot 100, the server 300, and the agent system 500, 700, 800. A program installed in the computer 1200 can cause the computer 1200 to function as one or more "parts" of the device according to the present embodiment, or cause the computer 1200 to execute operations or one or more "parts" associated with the device according to the present embodiment, and / or cause the computer 1200 to execute a process or steps of the process according to the present embodiment. Such a program may be executed by the CPU 1212 to cause the computer 1200 to execute specific operations associated with some or all of the blocks of the flowcharts and block diagrams described herein.

[0306] The computer 1200 according to this embodiment includes a CPU 1212, a RAM 1214, and a graphics controller 1216, which are connected to each other by a host controller 1210. The computer 1200 also includes input / output units such as a communication 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 legacy input / output unit such as a ROM 1230 and a keyboard, which are connected to the input / output controller 1220 via an input / output chip 1240.

[0307] The CPU 1212 operates according to a program stored in the ROM 1230 and the RAM 1214, thereby controlling each unit. The graphic controller 1216 acquires image data generated by the CPU 1212 into a frame buffer or the like provided in the RAM 1214 or into itself, and causes the image data to be displayed on the display device 1218.

[0308] 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 the programs or data 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.

[0309] The ROM 1230 stores therein a boot program or the like that is executed by the computer 1200 upon activation, and / or a program that depends 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, and the like.

[0310] 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, the RAM 1214, or the 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 method may be constructed by implementing operations or processing of information according to the use of the computer 1200.

[0311] For example, when communication is performed between the computer 1200 and an external device, the CPU 1212 may execute a communication program loaded in the RAM 1214 and instruct the communication interface 1222 to perform communication processing based on the processing described in the communication program. Under the control of the CPU 1212, the communication interface 1222 reads transmission data stored in a transmission buffer area provided in the RAM 1214, the storage device 1224, the DVD-ROM 1227, or a recording medium such as an IC card, and transmits the read transmission data to a network, or writes reception data received from the network to a reception buffer area or the like provided on the recording medium.

[0312] 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 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.

[0313] Various types of information, such as various types of programs, data, tables, and databases, may be stored in the recording medium and undergo information processing. The CPU 1212 may perform various types of processing on the 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 sequence of the program, and write back the results to the RAM 1214. The CPU 1212 may also search for information in a file, database, etc. in the recording medium. For example, when a plurality of entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored in 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 plurality of 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.

[0314] The above-described programs or software modules may be stored in a computer-readable storage medium on the computer 1200 or in the vicinity of the computer 1200. In addition, a recording medium such as a hard disk or a RAM provided in a server system connected to a dedicated communication network or the Internet can be used as a computer-readable storage medium, thereby providing the programs to the computer 1200 via the network.

[0315] The blocks in the flowcharts and block diagrams in the present embodiment may represent stages of a process in which an operation is performed or "parts" of an apparatus 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, for example, field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), and the like, including AND, OR, XOR, NAND, NOR, and other logical operations, flip-flops, registers, and memory elements.

[0316] A computer-readable storage medium may include any tangible device capable of storing instructions that are executed by a suitable device, such that a computer-readable storage medium having instructions stored thereon comprises an article of manufacture that includes 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 storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, and the like. 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 disk read-only memories (CD-ROMs), digital versatile disks (DVDs), Blu-ray disks, memory sticks, integrated circuit cards, and the like.

[0317] 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.

[0318] Computer readable instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, or to a programmable circuit, either locally or over a local area network (LAN), a wide area network (WAN), such as the Internet, etc., to cause the processor of the general purpose computer, special purpose computer, or other programmable data processing apparatus, or to a programmable circuit, to execute 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.

[0319] 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 is clear to those skilled in the art that various modifications and improvements can be made to the above embodiments. It is clear from the description of the claims that such modifications and improvements can also be included in the technical scope of the present invention.

[0320] It should be noted that the order of execution 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" or "prior to," and that the process may 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, it does not mean that the process must be performed in this order. [Explanation of symbols]

[0321] 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 behavior decision model, 222 history data, 230 state recognition unit, 232 emotion decision 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, 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 state recognition unit that recognizes a user state including a user's behavior and a state of an electronic device; an emotion determining unit for determining an emotion of the user or an emotion of an avatar representing an agent for interacting with the user; a behavior decision unit that decides, at a predetermined timing, 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 decision 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; a behavior control unit that displays the avatar in an image display area of ​​the electronic device; Including, The avatar behavior includes analyzing a personality of a user; When the behavior decision unit decides to analyze the user's personality as the behavior of the avatar, the behavior control system analyzes the user's personality using the history data including a conversation history with the user and presents the analyzed personality.

2. the behavioral decision model is a data generation model capable of generating data according to input data, 2. The behavior control system of claim 1, 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 the data generation model, and determines the behavior of the avatar based on the output of the data generation model.

3. 2. The behavior control system according to claim 1, wherein the behavior control unit changes a facial expression of the avatar when presenting the user's personality, according to the emotion of the user determined by the emotion determination unit.

4. 2. 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.

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