System

Abstract

An object of a system according to an embodiment is to improve the performance of a player and efficiently perform health management.SOLUTION: A system according to an embodiment includes a play-video analyzer, a coaching advice provider, a performance visualizer, and a health manager. The play-video analysis unit analyzes a play-video. The coaching advice providing unit provides coaching advice on the basis of the play-video analyzed by the play-video analysis unit. The performance visualization unit visually displays the performance of the player. The health management unit analyzes the health data of the player to predict the risk of injury.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] Previous technology has had the problem of not being able to provide efficient coaching or predict injury risk when it comes to improving the performance and managing the health of sports and eSports players.

[0005] The system according to the embodiment aims to improve the performance of the player and efficiently manage their health. [Means for solving the problem]

[0006] The system according to the embodiment includes a play video analysis unit, a coaching advice providing unit, a performance visualization unit, and a health management unit. The play video analysis unit analyzes play videos. The coaching advice providing unit provides coaching advice based on the play videos analyzed by the play video analysis unit. The performance visualization unit visually displays the player's performance. The health management unit analyzes the player's health data to predict the risk of injury. [Effects of the Invention]

[0007] The system according to the embodiment can improve the player's performance and efficiently manage their health. [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) An AI coaching tool according to an embodiment of the present invention is a system that improves player performance, improves team win rates, increases fan engagement, manages player health, and reduces the risk of injury. As a result, the AI ​​coaching tool can improve player performance, improve team win rates, increase fan engagement, manage player health, and reduce the risk of injury in both sports and eSports.

[0029] An AI coaching tool according to an embodiment includes a gameplay video analysis unit, a coaching advice providing unit, a performance visualization unit, and a health management unit. The gameplay video analysis unit analyzes gameplay videos. For example, the gameplay video analysis unit analyzes soccer game videos and identifies areas for improvement in player movements and positioning. The gameplay video analysis unit also analyzes eSports game videos and suggests areas for improvement in a player's strategy and operation. The coaching advice providing unit provides coaching advice based on the gameplay videos analyzed by the gameplay video analysis unit. For example, the coaching advice providing unit generates specific advice regarding player movements and tactics using a generation AI (e.g., a text generation AI or a multimodal generation AI). The performance visualization unit visually displays a player's performance. For example, the performance visualization unit displays data such as a player's running distance, shooting success rate, and reaction speed in graphs and charts. The health management unit analyzes a player's health data to predict injury risk. For example, the health management unit analyzes a player's movements and load, identifies areas of excessive load, and suggests appropriate training and rest. This enables the AI ​​coaching tool according to the embodiment to improve player performance, increase team win rates, increase fan engagement, manage player health, and reduce injury risk. For example, the output unit displays scoring results to students and teachers via a web application or mobile application. If students or teachers prefer paper feedback, the output unit prints the results using a printer. Sending the results via email provides quick feedback by sending them directly to students and parents.

[0030] The play video analysis unit analyzes the player's subtle movements and facial expressions, and the coaching advice provision unit can provide advice that includes not only technical improvements but also psychological approaches. The play video analysis unit, for example, analyzes play videos and analyzes the player's subtle movements and facial expressions in detail. For example, it analyzes the position of the player's feet and hand movements when shooting to point out technical improvements. The play video analysis unit also analyzes the player's facial expressions and suggests psychological approaches. For example, if a player is lacking concentration, it suggests mental training and stress management methods. This makes it possible to support the improvement of a player's performance from both a technical and psychological perspective.

[0031] The play video analysis unit studies the player's past play data, and the coaching advice provision unit can propose strategies for long-term performance improvement. The play video analysis unit, for example, studies the player's past play data and proposes strategies for long-term performance improvement. For example, it points out areas for improvement in specific techniques or tactics based on past match data. The play video analysis unit also analyzes the player's growth process and proposes long-term training plans. This makes it possible to support long-term performance improvement based on past data.

[0032] The performance visualization unit can visualize performance data using a 3D model and display the player's movements in three dimensions. For example, the performance visualization unit generates a 3D model based on the player's performance data and displays the player's movements in three dimensions. For example, the player's running distance and movements are visualized using a 3D model. The performance visualization unit also builds a system that displays the player's movements in 3D in real time. This makes it easier to understand the player's movements in three dimensions.

[0033] The performance visualization unit can provide a dashboard that allows fans to interactively manipulate data based on player performance data. The performance visualization unit provides a dashboard that allows fans to interactively manipulate data based on player performance data, for example. For example, a player's running distance and shooting success rate can be freely manipulated. The performance visualization unit also provides data filtering and graph manipulation functions so that fans can analyze player performance data in detail. This makes it easier for fans to analyze player performance data in detail.

[0034] The performance visualization unit can provide a timeline that allows fans to track a player's development process based on the performance data. The performance visualization unit provides a timeline that allows fans to track a player's development process based on the player's performance data, for example, by displaying the player's past match data and training data. The performance visualization unit also builds a system that displays the player's development process in chronological order, making it easier for fans to track the player's development process in detail.

[0035] The performance visualization unit can use the generation AI to create games and quizzes based on the player's performance data and provide content that fans can enjoy. The performance visualization unit generates games and quizzes that fans can enjoy, for example, based on the player's performance data. For example, it provides quizzes about the player's achievements and skills. The performance visualization unit also uses the generation AI (for example, text generation AI or image generation AI) to provide interactive content based on the player's performance data. This provides content that fans can enjoy and improves fan engagement.

[0036] The health management unit can integrate gameplay videos and health data to perform a detailed analysis of a player's movements and stress, and predict the risk of injury. The health management unit, for example, can integrate gameplay videos and health data to perform a detailed analysis of a player's movements and stress, and predict the risk of injury. For example, it can analyze a player's movements and stress and identify areas where excessive stress is being applied. The health management unit can also predict a player's risk of injury based on gameplay videos and health data, and suggest appropriate training and rest. This allows for a detailed analysis of a player's movements and stress, and predict the risk of injury.

[0037] The health management unit can learn from the player's past health data and propose a strategy for long-term health management. For example, the health management unit can learn from the player's past health data and propose a strategy for long-term health management. For example, based on the past data, it can analyze the changes in the player's health status and propose a long-term health management plan. The health management unit can also propose injury prevention measures based on the player's past health data. This makes it possible to support long-term health management based on past data.

[0038] The Health Management Department uses health data to find commonalities in health management between different sports and eSports events, which can then be applied to other events. For example, the Health Management Department analyzes health data from different sports and eSports to find commonalities in health management. For example, it compares health management data from soccer and basketball and suggests common areas for improvement. The Health Management Department also proposes health management measures that can be applied to other events based on the commonalities between different events. For example, it proposes training methods that can be applied to athletes in other sports based on the health management data of eSports players. This allows commonalities in health management between different events to be found, which can then be applied to other events.

[0039] The health management unit uses generative AI to monitor the health status of athletes in real time and provide immediate health management advice. The health management unit, for example, uses generative AI to monitor the health status of athletes in real time and provide immediate health management advice. For example, it analyzes the athletes' heart rate and body temperature in real time and provides appropriate advice. The health management unit also uses generative AI to propose real-time training plans based on the athletes' health status. This allows the health status of athletes to be monitored in real time and provide immediate health management advice.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The AI ​​coaching tool can also be equipped with a nutrition management section for players. The nutrition management section analyzes the player's dietary data and proposes an optimal nutritional intake plan. For example, it adjusts the timing of energy replenishment and nutritional balance based on the player's training schedule and game schedule. The nutrition management section also suggests necessary supplements and ingredients based on the player's physical condition and performance data. This can optimize the player's nutritional status and help improve performance.

[0042] The AI ​​coaching tool can also be equipped with a sleep management module for players. The sleep management module analyzes the player's sleep data and suggests the optimal sleeping environment and sleep schedule. For example, it analyzes the player's sleep patterns and quality and points out areas for improvement. The sleep management module also suggests appropriate sleep duration and recovery methods based on the player's training and match schedule. This can optimize the player's sleep state and help improve performance.

[0043] The AI ​​coaching tool can also be equipped with a player feedback management module. The feedback management module collects and analyzes feedback from coaches and teammates based on the player's performance data and suggests areas for improvement. For example, it can analyze a player's play video and performance data and provide specific feedback. The feedback management module can also track the player's development and propose long-term feedback plans, thereby helping to improve the player's performance.

[0044] The AI ​​coaching tool can also be equipped with a player training management section. The training management section analyzes the player's training data and proposes the optimal training plan. For example, it can analyze the player's physical strength and technical level and provide an individually customized training menu. The training management section also tracks the player's growth process and proposes long-term training plans. This can optimize the player's training and help improve performance.

[0045] The AI ​​coaching tool can further include a player performance prediction unit. The performance prediction unit learns a player's past performance data and predicts future performance. For example, it predicts a player's performance in the next game based on the player's past match and training data. The performance prediction unit also analyzes a player's growth process and makes long-term performance predictions. This makes it possible to predict a player's future performance and suggest appropriate training and strategies.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The gameplay video analysis unit analyzes gameplay videos. For example, it analyzes soccer game videos and points out areas for improvement in player movements and positioning. It also analyzes eSports game videos and suggests areas for improvement in player strategies and operations. Step 2: The coaching advice providing unit provides coaching advice based on the play video analyzed by the play video analysis unit. For example, specific advice regarding player movements and tactics is generated using a generation AI (e.g., a text generation AI or a multimodal generation AI). Step 3: The performance visualization unit visually displays the player's performance, for example, by displaying data such as the player's running distance, shooting success rate, and reaction time in graphs and charts. Step 4: The Health Management Department analyzes the player's health data to predict injury risk. For example, they analyze the player's movements and loads, identify areas of excessive strain, and suggest appropriate training and rest.

[0048] (Example 2) An AI coaching tool according to an embodiment of the present invention is a system that improves player performance, improves team win rates, increases fan engagement, manages player health, and reduces the risk of injury. As a result, the AI ​​coaching tool can improve player performance, improve team win rates, increase fan engagement, manage player health, and reduce the risk of injury in both sports and eSports.

[0049] An AI coaching tool according to an embodiment includes a gameplay video analysis unit, a coaching advice providing unit, a performance visualization unit, and a health management unit. The gameplay video analysis unit analyzes gameplay videos. For example, the gameplay video analysis unit analyzes soccer game videos and identifies areas for improvement in player movements and positioning. The gameplay video analysis unit also analyzes eSports game videos and suggests areas for improvement in a player's strategy and operation. The coaching advice providing unit provides coaching advice based on the gameplay videos analyzed by the gameplay video analysis unit. For example, the coaching advice providing unit generates specific advice regarding player movements and tactics using a generation AI (e.g., a text generation AI or a multimodal generation AI). The performance visualization unit visually displays a player's performance. For example, the performance visualization unit displays data such as a player's running distance, shooting success rate, and reaction speed in graphs and charts. The health management unit analyzes a player's health data to predict injury risk. For example, the health management unit analyzes a player's movements and load, identifies areas of excessive load, and suggests appropriate training and rest. This enables the AI ​​coaching tool according to the embodiment to improve player performance, increase team win rates, increase fan engagement, manage player health, and reduce injury risk. For example, the output unit displays scoring results to students and teachers via a web application or mobile application. If students or teachers prefer paper feedback, the output unit prints the results using a printer. Sending the results via email provides quick feedback by sending them directly to students and parents.

[0050] The play video analysis unit estimates the emotional state of the player, and the coaching advice providing unit can provide coaching advice based on the emotional state. The play video analysis unit, for example, analyzes play video and estimates the emotional state from the player's facial expressions and movements. For example, if the player is nervous, the unit can provide advice on breathing techniques or mental techniques to help him relax. This allows appropriate advice to be provided according to the player's emotional state.

[0051] The play video analysis unit analyzes the player's subtle movements and facial expressions, and the coaching advice provision unit can provide advice that includes not only technical improvements but also psychological approaches. The play video analysis unit, for example, analyzes play videos and analyzes the player's subtle movements and facial expressions in detail. For example, it analyzes the position of the player's feet and hand movements when shooting to point out technical improvements. The play video analysis unit also analyzes the player's facial expressions and suggests psychological approaches. For example, if a player is lacking concentration, it suggests mental training and stress management methods. This makes it possible to support the improvement of a player's performance from both a technical and psychological perspective.

[0052] The play video analysis unit studies the player's past play data, and the coaching advice provision unit can propose strategies for long-term performance improvement. The play video analysis unit, for example, studies the player's past play data and proposes strategies for long-term performance improvement. For example, it points out areas for improvement in specific techniques or tactics based on past match data. The play video analysis unit also analyzes the player's growth process and proposes long-term training plans. This makes it possible to support long-term performance improvement based on past data.

[0053] The performance visualization unit can perform emotion estimation based on the player's performance data, allowing fans to understand the player's emotional state. For example, the performance visualization unit can perform emotion estimation based on the player's performance data, allowing fans to understand the player's emotional state. For example, the performance visualization unit can display the moment at which the player is most focused. The performance visualization unit can also visually display the player's emotional state using graphs and charts, making it easier for fans to understand the player's emotional state.

[0054] The performance visualization unit can visualize performance data using a 3D model and display the player's movements in three dimensions. For example, the performance visualization unit generates a 3D model based on the player's performance data and displays the player's movements in three dimensions. For example, the player's running distance and movements are visualized using a 3D model. The performance visualization unit also builds a system that displays the player's movements in 3D in real time. This makes it easier to understand the player's movements in three dimensions.

[0055] The performance visualization unit can provide a dashboard that allows fans to interactively manipulate data based on player performance data. The performance visualization unit provides a dashboard that allows fans to interactively manipulate data based on player performance data, for example. For example, a player's running distance and shooting success rate can be freely manipulated. The performance visualization unit also provides data filtering and graph manipulation functions so that fans can analyze player performance data in detail. This makes it easier for fans to analyze player performance data in detail.

[0056] The performance visualization unit can provide a timeline that allows fans to track a player's development process based on the performance data. The performance visualization unit provides a timeline that allows fans to track a player's development process based on the player's performance data, for example, by displaying the player's past match data and training data. The performance visualization unit also builds a system that displays the player's development process in chronological order, making it easier for fans to track the player's development process in detail.

[0057] The performance visualization unit can use the generation AI to create games and quizzes based on the player's performance data and provide content that fans can enjoy. The performance visualization unit generates games and quizzes that fans can enjoy, for example, based on the player's performance data. For example, it provides quizzes about the player's achievements and skills. The performance visualization unit also uses the generation AI (for example, text generation AI or image generation AI) to provide interactive content based on the player's performance data. This provides content that fans can enjoy and improves fan engagement.

[0058] The performance visualization unit uses the emotion estimation function to enable fans to predict the emotional state of a player and provide content based on the prediction results. For example, the performance visualization unit uses the emotion estimation function to build a system that allows fans to predict the emotional state of a player. For example, the emotional state is estimated from the player's facial expressions and movements and provided to fans. The performance visualization unit also uses the emotion estimation function to provide interactive content based on the player's emotional state. This makes it easier for fans to predict the player's emotional state, and makes it possible to provide enjoyable content.

[0059] The health management unit can estimate the emotional state of a player and propose health management and injury risk reduction measures based on the emotion. The health management unit, for example, estimates the emotional state of a player and proposes health management and injury risk reduction measures based on the emotion. For example, if a player is feeling stressed, it proposes relaxation methods. The health management unit also analyzes the emotional state of a player and proposes appropriate training and rest. This makes it possible to provide health management and injury risk reduction measures according to the player's emotional state.

[0060] The health management unit can integrate gameplay videos and health data to perform a detailed analysis of a player's movements and stress, and predict the risk of injury. The health management unit, for example, can integrate gameplay videos and health data to perform a detailed analysis of a player's movements and stress, and predict the risk of injury. For example, it can analyze a player's movements and stress and identify areas where excessive stress is being applied. The health management unit can also predict a player's risk of injury based on gameplay videos and health data, and suggest appropriate training and rest. This allows for a detailed analysis of a player's movements and stress, and predict the risk of injury.

[0061] The health management unit can learn from the player's past health data and propose a strategy for long-term health management. For example, the health management unit can learn from the player's past health data and propose a strategy for long-term health management. For example, based on the past data, it can analyze the changes in the player's health status and propose a long-term health management plan. The health management unit can also propose injury prevention measures based on the player's past health data. This makes it possible to support long-term health management based on past data.

[0062] The Health Management Department uses health data to find commonalities in health management between different sports and eSports events, which can then be applied to other events. For example, the Health Management Department analyzes health data from different sports and eSports to find commonalities in health management. For example, it compares health management data from soccer and basketball and suggests common areas for improvement. The Health Management Department also proposes health management measures that can be applied to other events based on the commonalities between different events. For example, it proposes training methods that can be applied to athletes in other sports based on the health management data of eSports players. This allows commonalities in health management between different events to be found, which can then be applied to other events.

[0063] The health management unit uses generative AI to monitor the health status of athletes in real time and provide immediate health management advice. The health management unit, for example, uses generative AI to monitor the health status of athletes in real time and provide immediate health management advice. For example, it analyzes the athletes' heart rate and body temperature in real time and provides appropriate advice. The health management unit also uses generative AI to propose real-time training plans based on the athletes' health status. This allows the health status of athletes to be monitored in real time and provide immediate health management advice.

[0064] The health management unit can use the emotion estimation function to monitor the player's emotional state in real time and provide health management advice according to the emotion. The health management unit, for example, uses the emotion estimation function to monitor the player's emotional state in real time and provide health management advice according to the emotion. For example, if the player is feeling stressed, the health management unit can suggest relaxation methods. The health management unit can also suggest appropriate training and rest based on the player's emotional state. This makes it possible to provide health management advice according to the player's emotional state in real time.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The AI ​​coaching tool can also be equipped with a nutrition management section for players. The nutrition management section analyzes the player's dietary data and proposes an optimal nutritional intake plan. For example, it adjusts the timing of energy replenishment and nutritional balance based on the player's training schedule and game schedule. The nutrition management section also suggests necessary supplements and ingredients based on the player's physical condition and performance data. This can optimize the player's nutritional status and help improve performance.

[0067] The AI ​​coaching tool can also be equipped with a sleep management module for players. The sleep management module analyzes the player's sleep data and suggests the optimal sleeping environment and sleep schedule. For example, it analyzes the player's sleep patterns and quality and points out areas for improvement. The sleep management module also suggests appropriate sleep duration and recovery methods based on the player's training and match schedule. This can optimize the player's sleep state and help improve performance.

[0068] The AI ​​coaching tool can also be equipped with a motivation management module for players. This module estimates the player's emotional state and provides advice to maintain and improve their motivation. For example, if a player is feeling down, it can send encouraging messages or suggest ways to reflect on successful experiences. The motivation management module can also analyze the player's goal setting and achievement level and provide appropriate feedback. This can increase the player's motivation and help improve their performance.

[0069] The AI ​​coaching tool can also be equipped with a player communication management module. This module analyzes communication between players and with their coaches and suggests areas for improvement. For example, it can analyze communication patterns between players and provide advice to improve teamwork. The communication management module can also estimate players' emotional states and suggest appropriate communication methods. This can facilitate smooth communication within a team and help improve performance.

[0070] The AI ​​coaching tool can also be equipped with a player stress management module. The stress management module estimates the player's emotional state and provides advice to reduce stress. For example, if a player is feeling stressed, it can suggest relaxation methods and mental health care methods. The stress management module can also suggest appropriate rest and recovery methods based on the player's training and match schedule. This can help reduce player stress and improve performance.

[0071] The AI ​​coaching tool can also be equipped with a player feedback management module. The feedback management module collects and analyzes feedback from coaches and teammates based on the player's performance data and suggests areas for improvement. For example, it can analyze a player's play video and performance data and provide specific feedback. The feedback management module can also track the player's development and propose long-term feedback plans, thereby helping to improve the player's performance.

[0072] The AI ​​coaching tool can also be equipped with a player recovery management section. The recovery management section analyzes a player's training and health data to propose an optimal recovery plan. For example, it can analyze a player's fatigue level and injury risk and propose an appropriate recovery method. The recovery management section can also estimate a player's emotional state and suggest the mental care necessary for recovery. This can optimize a player's recovery and help improve their performance.

[0073] The AI ​​coaching tool can also be equipped with a player training management section. The training management section analyzes the player's training data and proposes the optimal training plan. For example, it can analyze the player's physical strength and technical level and provide an individually customized training menu. The training management section also tracks the player's growth process and proposes long-term training plans. This can optimize the player's training and help improve performance.

[0074] The AI ​​coaching tool can further include a player performance prediction unit. The performance prediction unit learns a player's past performance data and predicts future performance. For example, it predicts a player's performance in the next game based on the player's past match and training data. The performance prediction unit also analyzes a player's growth process and makes long-term performance predictions. This makes it possible to predict a player's future performance and suggest appropriate training and strategies.

[0075] The AI ​​coaching tool can also be equipped with a player's mental health management function. This function estimates the player's emotional state and provides advice for mental health care. For example, if the player is feeling anxious or stressed, it can suggest relaxation methods or counseling. The mental health management function can also provide appropriate mental training and support based on the player's emotional state. This can optimize the player's mental health and help improve their performance.

[0076] The processing flow of the second embodiment will be briefly explained below.

[0077] Step 1: The gameplay video analysis unit analyzes gameplay videos. For example, it analyzes soccer game videos and points out areas for improvement in player movements and positioning. It also analyzes eSports game videos and suggests areas for improvement in player strategies and operations. Step 2: The coaching advice providing unit provides coaching advice based on the play video analyzed by the play video analysis unit. For example, specific advice regarding player movements and tactics is generated using a generation AI (e.g., a text generation AI or a multimodal generation AI). Step 3: The performance visualization unit visually displays the player's performance, for example, by displaying data such as the player's running distance, shooting success rate, and reaction time in graphs and charts. Step 4: The Health Management Department analyzes the player's health data to predict injury risk. For example, they analyze the player's movements and loads, identify areas of excessive strain, and suggest appropriate training and rest.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0082] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0097] 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.

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

[0099] The 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.

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

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

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

[0103] Fig. 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.

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

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

[0106] In the 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.

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

[0108] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0110] The data processing system 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.

[0111] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0112] 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.

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

[0114] The 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.

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

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0118] 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.

[0119] 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.

[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 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.

[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 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.

[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 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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."

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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]

[0145] 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 gameplay video analysis unit that analyzes gameplay videos; a coaching advice providing unit that provides coaching advice based on the gameplay video analyzed by the gameplay video analyzing unit; a performance visualization unit that visually displays the player's performance; a health management unit that analyzes the health data of the athlete and predicts the risk of injury; A system characterized by:

2. The gameplay video analysis unit The emotional state of the player is estimated, and the coaching advice providing unit provides coaching advice based on the emotional state.

2. The system of claim 1.

3. The performance visualization unit Emotion estimation is performed based on the performance data of the player, allowing fans to understand the emotional state of the player.

2. The system of claim 1.

4. The health management department The game video and the health data are integrated to perform a detailed analysis of the player's movements and stress, and to predict the risk of injury.

2. The system of claim 1.

5. The health management department Estimate the player's emotional state and propose health management and injury risk reduction measures based on the player's emotions.

2. The system of claim 1.

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