system

Abstract

An object of the system according to the embodiment is to effectively learn assembly and strategy of a game.SOLUTION: A system according to an embodiment includes a game data learning unit, a professional game playing learning unit, and a VR game experience unit. The game data learning unit learns game data. The professional game performance learning unit learns a professional game performance. The VR game experience unit provides a game experience using VR goggles.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 lacks effective means for learning game structure and strategy, and there is room for improvement, especially for people with a certain level of skill.

[0005] The system according to the embodiment aims to effectively learn match construction and strategy. [Means for solving the problem]

[0006] The system according to the embodiment includes a match data learning unit, a professional match management learning unit, and a VR match experience unit. The match data learning unit learns match data. The professional match management learning unit learns how professional players manage their matches. The VR match experience unit provides a match experience using VR goggles. [Effects of the Invention]

[0007] The system according to the embodiment can effectively learn game construction and strategy. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) A game management lesson system according to an embodiment of the present invention is a system that learns game data, learns how professional players play games, and provides a game experience using VR goggles. This allows users to learn game data, learn how professional players play games, and enjoy a game experience using VR goggles.

[0029] A game management lesson system according to an embodiment includes a game data learning unit, a professional game management learning unit, and a VR game experience unit. The game data learning unit learns game data. For example, the generation AI learns past game data and analyzes tactics such as players' hitting paths and positioning. The generation AI also analyzes which path professional players hit and which position they move to under certain circumstances, and proposes optimal game management based on that data. The professional game management learning unit learns professional game management. For example, the generation AI may contract with professional players and provide content that allows users to learn professional game management. The generation AI also analyzes effective tactics based on video and data from actual games played by professional players, and provides the analysis to users. The VR game experience unit provides a game experience using VR goggles. For example, a user can wear VR goggles and experience the sensation of playing a game while watching the video. The generation AI also generates video in real time according to the user's movements and viewpoint, providing a realistic game experience. As a result, the game management lesson system according to the embodiment allows users to study game data, learn how professionals play games, and enjoy a game experience using VR goggles.

[0030] The match data learning unit can estimate a player's psychological state from match data and analyze tactical selection based on that psychological state. The match data learning unit, for example, analyzes match data and estimates the player's psychological state based on the player's behavioral patterns and reaction times. For example, it identifies when a player is nervous or relaxed and analyzes tactical selection based on that psychological state. The match data learning unit also analyzes a player's facial expressions and body movements to estimate the player's psychological state. For example, it identifies when a player is anxious or confident and proposes optimal tactics based on that psychological state. The match data learning unit also analyzes a player's heart rate and breathing patterns during a match to estimate the player's psychological state. For example, it identifies when a player is feeling pressured or concentrating and analyzes tactical selection based on that psychological state. In this way, tactical selection can be analyzed based on a player's psychological state.

[0031] The game data learning unit analyzes minute motion data included in the game data to estimate the physical strength and fatigue level of the players, thereby optimizing tactics. The game data learning unit, for example, analyzes the movement speed and movement distance of the players from the game data to estimate their physical strength and fatigue level. For example, it identifies situations where the players' movements slow down or their movement distances become shorter, and optimizes tactics based on that data. The game data learning unit also analyzes the frequency of the players' jumps and sprints to estimate their physical strength and fatigue level. For example, it identifies situations where the players frequently jump or sprint, and optimizes tactics based on that data. The game data learning unit also analyzes the players' heart rates and breathing patterns during the game to estimate their physical strength and fatigue level. For example, it identifies situations where the players' heart rates increase or their breathing becomes rough, and optimizes tactics based on that data. In this way, tactics can be optimized based on the players' physical strength and fatigue level.

[0032] The professional game execution learning unit can use generative AI to recreate the game execution of professional players in a 3D model, allowing users to visually understand it. For example, the professional game execution learning unit generates a 3D model based on the game data of professional players, allowing users to visually learn game execution. For example, it recreates player movements and positioning in a 3D model. The professional game execution learning unit also analyzes game footage of professional players and converts it into a 3D model. For example, it recreates player movements and tactics in a 3D model, allowing users to visually understand it. The professional game execution learning unit also uses the 3D model to simulate the game execution of professional players, allowing users to experience an actual game. For example, the user operates the 3D model to recreate the movements of professional players. This allows users to visually understand the game execution of professional players.

[0033] The professional game management learning unit can analyze game data of professional players and learn differences in tactics based on the individual playing styles of the players. The professional game management learning unit, for example, analyzes game data of professional players and identifies the playing style of each player. For example, it analyzes the differences between offensive players and defensive players and learns tactics based on that data. The professional game management learning unit also analyzes game footage of professional players and identifies differences in playing styles. For example, it analyzes differences in player movements and positioning and learns tactics based on that data. The professional game management learning unit also simulates differences in playing styles based on game data of professional players. For example, it simulates differences between offensive tactics and defensive tactics so that the user can learn. This makes it possible to learn differences in tactics based on the individual playing styles of players.

[0034] The VR match experience unit uses generative AI to provide real-time feedback based on the user's movements, improving the accuracy of the match experience. For example, the VR match experience unit analyzes the user's movements in real time and provides feedback on optimal movements and positioning. For example, it checks whether the user is moving with the correct form and suggests necessary adjustments. The VR match experience unit also suggests tactical changes in real time based on the user's movements. For example, if the user is making an offensive move, it instructs the user to change to a defensive tactic. The VR match experience unit also provides real-time feedback on the progress of the match based on the user's movements. For example, it instructs the user in real time on where they should be and which course they should hit the ball. This provides real-time feedback based on the user's movements, improving the accuracy of the match experience.

[0035] The VR match experience unit collects the user's gaze tracking data through the VR goggles and can suggest tactical improvements based on that data. For example, the VR match experience unit analyzes the user's gaze tracking data to identify gaze movements and points of gaze. For example, it analyzes which parts the user is focusing on and suggests tactical improvements based on that data. The VR match experience unit also evaluates the user's visual attention based on the gaze tracking data and suggests tactical improvements. For example, if the user misses an important scene, it instructs the user to move their gaze. The VR match experience unit also analyzes the user's gaze tracking data in real time and suggests tactical changes based on their gaze movements. For example, if the user is paying attention to a particular player, it suggests tactics for that player. In this way, it is possible to suggest tactical improvements based on the user's gaze tracking data.

[0036] The VR game experience unit can incorporate game simulations of different sports to provide a cross-sports experience. For example, the VR game experience unit provides game simulations of different sports, such as soccer and basketball, using VR goggles, allowing users to enjoy a cross-sports experience. For example, it can learn the tactics and movements of different sports. The VR game experience unit can also analyze common tactical patterns based on the game simulations of different sports and provide them to users. For example, it can compare positioning and attack timing to provide a cross-sports experience. The VR game experience unit can also integrate game simulations of different sports to allow users to experience multiple sports. For example, it can alternate between experiencing soccer and basketball game simulations. This allows for the incorporation of game simulations of different sports to provide a cross-sports experience.

[0037] The VR match experience unit can collect the user's physical data and provide an individual training plan based on that data. For example, the VR match experience unit collects the user's heart rate and breathing patterns and provides an individual training plan based on that data. For example, it suggests a training menu based on the user's physical strength and fatigue level. The VR match experience unit also collects the user's movement data and provides an individual training plan based on that data. For example, it analyzes the user's movements and form and points out areas for improvement. The VR match experience unit also collects the user's physical data in real time and provides a training plan based on that data. For example, it adjusts the training menu in real time based on the user's physical strength and fatigue level. This makes it possible to provide an individual training plan based on the user's physical data.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The match data learning unit can analyze match data, estimate a player's nutritional status, and analyze tactical selection based on that nutritional status. For example, it can analyze a player's weight fluctuations and energy consumption to estimate their nutritional status. It can also analyze a player's food records and supplement intake data to estimate their nutritional status. It can also analyze a player's blood test data to identify vitamin and mineral deficiencies and analyze tactical selection based on that nutritional status. This allows for optimal tactical selection based on a player's nutritional status.

[0040] The match data learning unit can analyze match data, estimate a player's sleep state, and analyze tactical selection based on the sleep state. For example, it can analyze a player's sleep duration and sleep quality to estimate the sleep state. It can also analyze a player's activity tracker data to identify sleep patterns and analyze tactical selection based on the sleep state. It can also analyze a player's electroencephalogram data to identify the proportion of deep sleep and light sleep and analyze tactical selection based on the sleep state. This makes it possible to optimize tactical selection based on a player's sleep state.

[0041] The match data learning unit can analyze match data, estimate the muscle fatigue level of a player, and analyze tactical selection based on the fatigue level. For example, it can analyze the player's electromyogram data to estimate the muscle fatigue level. It can also analyze the player's movement data to identify the frequency and strength of muscle use and analyze tactical selection based on the fatigue level. It can also analyze the player's self-reported data to identify muscle fatigue and analyze tactical selection based on the fatigue level. This makes it possible to optimize tactical selection based on the player's muscle fatigue level.

[0042] The VR game experience unit can also analyze the user's movement data and provide a training plan based on the user's athletic ability. For example, it can analyze the user's movement speed and reaction time to evaluate athletic ability. It can also analyze the user's jumping power and endurance and provide a training plan based on that athletic ability. It can also analyze the user's muscle strength and flexibility and provide a training plan based on that athletic ability. This allows it to provide a training plan based on the user's athletic ability and realize training that meets individual needs.

[0043] The VR match experience unit can analyze the user's gaze tracking data and provide a training plan to improve the user's visual attention. For example, the unit can analyze the user's gaze movements and gaze points to evaluate the user's visual attention. It can also provide a training menu to improve the user's visual attention based on the user's gaze tracking data. Furthermore, it can analyze the user's gaze tracking data in real time and provide feedback to improve the user's visual attention. This allows the unit to provide a training plan to improve the user's visual attention and improve performance during a match.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The match data learning unit learns match data. For example, the generation AI learns past match data and analyzes tactics such as players' hitting trajectories and positioning. The generation AI also analyzes which trajectory professional players will hit and to which position they will move in different situations, and based on that data, suggests optimal game strategies. Step 2: The professional game management learning unit learns how professional players manage their games. For example, the generation AI may sign a contract with a professional player and provide content that allows users to learn how professional players manage their games. The generation AI may also analyze which tactics were effective based on footage and data from actual games played by professional players and provide that information to users. Step 3: The VR match experience unit provides a match experience using VR goggles. For example, users can wear the VR goggles and experience the sensation of playing a match while watching the video. The generation AI also generates video in real time according to the user's movements and viewpoint, providing a realistic match experience.

[0046] (Example 2) A game management lesson system according to an embodiment of the present invention is a system that learns game data, learns how professional players play games, and provides a game experience using VR goggles. This allows users to learn game data, learn how professional players play games, and enjoy a game experience using VR goggles.

[0047] A game management lesson system according to an embodiment includes a game data learning unit, a professional game management learning unit, and a VR game experience unit. The game data learning unit learns game data. For example, the generation AI learns past game data and analyzes tactics such as players' hitting paths and positioning. The generation AI also analyzes which path professional players hit and which position they move to under certain circumstances, and proposes optimal game management based on that data. The professional game management learning unit learns professional game management. For example, the generation AI may contract with professional players and provide content that allows users to learn professional game management. The generation AI also analyzes effective tactics based on video and data from actual games played by professional players, and provides the analysis to users. The VR game experience unit provides a game experience using VR goggles. For example, a user can wear VR goggles and experience the sensation of playing a game while watching the video. The generation AI also generates video in real time according to the user's movements and viewpoint, providing a realistic game experience. As a result, the game management lesson system according to the embodiment allows users to study game data, learn how professionals play games, and enjoy a game experience using VR goggles.

[0048] The match data learning unit can estimate a player's psychological state from match data and analyze tactical selection based on that psychological state. The match data learning unit, for example, analyzes match data and estimates the player's psychological state based on the player's behavioral patterns and reaction times. For example, it identifies when a player is nervous or relaxed and analyzes tactical selection based on that psychological state. The match data learning unit also analyzes a player's facial expressions and body movements to estimate the player's psychological state. For example, it identifies when a player is anxious or confident and proposes optimal tactics based on that psychological state. The match data learning unit also analyzes a player's heart rate and breathing patterns during a match to estimate the player's psychological state. For example, it identifies when a player is feeling pressured or concentrating and analyzes tactical selection based on that psychological state. In this way, tactical selection can be analyzed based on a player's psychological state.

[0049] The game data learning unit analyzes minute motion data included in the game data to estimate the physical strength and fatigue level of the players, thereby optimizing tactics. The game data learning unit, for example, analyzes the movement speed and movement distance of the players from the game data to estimate their physical strength and fatigue level. For example, it identifies situations where the players' movements slow down or their movement distances become shorter, and optimizes tactics based on that data. The game data learning unit also analyzes the frequency of the players' jumps and sprints to estimate their physical strength and fatigue level. For example, it identifies situations where the players frequently jump or sprint, and optimizes tactics based on that data. The game data learning unit also analyzes the players' heart rates and breathing patterns during the game to estimate their physical strength and fatigue level. For example, it identifies situations where the players' heart rates increase or their breathing becomes rough, and optimizes tactics based on that data. In this way, tactics can be optimized based on the players' physical strength and fatigue level.

[0050] The match data learning unit can use the emotion estimation function to analyze the emotional state of a player and suggest tactical selection based on that emotion. The match data learning unit, for example, analyzes the player's facial expressions and tone of voice from match data to estimate the emotional state. For example, it identifies scenes in which a player is happy or angry and suggests tactics based on that emotional state. The match data learning unit also analyzes the player's behavioral patterns and reaction times to estimate the emotional state. For example, it identifies scenes in which a player is impatient or relaxed and suggests optimal tactics based on that emotional state. The match data learning unit also analyzes the player's heart rate and breathing patterns during a match to estimate the emotional state. For example, it identifies scenes in which a player is feeling pressure or concentrating and suggests tactics based on that emotional state. This makes it possible to suggest tactical selection based on the player's emotional state.

[0051] The professional game execution learning unit can use generative AI to recreate the game execution of professional players in a 3D model, allowing users to visually understand it. For example, the professional game execution learning unit generates a 3D model based on the game data of professional players, allowing users to visually learn game execution. For example, it recreates player movements and positioning in a 3D model. The professional game execution learning unit also analyzes game footage of professional players and converts it into a 3D model. For example, it recreates player movements and tactics in a 3D model, allowing users to visually understand it. The professional game execution learning unit also uses the 3D model to simulate the game execution of professional players, allowing users to experience an actual game. For example, the user operates the 3D model to recreate the movements of professional players. This allows users to visually understand the game execution of professional players.

[0052] The professional game management learning unit can analyze game data of professional players and learn differences in tactics based on the individual playing styles of the players. The professional game management learning unit, for example, analyzes game data of professional players and identifies the playing style of each player. For example, it analyzes the differences between offensive players and defensive players and learns tactics based on that data. The professional game management learning unit also analyzes game footage of professional players and identifies differences in playing styles. For example, it analyzes differences in player movements and positioning and learns tactics based on that data. The professional game management learning unit also simulates differences in playing styles based on game data of professional players. For example, it simulates differences between offensive tactics and defensive tactics so that the user can learn. This makes it possible to learn differences in tactics based on the individual playing styles of players.

[0053] The professional game management learning unit can use the emotion estimation function to analyze the emotional state of a professional player and learn to select tactics based on that emotion. The professional game management learning unit, for example, analyzes the game data of the professional player to estimate the emotional state. For example, it identifies scenes in which the player is nervous or relaxed, and learns tactics based on that emotional state. The professional game management learning unit also analyzes the facial expressions and tone of voice of the professional player to estimate the emotional state. For example, it identifies scenes in which the player is happy or angry, and learns tactics based on that emotional state. The professional game management learning unit also analyzes the heart rate and breathing patterns of the professional player to estimate the emotional state. For example, it identifies scenes in which the player is feeling pressure or concentrating, and learns tactics based on that emotional state. In this way, it is possible to learn to select tactics based on the emotional state of the professional player.

[0054] The VR match experience unit uses generative AI to provide real-time feedback based on the user's movements, improving the accuracy of the match experience. For example, the VR match experience unit analyzes the user's movements in real time and provides feedback on optimal movements and positioning. For example, it checks whether the user is moving with the correct form and suggests necessary adjustments. The VR match experience unit also suggests tactical changes in real time based on the user's movements. For example, if the user is making an offensive move, it instructs the user to change to a defensive tactic. The VR match experience unit also provides real-time feedback on the progress of the match based on the user's movements. For example, it instructs the user in real time on where they should be and which course they should hit the ball. This provides real-time feedback based on the user's movements, improving the accuracy of the match experience.

[0055] The VR match experience unit collects the user's gaze tracking data through the VR goggles and can suggest tactical improvements based on that data. For example, the VR match experience unit analyzes the user's gaze tracking data to identify gaze movements and points of gaze. For example, it analyzes which parts the user is focusing on and suggests tactical improvements based on that data. The VR match experience unit also evaluates the user's visual attention based on the gaze tracking data and suggests tactical improvements. For example, if the user misses an important scene, it instructs the user to move their gaze. The VR match experience unit also analyzes the user's gaze tracking data in real time and suggests tactical changes based on their gaze movements. For example, if the user is paying attention to a particular player, it suggests tactics for that player. In this way, it is possible to suggest tactical improvements based on the user's gaze tracking data.

[0056] The VR game experience unit can incorporate game simulations of different sports to provide a cross-sports experience. For example, the VR game experience unit provides game simulations of different sports, such as soccer and basketball, using VR goggles, allowing users to enjoy a cross-sports experience. For example, it can learn the tactics and movements of different sports. The VR game experience unit can also analyze common tactical patterns based on the game simulations of different sports and provide them to users. For example, it can compare positioning and attack timing to provide a cross-sports experience. The VR game experience unit can also integrate game simulations of different sports to allow users to experience multiple sports. For example, it can alternate between experiencing soccer and basketball game simulations. This allows for the incorporation of game simulations of different sports to provide a cross-sports experience.

[0057] The VR match experience unit can collect the user's physical data and provide an individual training plan based on that data. For example, the VR match experience unit collects the user's heart rate and breathing patterns and provides an individual training plan based on that data. For example, it suggests a training menu based on the user's physical strength and fatigue level. The VR match experience unit also collects the user's movement data and provides an individual training plan based on that data. For example, it analyzes the user's movements and form and points out areas for improvement. The VR match experience unit also collects the user's physical data in real time and provides a training plan based on that data. For example, it adjusts the training menu in real time based on the user's physical strength and fatigue level. This makes it possible to provide an individual training plan based on the user's physical data.

[0058] The VR match experience unit can use the emotion estimation function to analyze the user's emotional reactions and provide feedback to enhance the realism of the match experience. The VR match experience unit, for example, analyzes the user's facial expressions and tone of voice to estimate their emotional state. For example, it identifies scenes in which the user is excited or relaxed and provides match experience feedback based on that emotional state. The VR match experience unit can also analyze the user's heart rate and breathing patterns to estimate their emotional state. For example, it can identify scenes in which the user is feeling pressured or concentrating and provide match experience feedback based on that emotional state. The VR match experience unit can also analyze the user's behavioral patterns and reaction time to estimate their emotional state. For example, it can identify scenes in which the user is anxious or relaxed and provide match experience feedback based on that emotional state. This allows for feedback to be provided to enhance the realism of the match experience based on the user's emotional reactions.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The match data learning unit can analyze match data, estimate a player's nutritional status, and analyze tactical selection based on that nutritional status. For example, it can analyze a player's weight fluctuations and energy consumption to estimate their nutritional status. It can also analyze a player's food records and supplement intake data to estimate their nutritional status. It can also analyze a player's blood test data to identify vitamin and mineral deficiencies and analyze tactical selection based on that nutritional status. This allows for optimal tactical selection based on a player's nutritional status.

[0061] The match data learning unit can analyze match data, estimate a player's sleep state, and analyze tactical selection based on the sleep state. For example, it can analyze a player's sleep duration and sleep quality to estimate the sleep state. It can also analyze a player's activity tracker data to identify sleep patterns and analyze tactical selection based on the sleep state. It can also analyze a player's electroencephalogram data to identify the proportion of deep sleep and light sleep and analyze tactical selection based on the sleep state. This makes it possible to optimize tactical selection based on a player's sleep state.

[0062] The match data learning unit can analyze match data, estimate a player's stress level, and analyze tactical selection based on the stress level. For example, the unit can analyze a player's heart rate variability and electrodermal activity to estimate the player's stress level. It can also analyze a player's hormone levels, identify the amount of stress hormone secretion, and analyze tactical selection based on the player's stress level. It can also analyze a player's self-reported data, identify a player's subjective assessment of stress, and analyze tactical selection based on the player's stress level. This allows tactical selection to be optimized based on a player's stress level.

[0063] The professional game management learning unit can analyze the match data of professional players, estimate their motivation level, and learn to select tactics based on that motivation level. For example, it can analyze the players' interviews and comments before and after a match to estimate their motivation level. It can also analyze the players' performance data during a match to identify fluctuations in their motivation and learn to select tactics based on that motivation level. It can also analyze the players' social media posts to identify signs of motivation and learn to select tactics based on that motivation level. This makes it possible to learn to select tactics based on the motivation level of professional players.

[0064] The VR match experience unit can also use the user's emotion estimation function to provide a match scenario that corresponds to the user's emotional state. For example, if the user is nervous, it can provide a relaxing scenario. If the user is excited, it can provide a more challenging scenario. Furthermore, if the user is depressed, it can provide a motivational scenario. This makes it possible to provide a match scenario that corresponds to the user's emotional state, providing a more personalized match experience.

[0065] The match data learning unit can analyze match data, estimate the muscle fatigue level of a player, and analyze tactical selection based on the fatigue level. For example, it can analyze the player's electromyogram data to estimate the muscle fatigue level. It can also analyze the player's movement data to identify the frequency and strength of muscle use and analyze tactical selection based on the fatigue level. It can also analyze the player's self-reported data to identify muscle fatigue and analyze tactical selection based on the fatigue level. This makes it possible to optimize tactical selection based on the player's muscle fatigue level.

[0066] The professional game management learning unit can analyze the game data of professional players, estimate the players' concentration levels, and learn to select tactics based on those levels. For example, it can analyze the players' eye-tracking data to identify fluctuations in concentration. It can also analyze the players' brain wave data to identify highs and lows of concentration and learn to select tactics based on those levels. It can also analyze the players' performance data to identify the effects of concentration and learn to select tactics based on those levels. This makes it possible to learn to select tactics based on the concentration levels of professional players.

[0067] The VR game experience unit can also analyze the user's movement data and provide a training plan based on the user's athletic ability. For example, it can analyze the user's movement speed and reaction time to evaluate athletic ability. It can also analyze the user's jumping power and endurance and provide a training plan based on that athletic ability. It can also analyze the user's muscle strength and flexibility and provide a training plan based on that athletic ability. This allows it to provide a training plan based on the user's athletic ability and realize training that meets individual needs.

[0068] The VR match experience unit can analyze the user's gaze tracking data and provide a training plan to improve the user's visual attention. For example, the unit can analyze the user's gaze movements and gaze points to evaluate the user's visual attention. It can also provide a training menu to improve the user's visual attention based on the user's gaze tracking data. Furthermore, it can analyze the user's gaze tracking data in real time and provide feedback to improve the user's visual attention. This allows the unit to provide a training plan to improve the user's visual attention and improve performance during a match.

[0069] The VR match experience unit can also use the user's emotion estimation function to provide a training plan that matches the user's emotional state. For example, if the user is feeling stressed, a relaxing training menu can be provided. If the user wants to increase their motivation, a challenging training menu can be provided. Furthermore, if the user wants to improve their concentration, a training menu to cultivate concentration can be provided. This allows for the provision of a training plan that matches the user's emotional state, resulting in more effective training.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The match data learning unit learns match data. For example, the generation AI learns past match data and analyzes tactics such as players' hitting trajectories and positioning. The generation AI also analyzes which trajectory professional players will hit and to which position they will move in different situations, and based on that data, suggests optimal game strategies. Step 2: The professional game management learning unit learns how professional players manage their games. For example, the generation AI may sign a contract with a professional player and provide content that allows users to learn how professional players manage their games. The generation AI may also analyze which tactics were effective based on footage and data from actual games played by professional players and provide that information to users. Step 3: The VR match experience unit provides a match experience using VR goggles. For example, users can wear the VR goggles and experience the sensation of playing a match while watching the video. The generation AI also generates video in real time according to the user's movements and viewpoint, providing a realistic match experience.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0085] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0100] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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, to avoid confusion and 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.

[0138] 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]

[0139] 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 match data learning unit that learns match data; The Professional Match Management Learning Department, where you can learn how to manage a professional match, A VR match experience section that provides match experience using VR goggles is also provided. A system characterized by:

2. The game data learning unit The psychological state of the players is estimated from the match data, and tactical selection is analyzed based on the psychological state.

2. The system of claim 1.

3. The game data learning unit By analyzing the detailed movement data contained in the match data, players' physical strength and fatigue levels can be estimated and tactics optimized.

2. The system of claim 1.

4. The game data learning unit Analyze players' emotional states and suggest tactical choices based on their emotions 2. The system of claim 1.

5. The professional match management learning department Using generative AI to recreate professional players' game movements in 3D models, allowing users to visually understand them.

2. The system of claim 1.

Images