JP2024159676A - Behavior Control System - Google Patents

Abstract

An electronic device is caused to execute an appropriate action in response to a user's action.
[Solution] The behavior control system includes a user state recognition unit that recognizes a user state including a user's behavior, an emotion determination unit that determines an emotion of the user or an emotion of an electronic device, and a behavior determination unit that determines an action of the electronic device corresponding to the user state and the emotion of the user or the emotion of the electronic device based on a sentence generation model having a dialogue function for allowing the user and the electronic device to dialogue. The behavior determination unit increases the hunger level of the electronic device according to elapsed time, and decreases the hunger level of the electronic device based on a predetermined behavior by the user.
[Selected figure] Figure 2

Description

The present invention relates to a behavior control system.

Patent Document 1 discloses a technology for determining an appropriate robot behavior in response to a user's state. The conventional technology in Patent Document 1 recognizes the user's reaction when the robot performs a specific action, and if the robot is unable to determine an action to be taken in response to the recognized user reaction, it updates the robot's behavior by receiving information about an action appropriate to the recognized user's state from a server.

Patent No. 6053847

However, conventional technology leaves room for improvement in enabling electronic devices to perform appropriate actions in response to user actions.

According to a first aspect of the present invention, there is provided a behavior control system including a user state recognition unit that recognizes a user state including a user's behavior, an emotion determination unit that determines an emotion of the user or an emotion of an electronic device, and a behavior determination unit that determines an action of the electronic device corresponding to the user state and the emotion of the user or the emotion of the electronic device based on a sentence generation model having a dialogue function for allowing the user and the electronic device to dialogue with each other, and the behavior determination unit increases a hunger level of the electronic device according to elapsed time and decreases the hunger level of the electronic device based on a predetermined behavior by the user.
A behavior control system according to a second aspect of the present invention is the behavior control system of the first aspect, wherein the electronic device is a robot. Here, the robot includes a device that performs a physical action, a device that outputs video and audio without performing a physical action, and an agent that operates on software.

1 illustrates a schematic diagram of an example of a system 5 according to a first embodiment. 2 illustrates a schematic functional configuration of a robot 100 according to a first embodiment. 13 illustrates an example of an operation flow of a collection process by the robot 100 according to the first embodiment. 13 illustrates an example of an operation flow of a response process by the robot 100 according to the first embodiment. 4 illustrates an example of an operation flow of autonomous processing by the robot 100 according to the first embodiment. 4 shows an emotion map 400 onto which multiple emotions are mapped. 9 shows an emotion map 900 onto which multiple emotions are mapped. 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. FIG. 11 is a rear front view of a stuffed animal 100N according to a second embodiment. 13 shows a schematic functional configuration of a stuffed animal 100N according to a second embodiment. 13 shows an outline of the functional configuration of an agent system 500 according to a third embodiment. An example of the operation of the agent system is shown. An example of the operation of the agent system is shown. 1 illustrates an example of a hardware configuration of a computer 1200.

The present invention will be described below through embodiments of the invention, but the following embodiments do not limit 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.

[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.

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 the communication network 20 to converse with the user 10 and provide images, etc. to the user 10. For example, the robot 100 not only learns appropriate conversation by itself, but also cooperates with the server 300 to learn how to have a more appropriate conversation with the user 10. The robot 100 also records captured image data of the user 10 in the server 300, and requests the image data, etc. from the server 300 as necessary to provide it to the user 10.

The robot 100 also has an emotion value that represents the type of its own emotion. For example, the robot 100 has emotion values that represent the strength of each of the emotions of "happiness," "anger," "sorrow," "pleasure," "discomfort," "relief," "anxiety," "sorrow," "excitement," "worry," "relief," "fulfillment," "emptiness," and "neutral." When the robot 100 converses with the user 10 when its excitement emotion value is high, for example, it speaks at a fast speed. In this way, the robot 100 can express its own emotions through its actions.

The robot 100 may be configured to determine the behavior of the robot 100 corresponding to the emotion of the user 10 by matching a sentence generation model using AI (Artificial Intelligence) with an emotion engine. 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.

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 publicly known as disclosed in, for example, JP 2018-081444 A and ChatGPT (Internet search <URL: https://openai.com/blog/chatgpt>), and therefore a detailed description thereof will be omitted. Such a sentence generation model is configured by a large language model (LLM: Large Language Model).

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

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 the facial image of the user 10 acquired by the camera function and the voice of the user 10 acquired by the 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.

As an example of a behavioral decision model, the robot 100 stores 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 behavior of the user 10, and performs various behaviors according to the rules.

Specifically, the robot 100 has reaction rules for determining the behavior of the robot 100 based on the emotions of the user 10, the emotions of the robot 100, and the behavior of the user 10, 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". The reaction rules also define the behavior of the robot 100 as "apologizing" when the behavior of the user 10 is "angry". The reaction rules also define the behavior of the robot 100 as "answering" when the behavior of the user 10 is "asking a question". The reaction rules also define the behavior of the robot 100 as "calling out" when the behavior of the user 10 is "sad".

When the robot 100 recognizes that the behavior of the user 10 is "angry" 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 words "apologize".

In addition, when the emotion of the robot 100 is "normal" (i.e., "happy" = 0, "anger" = 0, "sad" = 0, "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."

When the robot 100 recognizes based on the reaction rules 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 rules as the action to be performed toward the user 10. For example, when the robot 100 selects the action of "calling out", it converts the words "What's wrong?", which express concern, into a worried voice and outputs it.

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

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

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.

Figure 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.

The controlled object 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.

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 images 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.

Note that, among the components of the robot 100 shown in FIG. 2, the components other than the control target 252 and the sensor unit 200 are examples of components of the behavior control system of the robot 100. The behavior control system of the robot 100 controls the control target 252.

The storage unit 220 includes a behavior decision model 221, history data 222, collected data 223, and behavior 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 the history of behavior, 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 the history of the behavior 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, the attribute information of the user 10, and the like. Note that the functions of the components of the robot 100 shown in FIG. 2, except for the control target 252, the sensor unit 200, and the storage unit 220, can be realized by the CPU operating based on a program. For example, the functions of these components can be implemented as CPU operations using operating system (OS) and programs that run on the OS.

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.

The voice emotion recognition unit 211 of the sensor module unit 210 analyzes the voice of the user 10 detected by the microphone 201 to recognize the emotion of the user 10. For example, the voice emotion recognition unit 211 extracts features such as frequency components of the voice, and recognizes the emotion of the user 10 based on the extracted features. 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 user 10's utterance.

The facial expression recognition unit 213 recognizes the facial expression and emotions 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 emotions of the user 10 based on the shape, positional relationship, etc. of the eyes and mouth.

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.

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

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 charge of the robot 100 and the brightness of the surrounding environment of the robot 100 as the state of the robot 100.

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.

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

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.

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

Specifically, the emotion determination unit 232 determines an emotion value indicating the emotion of the robot 100 according to rules for updating the emotion value of the robot 100 that are 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.

For example, if the state recognition unit 230 recognizes that the user 10 looks lonely, the emotion determination unit 232 increases the "sad" emotion value of the robot 100. Also, if the state recognition unit 230 recognizes that the user 10 is smiling, the emotion determination unit 232 increases the "happy" emotion value of the robot 100.

The emotion determination 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 completely dark, 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.

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 number of predetermined behavioral categories (e.g., "laughing," "anger," "asking a question," "sad") is obtained, and the behavioral category with the highest probability is recognized as the behavior of the user 10.

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, and the behavior control system of the robot 100 according to this embodiment takes into consideration the protection of the personal information and privacy of the user 10.

Next, we will explain the processing of the behavior decision unit 236 when performing response processing in which the robot 100 responds to the behavior of the user 10.

The behavior determination unit 236 determines an action corresponding to the behavior of the user 10 recognized by the behavior recognition unit 234 based on the current emotion value of the user 10 determined by the emotion determination unit 232, the history data 222 of past emotion values determined by the emotion determination unit 232 before the current emotion value of the user 10 was determined, and the emotion value of the robot 100. In this embodiment, the behavior determination unit 236 uses one most recent emotion value included in the history data 222 as the past emotion value of the user 10, but the disclosed technology is not limited to this aspect. For example, the behavior determination unit 236 may use the most recent multiple 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 determination unit 236 may determine an action 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 determination unit 236 includes gestures performed by the robot 100 or the contents of speech uttered by the robot 100.

The behavior decision unit 236 according to this embodiment decides the behavior of the robot 100 as the behavior corresponding to the behavior of the user 10, based on a combination of the past and current emotion values 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 the behavior corresponding to the behavior of the user 10 as the behavior for changing the emotion value of the user 10 to a positive value.

The reaction rules as the behavior decision model 221 define the behavior of the robot 100 according to a combination of the past and current emotional values 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 defined as the behavior of the robot 100.

For example, the reaction rule as the behavior decision model 221 defines the behavior of the robot 100 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 defined according to the behavior patterns of the user 10 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 the behavior decision unit 236 may transition to an operation mode that determines the behavior of the robot 100 using the history data 222, for example, 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 reaction rules as the behavior decision model 221 may define 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 decision model 221 may define at least one of a gesture and a statement as the behavior of the robot 100, for each group of patterns of the emotion value of the robot 100.

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

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.

Specifically, if the total intensity value, which is 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.

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).

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.

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

In addition, after the behavior control unit 250 executes the behavior determined by the behavior determination unit 236 in the execution form determined according to the emotion of the robot 100, the emotion determination unit 232 further changes the emotion value of the robot 100 based on the user's reaction to the execution of the behavior. Specifically, the emotion determination unit 232 increases the emotion value of "happiness" of the robot 100 when the user's reaction to the behavior determined by the behavior determination unit 236 being performed on the user in the execution form determined by the behavior control unit 250 is not bad. In addition, the emotion determination unit 232 increases the emotion value of "sadness" of the robot 100 when the user's reaction to the behavior determined by the behavior determination unit 236 being performed on the user in the execution form determined by the behavior control unit 250 is bad.

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, it 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, it controls the control object 252 to make the robot 100 assume a droopy posture.

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 updated reaction rules from the server 300, it updates the reaction rules as the behavioral decision model 221.

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

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

Specifically, the related information collection unit 270 acquires preference information indicating matters of interest to the user 10 from the contents of the speech of the user 10 or from a setting operation by the user 10. The related information collection unit 270 periodically collects news related to the preference information from external data, for example, using ChatGPT Plugins (Internet search <URL: https://openai.com/blog/chatgpt-plugins>). For example, if it is acquired as preference information that the user 10 is a fan of a specific professional baseball team, the related information collection 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.

The emotion determination unit 232 determines the emotion of the robot 100 based on information related to the preference information collected by the related information collection unit 270.

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

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.

Next, we will explain the processing of the behavior decision unit 236 when the robot 100 performs autonomous processing to act autonomously.

The behavior decision unit 236 uses 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 at a predetermined timing to decide one of a plurality of types of robot behaviors, including no behavior, as the behavior of the robot 100. Here, an example will be described in which a sentence generation model with a dialogue function is used as the behavior decision model 221.

Specifically, the behavior decision 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 decides the behavior of the robot 100 based on the output of the sentence generation model.

For example, the multiple types of robot behaviors include (1) to (10) below.

(1) The robot does nothing.
(2) Robots dream.
(3) The robot speaks to the user.
(4) The robot creates a picture diary.
(5) The robot suggests an activity.
(6) The robot suggests people for the user to meet.
(7) The robot introduces news that may be of interest to the user.
(8) The robot edits photos and videos.
(9) The robot studies together with the user.
(10) Robots evoke memories.

The behavior decision unit 236 inputs the state of the user 10 and the state of the robot 100 recognized by the state recognition unit 230, text representing the current emotion value of the user 10 and the current emotion value of the robot 100 determined by the emotion decision unit 232, and text asking about one of multiple types of robot behaviors including not taking any action, into the sentence generation model every time a certain period of time has elapsed, and determines the behavior of the robot 100 based on the output of the sentence generation model. Here, if 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, or may only include information indicating that the user 10 is not present.

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 (10) is the best behavior for the robot?"
(1) The robot does nothing.
(2) Robots dream.
(3) The robot talks to the user.
..." 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 is the most appropriate behavior," the behavior of the robot 100 is determined to be "(1) doing nothing" or "(2) the robot dreams."

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.
. . " 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."

When the behavior decision unit 236 decides to create an original event, i.e., "(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.

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, it uses a sentence generation model to decide the robot's utterance content corresponding to the user state and the user's emotion or the robot's emotion. At this time, the behavior control unit 250 causes a sound representing the determined robot's utterance content to be output from a speaker included in the control target 252. Note that, when the user 10 is not present around the robot 100, the behavior control unit 250 stores the determined robot's utterance content in the behavior schedule data 224 without outputting a sound representing the determined robot's utterance content.

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

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

When the behavior decision unit 236 determines that the robot behavior is "(8) The robot edits photos and videos," i.e., that an image is to be edited, it selects event data from the history data 222 based on the emotion value, and edits and outputs the image data of the selected event data. Note that when the user 10 is not present near 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.

When the behavior decision unit 236 determines that the robot behavior is "(5) The robot proposes an activity," i.e., that the robot proposes an action for the user 10, it uses a sentence generation model to determine the proposed user action based on the event data stored in the history data 222. At this time, the behavior control unit 250 causes a sound proposing the user action to be output from a speaker included in the control target 252. Note that, when the user 10 is not present around the robot 100, the behavior control unit 250 stores in the action schedule data 224 the suggestion of the user action without outputting the sound proposing the user action.

When the behavior decision unit 236 determines that the robot behavior is "(6) The robot proposes people that the user should meet," that is, to propose people that the user 10 should have contact with, it uses a sentence generation model based on the event data stored in the history data 222 to determine people that the proposed user should have contact with. At this time, the behavior control unit 250 causes a speaker included in the control target 252 to output a sound indicating that a person that the user should have contact with is being proposed. 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 suggestion of people that the user should have contact with, without outputting a sound indicating that a person that the user should have contact with is being proposed.

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

When the behavior decision unit 236 decides that the robot behavior is "(10) The robot recalls a memory," i.e., that the robot recalls event data, it selects the event data from the history data 222. At this time, the emotion decision unit 232 judges the emotion of the robot 100 based on the selected event data. Furthermore, the behavior decision unit 236 uses a sentence generation model based on the selected event data to create an emotion change event that represents the speech 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 scheduled behavior data 224.

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, "Which of the following would you like to say to the user the next time you meet them on the topic of pandas? 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 the user be most happy about?" 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.

In addition, for example, event data with a high emotion value for the robot 100 is selected as an impressive memory for the robot 100. This makes it possible to create an emotion change event based on the event data selected as an impressive memory.

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.

For example, if the user 10 is not present near 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 on the behavior of the robot 100. Also, if 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 on the behavior of the robot 100.

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

First, in step S90, the related information collection unit 270 acquires preference information that represents matters of interest to the user 10.

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

In step S94, the emotion determination unit 232 determines the emotion value of the robot 100 based on information related to the preference information collected by the related information collection unit 270.

In step S96, the memory control unit 238 determines whether 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 process 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 process proceeds to step S998.

In step S98, the memory control unit 238 stores the information related to the collected preference information in the collected data 223 and terminates the process.

Figure 4A shows an example of an outline of an operation flow relating to the operation of determining an action in the robot 100 when performing a response process in which the robot 100 responds to the action of the user 10. The operation flow shown in Figure 4A is executed repeatedly. At this time, it is assumed that information analyzed by the sensor module unit 210 has been input.

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.

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.

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 value of the user 10 and the emotion value of the robot 100 to the history data 222.

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

In step S106, the behavior decision unit 236 decides the behavior of the robot 100 based on a combination of the current emotion value of the user 10 determined in step S102 and the past emotion values included in the history data 222, the emotion value of the robot 100, the behavior of the user 10 recognized in step S104, and the behavior decision model 221.

In step S108, the behavior control unit 250 controls the control object 252 based on the behavior determined by the behavior determination unit 236.

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

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

In step S114, event data including the action determined by the action 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.

Figure 4B shows an example of an outline of an operation flow relating to the operation of determining the behavior of the robot 100 when the robot 100 performs autonomous processing to act autonomously. The operation flow shown in Figure 4B is automatically executed repeatedly, 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 Figure 4A above.

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.

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.

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 value of the user 10 and the emotion value of the robot 100 to the history data 222.

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

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

In step S201, the behavior decision unit 236 determines whether or not it was decided in step S200 above that no action should be taken. If it was decided that no action should be taken as the action of the robot 100, the process ends. On the other hand, if it was not decided that no action should be taken as the action of the robot 100, the process proceeds to step S202.

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

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

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

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.

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 (e.g., the facial expression, emotions, etc. of the user 10), and data on the sound, image, smell, etc. of the location.

According to the robot 100, the robot 100 can be made to perform an appropriate action in response to the action of the user 10. Conventionally, the user's actions were classified and the action including the robot's facial expression and appearance was 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 something better happened than yesterday?" Furthermore, for example, the robot 100 can say to a user 10 whose emotion value is equal to or greater than 0 and whose emotion value fluctuation range continues to be within a certain range, "You've been feeling stable lately, which is good."

For example, the robot 100 can ask the user 10, "Did you finish the homework I told you about yesterday?", and if the user 10 responds, "I did it," it can utter a positive utterance such as "Great!" and make a positive gesture such as clapping or a thumbs up. 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 great job!" and make the above-mentioned positive gesture. In this way, the robot 100 can be expected to make the user 10 feel a sense of closeness to the robot 100 by performing actions based on the state history of the user 10.

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 as event data in the history data 222.

Using the data stored 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 that expresses the user's happiness.

In addition, 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.

Furthermore, in the autonomous processing, the robot 100 can create emotion change events for increasing positive emotions by repeatedly generating questions, inputting them into a sentence generation model, and obtaining the output of the sentence generation model as an answer to the question, and storing these in the behavior schedule data 224. In this way, the robot 100 can execute self-learning.

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

In addition, the related information collection unit 270 can perform self-learning by automatically performing a keyword search corresponding to the preference information about the user and repeating the search execution step of obtaining search results.

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 when no external trigger is received.

In addition, the emotion determination unit 232 may determine the user's emotion according to a specific mapping. Specifically, the emotion determination unit 232 may determine the user's emotion according to an emotion map (see FIG. 5), which is a specific mapping.

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

(1) For example, if the emotion engine, which is the emotion determination unit 232 of the robot 100, detects emotions at approximately 100 msec, the frequency of the determination of the reaction action of the robot 100 (e.g., a backchannel) may be set to at least the same timing as the detection frequency of the emotion engine (100 msec), or may be set to an earlier timing. The detection frequency of the emotion engine may be interpreted as the sampling rate.

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. Note that 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.

(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 or security, the robot 100 may nod and continue listening. If the robot 100 feels anxious, confused, or suspicious, the robot 100 may tilt its head or stop shaking its head.

These emotions are distributed in the three o'clock direction of emotion map 400, and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness takes precedence over internal sensations, resulting in a sense of calm.

(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 hurt 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.

(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.

When the robot 100 feels a favorable feeling in its situational awareness while also feeling an internal sensation (reaction) 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, acceptance and tolerance toward the other person. Such emotions are distributed around 12 o'clock on the emotion map 400.

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 eye LEDs red and glare at the other person when it feels hatred. These types of emotions are distributed around the 6 o'clock position on the emotion map 400.

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

(6) When listening to someone with a sense of relief, which is distributed around the 3 o'clock area of the emotion map 400, the robot 100 may lightly nod its head and say "hmm," but when it comes to love, which is distributed around 12 o'clock, it may nod vigorously, nodding its head deeply.

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

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

The emotion determination unit 232 inputs the information analyzed by the sensor module unit 210 and the recognized state of the user 10 into a pre-trained neural network, obtains emotion values indicating each emotion shown in the emotion map 400, and determines the emotion of the user 10. This neural network is pre-trained based on multiple 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 located close to each other have similar values, as in the emotion map 900 shown in Figure 6. Figure 6 shows an example in which multiple emotions, such as "peace of mind," "calm," and "reassuring," have similar emotion values.

Furthermore, the emotion determination unit 232 may determine the emotion of the robot 100 according to a specific mapping. Specifically, the emotion determination unit 232 inputs the information analyzed by the sensor module unit 210, the state of the user 10 recognized by the state recognition unit 230, and the state of the robot 100 into a pre-trained neural network, obtains emotion values indicating each emotion shown in the emotion map 400, and determines the emotion of the robot 100. This neural network is pre-trained based on multiple 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 the robot 100 is recognized as being stroked by the user 10 from the output of a touch sensor (not shown), the emotional value becomes "happy" at "3," and that when the robot 100 is recognized as being hit by the user 10 from the output of the acceleration sensor 206, the emotional value becomes "anger" at "3." Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 6.

The behavior decision unit 236 generates the robot's behavior by adding fixed sentences to the text representing the user's behavior, the user's emotions, and the robot's emotions, and inputting the results into a sentence generation model with a dialogue function.

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

When the emotion of the robot 100 determined by the emotion determination unit 232 corresponds to index number "2", the text "very happy state" is obtained. Note that when the emotion of the robot 100 corresponds to multiple index numbers, multiple pieces of text representing the state of the robot 100 are obtained.

In addition, an emotion table like that shown in Table 2 is prepared for the emotions of user 10.

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 having a lot of fun. The user is having a normal amount of fun. The user says to the robot, 'Let's play together.' How will you respond as the robot?" is input into the sentence generation model to obtain the robot's behavior. The behavior decision unit 236 decides on the robot's behavior from this behavior.

In this way, the behavior decision unit 236 decides the behavior of the robot 100 in response to the state of the robot 100's emotion, which is predetermined for each type of emotion of the robot 100 and for each strength of the emotion, and the behavior of the user 10. In this form, the speech content of the robot 100 during a dialogue with the user 10 can be branched according to the state of the robot 100's emotion. In other words, since the robot 100 can change its behavior according to an 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.

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

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, obtains emotion values indicating each emotion shown in the emotion map 400, and integrates the obtained 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 emotion obtained 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.

For example, if the speech content 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 speech content is input to the neural network, a high emotion value is obtained for the emotion "happy," and the emotion of the robot 100 is updated so that the emotion value of the emotion "happy" becomes higher.

In the robot 100, a sentence generation model such as ChatGPT is linked to the emotion determination unit 232 to implement a method in which the robot has a sense of self and continues to grow with various parameters even when the user is not speaking.

ChatGPT is a large-scale language model that uses deep learning techniques. ChatGPT can also refer to external data. For example, ChatGPT plugins are known to provide as accurate an answer as possible by referring to various external data such as weather information and hotel reservation information through dialogue. For example, ChatGPT can automatically generate source code in various programming languages when a goal is given in natural language. For example, ChatGPT can also debug and find the problem when problematic source code is given, and automatically generate improved source code. Combining these, autonomous agents have emerged that, when a goal is given 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.

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 stored for a long time and event data for which the robot felt little emotion is quickly forgotten.

The robot 100 may also record video data of the user 10 acquired by the camera function in the history data 222. The robot 100 may acquire video data from the history data 222 as 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, it may switch to recording information in a low-compression format such as HD video when the emotion value of excitement exceeds a threshold. The robot 100 can leave a record of high-definition video data when the robot 100's emotion becomes heightened, for example.

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 robot's emotions using the emotion determination unit 232. When the robot 100 is not talking to the user 10 and the robot 100's emotions become emotions that encourage learning, the robot 100 may create an emotion change event for changing the user 10's emotions 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.

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

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

When the robot 100 is in a positive state, it may treat "desire" in the emotion map as an emotion that encourages learning. When the robot 100 is in a positive state, it 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".

The robot 100 may be configured not to execute autonomous learning when the robot 100 is experiencing emotions other than the emotions that encourage learning as described above. This can prevent the robot 100 from executing autonomous learning, for example, when the robot 100 is extremely angry or when the robot 100 is blindly feeling love.

An emotion-changing event, for example, is a suggestion of an action that follows a memorable event. An action that follows a memorable event is an emotion label that is on the outermost side of the emotion map. For example, beyond "love" are actions such as "tolerance" and "acceptance."

In the autonomous learning that is performed 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 memorable memories and the user itself using a sentence generation model.

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."

While not talking to the user 10, the robot 100 can continue to grow with various parameters by executing autonomous processing. Specifically, for example, the event data "a friend was hit and looked disgusted" is loaded as the top event data arranged in order of emotional value strength from the history data 222. The loaded event data is linked to the emotion of the robot 100, "anxiety" of strength 4, and here, the emotion of the friend, user 10, is linked to "disgust" of strength 5. If the current emotional value of the robot 100 is "relief" of strength 3 before loading, after loading, the influence of "anxiety" of strength 4 and "disgust" of strength 5 are added, and the emotional value of the robot 100 may change to "regret" meaning disappointment (regret). 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; 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 in the future.

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

"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 that it is not related to the time of day they will 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: (Words the robot should say to the user)
Candidate 3: (What the robot should say to the user)

In this case, the output of the sentence generation model would look something like this:

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 about you. Can you tell me something?"

Furthermore, based on the information obtained by creating an emotion change event, the robot 100 may automatically generate input text such as the following:

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, Sad C, Happy 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 about you. Can you tell me something?"

In this case, the output of the sentence generation model would look something like this:

"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, Sorrow 3, Pleasure 3"

In this way, the robot 100 may perform a musing process after creating an emotion change event.

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

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

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.

Specific examples are described below.

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.

For example, even when the robot 100 is not talking to the user, it can check information about the user's birthdays and anniversaries and think up a congratulatory message.

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

For example, even when the robot 100 is not talking to the user, it can check weather information and provide advice tailored to the user's schedule and plans.

For example, even when the robot 100 is not talking to the user, it can look up information about local events and festivals and suggest them to the user.

For example, even when the robot 100 is not talking to the user, it can check the results and news of sports that interest the user and provide topics of conversation.

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

For example, even when the robot 100 is not talking to the user, it can look up information about social issues or news that concern the user and provide its opinion.

For example, even when the robot 100 is not talking to the user, it can look up information about the user's hometown or birthplace and provide topics of conversation.

For example, the robot 100 can look up information about the user's work or school and provide advice, even when it is not talking to the user.

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.

For example, the robot 100 may check information about the user's health and provide advice even when it is not speaking to the user.

For example, the robot 100 may look up information about the user's travel plans and provide advice even when it is not speaking to the user.

For example, the robot 100 can look up information and provide advice on repairs and maintenance for the user's home or car, even when it is not speaking to the user.

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

For example, the robot 100 can look up information about the user's pet and provide advice even when it is not talking to the user.

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

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

For example, even when the robot 100 is not talking to the user, it can collect information and provide advice about important decisions that affect the user's life.

For example, the robot 100 looks up information about someone the user is concerned about and provides advice, even when it is not speaking to the user.

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

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

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

As shown in FIG. 7A, in this embodiment (other embodiments), the stuffed toy 100N has the shape of a bear covered with soft fabric, and the sensor unit 200A and the control target 252A are arranged in the space 52 formed inside the stuffed toy 100N as input/output devices (see FIG. 9). The sensor unit 200A includes a microphone 201 and a 2D camera 203. Specifically, as shown in FIG. 7B, the microphone 201 of the sensor unit 200 is arranged in the part corresponding to the ear 54 in the space 52, the 2D camera 203 of the sensor unit 200 is arranged in the part corresponding to the eye 56, and the speaker 60 constituting a part of the control target 252A is arranged in the part 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, this is not limited to this. The plush toy 100N may also have the shape of a specific character.

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

The smartphone 50 housed in the stuffed toy 100N of this embodiment executes the same processing as the robot 100 of the first embodiment. That is, the smartphone 50 has a function as the sensor module unit 210, a function as the storage unit 220, and a function as the control unit 228 shown in FIG. 9.

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

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. 7B), thereby providing the same functionality as the robot 100 of the first embodiment.

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 wireless power.

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

The stuffed animal 100N placed on this mounting base 70 can be viewed as an ornament in its natural state.

In addition, this base portion is made thinner than the surface thickness of other parts of the stuffed animal 100N, so that it is held closer to the mounting base 70.

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 receiving coil 66A on the power receiving plate 66. When the power receiving coil 66A is found, a current flows through the power transmission coil 72A, generating a magnetic field, and the power receiving coil 66A reacts to the magnetic field, starting electromagnetic induction. As a result, a current flows through the power receiving coil 66A, and power is stored in the battery (not shown) of the smartphone 50 via the USB hub 64.

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

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 to this. For example, a control device with a wireless function (e.g., "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 placing the smartphone 50 in the space 52, and the external smartphone 50 connects to each input/output device via the control device, thereby providing the same functions as 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.

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

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 through images reflected in the eyes, or a window may be provided in the abdomen through which the monitor of the built-in smartphone 50 can be projected. Furthermore, the eyes 56 may be used as a projector to express joy, anger, sadness, and happiness through images projected onto a wall.

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

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.

When trying to use wireless charging for the smartphone 50, the smartphone 50 must be placed as far out as possible when viewed from the inside of the stuffed toy 100N, which makes the stuffed toy 100N feel rough when touched from the outside.

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

The rest of the configuration and operation of the stuffed animal 100N of the second embodiment is the same as that of the robot 100 of the first embodiment, so a description thereof will be omitted.

[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.

Figure 10 is a functional block diagram of an agent system 500 that is configured using some or all of the functions of a behavior control system.

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

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

The agent system 500 may be installed in, for example, a robot, a doll, a stuffed toy, a wearable device (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 device such as a smartphone owned by the user.

The agent system 500 plays the 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 based on the user's preferences. The agent system 500 also makes reservations, orders, or makes payments to service providers.

The emotion determination unit 232 determines the emotions of the user 10 and the agent itself, as in the first embodiment. The behavior determination unit 236 determines the behavior of the robot 100 while taking into account the emotions of the user 10 and the agent. In other words, the agent system 500 understands the emotions of the user 10, reads the mood, and provides heartfelt support, assistance, advice, and services. The agent system 500 also listens to the worries of the user 10, comforts, encourages, and cheers them up. The agent system 500 also plays with the user 10, draws picture diaries, and helps them reminisce about the past. The agent system 500 takes actions that increase the user 10's sense of happiness.

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.

As in the first embodiment, the behavior decision unit 236 decides the agent's speech content for dialogue with the user 10 as the agent's behavior. The behavior control unit 250 outputs the agent's speech content as voice and/or text through a speaker or display as a control object 252B.

The character setting unit 276 sets the character of the agent when the agent system 500 interacts 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 characters. It is also possible to set fictional characters that appear in comics, movies, or animations. For example, it is possible to set "Princess Anne" played by "Audrey Hepburn" in the movie "Roman Holiday" as the agent character. If 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 choice, 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 interaction with the user 10. That is, the action control unit 250 synthesizes a voice corresponding to the character set by the character setting unit 276, and outputs the agent's speech content using the synthesized voice. This allows the user 10 to feel as if they are interacting with their favorite character (e.g., their favorite actor) in person.

When the agent system 500 is mounted on a device with 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 conducted while the image of the agent makes gestures according to the emotions of the user 10, the emotions of the agent, and the content of the agent's speech. Note that the agent system 500 may output only audio without outputting an image when engaging in a dialogue with the user 10.

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.

The agent's emotions are also reflected when the agent system 500 takes an action 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 by the degree of "sadness" the agent is feeling. If the character is feeling positive emotions, the character will have friendly conversations or actions toward the user 10, and if the character is feeling negative emotions, the character will have hostile conversations or actions toward the user 10.

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 action content 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 agent's utterance content based on the dialogue history stored in the history data 222. The history data 222 stores personal information of the user 10, such as the name, address, telephone number, and credit card number, obtained through the dialogue with the user 10.

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

The command acquisition unit 272 uses the output of the speech understanding unit 212 to acquire commands for the agent from the voice or text uttered by the user 10 through dialogue with the user 10. The commands include the content of actions to be performed by the agent system 500, such as, for example, searching for information, making a reservation at a store, arranging tickets, purchasing a product or service, paying for it, getting route guidance to a destination, and providing recommendations.

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

The RPA 274 reads out from the history data 222 the personal information of the user 10 required to execute actions related to the use of the service provider, and uses it. For example, when the agent system 500 purchases a product at the request of the user 10, it reads out and uses the 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 be required to input personal information in the initial settings. In the agent system 500 according to this embodiment, instead of requiring the user 10 to input personal information in the initial settings, the personal information acquired through the dialogue with the user 10 is stored, and is read out and used as necessary. This makes it possible to avoid making the user feel uncomfortable, and improves user convenience.

The agent system 500 executes the dialogue processing, for example, through the following steps 1 to 5.

(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 the designation from the user 10.

(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 the history data 222. Specifically, the same processing as in steps S100 to S103 above is performed to acquire the state of the user 10, including the voice or text input from the user 10, the emotion value of the user 10, the emotion value of the agent, and the history data 222.

(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 agent's speech content.

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.

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 will be "Understood," and "Here are some recommended restaurants: 1. AAAA. 2. BBBB. 3. CCCC. 4. DDDD."

In addition, if the text or voice input by the user 10 is "Number 4, DDDD, would be good," the agent's utterance will be "Understood. I will try to make a reservation. How many seats are there?"

(Step 4) The agent system 500 outputs the agent's utterance content.
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 speech in the synthesized voice.

(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.

(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 issued 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.

If the command is "reserve a restaurant," the reservation information obtained through dialogue with the user 10, the restaurant information, and the API are used to place a call to the restaurant using 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 in response to the voice input from the other party. The behavior decision unit 236 then inputs the result of the restaurant reservation (whether the reservation was successful or not) into the sentence generation model to generate the agent's utterance. 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 using the synthesized voice.

Then go back to step 2 above.

In this way, the agent system 500 can execute interactive processing and, if necessary, take action related to the use of the service provider.

Figures 11 and 12 are diagrams showing an example of the operation of the agent system 500. Figure 11 illustrates an example of the agent system 500 making a restaurant reservation through dialogue with the user 10. In Figure 11, the left side shows the agent's speech, and the right side shows the user's speech. 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 recommended list of restaurants that match the preferences of the user 10, and make a reservation at the selected restaurant.

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 agent's speech is shown on the left side, and the user's speech is shown on the right side. The agent system 500 can estimate the remaining amount of a beverage stocked by the user based on the dialogue history with the user 10, and can suggest and execute the purchase of the beverage to the user 10. The agent system 500 can also understand the user's preferences based on the past dialogue history with the user 10, and can recommend snacks that the user prefers.

Note that 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, so a description thereof will be omitted.

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.

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

[Fourth embodiment]
The fourth embodiment is an example in which the response processing and autonomous processing in the behavior control system of the first embodiment, and the agent function of the third embodiment are applicable to the stuffed toy of the second embodiment. Hereinafter, parts having the same configuration as those of the first to third embodiments will be given the same reference numerals and will not be described.

The robot 100 of this embodiment (corresponding to the smartphone 50 housed in the stuffed toy 100N in this embodiment) executes the process of increasing or decreasing the hunger level through the following steps 1 to 4-2.

(Step 1) The robot 100 acquires the state of the user 10, the emotional value of the user 10, the emotional value of the robot 100, and the history data 222. Specifically, the robot 100 performs the same process as steps S100 to S103 described above to acquire the state of the user 10, the emotional value of the user 10, the emotional value of the robot 100, and the history data 222. In this embodiment, the concept of hunger level is added to the robot 100, and the robot 100 acquires the hunger level of the robot 100 separately from the emotional value. For example, data on the hunger level of the robot 100 is stored in the storage unit 220, and the hunger level is updated every time a predetermined time has passed. Specifically, the hunger level increases according to the elapsed time. The more the hunger level increases, the more the robot 100 virtually becomes hungry, and the more likely the robot 100 is to feel unpleasant emotions such as "anger", "sorrow", "anger", "pain", and "sorrow".

(Step 2) When the hunger level of the robot 100 reaches or exceeds a predetermined threshold, the robot 100 determines the behavior of the robot 100 to be an utterance indicating hunger to the user 10, and the behavior control unit 250 controls the control object 252 to make an utterance indicating that the robot 100 is hungry to the user 10. For example, the robot 100 utters through the speaker 60, "I'm hungry, so I want to eat a banana."

(Step 3) The robot 100 acquires the reaction of the user 10. Specifically, the user state recognition unit 230 recognizes that the user 10 has made a statement related to bananas based on the information analyzed by the sensor module unit 210. For example, a rule may be set such that the user 10 gives the robot 100 a banana by uttering the name of a food that the robot 100 wants to eat. In this case, if the user 10 utters "banana", the robot 100 is virtually given a banana. In addition, in order to distinguish from a case where the user 10 utters "Do you like bananas?" or the like, a process may be executed to virtually give food by repeating the same word a predetermined number of times. For example, when the user 10 utters "banana" three times within a predetermined time, a process may be executed so that the robot 100 is virtually given a banana. Specifically, the robot 100 may analyze the voice acquired from the microphone 201, and when it is confirmed that the word "banana" has been uttered three times by the user's voice, it may recognize that the user 10 has repeatedly uttered "banana" three times within a predetermined time. Note that the food is not limited to "banana" and may be other foods. Also, only the food that the robot 100 says it wants to eat may be virtually given to it. In other words, when the robot 100 says "I want to eat an apple," even if the user 10 says "banana," a process of giving a banana to the robot 100 is not executed.

(Step 4-1) If the user 10 utters "banana" three times within a predetermined time, the robot 100 is virtually given a banana, and the hunger level of the robot 100 decreases by a certain amount. Specifically, the robot 100 updates the hunger level stored in the storage unit 220 to a value that is smaller by a certain amount. At this time, the amount of decrease in hunger level may be changed depending on the type of food given to the robot 100. For example, the hunger level update process may be performed so that the decrease in hunger level is greater for curry rice than for bananas.

(Step 4-2) If the user 10 does not say "banana" three times within the specified time, the process of giving the robot 100 a banana is not executed. Therefore, the hunger level of the robot 100 does not decrease, and the hunger level increases further according to the elapsed time. Then, the process returns to step 2 above, and the robot 100 says, for example, via the speaker 60, "I'm hungry, please give me a banana quickly."

Also, as in the first embodiment, the above-mentioned emotion table (see Table 2) may be used to determine the behavior of the robot 100. For example, if the user's behavior is to say "I'll give you a banana today," the emotion of the robot 100 is index number "2," and the emotion of the user 10 is index number "3," then:
"The robot is in a very happy state. The user is in a normal happy state. The user says, 'I'll give you a banana.' How would you, as the robot, respond?"
The above 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.

The above processing described in the fourth embodiment may be executed in each of the response processing and autonomous processing in the behavior control system of the first embodiment, or may be executed in the agent function of the third embodiment.

13 is a schematic diagram showing an example of a hardware configuration of a computer 1200 functioning as a smartphone 50, a robot 100, a server 300, and an agent system 500. A program installed on the computer 1200 can cause the computer 1200 to function as one or more "parts" of an apparatus according to the present embodiment, or to execute an operation or one or more "parts" associated with an apparatus according to the present embodiment, and/or to execute a process or a step 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 certain operations associated with some or all of the blocks of the flowcharts and block diagrams described herein.

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 ROM 1230 and a legacy input/output unit such as a keyboard, which are connected to the input/output controller 1220 via an input/output chip 1240.

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

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

ROM 1230 stores therein a boot program or the like executed by computer 1200 upon activation, and/or a program that depends on the hardware of computer 1200. I/O chip 1240 may also connect various I/O units to I/O controller 1220 via USB ports, parallel ports, serial ports, keyboard ports, mouse ports, etc.

The programs are provided by a computer-readable storage medium such as a DVD-ROM 1227 or an IC card. The programs are read from the computer-readable storage medium, installed in the storage device 1224, RAM 1214, or ROM 1230, which are also examples of computer-readable storage media, and executed by the CPU 1212. The 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 realizing the operation or processing of information according to the use of the computer 1200.

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

The CPU 1212 may also cause all or a necessary portion of a file or database stored in an external recording medium such as the storage device 1224, DVD drive 1226 (DVD-ROM 1227), 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.

Various types of information, such as various types of programs, data, tables, and databases, may be stored in the recording medium and may 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 writes 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 multiple 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 multiple entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

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 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.

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

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 flowchart or block diagram. 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.

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.

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

The present invention has been described above using an embodiment, but the technical scope of the present invention is not limited to the scope described in the above embodiment. It is clear to those skilled in the art that various modifications and improvements can be made to the above embodiment. It is clear from the claims that forms with such modifications or improvements can also be included in the technical scope of the present invention.

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 it should be noted that the processes 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 explained using "first," "next," etc. for convenience, it does not mean that it is necessary to perform the processes in that order.

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 Action decision model, 222 History data, 230 State recognition unit, 232 Emotion decision unit, 234 Action recognition unit, 236 Action decision unit, 238 Memory control unit, 250 Action control unit, 252 Control target, 270 Related information collection unit, 280 Communication processing unit, 300 Server, 500 Agent system, 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 (7)

A user state recognition unit that recognizes a user state including a user's behavior;
an emotion determining unit for determining an emotion of a user or an emotion of an electronic device;
a behavior determination unit that determines an action of the electronic device corresponding to the user state and an emotion of the user or an emotion of the electronic device based on a sentence generation model having an interaction function that allows a user and the electronic device to interact with each other;
the behavior determining unit increases the hunger level of the electronic device in accordance with an elapsed time, and decreases the hunger level of the electronic device based on a predetermined behavior of a user;
Behavioral control system. The behavior control system according to claim 1, wherein the behavior decision unit reduces the hunger level of the electronic device when it recognizes that the user has spoken the name of a food a predetermined number of times within a predetermined time. The behavior control system according to claim 1, wherein the electronic device is mounted on the stuffed toy or is connected wirelessly or by wire to a control target device mounted on the stuffed toy. the control target device is a speaker,
4. The behavior control system according to claim 3, wherein the stuffed toy is equipped with a microphone or a camera. The behavior control system according to claim 4, wherein the camera is attached to the eyes that constitute the face of the stuffed animal, the microphone is attached to the ears, and the speaker is attached to the mouth. A wireless power receiving unit that receives wireless power from an external wireless power transmitting unit is disposed inside the stuffed toy,
4. The behavior control system according to claim 3, wherein the controlled device or the electronic device receives power via the wireless power receiving unit. The behavior control system according to any one of claims 1 to 6, wherein the electronic device is a robot.