System

Abstract

An object of a system according to an embodiment is to analyze a behavior pattern and a psychological state of a player and generate a more human-like reaction.SOLUTION: A system according to an embodiment includes a behavior pattern analysis unit, a speech analysis unit, a facial expression analysis unit, and a reaction generation unit. The behavior pattern analysis unit analyzes a behavior pattern of the player. The statement analysis unit estimates the psychological state of the player based on the action pattern of the player analyzed by the action pattern analysis unit. The facial expression analysis unit analyzes a facial expression of the player based on the psychological state of the player estimated by the statement analysis unit. The reaction generation unit infers a psychological state of the player based on the facial expression of the player analyzed by the facial expression analysis unit, and generates a human-like reaction according to the psychological state of the player.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has difficulty recreating the psychological battle between players, and there is room for improvement in generating more human-like reactions.

[0005] The system according to the embodiment aims to analyze the player's behavioral patterns and psychological state and generate more human-like responses. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavior pattern analysis unit, a utterance analysis unit, a facial expression analysis unit, and a reaction generation unit. The behavior pattern analysis unit analyzes the behavior patterns of the player. The utterance analysis unit infers the psychological state of the player based on the behavior patterns of the player analyzed by the behavior pattern analysis unit. The facial expression analysis unit analyzes the facial expression of the player based on the psychological state of the player inferred by the utterance analysis unit. The reaction generation unit infers the psychological state of the player based on the facial expression of the player analyzed by the facial expression analysis unit, and generates a human-like reaction according to the psychological state of the player. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the player's behavioral patterns and psychological state to generate more human-like responses. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The game CPU system according to the embodiment of the present invention is a system that reads the psychology of players from their behavioral patterns, remarks, and facial expressions, and responds in a more human-like manner. This allows the game CPU system to understand the psychological battles between players and provide a more realistic game experience.

[0029] The game CPU system according to the embodiment includes a behavior pattern analysis unit, a utterance analysis unit, a facial expression analysis unit, and a human-like reaction generation unit. The behavior pattern analysis unit analyzes a player's behavior patterns. For example, it observes the player's behavior, such as the timing at which the player plays a card and the type of hand the player chooses, and learns those patterns. The behavior pattern analysis unit can also analyze a player's movement patterns. For example, it observes how the player moves and learns those patterns. The behavior pattern analysis unit can also analyze a player's reaction speed. For example, it observes how quickly the player reacts and learns those patterns. The utterance analysis unit infers a player's psychological state based on the player's behavior patterns analyzed by the behavior pattern analysis unit. For example, it infers a player's psychological state based on information such as the words the player uses and the tone of their speech. The utterance analysis unit can also analyze the context of a player's utterances. For example, it analyzes the context before and after a player's utterance and analyzes the relevance therebetween. The utterance analysis unit can also analyze a player's utterance speed and pauses. For example, it analyzes the speed at which a player speaks and the pauses they take, and uses this information to infer their psychological state. The facial expression analysis unit analyzes the player's facial expression based on the psychological state inferred by the utterance analysis unit. For example, it observes changes in facial expression, such as whether the player smiles or frowns, and infers the player's psychological state from these changes. The facial expression analysis unit can also analyze subtle changes in the player's facial expression. For example, it detects subtle changes in the player's facial expression and uses that information to analyze momentary changes in emotion. The facial expression analysis unit can also analyze changes in the player's facial temperature. For example, it detects changes in the player's facial temperature and uses that information to infer stress or excitement. The human-like reaction generation unit infers the player's psychological state based on the player's facial expression analyzed by the facial expression analysis unit, and generates a human-like reaction corresponding to the player's psychological state. For example, if it determines that the player is nervous, the generation AI will use that nervousness to react in a way that shakes the player up.The human-like reaction generation unit can also refer to the player's past game data and select the most effective reaction. For example, it can select the most effective reaction in a specific situation. The human-like reaction generation unit can also generate customized reactions by taking into account the player's individual personality and play style. For example, it can generate reactions that correspond to a specific personality or play style. This allows the game CPU system according to the embodiment to infer the player's psychological state from the player's behavioral patterns, remarks, and facial expressions and generate human-like reactions. For example, by analyzing the player's behavioral patterns and inferring the player's psychological state, a more realistic gaming experience can be provided. Furthermore, by analyzing the player's remarks and facial expressions and inferring the player's psychological state, more human-like reactions can be generated. Furthermore, customized reactions can be generated by taking into account the player's past game data, individual personality, and play style.

[0030] The behavior pattern analysis unit can refer to a player's past game history and learn long-term behavioral trends. For example, the behavior pattern analysis unit stores a player's past game history in a database, and the generation AI analyzes that data. For example, it learns how a player will behave in specific situations and predicts that behavior in the next game. The behavior pattern analysis unit can also analyze a player's behavioral trends based on the player's past game history. For example, it learns behavioral trends such as what hand a player will choose and when they will play cards. The behavior pattern analysis unit can also analyze a player's reaction speed based on the player's past game history. For example, it learns how quickly a player reacts and predicts their reaction in the next game based on that information. In this way, it is possible to refer to a player's past game history and learn long-term behavioral trends.

[0031] The behavior pattern analysis unit collects physiological data from the player, enabling more accurate estimation of their psychological state. The behavior pattern analysis unit, for example, monitors the player's heart rate and electrodermal response in real time and inputs that data into the generation AI. For example, it detects tension or excitement and analyzes behavior patterns based on that information. The behavior pattern analysis unit can also monitor the player's breathing pattern. For example, it can analyze the rhythm and depth of the player's breathing and infer their psychological state based on that information. The behavior pattern analysis unit can also monitor the player's body temperature. For example, it can analyze changes in the player's body temperature and infer their psychological state based on that information. This allows the system to collect physiological data from the player and more accurately estimate their psychological state.

[0032] The behavior pattern analysis unit can learn behavior patterns in different game genres and construct a general-purpose behavior analysis model. The behavior pattern analysis unit, for example, collects behavior data from different game genres, and the generation AI learns that data. For example, the behavior patterns in strategy games and simulation games are analyzed to construct a general-purpose behavior analysis model. The behavior pattern analysis unit can also use a specific algorithm to analyze behavior patterns in different game genres. For example, a clustering algorithm can be used to classify behavior patterns in different game genres, and a behavior analysis model can be constructed based on that information. The behavior pattern analysis unit can also use deep learning technology to analyze behavior patterns in different game genres. For example, a neural network can be used to learn behavior patterns in different game genres, and a behavior analysis model can be constructed based on that information. In this way, behavior patterns in different game genres can be learned and a general-purpose behavior analysis model can be constructed.

[0033] The behavior pattern analysis unit can analyze group dynamics by taking into account interactions between multiple players. For example, the behavior pattern analysis unit collects behavioral data between multiple players, and the generation AI analyzes the data. For example, the behavior pattern analysis unit can analyze group dynamics by taking into account interactions between players. The behavior pattern analysis unit can also analyze the influence of leadership within a group. For example, the behavior pattern analysis unit can analyze the influence of a leader's behavior on other players and analyze group dynamics based on that information. The behavior pattern analysis unit can also analyze the division of roles within a group. For example, the behavior pattern analysis unit can analyze the influence of each player's role on the group as a whole and analyze group dynamics based on that information. This makes it possible to analyze group dynamics by taking into account interactions between multiple players.

[0034] The utterance analysis unit can analyze the relevance of a utterance with previous and following utterances, taking into account the context of the utterance. For example, the utterance analysis unit collects utterance data from players and the generation AI analyzes that data. For example, the utterance analysis unit can analyze the relevance of a utterance with previous and following utterances, taking into account the context of the utterance. The utterance analysis unit can also analyze the intention of a player's utterance. For example, the utterance analysis unit can analyze the intention of a player to make a specific utterance and analyze the relevance of the utterance based on that information. The utterance analysis unit can also analyze the topic of a player's utterance. For example, the utterance analysis unit can extract the topic of a player's utterance and analyze the relevance of the utterance based on that information. This makes it possible to analyze the relevance of a utterance with previous and following utterances, taking into account the context of the utterance.

[0035] The speech analysis unit can analyze the speed at which a player speaks or the pauses they take, and infer their psychological state, such as tension or impatience. For example, the speech analysis unit monitors the player's speech speed and pauses in real time and inputs that data into the generation AI. For example, if the speech speed is fast, it can determine that the player is nervous and infer their psychological state based on that information. The speech analysis unit can also analyze the rhythm of the player's speech. For example, it can analyze the rhythm of the player's speech and infer their psychological state based on that information. The speech analysis unit can also analyze the intervals between the player's speech. For example, it can analyze the intervals between the player's speech and infer their psychological state based on that information. In this way, it is possible to analyze the player's speech speed and pauses and infer their psychological state, such as tension or impatience.

[0036] The utterance analysis unit can build a multilingual analysis model that can handle different languages ​​and dialects. For example, the utterance analysis unit collects utterance data in different languages ​​and dialects, and the generation AI learns from that data. For example, it analyzes utterance data in English, French, Japanese, etc., and builds a multilingual analysis model. The utterance analysis unit can also use machine translation technology to analyze utterances in different languages ​​and dialects. For example, it can automatically translate players' utterances and analyze the utterances based on that information. The utterance analysis unit can also train a language model to analyze utterances in different languages ​​and dialects. For example, it can train a language model using utterance data in a specific language or dialect, and analyze utterances based on that information. This makes it possible to build a multilingual analysis model that can handle different languages ​​and dialects.

[0037] The utterance analysis unit can simultaneously analyze not only voice data but also text data, allowing for more accurate inference of psychological states. For example, the utterance analysis unit simultaneously collects a player's voice data and text data, and the generation AI analyzes the data. For example, the utterance analysis unit can combine the voice data and text data to infer a psychological state. The utterance analysis unit can also use voice recognition technology to analyze the player's voice data. For example, the utterance analysis unit can convert the player's voice into text and infer a psychological state based on that information. The utterance analysis unit can also use natural language processing technology to analyze the player's text data. For example, the utterance analysis unit can analyze the text data of the player's utterances and infer a psychological state based on that information. This allows for more accurate inference of psychological states by simultaneously analyzing not only voice data but also text data.

[0038] The facial expression analysis unit can detect subtle changes in a player's facial expression and analyze momentary changes in emotion. For example, the facial expression analysis unit collects the player's facial expression data using a high-resolution camera, and the generation AI analyzes the data. For example, it detects subtle changes in expression and analyzes momentary changes in emotion based on that information. The facial expression analysis unit can also analyze the movement of a player's facial muscles. For example, it analyzes the movement of a player's facial muscles and detects subtle changes in expression based on that information. The facial expression analysis unit can also analyze the movement of a player's eyes. For example, it analyzes the movement of a player's eyes and analyzes momentary changes in emotion based on that information. This makes it possible to detect subtle changes in a player's facial expression and analyze momentary changes in emotion.

[0039] The facial expression analysis unit can detect temperature changes in the player's face and infer their stress or excitement state. For example, the facial expression analysis unit collects temperature data of the player's face using thermography, and the generation AI analyzes the data. For example, it detects temperature changes in the face and infers their stress or excitement state based on that information. The facial expression analysis unit can also use an infrared camera to analyze temperature changes in the player's face. For example, it can measure the temperature of the player's face with an infrared camera and analyze the temperature change based on that information. The facial expression analysis unit can also use a temperature sensor to analyze temperature changes in the player's face. For example, it can measure the temperature of the player's face with a temperature sensor and analyze the temperature change based on that information. In this way, it can detect temperature changes in the player's face and infer their stress or excitement state.

[0040] The facial expression analysis unit can learn facial expression data from different cultures and races and build a global facial expression analysis model. For example, the facial expression analysis unit collects facial expression data from different cultures and races, and the generation AI learns from that data. For example, it analyzes facial expression data from Asians, Europeans, Africans, etc. and builds a global facial expression analysis model. The facial expression analysis unit can also use a specific algorithm to analyze facial expressions from different cultures and races. For example, it can use a clustering algorithm to classify facial expression data from different cultures and races and build an facial expression analysis model based on that information. The facial expression analysis unit can also use deep learning technology to analyze facial expressions from different cultures and races. For example, it can use a neural network to learn facial expression data from different cultures and races and build an facial expression analysis model based on that information. In this way, it is possible to learn facial expression data from different cultures and races and build a global facial expression analysis model.

[0041] The facial expression analysis unit can simultaneously analyze the player's entire body movements and posture, allowing for more accurate inference of the player's psychological state. For example, the facial expression analysis unit collects data on the player's entire body movements and posture, and the generation AI analyzes that data. For example, the psychological state can be inferred by combining facial expressions and body movements. The facial expression analysis unit can also use motion capture technology to analyze the player's entire body movements. For example, the player's body movements can be collected using motion capture technology, and the psychological state can be inferred based on that information. The facial expression analysis unit can also use posture recognition technology to analyze the player's posture. For example, the player's posture can be analyzed using posture recognition technology, and the psychological state can be inferred based on that information. This allows for simultaneous analysis of the player's entire body movements and posture, allowing for more accurate inference of the player's psychological state.

[0042] The human-like reaction generation unit can refer to past game data and select the most effective reaction when generating a reaction according to a player's psychological state. For example, the human-like reaction generation unit collects the player's past game data, and the generation AI analyzes that data. For example, it selects the most effective reaction in a specific situation. The human-like reaction generation unit can also analyze the player's behavioral patterns based on the player's past game data. For example, it can learn the player's behavioral patterns, such as what hand the player chooses and when the player plays cards, and select the most effective reaction based on that information. The human-like reaction generation unit can also analyze the player's reaction speed based on the player's past game data. For example, it can learn how quickly the player reacts and select the most effective reaction based on that information. This makes it possible to refer to past game data and select the most effective reaction when generating a reaction according to a player's psychological state.

[0043] The human-like reaction generation unit can generate customized reactions by taking into account the individual personality and play style of the player when generating reactions according to the player's psychological state. The human-like reaction generation unit, for example, collects personality and play style data of the player, and the generation AI analyzes the data. For example, it generates reactions according to a specific personality or play style. The human-like reaction generation unit can also use a personality diagnostic test to analyze the player's personality. For example, it can analyze the player's personality based on the results of the personality diagnostic test, and generate customized reactions based on that information. The human-like reaction generation unit can also use behavioral pattern analysis to analyze the player's play style. For example, it can analyze the player's behavioral patterns, infer the play style based on that information, and generate customized reactions. In this way, it can generate customized reactions by taking into account the individual personality and play style of the player when generating reactions according to the player's psychological state.

[0044] The human-like reaction generation unit can learn reactions in different game genres and build a general-purpose reaction generation model. The human-like reaction generation unit, for example, collects reaction data from different game genres, and the generation AI learns from that data. For example, reaction data from action games and RPGs is analyzed to build a general-purpose reaction generation model. The human-like reaction generation unit can also use a specific algorithm to analyze reactions in different game genres. For example, a clustering algorithm can be used to classify reaction data from different game genres, and a reaction generation model can be built based on that information. The human-like reaction generation unit can also use deep learning technology to analyze reactions in different game genres. For example, a neural network can be used to learn reaction data from different game genres, and a reaction generation model can be built based on that information. This makes it possible to learn reactions in different game genres and build a general-purpose reaction generation model.

[0045] The human-like reaction generation unit can take into account interactions between multiple players and generate reactions based on group dynamics when generating reactions according to the psychological states of players. The human-like reaction generation unit, for example, collects interaction data between multiple players and the generation AI analyzes the data. For example, it takes into account interactions between players and generates reactions based on group dynamics. The human-like reaction generation unit can also analyze the influence of leadership within a group. For example, it can analyze the influence of a leader's actions on other players and generate reactions based on group dynamics based on that information. The human-like reaction generation unit can also analyze the division of roles within a group. For example, it can analyze the influence of each player's role on the entire group and generate reactions based on group dynamics based on that information. As a result, it is possible to take into account interactions between multiple players and generate reactions based on group dynamics when generating reactions according to the psychological states of players.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The game CPU system can also be equipped with a voice recognition unit. The voice recognition unit converts the player's utterances into text in real time and provides the text data to the utterance analysis unit. For example, the voice recognition unit converts the player's instructions and comments during the game into text, and the utterance analysis unit infers the player's psychological state based on that information. The voice recognition unit can also analyze the tone and speed of the player's utterances. For example, if the player is excited, the tone of their utterances often becomes higher, so their psychological state can be inferred based on that information. Furthermore, the voice recognition unit can analyze the content of the player's utterances and detect specific keywords and phrases. For example, if a player utters phrases such as "I want to win" or "I don't want to lose," the player's motivation and tension can be inferred based on that information.

[0048] The game CPU system can further include an environmental data acquisition unit. The environmental data acquisition unit collects data on the environment in which the player is playing and provides that data to the behavioral pattern analysis unit. For example, it can monitor the brightness, temperature, and noise level of the room in which the player is playing, and analyze the player's behavioral patterns based on that information. The environmental data acquisition unit can also monitor the vibrations and posture of the player's chair. For example, if the player is nervous, the chair vibrations often increase, and this information can be used to infer the player's psychological state. Furthermore, the environmental data acquisition unit can monitor the movements and sounds of people around the player. For example, if the player's friends or family are nearby, their influence can be taken into account when analyzing behavioral patterns.

[0049] The game CPU system may further include a biometric data acquisition unit. The biometric data acquisition unit collects the player's biometric data and provides that data to the behavior pattern analysis unit. For example, it may monitor the player's heart rate, blood pressure, and skin galvanic response, and use that information to infer the player's psychological state. The biometric data acquisition unit may also monitor the player's brain waves. For example, when a player is concentrating, a specific brain wave pattern appears, and the player's psychological state may be inferred based on that information. The biometric data acquisition unit may also monitor the player's muscle tension. For example, when a player is nervous, muscle tension often increases, and the player's psychological state may be inferred based on that information.

[0050] The game CPU system can further include an eye tracking unit. The eye tracking unit monitors the player's eye movements and provides the data to the behavior pattern analysis unit. For example, it analyzes which part the player is focusing on and uses that information to infer the player's interest and direction of attention. The eye tracking unit can also analyze the length of time the player's eyes remain fixed. For example, if the player fixates their gaze on a particular object for a long period of time, it can be inferred that the player has a high level of interest in that object. The eye tracking unit can also analyze the speed and pattern of the player's eye movements. For example, if the player is anxious, their eye movements often become faster, and this information can be used to infer their psychological state.

[0051] The game CPU system may further include a haptic feedback unit. The haptic feedback unit provides haptic feedback to the player and provides the player's reaction to the behavior pattern analysis unit. For example, when the player performs a specific action, the controller vibrates to provide feedback and analyze the reaction. The haptic feedback unit may also monitor the amount of sweat on the player's palms. For example, if the player is nervous, the amount of sweat on the palms often increases, and this information can be used to infer the player's psychological state. The haptic feedback unit may also monitor the player's finger movements. For example, if the player is impatient, the player's finger movements often become faster, and this information can be used to infer the player's psychological state.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The behavioral pattern analysis unit analyzes the player's behavioral patterns. For example, it observes the player's behavior, such as when they play cards and what hand they choose, and learns those patterns. It also analyzes the player's movement patterns and reaction speed. Step 2: The speech analysis unit infers the player's psychological state based on the player's behavioral patterns analyzed by the behavioral pattern analysis unit. For example, it analyzes the words the player uses, the tone of their speech, the context, speed, and pauses of their speech. Step 3: The facial expression analysis unit analyzes the player's facial expressions based on the psychological state inferred by the utterance analysis unit. For example, it observes changes in facial expressions such as smiling or frowning, subtle changes in facial expression, and changes in facial temperature, and infers the player's psychological state from these changes. Step 4: The reaction generation unit infers the player's psychological state based on the player's facial expressions analyzed by the facial expression analysis unit, and generates a human-like reaction that corresponds to the player's psychological state. For example, if it determines that the player is nervous, it will use that nervousness to react in a way that shakes the player up. It also generates customized reactions that take into account the player's past game data, individual personality, and playing style.

[0054] (Example 2) The game CPU system according to the embodiment of the present invention is a system that reads the psychology of players from their behavioral patterns, remarks, and facial expressions, and responds in a more human-like manner. This allows the game CPU system to understand the psychological battles between players and provide a more realistic game experience.

[0055] The game CPU system according to the embodiment includes a behavior pattern analysis unit, a utterance analysis unit, a facial expression analysis unit, and a human-like reaction generation unit. The behavior pattern analysis unit analyzes a player's behavior patterns. For example, it observes the player's behavior, such as the timing at which the player plays a card and the type of hand the player chooses, and learns those patterns. The behavior pattern analysis unit can also analyze a player's movement patterns. For example, it observes how the player moves and learns those patterns. The behavior pattern analysis unit can also analyze a player's reaction speed. For example, it observes how quickly the player reacts and learns those patterns. The utterance analysis unit infers a player's psychological state based on the player's behavior patterns analyzed by the behavior pattern analysis unit. For example, it infers a player's psychological state based on information such as the words the player uses and the tone of their speech. The utterance analysis unit can also analyze the context of a player's utterances. For example, it analyzes the context before and after a player's utterance and analyzes the relevance therebetween. The utterance analysis unit can also analyze a player's utterance speed and pauses. For example, it analyzes the speed at which a player speaks and the pauses they take, and uses this information to infer their psychological state. The facial expression analysis unit analyzes the player's facial expression based on the psychological state inferred by the utterance analysis unit. For example, it observes changes in facial expression, such as whether the player smiles or frowns, and infers the player's psychological state from these changes. The facial expression analysis unit can also analyze subtle changes in the player's facial expression. For example, it detects subtle changes in the player's facial expression and uses that information to analyze momentary changes in emotion. The facial expression analysis unit can also analyze changes in the player's facial temperature. For example, it detects changes in the player's facial temperature and uses that information to infer stress or excitement. The human-like reaction generation unit infers the player's psychological state based on the player's facial expression analyzed by the facial expression analysis unit, and generates a human-like reaction corresponding to the player's psychological state. For example, if it determines that the player is nervous, the generation AI will use that nervousness to react in a way that shakes the player up.The human-like reaction generation unit can also refer to the player's past game data and select the most effective reaction. For example, it can select the most effective reaction in a specific situation. The human-like reaction generation unit can also generate customized reactions by taking into account the player's individual personality and play style. For example, it can generate reactions that correspond to a specific personality or play style. This allows the game CPU system according to the embodiment to infer the player's psychological state from the player's behavioral patterns, remarks, and facial expressions and generate human-like reactions. For example, by analyzing the player's behavioral patterns and inferring the player's psychological state, a more realistic gaming experience can be provided. Furthermore, by analyzing the player's remarks and facial expressions and inferring the player's psychological state, more human-like reactions can be generated. Furthermore, customized reactions can be generated by taking into account the player's past game data, individual personality, and play style.

[0056] The behavior pattern analysis unit can refer to a player's past game history and learn long-term behavioral trends. For example, the behavior pattern analysis unit stores a player's past game history in a database, and the generation AI analyzes that data. For example, it learns how a player will behave in specific situations and predicts that behavior in the next game. The behavior pattern analysis unit can also analyze a player's behavioral trends based on the player's past game history. For example, it learns behavioral trends such as what hand a player will choose and when they will play cards. The behavior pattern analysis unit can also analyze a player's reaction speed based on the player's past game history. For example, it learns how quickly a player reacts and predicts their reaction in the next game based on that information. In this way, it is possible to refer to a player's past game history and learn long-term behavioral trends.

[0057] The behavior pattern analysis unit collects physiological data from the player, enabling more accurate estimation of their psychological state. The behavior pattern analysis unit, for example, monitors the player's heart rate and electrodermal response in real time and inputs that data into the generation AI. For example, it detects tension or excitement and analyzes behavior patterns based on that information. The behavior pattern analysis unit can also monitor the player's breathing pattern. For example, it can analyze the rhythm and depth of the player's breathing and infer their psychological state based on that information. The behavior pattern analysis unit can also monitor the player's body temperature. For example, it can analyze changes in the player's body temperature and infer their psychological state based on that information. This allows the system to collect physiological data from the player and more accurately estimate their psychological state.

[0058] The behavior pattern analysis unit can use the emotion estimation function to analyze changes in emotions based on the player's behavior patterns in real time and predict behavior patterns corresponding to those emotions. The behavior pattern analysis unit, for example, uses the emotion estimation function to analyze changes in emotions based on the player's behavior patterns in real time. For example, it estimates the player's emotions when taking a specific action and predicts the player's next action based on that information. The behavior pattern analysis unit can also use facial expression recognition technology to analyze changes in the player's emotions. For example, it analyzes changes in the player's facial expressions and estimates changes in emotions based on that information. The behavior pattern analysis unit can also use voice analysis technology to analyze changes in the player's emotions. For example, it analyzes the tone and speed of the player's voice and estimates changes in emotions based on that information. In this way, it is possible to use the emotion estimation function to analyze changes in emotions based on the player's behavior patterns in real time and predict behavior patterns corresponding to those emotions.

[0059] The behavior pattern analysis unit can learn behavior patterns in different game genres and construct a general-purpose behavior analysis model. The behavior pattern analysis unit, for example, collects behavior data from different game genres, and the generation AI learns that data. For example, the behavior patterns in strategy games and simulation games are analyzed to construct a general-purpose behavior analysis model. The behavior pattern analysis unit can also use a specific algorithm to analyze behavior patterns in different game genres. For example, a clustering algorithm can be used to classify behavior patterns in different game genres, and a behavior analysis model can be constructed based on that information. The behavior pattern analysis unit can also use deep learning technology to analyze behavior patterns in different game genres. For example, a neural network can be used to learn behavior patterns in different game genres, and a behavior analysis model can be constructed based on that information. In this way, behavior patterns in different game genres can be learned and a general-purpose behavior analysis model can be constructed.

[0060] The behavior pattern analysis unit can analyze group dynamics by taking into account interactions between multiple players. For example, the behavior pattern analysis unit collects behavioral data between multiple players, and the generation AI analyzes the data. For example, the behavior pattern analysis unit can analyze group dynamics by taking into account interactions between players. The behavior pattern analysis unit can also analyze the influence of leadership within a group. For example, the behavior pattern analysis unit can analyze the influence of a leader's behavior on other players and analyze group dynamics based on that information. The behavior pattern analysis unit can also analyze the division of roles within a group. For example, the behavior pattern analysis unit can analyze the influence of each player's role on the group as a whole and analyze group dynamics based on that information. This makes it possible to analyze group dynamics by taking into account interactions between multiple players.

[0061] The behavior pattern analysis unit uses the emotion estimation function to analyze changes in emotions based on the player's behavior patterns and predict the impact on other players. The behavior pattern analysis unit, for example, uses the emotion estimation function to analyze changes in emotions based on the player's behavior patterns. For example, it predicts what emotions a specific behavior will evoke in other players. The behavior pattern analysis unit can also use facial expression recognition technology to analyze changes in the player's emotions. For example, it analyzes changes in the player's facial expressions and predicts changes in emotions based on that information. The behavior pattern analysis unit can also use voice analysis technology to analyze changes in the player's emotions. For example, it analyzes the tone and speed of the player's voice and predicts changes in emotions based on that information. In this way, it is possible to use the emotion estimation function to analyze changes in emotions based on the player's behavior patterns and predict the impact on other players.

[0062] The utterance analysis unit can analyze the relevance of a utterance with previous and following utterances, taking into account the context of the utterance. For example, the utterance analysis unit collects utterance data from players and the generation AI analyzes that data. For example, the utterance analysis unit can analyze the relevance of a utterance with previous and following utterances, taking into account the context of the utterance. The utterance analysis unit can also analyze the intention of a player's utterance. For example, the utterance analysis unit can analyze the intention of a player to make a specific utterance and analyze the relevance of the utterance based on that information. The utterance analysis unit can also analyze the topic of a player's utterance. For example, the utterance analysis unit can extract the topic of a player's utterance and analyze the relevance of the utterance based on that information. This makes it possible to analyze the relevance of a utterance with previous and following utterances, taking into account the context of the utterance.

[0063] The speech analysis unit can analyze the speed at which a player speaks or the pauses they take, and infer their psychological state, such as tension or impatience. For example, the speech analysis unit monitors the player's speech speed and pauses in real time and inputs that data into the generation AI. For example, if the speech speed is fast, it can determine that the player is nervous and infer their psychological state based on that information. The speech analysis unit can also analyze the rhythm of the player's speech. For example, it can analyze the rhythm of the player's speech and infer their psychological state based on that information. The speech analysis unit can also analyze the intervals between the player's speech. For example, it can analyze the intervals between the player's speech and infer their psychological state based on that information. In this way, it is possible to analyze the player's speech speed and pauses and infer their psychological state, such as tension or impatience.

[0064] The utterance analysis unit can use the emotion estimation function to analyze changes in emotion based on the player's utterances in real time and predict the content of the utterances according to those emotions. The utterance analysis unit, for example, uses the emotion estimation function to analyze changes in emotion based on the player's utterances in real time. For example, it estimates what emotion a specific utterance will evoke and predicts the content of the next utterance based on that information. The utterance analysis unit can also use text analysis technology to analyze the emotions of the player's utterances. For example, it analyzes text data of the player's utterances and estimates changes in emotion based on that information. The utterance analysis unit can also use voice analysis technology to analyze the emotions of the player's utterances. For example, it analyzes the tone and speed of the player's voice and estimates changes in emotion based on that information. In this way, the emotion estimation function can be used to analyze changes in emotion based on the player's utterances in real time and predict the content of the utterances according to those emotions.

[0065] The utterance analysis unit can build a multilingual analysis model that can handle different languages ​​and dialects. For example, the utterance analysis unit collects utterance data in different languages ​​and dialects, and the generation AI learns from that data. For example, it analyzes utterance data in English, French, Japanese, etc., and builds a multilingual analysis model. The utterance analysis unit can also use machine translation technology to analyze utterances in different languages ​​and dialects. For example, it can automatically translate players' utterances and analyze the utterances based on that information. The utterance analysis unit can also train a language model to analyze utterances in different languages ​​and dialects. For example, it can train a language model using utterance data in a specific language or dialect, and analyze utterances based on that information. This makes it possible to build a multilingual analysis model that can handle different languages ​​and dialects.

[0066] The utterance analysis unit can simultaneously analyze not only voice data but also text data, allowing for more accurate inference of psychological states. For example, the utterance analysis unit simultaneously collects a player's voice data and text data, and the generation AI analyzes the data. For example, the utterance analysis unit can combine the voice data and text data to infer a psychological state. The utterance analysis unit can also use voice recognition technology to analyze the player's voice data. For example, the utterance analysis unit can convert the player's voice into text and infer a psychological state based on that information. The utterance analysis unit can also use natural language processing technology to analyze the player's text data. For example, the utterance analysis unit can analyze the text data of the player's utterances and infer a psychological state based on that information. This allows for more accurate inference of psychological states by simultaneously analyzing not only voice data but also text data.

[0067] The utterance analysis unit uses the emotion estimation function to analyze changes in emotion based on the player's utterances and predict the impact on other players. The utterance analysis unit, for example, uses the emotion estimation function to analyze changes in emotion based on the player's utterances. For example, it estimates what emotions a specific utterance will evoke in other players and predicts the next utterance content based on that information. The utterance analysis unit can also use text analysis technology to analyze the emotions in the player's utterances. For example, it analyzes text data of the player's utterances and estimates changes in emotion based on that information. The utterance analysis unit can also use voice analysis technology to analyze the emotions in the player's utterances. For example, it analyzes the tone and speed of the player's voice and estimates changes in emotion based on that information. In this way, it is possible to use the emotion estimation function to analyze changes in emotion based on the player's utterances and predict the impact on other players.

[0068] The facial expression analysis unit can detect subtle changes in a player's facial expression and analyze momentary changes in emotion. For example, the facial expression analysis unit collects the player's facial expression data using a high-resolution camera, and the generation AI analyzes the data. For example, it detects subtle changes in expression and analyzes momentary changes in emotion based on that information. The facial expression analysis unit can also analyze the movement of a player's facial muscles. For example, it analyzes the movement of a player's facial muscles and detects subtle changes in expression based on that information. The facial expression analysis unit can also analyze the movement of a player's eyes. For example, it analyzes the movement of a player's eyes and analyzes momentary changes in emotion based on that information. This makes it possible to detect subtle changes in a player's facial expression and analyze momentary changes in emotion.

[0069] The facial expression analysis unit can detect temperature changes in the player's face and infer their stress or excitement state. For example, the facial expression analysis unit collects temperature data of the player's face using thermography, and the generation AI analyzes the data. For example, it detects temperature changes in the face and infers their stress or excitement state based on that information. The facial expression analysis unit can also use an infrared camera to analyze temperature changes in the player's face. For example, it can measure the temperature of the player's face with an infrared camera and analyze the temperature change based on that information. The facial expression analysis unit can also use a temperature sensor to analyze temperature changes in the player's face. For example, it can measure the temperature of the player's face with a temperature sensor and analyze the temperature change based on that information. In this way, it can detect temperature changes in the player's face and infer their stress or excitement state.

[0070] The facial expression analysis unit can use the emotion estimation function to analyze changes in emotion based on the player's facial expression in real time and predict changes in emotion corresponding to those emotions. The facial expression analysis unit, for example, uses the emotion estimation function to analyze changes in emotion based on the player's facial expression in real time. For example, it estimates what emotion a specific facial expression indicates and predicts the next change in emotion based on that information. The facial expression analysis unit can also use facial expression recognition technology to analyze changes in the player's facial expression. For example, it analyzes changes in the player's facial expression and estimates changes in emotion based on that information. The facial expression analysis unit can also use deep learning technology to analyze changes in the player's facial expression. For example, it uses a neural network to learn changes in the player's facial expression and estimate changes in emotion based on that information. In this way, the emotion estimation function can be used to analyze changes in emotion based on the player's facial expression in real time and predict changes in emotion corresponding to those emotions.

[0071] The facial expression analysis unit can learn facial expression data from different cultures and races and build a global facial expression analysis model. For example, the facial expression analysis unit collects facial expression data from different cultures and races, and the generation AI learns from that data. For example, it analyzes facial expression data from Asians, Europeans, Africans, etc. and builds a global facial expression analysis model. The facial expression analysis unit can also use a specific algorithm to analyze facial expressions from different cultures and races. For example, it can use a clustering algorithm to classify facial expression data from different cultures and races and build an facial expression analysis model based on that information. The facial expression analysis unit can also use deep learning technology to analyze facial expressions from different cultures and races. For example, it can use a neural network to learn facial expression data from different cultures and races and build an facial expression analysis model based on that information. In this way, it is possible to learn facial expression data from different cultures and races and build a global facial expression analysis model.

[0072] The facial expression analysis unit can simultaneously analyze the player's entire body movements and posture, allowing for more accurate inference of the player's psychological state. For example, the facial expression analysis unit collects data on the player's entire body movements and posture, and the generation AI analyzes that data. For example, the psychological state can be inferred by combining facial expressions and body movements. The facial expression analysis unit can also use motion capture technology to analyze the player's entire body movements. For example, the player's body movements can be collected using motion capture technology, and the psychological state can be inferred based on that information. The facial expression analysis unit can also use posture recognition technology to analyze the player's posture. For example, the player's posture can be analyzed using posture recognition technology, and the psychological state can be inferred based on that information. This allows for simultaneous analysis of the player's entire body movements and posture, allowing for more accurate inference of the player's psychological state.

[0073] The facial expression analysis unit can use the emotion estimation function to analyze changes in emotion based on the player's facial expression and predict the impact on other players. The facial expression analysis unit, for example, uses the emotion estimation function to analyze changes in emotion based on the player's facial expression. For example, it estimates what emotion a particular facial expression evokes in other players and predicts the next change in expression based on that information. The facial expression analysis unit can also use facial expression recognition technology to analyze changes in the player's facial expression. For example, it analyzes changes in the player's facial expression and estimates changes in emotion based on that information. The facial expression analysis unit can also use deep learning technology to analyze changes in the player's facial expression. For example, it uses a neural network to learn changes in the player's facial expression and estimate changes in emotion based on that information. In this way, it is possible to use the emotion estimation function to analyze changes in emotion based on the player's facial expression and predict the impact on other players.

[0074] The human-like reaction generation unit can refer to past game data and select the most effective reaction when generating a reaction according to a player's psychological state. For example, the human-like reaction generation unit collects the player's past game data, and the generation AI analyzes that data. For example, it selects the most effective reaction in a specific situation. The human-like reaction generation unit can also analyze the player's behavioral patterns based on the player's past game data. For example, it can learn the player's behavioral patterns, such as what hand the player chooses and when the player plays cards, and select the most effective reaction based on that information. The human-like reaction generation unit can also analyze the player's reaction speed based on the player's past game data. For example, it can learn how quickly the player reacts and select the most effective reaction based on that information. This makes it possible to refer to past game data and select the most effective reaction when generating a reaction according to a player's psychological state.

[0075] The human-like reaction generation unit can generate customized reactions by taking into account the individual personality and play style of the player when generating reactions according to the player's psychological state. The human-like reaction generation unit, for example, collects personality and play style data of the player, and the generation AI analyzes the data. For example, it generates reactions according to a specific personality or play style. The human-like reaction generation unit can also use a personality diagnostic test to analyze the player's personality. For example, it can analyze the player's personality based on the results of the personality diagnostic test, and generate customized reactions based on that information. The human-like reaction generation unit can also use behavioral pattern analysis to analyze the player's play style. For example, it can analyze the player's behavioral patterns, infer the play style based on that information, and generate customized reactions. In this way, it can generate customized reactions by taking into account the individual personality and play style of the player when generating reactions according to the player's psychological state.

[0076] The human-like reaction generation unit can use the emotion estimation function to analyze changes in emotions based on the player's psychological state in real time and generate a reaction corresponding to the emotion. The human-like reaction generation unit can, for example, use the emotion estimation function to analyze changes in emotions based on the player's psychological state in real time. For example, it can estimate what kind of reaction a specific emotion will cause and generate the next reaction based on that information. The human-like reaction generation unit can also use facial expression recognition technology to analyze changes in the player's emotions. For example, it can analyze changes in the player's facial expressions, estimate changes in emotions based on that information, and generate a reaction corresponding to that emotion. The human-like reaction generation unit can also use voice analysis technology to analyze changes in the player's emotions. For example, it can analyze the tone and speed of the player's voice, estimate changes in emotions based on that information, and generate a reaction corresponding to that emotion. In this way, the emotion estimation function can be used to analyze changes in emotions based on the player's psychological state in real time and generate a reaction corresponding to that emotion.

[0077] The human-like reaction generation unit can learn reactions in different game genres and build a general-purpose reaction generation model. The human-like reaction generation unit, for example, collects reaction data from different game genres, and the generation AI learns from that data. For example, reaction data from action games and RPGs is analyzed to build a general-purpose reaction generation model. The human-like reaction generation unit can also use a specific algorithm to analyze reactions in different game genres. For example, a clustering algorithm can be used to classify reaction data from different game genres, and a reaction generation model can be built based on that information. The human-like reaction generation unit can also use deep learning technology to analyze reactions in different game genres. For example, a neural network can be used to learn reaction data from different game genres, and a reaction generation model can be built based on that information. This makes it possible to learn reactions in different game genres and build a general-purpose reaction generation model.

[0078] The human-like reaction generation unit can take into account interactions between multiple players and generate reactions based on group dynamics when generating reactions according to the psychological states of players. The human-like reaction generation unit, for example, collects interaction data between multiple players and the generation AI analyzes the data. For example, it takes into account interactions between players and generates reactions based on group dynamics. The human-like reaction generation unit can also analyze the influence of leadership within a group. For example, it can analyze the influence of a leader's actions on other players and generate reactions based on group dynamics based on that information. The human-like reaction generation unit can also analyze the division of roles within a group. For example, it can analyze the influence of each player's role on the entire group and generate reactions based on group dynamics based on that information. As a result, it is possible to take into account interactions between multiple players and generate reactions based on group dynamics when generating reactions according to the psychological states of players.

[0079] The human-like reaction generation unit can use the emotion estimation function to analyze changes in emotions based on the player's psychological state and predict the impact on other players. The human-like reaction generation unit, for example, uses the emotion estimation function to analyze changes in emotions based on the player's psychological state. For example, it estimates how a specific emotion will affect other players and generates the next reaction based on that information. The human-like reaction generation unit can also use facial expression recognition technology to analyze changes in emotions of the player. For example, it can analyze changes in the player's facial expression, estimate changes in emotions based on that information, and generate a reaction corresponding to that emotion. The human-like reaction generation unit can also use voice analysis technology to analyze changes in emotions of the player. For example, it can analyze the tone and speed of the player's voice, estimate changes in emotions based on that information, and generate a reaction corresponding to that emotion. In this way, the emotion estimation function can be used to analyze changes in emotions based on the player's psychological state and predict the impact on other players.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The game CPU system can also be equipped with a voice recognition unit. The voice recognition unit converts the player's utterances into text in real time and provides the text data to the utterance analysis unit. For example, the voice recognition unit converts the player's instructions and comments during the game into text, and the utterance analysis unit infers the player's psychological state based on that information. The voice recognition unit can also analyze the tone and speed of the player's utterances. For example, if the player is excited, the tone of their utterances often becomes higher, so their psychological state can be inferred based on that information. Furthermore, the voice recognition unit can analyze the content of the player's utterances and detect specific keywords and phrases. For example, if a player utters phrases such as "I want to win" or "I don't want to lose," the player's motivation and tension can be inferred based on that information.

[0082] The game CPU system can further include an environmental data acquisition unit. The environmental data acquisition unit collects data on the environment in which the player is playing and provides that data to the behavioral pattern analysis unit. For example, it can monitor the brightness, temperature, and noise level of the room in which the player is playing, and analyze the player's behavioral patterns based on that information. The environmental data acquisition unit can also monitor the vibrations and posture of the player's chair. For example, if the player is nervous, the chair vibrations often increase, and this information can be used to infer the player's psychological state. Furthermore, the environmental data acquisition unit can monitor the movements and sounds of people around the player. For example, if the player's friends or family are nearby, their influence can be taken into account when analyzing behavioral patterns.

[0083] The game CPU system may further include a biometric data acquisition unit. The biometric data acquisition unit collects the player's biometric data and provides that data to the behavior pattern analysis unit. For example, it may monitor the player's heart rate, blood pressure, and skin galvanic response, and use that information to infer the player's psychological state. The biometric data acquisition unit may also monitor the player's brain waves. For example, when a player is concentrating, a specific brain wave pattern appears, and the player's psychological state may be inferred based on that information. The biometric data acquisition unit may also monitor the player's muscle tension. For example, when a player is nervous, muscle tension often increases, and the player's psychological state may be inferred based on that information.

[0084] The game CPU system can further include an eye tracking unit. The eye tracking unit monitors the player's eye movements and provides the data to the behavior pattern analysis unit. For example, it analyzes which part the player is focusing on and uses that information to infer the player's interest and direction of attention. The eye tracking unit can also analyze the length of time the player's eyes remain fixed. For example, if the player fixates their gaze on a particular object for a long period of time, it can be inferred that the player has a high level of interest in that object. The eye tracking unit can also analyze the speed and pattern of the player's eye movements. For example, if the player is anxious, their eye movements often become faster, and this information can be used to infer their psychological state.

[0085] The game CPU system may further include a haptic feedback unit. The haptic feedback unit provides haptic feedback to the player and provides the player's reaction to the behavior pattern analysis unit. For example, when the player performs a specific action, the controller vibrates to provide feedback and analyze the reaction. The haptic feedback unit may also monitor the amount of sweat on the player's palms. For example, if the player is nervous, the amount of sweat on the palms often increases, and this information can be used to infer the player's psychological state. The haptic feedback unit may also monitor the player's finger movements. For example, if the player is impatient, the player's finger movements often become faster, and this information can be used to infer the player's psychological state.

[0086] The game CPU system can further use the emotion estimation function to analyze changes in emotions based on the player's psychological state in real time and generate in-game events according to those emotions. For example, if the player is nervous, an event can be generated in the game to relieve the tension. The emotion estimation function can also be used to analyze changes in the player's emotions and adjust the difficulty of the game based on that information. For example, if the player is feeling stressed, the game difficulty can be lowered to reduce the player's stress. The emotion estimation function can also be used to analyze changes in the player's emotions and adjust the reactions of in-game characters based on that information. For example, if the player is happy, an in-game character can react by congratulating the player.

[0087] The game CPU system can further use the emotion estimation function to analyze changes in emotions based on the player's psychological state and generate music and sound effects according to those emotions. For example, if the player is nervous, music that increases the sense of tension can be played. The emotion estimation function can also be used to analyze changes in the player's emotions and adjust the tempo and tone of the music based on that information. For example, if the player is relaxed, slow music can be played to maintain a sense of relaxation. The emotion estimation function can also be used to analyze changes in the player's emotions and adjust the type and volume of sound effects based on that information. For example, if the player is excited, the sense of excitement can be increased by increasing the volume of sound effects.

[0088] The game CPU system can further use an emotion estimation function to analyze changes in emotions based on the player's psychological state and generate an avatar's facial expression that corresponds to that emotion. For example, if the player is happy, the avatar can generate an expression that makes it appear to be smiling. The emotion estimation function can also be used to analyze changes in the player's emotions and adjust the avatar's movements based on that information. For example, if the player is nervous, the avatar can move in a way that indicates nervousness. The emotion estimation function can also be used to analyze changes in the player's emotions and adjust the tone and speed of the avatar's voice based on that information. For example, if the player is excited, the avatar's voice can be made higher in tone and faster to express excitement.

[0089] The game CPU system can further use its emotion estimation function to analyze changes in emotions based on the player's psychological state and change the in-game weather and time of day according to those emotions. For example, if the player is feeling down, the in-game weather can be changed to sunny to brighten the mood. The emotion estimation function can also be used to analyze changes in the player's emotions and adjust the in-game time of day based on that information. For example, if the player is relaxing, the time of day can be changed to a calm evening to maintain a sense of relaxation. The emotion estimation function can also be used to analyze changes in the player's emotions and adjust the in-game environmental sounds based on that information. For example, if the player is excited, the sense of excitement can be enhanced by emphasizing the chirping of birds and the sound of the wind.

[0090] The game CPU system can further use the emotion estimation function to analyze changes in a player's emotions based on their psychological state and provide in-game items or rewards according to those emotions. For example, if a player is working hard, it can increase their motivation by providing them with special items or rewards. The emotion estimation function can also be used to analyze changes in a player's emotions and adjust the difficulty of in-game challenges based on that information. For example, if a player is feeling stressed, it can reduce the difficulty of the challenges to alleviate the stress. The emotion estimation function can also be used to analyze changes in a player's emotions and adjust the lines and actions of in-game characters based on that information. For example, if a player is happy, a character can say something to congratulate them.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The behavioral pattern analysis unit analyzes the player's behavioral patterns. For example, it observes the player's behavior, such as when they play cards and what hand they choose, and learns those patterns. It also analyzes the player's movement patterns and reaction speed. Step 2: The speech analysis unit infers the player's psychological state based on the player's behavioral patterns analyzed by the behavioral pattern analysis unit. For example, it analyzes the words the player uses, the tone of their speech, the context, speed, and pauses of their speech. Step 3: The facial expression analysis unit analyzes the player's facial expressions based on the psychological state inferred by the utterance analysis unit. For example, it observes changes in facial expressions such as smiling or frowning, subtle changes in facial expression, and changes in facial temperature, and infers the player's psychological state from these changes. Step 4: The reaction generation unit infers the player's psychological state based on the player's facial expressions analyzed by the facial expression analysis unit, and generates a human-like reaction that corresponds to the player's psychological state. For example, if it determines that the player is nervous, it will use that nervousness to react in a way that shakes the player up. It also generates customized reactions that take into account the player's past game data, individual personality, and playing style.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0152] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a behavior pattern analysis unit that analyzes the player's behavior patterns; a statement analysis unit that infers a psychological state of the player based on the behavioral pattern of the player analyzed by the behavioral pattern analysis unit; an expression analysis unit that analyzes an expression of the player based on the psychological state of the player estimated by the utterance analysis unit; a reaction generation unit that infers the player's psychological state based on the player's facial expression analyzed by the facial expression analysis unit and generates a human-like reaction in accordance with the player's psychological state. A system characterized by:

2. The behavior pattern analysis unit Referencing the player's past game history to learn long-term behavioral trends 2. The system of claim 1.

3. The utterance analysis unit Consider the context of the player's statement and analyze its relevance to previous and subsequent statements.

2. The system of claim 1.

4. The facial expression analysis unit Detect subtle changes in the player's facial expressions and analyze momentary emotional changes.

2. The system of claim 1.

5. The human-like response generator includes: When generating a response according to the psychological state of the player, past game data is referenced to select the most effective response.

2. The system of claim 1.

6. The behavior pattern analysis unit Analyzing changes in emotions based on the player's behavioral patterns in real time and predicting behavioral patterns corresponding to those emotions 2. The system of claim 1.

7. The utterance analysis unit Analyze changes in emotions based on the player's comments in real time and predict what will be said based on those emotions.

2. The system of claim 1.

8. The facial expression analysis unit Analyzing changes in emotions based on the player's facial expressions in real time and predicting facial changes corresponding to those emotions 2. The system of claim 1.

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