System

Abstract

An object of a system according to an embodiment is to provide optimal coaching to each player even by a teacher who has no sports coaching shortage or no competition experience.SOLUTION: A system according to an embodiment includes a performance analyzer, a training plan generator, and a real-time feedback unit. The performance analysis unit analyzes performance data of a player. The training plan creation unit creates a training plan on the basis of the result analyzed by the performance analysis unit. The real-time feedback component monitors training and performance during the game based on the training plan generated by the training plan generator and provides feedback.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] With conventional technology, there is room for improvement due to a shortage of sports instructors and insufficient instruction by teachers with no athletic experience.

[0005] The system according to the embodiment aims to provide optimal instruction to each athlete, even when there is a shortage of sports instructors or when teachers have no athletic experience. [Means for solving the problem]

[0006] The system according to the embodiment includes a performance analysis unit, a training plan creation unit, and a real-time feedback unit. The performance analysis unit analyzes the performance data of the player. The training plan creation unit creates a training plan based on the results of the analysis by the performance analysis unit. The real-time feedback unit monitors performance during training and matches based on the training plan created by the training plan creation unit and provides feedback. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal instruction to each athlete even when there is a shortage of sports instructors or when teachers have no athletic experience. [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 sports coaching support system according to an embodiment of the present invention is a system that uses generative AI to provide optimal coaching for each athlete. This allows the sports coaching support system to solve the problems of a shortage of sports coaches and sports coaching by instructors with no athletic experience, and to help athletes improve their performance.

[0029] A sports coaching support system according to an embodiment includes a performance analysis unit, a training plan creation unit, and a real-time feedback unit. The performance analysis unit analyzes a player's performance data. For example, it collects data such as the player's running speed, jumping power, and shooting accuracy, and identifies the player's strengths and weaknesses based on the collected data. Furthermore, the performance analysis unit uses a generation AI to analyze the player's performance data and output specific advice. For example, the generation AI can output the analysis results using a text generation AI (e.g., LLM). The training plan creation unit creates a training plan based on the results of the analysis by the performance analysis unit. For example, it proposes a specific training menu to strengthen the player's weaknesses or a conditioning plan for a match. Furthermore, the training plan creation unit uses the generation AI to create a training plan based on the analysis results and provides the plan to the player or coach. For example, the generation AI adjusts the frequency and intensity of the training menu to create an optimal plan. The real-time feedback unit monitors performance during training and a match and provides feedback. For example, it monitors whether the player is using the correct form during training and issues corrective instructions as necessary. Furthermore, the real-time feedback unit uses the generation AI to analyze real-time performance data and generate feedback. For example, the generation AI analyzes the player's movements in real time and provides appropriate advice. As a result, the sports coaching support system according to the embodiment can analyze the player's performance, create an individual training plan, and provide feedback in real time to help the player improve their performance.

[0030] The performance analysis unit can analyze long-term performance fluctuations by taking into account a player's past match data and training history. For example, the performance analysis unit collects a player's past match data and training history, and the generation AI analyzes that data. For example, it analyzes a player's performance fluctuations based on the past year's match results and training content, and identifies long-term trends. By taking into account a player's past data, it is possible to analyze long-term performance fluctuations and provide more accurate training plans.

[0031] The performance analysis unit can input an athlete's dietary and sleep data and analyze the impact of lifestyle habits on performance. For example, the performance analysis unit collects an athlete's dietary and sleep data, and the generation AI analyzes that data. For example, it analyzes the impact of dietary nutritional balance and sleep duration on performance and proposes optimal meal plans and sleep schedules. By taking into account the athlete's lifestyle habits, it is possible to analyze the impact on performance and provide more effective training plans.

[0032] The training plan creation unit can propose the optimal training menu by taking into account the player's past training data and match results. For example, the training plan creation unit collects the player's past training data and match results, and the generation AI analyzes that data. For example, the optimal training plan can be proposed based on the player's past training menu and match results. This makes it possible to propose the optimal training menu by taking into account the player's past data.

[0033] The training plan creation unit can propose an individual health management plan by taking into account the physical characteristics and health condition of the athlete. For example, the training plan creation unit collects the athlete's physical characteristics and health condition, and the generation AI analyzes this data. For example, an individual health management plan can be proposed based on height, weight, muscle mass, health check results, etc. This makes it possible to propose an individual health management plan by taking into account the athlete's physical characteristics and health condition.

[0034] The real-time feedback unit can refer to a player's past performance data and provide feedback by comparing it with the past data. The real-time feedback unit, for example, collects a player's past performance data, and the generation AI analyzes that data. For example, based on past match results and training data, the unit provides feedback by comparing it with current performance. This makes it possible to provide feedback by referring to a player's past data and comparing it with the past data.

[0035] The real-time feedback unit can provide individualized feedback by taking into account the physical characteristics and health condition of the player. For example, the real-time feedback unit collects the physical characteristics and health condition of the player, and the generation AI analyzes that data. For example, the real-time feedback unit provides individualized feedback based on height, weight, muscle mass, health check results, etc. This makes it possible to provide individualized feedback by taking into account the physical characteristics and health condition of the player.

[0036] The real-time feedback unit can provide feedback from a cross-sports perspective by referring to feedback data from different sports. For example, the real-time feedback unit collects feedback data from different sports, and the generation AI analyzes the data. For example, feedback is provided to a basketball player by referring to feedback data from a soccer player. This allows feedback to be provided from a cross-sports perspective by referring to feedback data from different sports.

[0037] The real-time feedback unit can provide comprehensive feedback by taking into account the lifestyle habits of athletes. For example, the real-time feedback unit collects dietary and sleep data from athletes, and the generation AI analyzes the data. For example, it analyzes the impact of dietary nutritional balance and sleep duration on performance and provides optimal feedback. This allows for comprehensive feedback by taking into account the lifestyle habits of athletes.

[0038] The training plan creation unit can create a training plan from a cross-sports perspective by referring to training menus of different sports. For example, the training plan creation unit collects training menus of different sports, and the generation AI analyzes the data. For example, a training plan for a basketball player can be created by referring to the training menu of a soccer player. In this way, a training plan can be created from a cross-sports perspective by referring to the training menus of different sports.

[0039] The competition strategy proposal unit can propose optimal competition strategies by taking into account the player's past match data and training data. For example, the competition strategy proposal unit collects the player's past match data and training data, and the generation AI analyzes this data. For example, the unit proposes optimal competition strategies based on past match results and training content. This makes it possible to propose optimal competition strategies by taking into account the player's past data.

[0040] The competitive strategy proposal unit can analyze the opponent's past match data and tactics and propose strategies that exploit the opponent's weaknesses. For example, the competitive strategy proposal unit collects the opponent's past match data and tactics, and the generation AI analyzes that data. For example, it proposes an optimal competitive strategy based on the opponent's match results and tactics. In this way, by analyzing the opponent's past data, it is possible to propose strategies that exploit the opponent's weaknesses.

[0041] The competitive strategy proposal unit can propose strategies from a cross-sports perspective by referring to strategic data from different sports. For example, the competitive strategy proposal unit collects strategic data from different sports, and the generation AI analyzes that data. For example, a competitive strategy for basketball can be proposed by referring to strategic data from soccer. This makes it possible to propose strategies from a cross-sports perspective by referring to strategic data from different sports.

[0042] The competition strategy proposal unit can propose individual strategies by taking into account the physical characteristics and health condition of the player. For example, the competition strategy proposal unit collects the player's physical characteristics and health condition, and the generation AI analyzes this data. For example, it proposes individual competition strategies based on height, weight, muscle mass, health check results, etc. This makes it possible to propose individual strategies by taking into account the player's physical characteristics and health condition.

[0043] The mental care suggestion unit can propose optimal mental care by taking into account a player's past mental data and match results. For example, the mental care suggestion unit collects a player's past mental data and match results, and the generation AI analyzes this data. For example, it proposes optimal mental care based on the player's mental state before and after past matches. This makes it possible to propose optimal mental care by taking into account a player's past mental data and match results.

[0044] The mental care proposal unit can propose a comprehensive mental care plan taking into account the lifestyle habits of the player. For example, the mental care proposal unit collects the player's dietary data and sleep data, and the generation AI analyzes this data. For example, it analyzes the impact of the nutritional balance of meals and sleep duration on mental state and proposes an optimal mental care plan. This makes it possible to propose a comprehensive mental care plan by taking into account the player's lifestyle habits.

[0045] The mental care suggestion unit can suggest mental care from a cross-sports perspective by referring to mental care data from different sports. For example, the mental care suggestion unit collects mental care data from different sports, and the generation AI analyzes the data. For example, the mental care suggestion unit suggests mental care to basketball players by referring to mental care data from soccer players. In this way, by referring to mental care data from different sports, mental care can be suggested from a cross-sports perspective.

[0046] The mental care proposal unit can propose an individual mental care plan by taking into account the physical characteristics and health condition of the player. For example, the mental care proposal unit collects the player's physical characteristics and health condition, and the generation AI analyzes this data. For example, an individual mental care plan can be proposed based on height, weight, muscle mass, health check results, etc. This makes it possible to propose an individual mental care plan by taking into account the player's physical characteristics and health condition.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The sports coaching support system can also collect data on an athlete's social environment and analyze the impact of social factors on performance. For example, data such as an athlete's home environment, friendships, and stress levels at school are collected and analyzed by the generative AI. This allows the system to identify the impact of an athlete's social environment on performance and provide appropriate support. For example, it can suggest strengthening mental health care for athletes with unstable home environments. It can also provide stress management training for athletes experiencing high levels of stress at school. It can also recommend team-building activities for athletes with good friendships.

[0049] The sports coaching support system can also consider the athlete's hobbies and interests to propose training plans to increase motivation. For example, if an athlete likes music, it can recommend listening to their favorite music while training. If an athlete admires a particular athlete, it can provide a plan based on that athlete's training method. Furthermore, if an athlete likes outdoor activities, it can incorporate outdoor training. By considering the athlete's hobbies and interests, it is possible to increase motivation and maximize the effectiveness of training.

[0050] The sports coaching support system can also take into account an athlete's academic performance and propose a plan to balance their studies and sports. For example, if an athlete's academic performance is declining, it can adjust their training time to ensure they have time to focus on their studies. It can also encourage athletes with excellent academic performance to take on further academic challenges, helping them balance their sports and studies. Furthermore, it can provide advice on maintaining a balanced lifestyle for athletes who demonstrate high performance in both academics and sports. This allows it to provide the optimal plan for balancing their studies and sports by taking into account the athlete's academic performance.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The performance analysis unit analyzes the player's performance data. For example, it collects data such as the player's running speed, jumping power, and shooting accuracy, and uses this data to identify the player's strengths and weaknesses. The generation AI then analyzes the player's performance data and outputs specific advice. For example, the analysis results can be output using a text generation AI (e.g., LLM). Step 2: The training plan creation unit creates a training plan based on the results of the analysis by the performance analysis unit. For example, it proposes a specific training menu to strengthen the player's weaknesses or a conditioning plan for a match. The generation AI also creates a training plan based on the analysis results and provides it to the player and coach. For example, it adjusts the frequency and intensity of the training menu to create an optimal plan. Step 3: The real-time feedback unit monitors performance during training and matches and provides feedback. For example, it monitors whether a player is using the correct form during training and gives corrective instructions as necessary. The generation AI also analyzes real-time performance data and generates feedback. For example, it analyzes a player's movements in real time and provides appropriate advice.

[0053] (Example 2) The sports coaching support system according to an embodiment of the present invention is a system that uses generative AI to provide optimal coaching for each athlete. This allows the sports coaching support system to solve the problems of a shortage of sports coaches and sports coaching by instructors with no athletic experience, and to help athletes improve their performance.

[0054] A sports coaching support system according to an embodiment includes a performance analysis unit, a training plan creation unit, and a real-time feedback unit. The performance analysis unit analyzes a player's performance data. For example, it collects data such as the player's running speed, jumping power, and shooting accuracy, and identifies the player's strengths and weaknesses based on the collected data. Furthermore, the performance analysis unit uses a generation AI to analyze the player's performance data and output specific advice. For example, the generation AI can output the analysis results using a text generation AI (e.g., LLM). The training plan creation unit creates a training plan based on the results of the analysis by the performance analysis unit. For example, it proposes a specific training menu to strengthen the player's weaknesses or a conditioning plan for a match. Furthermore, the training plan creation unit uses the generation AI to create a training plan based on the analysis results and provides the plan to the player or coach. For example, the generation AI adjusts the frequency and intensity of the training menu to create an optimal plan. The real-time feedback unit monitors performance during training and a match and provides feedback. For example, it monitors whether the player is using the correct form during training and issues corrective instructions as necessary. Furthermore, the real-time feedback unit uses the generation AI to analyze real-time performance data and generate feedback. For example, the generation AI analyzes the player's movements in real time and provides appropriate advice. As a result, the sports coaching support system according to the embodiment can analyze the player's performance, create an individual training plan, and provide feedback in real time to help the player improve their performance.

[0055] The performance analysis unit can analyze long-term performance fluctuations by taking into account a player's past match data and training history. For example, the performance analysis unit collects a player's past match data and training history, and the generation AI analyzes that data. For example, it analyzes a player's performance fluctuations based on the past year's match results and training content, and identifies long-term trends. By taking into account a player's past data, it is possible to analyze long-term performance fluctuations and provide more accurate training plans.

[0056] The performance analysis unit can input an athlete's dietary and sleep data and analyze the impact of lifestyle habits on performance. For example, the performance analysis unit collects an athlete's dietary and sleep data, and the generation AI analyzes that data. For example, it analyzes the impact of dietary nutritional balance and sleep duration on performance and proposes optimal meal plans and sleep schedules. By taking into account the athlete's lifestyle habits, it is possible to analyze the impact on performance and provide more effective training plans.

[0057] The performance analysis unit can use the emotion estimation function to analyze the emotional state of a player and identify the impact that emotional state has on performance. The performance analysis unit, for example, uses the emotion estimation function to analyze the emotional state of a player. For example, emotional data such as pre-match tension and post-match satisfaction is collected, and the generation AI analyzes that data. In this way, by analyzing the player's emotional state, it is possible to identify the impact that emotions have on performance and provide appropriate feedback.

[0058] The training plan creation unit can propose the optimal training menu by taking into account the player's past training data and match results. For example, the training plan creation unit collects the player's past training data and match results, and the generation AI analyzes that data. For example, the optimal training plan can be proposed based on the player's past training menu and match results. This makes it possible to propose the optimal training menu by taking into account the player's past data.

[0059] The training plan creation unit can propose an individual health management plan by taking into account the physical characteristics and health condition of the athlete. For example, the training plan creation unit collects the athlete's physical characteristics and health condition, and the generation AI analyzes this data. For example, an individual health management plan can be proposed based on height, weight, muscle mass, health check results, etc. This makes it possible to propose an individual health management plan by taking into account the athlete's physical characteristics and health condition.

[0060] The training plan creation unit can use the emotion estimation function to analyze the player's emotional state and propose a training menu according to that emotional state. The training plan creation unit, for example, uses the emotion estimation function to analyze the player's emotional state. For example, emotional data such as tension before a game and satisfaction after a game is collected, and the generation AI proposes a training menu based on that data. In this way, by analyzing the player's emotional state, a training menu according to the player's emotions can be proposed.

[0061] The real-time feedback unit can refer to a player's past performance data and provide feedback by comparing it with the past data. The real-time feedback unit, for example, collects a player's past performance data, and the generation AI analyzes that data. For example, based on past match results and training data, the unit provides feedback by comparing it with current performance. This makes it possible to provide feedback by referring to a player's past data and comparing it with the past data.

[0062] The real-time feedback unit can provide individualized feedback by taking into account the physical characteristics and health condition of the player. For example, the real-time feedback unit collects the physical characteristics and health condition of the player, and the generation AI analyzes that data. For example, the real-time feedback unit provides individualized feedback based on height, weight, muscle mass, health check results, etc. This makes it possible to provide individualized feedback by taking into account the physical characteristics and health condition of the player.

[0063] The real-time feedback unit can use the emotion estimation function to analyze the emotional state of a player in real time and provide feedback according to the emotional state. The real-time feedback unit, for example, uses the emotion estimation function to analyze the emotional state of a player in real time. For example, emotional data such as tension during a match or satisfaction after a match is collected, and the generation AI provides feedback based on that data. In this way, by analyzing the emotional state of a player in real time, feedback according to the emotion can be provided.

[0064] The real-time feedback unit can provide feedback from a cross-sports perspective by referring to feedback data from different sports. For example, the real-time feedback unit collects feedback data from different sports, and the generation AI analyzes the data. For example, feedback is provided to a basketball player by referring to feedback data from a soccer player. This allows feedback to be provided from a cross-sports perspective by referring to feedback data from different sports.

[0065] The real-time feedback unit can provide comprehensive feedback by taking into account the lifestyle habits of athletes. For example, the real-time feedback unit collects dietary and sleep data from athletes, and the generation AI analyzes the data. For example, it analyzes the impact of dietary nutritional balance and sleep duration on performance and provides optimal feedback. This allows for comprehensive feedback by taking into account the lifestyle habits of athletes.

[0066] The real-time feedback unit can use the emotion estimation function to monitor the emotional state of the player in real time and provide feedback in real time according to fluctuations in the emotional state. The real-time feedback unit, for example, uses the emotion estimation function to monitor the emotional state of the player in real time. For example, the emotional fluctuations during a game are analyzed, and the generation AI provides feedback in real time based on that data. In this way, by monitoring the emotional state of the player in real time, feedback in real time according to the emotional fluctuations can be provided.

[0067] The training plan creation unit can create a training plan from a cross-sports perspective by referring to training menus of different sports. For example, the training plan creation unit collects training menus of different sports, and the generation AI analyzes the data. For example, a training plan for a basketball player can be created by referring to the training menu of a soccer player. In this way, a training plan can be created from a cross-sports perspective by referring to the training menus of different sports.

[0068] The training plan creation unit can use the emotion estimation function to monitor the player's emotional state in real time and adjust the training menu in real time according to fluctuations in the emotional state. The training plan creation unit, for example, uses the emotion estimation function to monitor the player's emotional state in real time. For example, the emotional fluctuations during training are analyzed, and the generation AI adjusts the training menu in real time based on that data. In this way, by monitoring the player's emotional state in real time, the training menu can be adjusted in real time according to emotional fluctuations.

[0069] The competition strategy proposal unit can propose optimal competition strategies by taking into account the player's past match data and training data. For example, the competition strategy proposal unit collects the player's past match data and training data, and the generation AI analyzes this data. For example, the unit proposes optimal competition strategies based on past match results and training content. This makes it possible to propose optimal competition strategies by taking into account the player's past data.

[0070] The competitive strategy proposal unit can analyze the opponent's past match data and tactics and propose strategies that exploit the opponent's weaknesses. For example, the competitive strategy proposal unit collects the opponent's past match data and tactics, and the generation AI analyzes that data. For example, it proposes an optimal competitive strategy based on the opponent's match results and tactics. In this way, by analyzing the opponent's past data, it is possible to propose strategies that exploit the opponent's weaknesses.

[0071] The competitive strategy proposal unit can use the emotion estimation function to analyze the emotional state of the players and propose competitive strategies that correspond to their emotional state. The competitive strategy proposal unit, for example, uses the emotion estimation function to analyze the emotional state of the players. For example, emotional data such as tension before a match or satisfaction after a match is collected, and the generation AI proposes competitive strategies based on that data. In this way, by analyzing the emotional state of the players, it is possible to propose competitive strategies that correspond to their emotions.

[0072] The competitive strategy proposal unit can propose strategies from a cross-sports perspective by referring to strategic data from different sports. For example, the competitive strategy proposal unit collects strategic data from different sports, and the generation AI analyzes that data. For example, a competitive strategy for basketball can be proposed by referring to strategic data from soccer. This makes it possible to propose strategies from a cross-sports perspective by referring to strategic data from different sports.

[0073] The competition strategy proposal unit can propose individual strategies by taking into account the physical characteristics and health condition of the player. For example, the competition strategy proposal unit collects the player's physical characteristics and health condition, and the generation AI analyzes this data. For example, it proposes individual competition strategies based on height, weight, muscle mass, health check results, etc. This makes it possible to propose individual strategies by taking into account the player's physical characteristics and health condition.

[0074] The competitive strategy proposal unit uses the emotion estimation function to monitor the emotional state of the players in real time, and can adjust the competitive strategy in real time according to the fluctuations in the emotional state. The competitive strategy proposal unit, for example, uses the emotion estimation function to monitor the emotional state of the players in real time. For example, the emotional fluctuations during a game are analyzed, and the generation AI adjusts the competitive strategy in real time based on that data. In this way, by monitoring the emotional state of the players in real time, the competitive strategy can be adjusted in real time according to the fluctuations in the emotions.

[0075] The mental care suggestion unit can propose optimal mental care by taking into account a player's past mental data and match results. For example, the mental care suggestion unit collects a player's past mental data and match results, and the generation AI analyzes this data. For example, it proposes optimal mental care based on the player's mental state before and after past matches. This makes it possible to propose optimal mental care by taking into account a player's past mental data and match results.

[0076] The mental care proposal unit can propose a comprehensive mental care plan taking into account the lifestyle habits of the player. For example, the mental care proposal unit collects the player's dietary data and sleep data, and the generation AI analyzes this data. For example, it analyzes the impact of the nutritional balance of meals and sleep duration on mental state and proposes an optimal mental care plan. This makes it possible to propose a comprehensive mental care plan by taking into account the player's lifestyle habits.

[0077] The mental care suggestion unit can use the emotion estimation function to analyze the player's emotional state in real time and suggest mental care that suits the emotional state. The mental care suggestion unit, for example, uses the emotion estimation function to analyze the player's emotional state in real time. For example, emotional data such as tension before a match and satisfaction after a match is collected, and the generation AI suggests mental care based on that data. In this way, by analyzing the player's emotional state in real time, it is possible to suggest mental care that suits the emotion.

[0078] The mental care suggestion unit can suggest mental care from a cross-sports perspective by referring to mental care data from different sports. For example, the mental care suggestion unit collects mental care data from different sports, and the generation AI analyzes the data. For example, the mental care suggestion unit suggests mental care to basketball players by referring to mental care data from soccer players. In this way, by referring to mental care data from different sports, mental care can be suggested from a cross-sports perspective.

[0079] The mental care proposal unit can propose an individual mental care plan by taking into account the physical characteristics and health condition of the player. For example, the mental care proposal unit collects the player's physical characteristics and health condition, and the generation AI analyzes this data. For example, an individual mental care plan can be proposed based on height, weight, muscle mass, health check results, etc. This makes it possible to propose an individual mental care plan by taking into account the player's physical characteristics and health condition.

[0080] The mental care suggestion unit can use the emotion estimation function to monitor the emotional state of the player in real time and adjust mental care in real time according to fluctuations in the emotional state. The mental care suggestion unit, for example, uses the emotion estimation function to monitor the emotional state of the player in real time. For example, it analyzes emotional fluctuations during a game, and the generation AI adjusts mental care in real time based on that data. In this way, by monitoring the emotional state of the player in real time, it is possible to adjust mental care in real time according to emotional fluctuations.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The sports coaching support system can also collect data on an athlete's social environment and analyze the impact of social factors on performance. For example, data such as an athlete's home environment, friendships, and stress levels at school are collected and analyzed by the generative AI. This allows the system to identify the impact of an athlete's social environment on performance and provide appropriate support. For example, it can suggest strengthening mental health care for athletes with unstable home environments. It can also provide stress management training for athletes experiencing high levels of stress at school. It can also recommend team-building activities for athletes with good friendships.

[0083] The sports coaching support system can also consider the athlete's hobbies and interests to propose training plans to increase motivation. For example, if an athlete likes music, it can recommend listening to their favorite music while training. If an athlete admires a particular athlete, it can provide a plan based on that athlete's training method. Furthermore, if an athlete likes outdoor activities, it can incorporate outdoor training. By considering the athlete's hobbies and interests, it is possible to increase motivation and maximize the effectiveness of training.

[0084] The sports coaching support system can also take into account an athlete's academic performance and propose a plan to balance their studies and sports. For example, if an athlete's academic performance is declining, it can adjust their training time to ensure they have time to focus on their studies. It can also encourage athletes with excellent academic performance to take on further academic challenges, helping them balance their sports and studies. Furthermore, it can provide advice on maintaining a balanced lifestyle for athletes who demonstrate high performance in both academics and sports. This allows it to provide the optimal plan for balancing their studies and sports by taking into account the athlete's academic performance.

[0085] The sports coaching support system can also analyze the emotional state of players and provide mental care tailored to their emotional state. For example, if a player feels nervous before a match, it can suggest relaxation techniques. If a player feels disappointed after a match, it can provide positive feedback to restore motivation. Furthermore, if a player's concentration is declining during training, it can suggest mental exercises to improve their concentration. In this way, by analyzing a player's emotional state, it is possible to provide mental care tailored to their emotions and improve their performance.

[0086] The sports coaching support system can also analyze the emotional state of players and suggest training menus according to their emotional state. For example, if a player feels nervous before a match, it can suggest light training to help them relax. If a player feels satisfied after a match, it can suggest challenging training to help them maintain their sense of accomplishment. Furthermore, if a player feels stressed during training, it can suggest relaxation exercises to reduce stress. In this way, by analyzing the emotional state of players, it is possible to suggest training menus according to their emotions and improve their performance.

[0087] The sports coaching support system can also analyze the emotional state of players and provide feedback according to their emotional state. For example, if a player is nervous during a match, it can provide advice on how to relax. If a player is disappointed after a match, it can provide positive feedback to restore their motivation. Furthermore, if a player's concentration is declining during training, it can provide advice on how to improve their concentration. In this way, by analyzing a player's emotional state, it is possible to provide feedback according to their emotions and improve their performance.

[0088] The sports coaching support system can also analyze the emotional state of players and suggest competitive strategies based on their emotional state. For example, if a player is nervous before a match, it can suggest a strategy to help them relax. Also, if a player's concentration drops during a match, it can suggest a strategy to improve their concentration. Furthermore, if a player is disappointed after a match, it can provide positive feedback and suggest a strategy for the next match. In this way, by analyzing a player's emotional state, it is possible to suggest competitive strategies based on their emotions and improve a player's performance.

[0089] The sports coaching support system can also analyze the emotional state of players and suggest mental care appropriate to their emotional state. For example, if a player is nervous before a match, it can suggest relaxation techniques. If a player is disappointed after a match, it can provide positive feedback to restore motivation. Furthermore, if a player's concentration is declining during training, it can suggest mental exercises to improve their concentration. In this way, by analyzing a player's emotional state, it is possible to suggest mental care appropriate to their emotions and improve a player's performance.

[0090] The sports coaching support system can also analyze the emotional state of players and provide feedback according to their emotional state. For example, if a player is nervous during a match, it can provide advice on how to relax. If a player is disappointed after a match, it can provide positive feedback to restore their motivation. Furthermore, if a player's concentration is declining during training, it can provide advice on how to improve their concentration. In this way, by analyzing a player's emotional state, it is possible to provide feedback according to their emotions and improve their performance.

[0091] The sports coaching support system can also analyze the emotional state of players and suggest competitive strategies based on their emotional state. For example, if a player is nervous before a match, it can suggest a strategy to help them relax. Also, if a player's concentration drops during a match, it can suggest a strategy to improve their concentration. Furthermore, if a player is disappointed after a match, it can provide positive feedback and suggest a strategy for the next match. In this way, by analyzing a player's emotional state, it is possible to suggest competitive strategies based on their emotions and improve a player's performance.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The performance analysis unit analyzes the player's performance data. For example, it collects data such as the player's running speed, jumping power, and shooting accuracy, and uses this data to identify the player's strengths and weaknesses. The generation AI then analyzes the player's performance data and outputs specific advice. For example, the analysis results can be output using a text generation AI (e.g., LLM). Step 2: The training plan creation unit creates a training plan based on the results of the analysis by the performance analysis unit. For example, it proposes a specific training menu to strengthen the player's weaknesses or a conditioning plan for a match. The generation AI also creates a training plan based on the analysis results and provides it to the player and coach. For example, it adjusts the frequency and intensity of the training menu to create an optimal plan. Step 3: The real-time feedback unit monitors performance during training and matches and provides feedback. For example, it monitors whether a player is using the correct form during training and gives corrective instructions as necessary. The generation AI also analyzes real-time performance data and generates feedback. For example, it analyzes a player's movements in real time and provides appropriate advice.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, a 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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 performance analysis department that analyzes the performance data of players; a training plan creation unit that creates a training plan based on the results of the analysis by the performance analysis unit; a real-time feedback unit that monitors performance during training and games based on the training plan created by the training plan creation unit and provides feedback. A system characterized by:

2. The performance analysis unit Analyze long-term performance fluctuations by taking into account the player's past match data and training history.

2. The system of claim 1.

3. The training plan creation unit Taking into account the player's past training data and match results, we propose the optimal training menu 2. The system of claim 1.

4. The real-time feedback unit Referencing the player's past performance data and comparing it with the past data to provide feedback 2. The system of claim 1.

5. The Competition Strategy Proposal Department Taking into account the player's past match and training data, we propose the optimal competition strategy.

2. The system of claim 1.

6. The performance analysis unit Analyzing the emotional state of the player and identifying the impact of the emotional state on the performance.

2. The system of claim 1.

7. The training plan creation unit Analyzing the emotional state of the player and proposing a training menu according to the emotional state 2. The system of claim 1.

8. The real-time feedback unit Analyzing the emotional state of the player in real time and providing feedback according to the emotional state.

2. The system of claim 1.

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