JP2024155852A - Electronics - Google Patents

Abstract

[Problem] To provide an electronic device that improves the relationship between a user and the electronic device. [Solution] In a behavior control system, a robot, which is an electronic device, includes a user state recognition unit that recognizes the state of the user, an emotion determination unit that is an estimation unit that estimates the emotion of the user based on the state of the user recognized by the user state recognition unit, and a behavior control unit that is a control unit that controls a controlled object so that the controlled object shows an emotion corresponding to the emotion of the user estimated by the estimation unit in synchronization with the state of the user recognized by the user state recognition unit. [Selected Figure] Figure 2

Description

The present invention relates to electronic devices.

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 terms of improving the relationship between users and electronic devices.

According to a first aspect of the present invention, an electronic device is provided. The electronic device includes a recognition unit, an estimation unit, and a control unit. The recognition unit recognizes a user's state. The estimation unit estimates the user's emotion based on the user's state recognized by the recognition unit. The control unit controls the control target to express an emotion corresponding to the user's emotion estimated by the estimation unit in synchronization with the user's state recognized by the recognition unit.

FIG. 1 is a diagram illustrating an example of a system 5 according to the present embodiment. FIG. 2 is a diagram illustrating a schematic functional configuration of the robot 100. FIG. 3 is a diagram illustrating an example of an operation flow of the robot 100. As shown in FIG. FIG. 4 is a diagram illustrating an example of a hardware configuration of the computer 1200. As shown in FIG. FIG. 5 is a diagram showing an emotion map onto which a plurality of emotions are mapped. FIG. 6 is a diagram showing another example of an emotion map. FIG. 7 is a diagram showing an example of an emotion table. FIG. 8 is a diagram showing an example of an emotion table.

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.

In the embodiment, the electronic device is a robot. FIG. 1 shows an example of a system 5 according to the embodiment. The system 5 includes a robot 100, a robot 101, a robot 102, and a server 300. User 10a, user 10b, user 10c, and user 10d are users of the robot 100. User 11a, user 11b, and user 11c are users of the robot 101. User 12a and user 12b are users of the robot 102. In the description of the embodiment, user 10a, user 10b, user 10c, and user 10d may be collectively referred to as user 10. User 11a, user 11b, and user 11c may be collectively referred to as user 11. User 12a and user 12b may be collectively referred to as user 12. The robots 101 and 102 have substantially the same functions as the robot 100. Therefore, the system 5 will be explained by focusing mainly on the functions of the robot 100. The robots 100 to 102 are examples of electronic devices.

The appearance of the robot may be a human-like appearance, like robot 100 and robot 101, or it may be a stuffed toy, like robot 102. Because robot 102 has the appearance of a stuffed toy, it is thought that children in particular will find it easy to relate to.

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 (so-called an AI (Artificial Intelligence) chat engine) 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 behavior of the user 10, 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 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>), so 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, in this embodiment, by combining a large language model and an emotion engine, it is possible to reflect the emotions of the user 10 and the robot 100 and various linguistic information in the behavior of the robot 100. In other words, according to this embodiment, a synergistic effect can be obtained by combining a sentence generation model and 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.

The robot 100 stores rules that define the actions that the robot 100 will take based on the emotions of the user 10, the emotions of the robot 100, and the actions of the user 10, and performs various actions 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. 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 user 10 is in is "alone and looks lonely", 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 user 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 control target 252, and a communication processing unit 280.

The control object 252 includes a display device 2521, a speaker 2522, a lamp 2523 (e.g., an LED in the eye), and a motor 2524 for driving the arms, hands, and legs. The posture and gestures of the robot 100 are controlled by controlling the motors 2524 of the arms, hands, and legs. Some of the emotions of the robot 100 can be expressed by controlling these motors 2524. In addition, the facial expressions of the robot 100 can also be expressed by controlling the light emission state of the LED in the eye of the robot 100. The display device 2521 may display the contents of the conversation with the user 10 as text. 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, and a distance sensor 204. 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 an acceleration sensor, a clock, a gyro sensor, a touch 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 reaction rules 221 and history data 222. The history data 222 includes the user 10's past emotional state and behavior history. For example, the history data 222 stores data on the user 10's past emotional values and behavior history. The emotional values and behavior history are recorded for each user 10, for example, by being associated with the user 10's identification information. At least a part of the storage unit 220 is implemented by a storage medium such as a memory. The storage unit 220 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 the operation of the CPU by the operating system (OS) and a program that operates 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 user 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 user 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 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 user 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 and the state of the user 10 recognized by the user 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 user state recognition unit 230.

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

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 reaction rules 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 for changing the emotion value of the user 10 to a positive value as the behavior corresponding to the behavior of the user 10.

The reaction rules 221 define the behavior of the robot 100 according to a combination of the past and current emotion values of the user 10, the emotion value of the robot 100, and the behavior of the user 10. For example, when the past emotion value of the user 10 is a positive value and the current emotion 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 221 defines the behavior of the robot 100 for all combinations of 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"); combination patterns of the past emotion values and the current emotion values of the user 10; and behavior patterns of the user 10. That is, for each pattern of the emotion values of the robot 100, 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 determination 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 221 may define at least one of a gesture and a statement as the behavior of the robot 100 for each of the patterns (1296 patterns) of the emotion value of the robot 100. Alternatively, the reaction rules 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.

For each gesture included in the behavior of the robot 100 defined in the reaction rules 221, the strength of the gesture is predefined. For each utterance content included in the behavior of the robot 100 defined in the reaction rules 221, the strength of the utterance content is predefined.

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.

The behavior decision unit 236 may determine a behavior corresponding to the behavior of the user 10 based on the emotion of the robot 100. For example, when the robot 100 is verbally abused by the user 10 or when the user 10 is arrogant (i.e., when the user's reaction is poor), when the surrounding noise is loud and the voice of the user 10 cannot be detected, when the battery level of the robot 100 is low, etc., if the emotion value of "anger" or "sorrow" of the robot 100 increases, the behavior decision unit 236 may determine a behavior corresponding to the behavior of the user 10 according to the increase in the emotion value of "anger" or "sorrow". Also, when the user's reaction is good or the battery level of the robot 100 is high, etc., if the emotion value of "joy" or "pleasure" of the robot 100 increases, the behavior decision unit 236 may determine a behavior corresponding to the behavior of the user 10 according to the increase in the emotion value of "joy" or "pleasure". Furthermore, the behavior decision unit 236 may decide that the behavior of the robot 100 toward the user 10 who has increased the emotional value of "joy" or "pleasure" is different from the behavior of the robot 100 toward the user 10 who has increased the emotional value of "anger" or "sadness." In this way, the behavior decision unit 236 may decide on different behaviors depending not only on the emotion of the robot 100 itself or the behavior of the user 10 but also on how the user 10 has changed their emotion.

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 surrounding information such as data on the sound, images, smells, etc. of the scene), and the state of the user 10 recognized by the user 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 that includes speaking, the behavior control unit 250 causes a speaker included in the control target 252 to output sound.

Incidentally, when users 10 converse using similar gestures, speaking styles, and language, the psychological connection between them improves and the relationship improves. In particular, when users 10 are in a positive emotional state, the relationship is more likely to improve.

Therefore, the behavior control unit 250 may control the control target 252 to express emotions corresponding to the estimated emotions of the user 10 in synchronization with the recognized state of the user 10. For example, in the robot 100 according to the embodiment, the sensor module unit 210 detects the user 10 by voice, image, or the like using the sensor unit 200, and the user state recognition unit 230 recognizes the state of the user 10 based on the information analyzed by the sensor module unit 210. Then, the emotion determination unit 232 determines the emotional state of the user 10 from the recognized state of the user 10, thereby estimating the emotion of the user 10. The behavior control unit 250 controls the control target 252 to express emotions corresponding to the emotion of the user 10 estimated by the emotion determination unit 232 in synchronization with the state of the user 10 recognized by the user state recognition unit 230. When the emotion of the user 10 estimated by the emotion determination unit 232 is a positive state, the behavior control unit 250 controls the control target 252 to express an emotion corresponding to the estimated emotion of the user 10 in synchronization with the state of the user 10 recognized by the user state recognition unit 230. For example, the behavior control unit 250 determines the state to be positive when the emotion values of positive emotions such as "joy", "pleasure", "comfort", "relief", "excitement", "relief", and "sense of fulfillment", "joy", "pleasure", and "pleasure" are large, and the emotion values of negative emotions such as "anger", "sorrow", "discomfort", "anxiety", "sadness", "worry", and "feeling of emptiness" are small. On the other hand, the behavior control unit 250 determines the state to be negative when the emotion values of positive emotions such as "joy", "pleasure", "comfort", "relief", "excitement", "relief", and "sense of fulfillment", "joy", "pleasure", and "pleasure" are small, and the emotion values of negative emotions such as "anger", "sorrow", "discomfort", "anxiety", "sadness", "worry", and "feeling of emptiness" are large. When the emotion of the user 10 is in a positive state, the behavior control unit 250 controls the control object 252 to express an emotion corresponding to the estimated emotion of the user 10 in the same state as the state of the user 10. For example, the user state recognition unit 230 determines the speech speed of the voice based on the emotion value of the user 10. Also, for example, the user state recognition unit 230 detects the user 10 by voice, image, or the like using the sensor unit 200, and recognizes any of the characteristics of the user 10's gestures, speech, or language. The behavior control unit 250 controls the control object 252 to express an emotion corresponding to the estimated emotion of the user 10, with any of the gestures, speech, or language being the same characteristics as the user 10. Also, for example, the user state recognition unit 230 recognizes any of the facial expression, tone of voice, or nuance of words of the user 10. The behavior control unit 250 controls the control object 252 to express an emotion corresponding to the estimated emotion of the user 10, with any of the facial expression, tone of voice, or nuance of words being the same state as the state of the user 10. By the robot 100 expressing emotions to the user 10 in this way in response to the emotions of the user 10, the psychological connection between the user 10 and the robot 100 is improved, and the relationship between them is improved.

The behavior control unit 250 may determine the speech rate of the voice based on the emotion value of the robot 100. For example, when the emotion of the user 10 is in a positive state, the behavior control unit 250 determines the speech rate of the voice based on the emotion value of the robot 100. For example, the behavior control unit 250 determines a faster speech rate as the emotion value of the robot 100 is higher. 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 a touch sensor included in the sensor unit 200. If an impact is detected by the touch sensor 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 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 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.

Figure 3 shows an example of an outline of an operation flow relating to the operation of determining an action in the robot 100. The operation flow shown in Figure 3 is executed repeatedly. At this time, it is assumed that information analyzed by the sensor module unit 210 is input. Note that "S" in the operation flow indicates the step that is executed.

First, in step S100, the state of the user 10 is recognized. For example, the user state recognition unit 230 recognizes the state of the user 10 based on information analyzed by the sensor module unit 210.

In step S102, the emotion of the user 10 is estimated based on the recognized state of the user 10. For example, 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 user 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 user state recognition unit 230. The emotion determination unit 232 adds the determined emotion value of the user 10 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 user 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 and the past emotion value of the user 10 determined in step S102, the emotion value of the robot 100, the behavior of the user 10 recognized by the behavior recognition unit 234, and the reaction rules 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. At this time, when the emotion of the user 10 estimated by the emotion determination unit 232 is in a positive state, the behavior control unit 250 controls the control object 252 to express an emotion corresponding to the estimated emotion of the user 10 in synchronization with the state of the user 10 recognized by the user state recognition unit 230. For example, when the emotion of the user 10 is in a positive state, the behavior control unit 250 controls the control object 252 to express an emotion corresponding to the estimated emotion of the user 10 in the same state as the state of the user 10. Furthermore, when the emotion of the user 10 is in a positive state, the behavior control unit 250 determines the speech speed of the voice based on the emotion value of the robot 100.

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 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 user state recognition unit 230 are stored in the history data 222.

As described above, the robot 100 includes a recognition unit (sensor unit 200, sensor module unit 210, user state recognition unit 230), an estimation unit (emotion determination unit 232), and a control unit (behavior control unit 250). The recognition unit recognizes the state of the user 10. The estimation unit estimates the emotion of the user 10 based on the state of the user 10 recognized by the recognition unit. The control unit controls the control target 252 to express an emotion corresponding to the emotion of the user 10 estimated by the estimation unit in synchronization with the state of the user 10 recognized by the recognition unit. This can improve the relationship between the user 10 and the robot 100.

Furthermore, when the emotion of the user 10 estimated by the estimation unit is positive, the control unit controls the control object to synchronize with the state of the user 10 recognized by the recognition unit and to express an emotion corresponding to the emotion of the user 10. By synchronizing with the state of the user 10 when the emotion of the user 10 is positive in this way, the psychological connection is improved, and the relationship between the user 10 and the robot 100 can be improved.

The control unit also controls the control object 252 so that the control object 252 expresses emotions corresponding to the emotion of the user 10 estimated by the estimation unit in the same state as the state of the user 10. By controlling the control object 252 so that the robot 100 expresses emotions in the same state as the state of the user 10 in this way, the psychological connection is improved, and the relationship between the user 10 and the robot 100 can be improved.

The recognition unit also recognizes the characteristics of the user 10, such as gestures, speech, or language. The control unit controls the control object 252 to express emotions corresponding to the emotions of the user 10 estimated by the estimation unit, using any of the gestures, speech, or language as the same characteristics as the user 10. By controlling the control object 252 to express emotions using any of the gestures, speech, or language as the same characteristics as the user 10 in this manner, the psychological connection between the user 10 and the robot 100 is improved, and the relationship between the user 10 and the robot 100 can be improved.

The recognition unit also recognizes the facial expression, tone of voice, or nuances of the words of the user 10. The control unit controls the control object 252 so that the facial expression, tone of voice, or nuances of the words are in the same state as the state of the user 10, and the control object 252 expresses an emotion corresponding to the emotion of the user 10 estimated by the estimation unit. By controlling the control object 252 to express an emotion in this manner, with the facial expression, tone of voice, or nuances of the words being in the same state as the user 10, the psychological connection is improved, and the relationship between the user 10 and the robot 100 can be improved.

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.

Figure 4 shows an example of a hardware configuration of a computer 1200 functioning as the robot 100 and the server 300. 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 operations or one or more "parts" associated with an apparatus according to the present embodiment, and/or to execute a process or steps of the process according to the present embodiment. Such a program can be executed by the CPU 1212 to cause the computer 1200 to execute specific operations associated with some or all of the blocks of the flowcharts and block diagrams described herein.

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.

(Other embodiments)
The robot 100 described above may be mounted on a stuffed toy, or may be applied to a control device connected wirelessly or by wire to a control target device (speaker or camera) mounted on the stuffed toy.

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 700 on which multiple emotions are mapped. In the emotion map 700, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. Emotions that represent states and actions arising from a state of mind are arranged on the outer sides of the concentric circles. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 700, 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, resulting in a natural dialogue that reads the atmosphere. The reaction (such as a backchannel) is performed according to the directionality and the degree (strength) of the mandala in the robot's 100 emotion map 700. 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 10, etc.

(2) The directionality of emotions and the strength of their intensity may be preset in reference to the emotion map 700, 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 700, and usually fluctuate between relief and anxiety. In the right half of emotion map 700, 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 700.

(4) In the left half of the emotion map 700, 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 700.

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 700.

(5) The inside of emotion map 700 represents what is going on inside one's mind, while the outside of emotion map 700 represents behavior, so the further out on the outside of emotion map 700 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 700, 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.

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 700, 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 700. 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 is a diagram showing another example of an emotion map. Figure 6 shows an example in which multiple emotions, such as "peace of mind," "calm," and "reassuring," have similar emotion values.

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 user 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 700, and determines the emotion of the robot 100. This neural network is pre-trained based on a plurality of learning data that are combinations of the information analyzed by the sensor module unit 210, the recognized state of the user 10, and the state of the robot 100, and the emotion values indicating each emotion shown in the emotion map 700. For example, the neural network is trained based on learning data that indicates that when it is recognized from the output of the touch sensor that the robot 100 is being stroked by the user 10, the emotion value becomes "3" for "happiness," and learning data that indicates that when it is recognized from the output of the acceleration sensor that the robot 100 is being hit by the user 10, the emotion value becomes "3" for "anger." Furthermore, this neural network is trained so that emotions that are close to each other have similar values, as shown in the emotion map 900 in Figure 6.

In addition, the emotion determination unit 232 may determine the emotion of the robot 100 based on the behavioral content of the robot 100 generated by the sentence generation model. Specifically, the emotion determination unit 232 inputs the behavioral content of the robot 100 generated by the sentence generation model into a pre-trained neural network, obtains emotion values indicating each emotion shown in the emotion map 700, and integrates the emotion values indicating each acquired emotion with the emotion values indicating each emotion of the current robot 100 to update the emotion of the robot 100. For example, the emotion values indicating each acquired emotion and the emotion values indicating each emotion of the current robot 100 are averaged and integrated. This neural network is pre-trained based on multiple learning data that are combinations of texts indicating the behavioral content of the robot 100 generated by the sentence generation model and emotion values indicating each emotion shown in the emotion map 700. 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 value is obtained as the emotion value of the emotion "happy," and the emotion of the robot 100 is updated so that the emotion value of the emotion "happy" becomes higher.

The behavior decision unit 236 generates the behavior of the robot 100 by adding fixed sentences to the text representing the behavior of the user 10, the emotions of the user 10, and the emotions of the robot 100, and inputting the added fixed sentences into a sentence generation model with a dialogue function to ask about the behavior of the robot 100.

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 FIG. 7. FIG. 7 is a diagram showing an example of an emotion table. 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.

Also, for the emotions of the user 10, an emotion table as shown in FIG. 8 is prepared. FIG. 8 is a diagram showing an example of an emotion table. Here, if the behavior of the user 10 is "talking to AAA", 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 has spoken to you with 'AAA'. How would you respond as the robot?" is input to the sentence generation model, and the action content of the robot 100 is obtained. The action decision unit 236 decides the action of the robot 100 from this action content. Note that "AAA" is the name (nickname) given to the robot 100 by the user 10.

In this way, the robot 100 can change its behavior according to the index number that corresponds to the emotion of the robot 100, so that the user 10 has the impression that the robot 100 has a heart, and is encouraged to take actions such as talking to the robot 100.

The behavior decision unit 236 may generate the behavioral content of the robot 100 by adding not only text representing the behavior of the user 10, the emotions of the user 10, and the emotions of the robot 100, but also text representing the contents of the history data 222, adding a fixed sentence for asking about the behavioral content of the robot 100 corresponding to the behavior of the user 10, and inputting the result into a sentence generation model having a dialogue function. This allows the robot 100 to change its behavior according to the history data representing the emotions and behavior of the user 10, so that the user 10 has the impression that the robot 100 has individuality, and is encouraged to take actions such as talking to the robot 100. The history data may further include the emotions and behavior of the robot 100.

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, 200 Sensor unit, 201 Microphone, 202 3D depth sensor, 203 2D 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 Response rules, 222 History data, 230 User state recognition unit, 232 Emotion determination unit, 234 Behavior recognition unit, 236 Behavior determination unit, 238 Memory control unit, 250 Behavior control unit, 252 Control target, 280 Communication processing unit, 300 Server, 1200 Computer, 1210 Host controller, 1212 CPU, 1214 RAM, 1216 Graphic controller, 1218 Display device, 1220 I/O controller, 1222 communication interface, 1224 storage device, 1226 DVD drive, 1227 DVD-ROM, 1230 ROM, 1240 I/O chip

Claims (6)

A recognition unit that recognizes a state of a user;
an estimation unit that estimates an emotion of a user based on the state of the user recognized by the recognition unit;
a control unit that controls a control target so as to show an emotion corresponding to the emotion of the user estimated by the estimation unit in synchronization with the state of the user recognized by the recognition unit;
An electronic device comprising: 2. The electronic device according to claim 1, wherein, when the user's emotion estimated by the estimation unit is a positive state, the control unit controls the control target to synchronize with the user's state recognized by the recognition unit and to express an emotion corresponding to the user's emotion. The electronic device according to claim 1 , wherein the control unit controls the controlled object so as to express an emotion corresponding to the emotion of the user estimated by the estimation unit in the same state as the state of the user. The recognition unit recognizes any one of the characteristics of a user's gestures, manner of speaking, or language,
The electronic device according to claim 3 , wherein the control unit controls the controlled object so as to express an emotion corresponding to the emotion of the user estimated by the estimation unit, using any one of a gesture, a manner of speech, and a choice of words as the same characteristic as that of the user. The recognition unit recognizes any one of a facial expression, a tone of voice, or a nuance of a user's words,
The electronic device according to claim 3 , wherein the control unit controls the controlled object so as to express an emotion corresponding to the emotion of the user estimated by the estimation unit by setting any one of a facial expression, a tone of voice, and a nuance of words to be the same as a state of the user. The electronic device includes:
The electronic device according to claim 1 , which is mounted on a stuffed toy or connected wirelessly or by wire to a control target device mounted on the stuffed toy.

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