system

Abstract

The system according to this embodiment aims to effectively disseminate tactics and training methods that enable amateur sports teams to defeat professional teams. [Solution] The system according to the embodiment comprises a tactical analysis unit, a real-time adjustment unit, a performance prediction unit, a training planning unit, and a match simulation unit. The tactical analysis unit analyzes the opposing team's match data. The real-time adjustment unit adjusts tactics based on the data analyzed by the tactical analysis unit. The performance prediction unit predicts player performance based on the tactics adjusted by the real-time adjustment unit. The training planning unit generates a training plan based on the data predicted by the performance prediction unit. The match simulation unit performs a match simulation based on the plan generated by the training planning unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to effectively spread the tactics and training methods for an amateur sports team to defeat a professional team.

[0005] The system according to the embodiment aims to effectively spread the tactics and training methods for an amateur sports team to defeat a professional team.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a tactical analysis unit, a real-time adjustment unit, a performance prediction unit, a training planning unit, and a match simulation unit. The tactical analysis unit analyzes the opposing team's match data. The real-time adjustment unit adjusts tactics based on the data analyzed by the tactical analysis unit. The performance prediction unit predicts player performance based on the tactics adjusted by the real-time adjustment unit. The training planning unit generates a training plan based on the data predicted by the performance prediction unit. The match simulation unit performs a match simulation based on the plan generated by the training planning unit. [Effects of the Invention]

[0007] The system according to this embodiment can effectively disseminate tactics and training methods that allow amateur sports teams to defeat professional teams. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8]This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG.  1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. 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, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The sports team support system according to an embodiment of the present invention is a system that utilizes an AI agent to disseminate tactics and training methods that enable amateur sports teams to defeat professional teams. This system integrates tactical analysis, real-time adjustments, player performance prediction, training plans, and match simulations, aiming to create strong teams even without excellent coaches. For example, the sports team support system analyzes the opposing team's match data and extracts tactical patterns. This allows it to propose optimal countermeasures for the team. For example, it analyzes the opposing team's offensive patterns and defensive weaknesses and adjusts the team's tactics based on that. Next, the sports team support system analyzes the players' movements during practice in real time and provides technical advice. The AI ​​agent monitors the players' movements and suggests areas for improvement in form and ways to make movements more efficient. This helps improve the players' skills. Furthermore, the sports team support system analyzes match data in real time and suggests tactical adjustments. The AI ​​agent grasps the situation during the match and changes tactics as needed to gain an advantage in the flow of the game. For example, it instantly adjusts the team's tactics in response to changes in the opposing team's tactics. In addition, the sports team support system predicts player performance based on past data and generates an optimal training plan. The AI ​​agent analyzes a player's past performance data and predicts their future growth. Based on this, it proposes an optimal training plan for each individual player. Finally, the sports team support system performs match simulations combining video and text data to verify the effectiveness of tactics in advance. Through the match simulation, the AI ​​agent confirms the effectiveness of tactics and modifies them as needed. This allows for improved tactical precision before the match. This system enables amateur sports teams to acquire tactics and training methods comparable to professional teams, even without top-tier coaches. This significantly improves the team's competitive level and increases the likelihood of winning matches. In this way, the sports team support system can disseminate tactics and training methods that allow amateur sports teams to beat professional teams.

[0029] The sports team support system according to this embodiment comprises a tactical analysis unit, a real-time adjustment unit, a performance prediction unit, a training planning unit, and a match simulation unit. The tactical analysis unit analyzes the opposing team's match data. The opposing team's match data includes, but is not limited to, scoring patterns, player movements, and tactical tendencies. For example, the tactical analysis unit analyzes the opposing team's scoring patterns to understand their offensive tendencies. The tactical analysis unit can also analyze player movements to identify defensive weaknesses. Furthermore, the tactical analysis unit can analyze tactical tendencies to predict changes in the opposing team's tactics. For example, the tactical analysis unit detects changes in tactics based on the opposing team's past match data. The real-time adjustment unit adjusts tactics based on the data analyzed by the tactical analysis unit. Tactical adjustments include, but are not limited to, changes in formation and changes in player positioning. For example, the real-time adjustment unit changes the formation to respond to the opposing team's attacks. The real-time adjustment unit can also strengthen defenses by changing player positioning. Furthermore, the real-time adjustment unit can also make tactical changes in real time. For example, the real-time adjustment unit analyzes data during a match and proposes tactical adjustments. The performance prediction unit predicts player performance based on the tactics adjusted by the real-time adjustment unit. Performance prediction includes, but is not limited to, analysis of historical data and use of statistical models. For example, the performance prediction unit predicts player performance based on past match data. The performance prediction unit can also predict player growth using statistical models. Furthermore, the performance prediction unit can predict player performance in real time. For example, the performance prediction unit analyzes data during a match and predicts player performance. The training planning unit generates a training plan based on the data predicted by the performance prediction unit. The training plan includes, but is not limited to, practice menus, training frequency and intensity. For example, the training planning unit proposes a training plan tailored to the player's fitness level.Furthermore, the training planning unit can propose training plans tailored to the skill level of the players. It can also propose training plans tailored to the players' goals. For example, the training planning unit adjusts the training plan based on the players' goals. The match simulation unit performs match simulations based on the plans generated by the training planning unit. Match simulations include, but are not limited to, the simulation scenario and the types of data used. For example, the match simulation unit performs match simulations combining video and text data. It can also perform simulations based on past match data. Furthermore, the match simulation unit can perform simulations that reflect the individual characteristics of the players. For example, it performs simulations considering the players' skills and abilities. As a result, the sports team support system according to this embodiment can disseminate tactics and training methods that enable amateur sports teams to defeat professional teams.

[0030] The tactical analysis unit analyzes the opposing team's match data. This data includes, but is not limited to, scoring patterns, player movements, and tactical tendencies. For example, the tactical analysis unit analyzes the opposing team's scoring patterns to understand their offensive tendencies. It can also analyze player movements to identify defensive weaknesses. Furthermore, it can analyze tactical tendencies to predict tactical changes by the opposing team. For example, the tactical analysis unit detects tactical changes based on the opposing team's past match data. The tactical analysis unit uses AI to quickly and accurately analyze large amounts of match data. The AI ​​uses deep learning technology to analyze match videos and player movement data to extract the opposing team's tactical patterns. For example, the AI ​​learns the opposing team's offensive formations and player movement characteristics to predict scoring patterns under specific circumstances. The AI ​​also analyzes player positioning and movement tendencies during defense to identify defensive weaknesses. In addition, the AI ​​detects signs of tactical changes by the opposing team and provides information to quickly respond to tactical changes during a match. This allows the tactical analysis department to gain a detailed understanding of the opposing team's tactics and provide crucial information for formulating match strategies.

[0031] The Real-Time Adjustment Unit adjusts tactics based on data analyzed by the Tactical Analysis Unit. These adjustments include, but are not limited to, changes in formation and player positioning. For example, the Real-Time Adjustment Unit might change the formation to counter the opposing team's attack. It can also strengthen defenses by changing player positioning. Furthermore, the Real-Time Adjustment Unit can make tactical changes in real time. For example, it might analyze in-game data and propose tactical adjustments. The Real-Time Adjustment Unit uses AI to analyze in-game data in real time and propose optimal tactical changes. The AI ​​monitors player movements and opposing team tactical changes in real time, quickly proposing tactical changes appropriate to the situation. For example, the AI ​​might detect the opposing team's attack patterns and change the defensive formation to neutralize their attack. The AI ​​also monitors player fatigue and performance, and optimizes overall team performance by proposing substitutions and positioning changes. Finally, the Real-Time Adjustment Unit evaluates the effectiveness of tactical changes based on in-game data and makes further adjustments as needed. This allows the real-time adjustment unit to respond quickly and flexibly to the situation during a match, maximizing the team's performance.

[0032] The performance prediction unit predicts player performance based on tactics adjusted by the real-time adjustment unit. Performance prediction includes, but is not limited to, analysis of historical data and the use of statistical models. For example, the performance prediction unit predicts player performance based on past match data. The performance prediction unit can also predict player growth using statistical models. Furthermore, the performance prediction unit can predict player performance in real time. For example, the performance prediction unit analyzes data during a match to predict player performance. The performance prediction unit uses AI to predict player performance with high accuracy. The AI ​​learns from the player's past match data and training data to build a model for predicting player performance. For example, the AI ​​analyzes data such as a player's scoring patterns, number of assists, and defensive success rate to predict player performance. The AI ​​also predicts player performance in real time, taking into account the player's physical condition, fatigue level, and injury risk. In addition, the AI ​​can predict player growth and propose training plans that anticipate future performance improvements. As a result, the performance prediction unit can predict player performance with high accuracy and contribute to team strategy planning and training plan development.

[0033] The training planning unit generates a training plan based on data predicted by the performance prediction unit. The training plan includes, but is not limited to, practice menus, training frequency, and intensity. For example, the training planning unit can propose a training plan tailored to the athlete's fitness level. It can also propose a training plan tailored to the athlete's skill level. Furthermore, it can propose a training plan tailored to the athlete's goals. For example, the training planning unit adjusts the training plan based on the athlete's goals. The training planning unit uses AI to analyze athlete data and generate an optimal training plan. The AI ​​proposes training menus tailored to the athlete's fitness level, skill level, and goals. For example, the AI ​​proposes training menus to improve endurance based on the athlete's fitness data. The AI ​​also analyzes the athlete's skill data and proposes practice menus to improve skill. In addition, the AI ​​adjusts the training frequency and intensity, taking the athlete's goals into consideration. For example, it adjusts the training plan so that the athlete can achieve peak performance for a specific match. This allows the training planning unit to provide an optimal training plan tailored to the individual needs of each athlete, supporting their performance improvement.

[0034] The match simulation unit performs match simulations based on plans generated by the training planning unit. Match simulations include, but are not limited to, the simulation scenario and the types of data used. For example, the match simulation unit can perform match simulations combining video and text data. It can also perform simulations based on past match data. Furthermore, it can perform simulations that reflect the individual characteristics of players. For example, it can perform simulations considering the skills and abilities of players. The match simulation unit uses AI to perform match simulations with high accuracy. The AI ​​learns from past match data and player characteristic data to simulate match scenarios. For example, the AI ​​simulates the opposing team's tactics and player movements to predict the flow of the match. The AI ​​also simulates in-game play considering the skills and abilities of players. Furthermore, the AI ​​updates the simulation in real time in response to changes in the match situation and proposes optimal tactics. This allows the match simulation unit to predict the flow of matches with high accuracy and provide information useful for team strategy planning and player training.

[0035] The tactical analysis unit can analyze the opposing team's attack patterns and defensive weaknesses. For example, the tactical analysis unit can analyze the opposing team's attack patterns to understand scoring methods and attack timing. It can also analyze the opposing team's defensive weaknesses to identify defensive positioning and player movements. For example, the tactical analysis unit can analyze the opposing team's attack patterns to identify scoring methods. It can also analyze the opposing team's defensive weaknesses to identify defensive positioning. Furthermore, the tactical analysis unit can comprehensively analyze the opposing team's attack patterns and defensive weaknesses and propose tactical adjustments. For example, the tactical analysis unit can analyze the opposing team's attack patterns and defensive weaknesses and propose tactical adjustments. In this way, by analyzing the opposing team's attack patterns and defensive weaknesses, it can propose effective tactics. Some or all of the above processing in the tactical analysis unit may be performed using AI, for example, or not. For example, the tactical analysis unit can input the opposing team's match data into a generating AI and have the generating AI perform the analysis of attack patterns and defensive weaknesses.

[0036] The real-time adjustment unit can analyze the movements of players during practice in real time and provide technical advice. For example, the real-time adjustment unit can monitor players' movements and suggest corrections to their form or ways to improve the efficiency of their movements. The real-time adjustment unit can also analyze players' movements and provide tactical instructions. For example, the real-time adjustment unit can monitor players' movements and suggest corrections to their form. The real-time adjustment unit can also analyze players' movements and suggest ways to improve the efficiency of their movements. The real-time adjustment unit can also analyze players' movements and provide tactical instructions. For example, the real-time adjustment unit can monitor players' movements and provide tactical instructions. This allows for the improvement of players' skills by analyzing their movements in real time during practice and providing technical advice. Some or all of the above processing in the real-time adjustment unit may be performed using AI, for example, or without AI. For example, the real-time adjustment unit can input player movement data into a generating AI and have the generating AI provide technical advice.

[0037] The real-time adjustment unit can analyze match data in real time and propose tactical adjustments. For example, the real-time adjustment unit can analyze match data and respond to changes in the opposing team's tactics. The real-time adjustment unit can also analyze match data and instantly adjust its own team's tactics. For example, the real-time adjustment unit can analyze match data and respond to changes in the opposing team's tactics. The real-time adjustment unit can also analyze match data and instantly adjust its own team's tactics. The real-time adjustment unit can also analyze match data and propose tactical adjustments. For example, the real-time adjustment unit can analyze match data and propose tactical adjustments. By analyzing match data in real time and proposing tactical adjustments, it is possible to gain an advantage in the flow of the match. Some or all of the above processing in the real-time adjustment unit may be performed using AI, for example, or without AI. For example, the real-time adjustment unit can input match data into a generating AI and have the generating AI perform tactical adjustments.

[0038] The performance prediction unit can predict a player's performance based on past data. For example, the performance prediction unit can analyze past match data to predict a player's performance. The performance prediction unit can also predict a player's growth using statistical models. For example, the performance prediction unit can analyze past match data to predict a player's performance. The performance prediction unit can also predict a player's growth using statistical models. Furthermore, the performance prediction unit can predict a player's performance in real time. For example, the performance prediction unit can analyze data during a match to predict a player's performance. This allows for the generation of an optimal training plan by predicting a player's performance based on past data. Some or all of the above processing in the performance prediction unit may be performed using AI, for example, or without AI. For example, the performance prediction unit can input past match data into a generating AI and have the generating AI perform the prediction of a player's performance.

[0039] The training planning unit can generate an optimal training plan for each individual athlete. For example, the training planning unit can propose a training plan that is appropriate for the athlete's physical fitness level. It can also propose a training plan that is appropriate for the athlete's skill level. For example, the training planning unit can propose a training plan that is appropriate for the athlete's physical fitness level. It can also propose a training plan that is appropriate for the athlete's skill level. Furthermore, the training planning unit can propose a training plan that is appropriate for the athlete's goals. For example, the training planning unit can adjust the training plan based on the athlete's goals. This allows for the generation of an optimal training plan for each individual athlete, thereby promoting their growth. Some or all of the above processes in the training planning unit may be performed using AI, for example, or without AI. For example, the training planning unit can input data on the athlete's physical fitness level and skill level into a generating AI and have the generating AI generate the training plan.

[0040] The match simulation unit can perform match simulations by combining video data and text data, allowing for the prior verification of tactical effectiveness. For example, the match simulation unit can perform match simulations by combining video data and text data. The match simulation unit can also perform simulations based on past match data. For example, the match simulation unit can perform match simulations by combining video data and text data. The match simulation unit can also perform simulations based on past match data. Furthermore, the match simulation unit can perform simulations that reflect the individual characteristics of players. For example, the match simulation unit can perform simulations considering the skills and abilities of players. This allows for the confirmation of the effectiveness of tactics through match simulations and the improvement of tactical accuracy before the match by modifying tactics as needed. Some or all of the above-described processes in the match simulation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the match simulation unit can input video data and text data into a generative AI and have the generative AI perform the match simulation.

[0041] The tactical analysis unit can detect changes in tactics by comparing the opponent team's match data with past match data. For example, the tactical analysis unit can analyze the data from the opponent team's last five matches and detect changes in tactics. The tactical analysis unit can also detect changes in the movements of specific players on the opponent team. For example, the tactical analysis unit can analyze the data from the opponent team's last five matches and detect changes in tactics. The tactical analysis unit can also detect changes in the movements of specific players on the opponent team. The tactical analysis unit can also detect changes in the tactics of the opponent team's coach. For example, the tactical analysis unit can detect changes in the tactics of the opponent team's coach. This allows the system to respond to changes in the opponent team's tactics by detecting changes in tactics by comparing them with past match data. Some or all of the above processing in the tactical analysis unit may be performed using AI, for example, or without AI. For example, the tactical analysis unit can input the opponent team's past match data into a generating AI and have the generating AI perform the detection of changes in tactics.

[0042] The tactical analysis unit can analyze the movements and performance of individual players on the opposing team and propose individual countermeasures. For example, the tactical analysis unit can analyze the movements of the opposing team's ace player and propose countermeasures. The tactical analysis unit can also analyze the weaknesses in the opposing team's defense and propose offensive tactics. For example, the tactical analysis unit can analyze the movements of the opposing team's ace player and propose countermeasures. The tactical analysis unit can also analyze the weaknesses in the opposing team's defense and propose offensive tactics. Furthermore, the tactical analysis unit can analyze the fatigue levels of the opposing team's players and predict the flow of the game. For example, the tactical analysis unit can analyze the fatigue levels of the opposing team's players and predict the flow of the game. This allows for the proposal of individual countermeasures by analyzing the movements and performance of individual players on the opposing team. Some or all of the above processing in the tactical analysis unit may be performed using AI, for example, or without AI. For example, the tactical analysis unit can input data on the movements and performance of opposing team players into a generating AI and have the generating AI execute the proposal of individual countermeasures.

[0043] The tactical analysis unit can consider external factors such as weather and match location when analyzing the opposing team's match data. For example, the tactical analysis unit can analyze match data from rainy weather and propose tactics. The tactical analysis unit can also propose tactics considering the characteristics of the match location. For example, the tactical analysis unit can analyze match data from rainy weather and propose tactics. The tactical analysis unit can also propose tactics considering the characteristics of the match location. The tactical analysis unit can also propose tactics considering the influence of the time of day. For example, the tactical analysis unit can propose tactics considering the influence of the time of day. By considering external factors such as weather and match location, it is possible to propose more realistic tactics. Some or all of the above processing in the tactical analysis unit may be performed using AI, for example, or without AI. For example, the tactical analysis unit can input weather and match location data into a generating AI and have the generating AI perform the consideration of external factors.

[0044] The tactical analysis unit can consider the tactical tendencies of the opposing team's coach when analyzing the opposing team's match data. For example, the tactical analysis unit can analyze the opposing team's coach's past tactical tendencies and propose tactics. The tactical analysis unit can also analyze patterns of tactical changes by the opposing team's coach. For example, the tactical analysis unit can analyze the opposing team's coach's past tactical tendencies and propose tactics. The tactical analysis unit can also analyze patterns of tactical changes by the opposing team's coach. Furthermore, the tactical analysis unit can predict the flow of the game based on the opposing team's coach's tactical tendencies. For example, the tactical analysis unit predicts the flow of the game based on the opposing team's coach's tactical tendencies. By considering the opposing team's coach's tactical tendencies, more effective tactics can be proposed. Some or all of the above processing in the tactical analysis unit may be performed using AI, for example, or without AI. For example, the tactical analysis unit can input data on the opposing team's coach's tactical tendencies into a generating AI and have the generating AI consider the tactical tendencies.

[0045] The real-time adjustment unit can analyze the movements of athletes during practice and provide technical advice while considering the athlete's fatigue level. For example, the real-time adjustment unit can monitor the athlete's fatigue level and suggest appropriate rest. The real-time adjustment unit can also adjust the practice menu according to the athlete's fatigue level. For example, the real-time adjustment unit can monitor the athlete's fatigue level and suggest appropriate rest. The real-time adjustment unit can also adjust the practice menu according to the athlete's fatigue level. The real-time adjustment unit can also suggest areas for improvement in form, taking the athlete's fatigue level into consideration. For example, the real-time adjustment unit can suggest areas for improvement in form, taking the athlete's fatigue level into consideration. In this way, by providing technical advice while considering the athlete's fatigue level, the athlete's skills can be improved. Some or all of the above processing in the real-time adjustment unit may be performed using AI, for example, or without AI. For example, the real-time adjustment unit can input athlete fatigue data into a generating AI and have the generating AI provide technical advice.

[0046] The real-time adjustment unit can provide alerts to immediately respond to changes in the opposing team's tactics when analyzing data during a match. For example, the real-time adjustment unit can detect changes in the opposing team's tactics and provide an alert. The real-time adjustment unit can also detect substitutions by the opposing team and provide an alert. For example, the real-time adjustment unit can detect changes in the opposing team's tactics and provide an alert. The real-time adjustment unit can also detect substitutions by the opposing team and provide an alert. The real-time adjustment unit can also detect changes in the opposing team's formation and provide an alert. For example, the real-time adjustment unit can detect changes in the opposing team's formation and provide an alert. By providing alerts to immediately respond to changes in the opposing team's tactics, it is possible to gain an advantage in the flow of the match. Some or all of the above processing in the real-time adjustment unit may be performed using AI, for example, or without AI. For example, the real-time adjustment unit can input data on changes in the opposing team's tactics into a generating AI and have the generating AI provide the alert.

[0047] The real-time adjustment unit can monitor the athlete's health status and suggest appropriate rest when analyzing the athlete's movements during practice. For example, the real-time adjustment unit can monitor the athlete's heart rate and suggest appropriate rest. It can also monitor the athlete's body temperature and suggest appropriate rest. For example, the real-time adjustment unit can monitor the athlete's heart rate and suggest appropriate rest. It can also monitor the athlete's body temperature and suggest appropriate rest. It can also monitor the athlete's fatigue level and suggest appropriate rest. For example, the real-time adjustment unit can monitor the athlete's fatigue level and suggest appropriate rest. In this way, the athlete's health can be maintained by monitoring their health status and suggesting appropriate rest. Some or all of the above processing in the real-time adjustment unit may be performed using AI, for example, or without AI. For example, the real-time adjustment unit can input data on the athlete's health status into a generating AI and have the generating AI suggest appropriate rest.

[0048] The real-time adjustment unit can adjust tactics by considering the reactions of the audience when analyzing data during a match. For example, the real-time adjustment unit can analyze the volume of the audience's cheers and adjust tactics. The real-time adjustment unit can also analyze the reactions of the audience and adjust tactics. For example, the real-time adjustment unit can analyze the volume of the audience's cheers and adjust tactics. The real-time adjustment unit can also analyze the trends in the cheering of the audience and adjust tactics. For example, the real-time adjustment unit can analyze the trends in the cheering of the audience and adjust tactics. By adjusting tactics while considering the reactions of the audience, it is possible to gain an advantage in the flow of the match. Some or all of the above processing in the real-time adjustment unit may be performed using AI, for example, or without AI. For example, the real-time adjustment unit can input data on the audience's reactions into a generating AI and have the generating AI perform tactical adjustments.

[0049] The performance prediction unit can consider the player's growth curve when predicting the player's performance based on past data. For example, the performance prediction unit predicts future growth based on the player's past growth data. The performance prediction unit can also analyze the player's growth curve and predict performance. For example, the performance prediction unit predicts future growth based on the player's past growth data. The performance prediction unit can also analyze the player's growth curve and predict performance. The performance prediction unit can also analyze the player's growth pattern and predict performance. For example, the performance prediction unit analyzes the player's growth pattern and predicts performance. By considering the player's growth curve, more accurate performance prediction becomes possible. Some or all of the above processing in the performance prediction unit may be performed using AI, for example, or without AI. For example, the performance prediction unit can input the player's growth data into a generating AI and have the generating AI perform the analysis of the growth curve.

[0050] The performance prediction unit can consider the athlete's nutritional status and sleep patterns when predicting the athlete's performance. For example, the performance prediction unit can monitor the athlete's nutritional status and predict performance. The performance prediction unit can also monitor the athlete's sleep patterns and predict performance. For example, the performance prediction unit can monitor the athlete's nutritional status and predict performance. The performance prediction unit can also monitor the athlete's sleep patterns and predict performance. The performance prediction unit can also analyze the athlete's nutritional status and sleep patterns and predict performance. For example, the performance prediction unit can analyze the athlete's nutritional status and sleep patterns and predict performance. This makes it possible to make more accurate performance predictions by considering the athlete's nutritional status and sleep patterns. Some or all of the above processing in the performance prediction unit may be performed using AI, for example, or without AI. For example, the performance prediction unit can input data on the athlete's nutritional status and sleep patterns into a generating AI and have the generating AI perform the performance prediction.

[0051] The performance prediction unit can consider the athlete's training history when predicting an athlete's performance based on past data. For example, the performance prediction unit predicts performance based on the athlete's past training data. The performance prediction unit can also analyze the athlete's training history and predict performance. For example, the performance prediction unit predicts performance based on the athlete's past training data. The performance prediction unit can also analyze the athlete's training history and predict performance. The performance prediction unit can also analyze the athlete's training patterns and predict performance. For example, the performance prediction unit analyzes the athlete's training patterns and predicts performance. By considering the athlete's training history, more accurate performance predictions become possible. Some or all of the above processing in the performance prediction unit may be performed using AI, for example, or without AI. For example, the performance prediction unit can input the athlete's training history data into a generating AI and have the generating AI perform the performance prediction.

[0052] The performance prediction unit can consider the psychological state of an athlete when predicting their performance. For example, the performance prediction unit can monitor the athlete's psychological state and predict their performance. The performance prediction unit can also analyze the athlete's psychological state and predict their performance. For example, the performance prediction unit can monitor the athlete's psychological state and predict their performance. The performance prediction unit can also analyze the athlete's psychological state and predict their performance. The performance prediction unit can also analyze the relationship between the athlete's psychological state and their performance and predict their performance. For example, the performance prediction unit can analyze the relationship between the athlete's psychological state and their performance and predict their performance. By considering the athlete's psychological state, more accurate performance predictions become possible. Some or all of the above processing in the performance prediction unit may be performed using AI, for example, or without AI. For example, the performance prediction unit can input data on the athlete's psychological state into a generating AI and have the generating AI perform the performance prediction.

[0053] The training planning unit can consider an athlete's past training data when generating an optimal training plan for each individual athlete. For example, the training planning unit can propose an optimal training plan based on the athlete's past training data. The training planning unit can also analyze an athlete's training history and propose an optimal training plan. For example, the training planning unit can propose an optimal training plan based on the athlete's past training data. The training planning unit can also analyze an athlete's training history and propose an optimal training plan. The training planning unit can also analyze an athlete's training pattern and propose an optimal training plan. For example, the training planning unit can analyze an athlete's training pattern and propose an optimal training plan. By considering an athlete's past training data, a more effective training plan can be generated. Some or all of the above processes in the training planning unit may be performed using AI, for example, or without AI. For example, the training planning unit can input an athlete's past training data into a generating AI and have the generating AI perform the generation of the training plan.

[0054] The training planning unit can incorporate the athlete's goals and preferences when generating training plans. For example, the training planning unit can interview the athlete to understand their goals and propose a training plan based on those goals. The training planning unit can also generate a plan that reflects the athlete's desired training content. For example, the training planning unit can interview the athlete to understand their goals and propose a training plan based on those goals. The training planning unit can also generate a plan that reflects the athlete's desired training content. Furthermore, the training planning unit can adjust the training plan considering the athlete's short-term and long-term goals. For example, the training planning unit can adjust the training plan considering the athlete's short-term and long-term goals. This allows for the generation of more effective training plans by incorporating the athlete's goals and preferences. Some or all of the above processes in the training planning unit may be performed using AI, for example, or without AI. For example, the training planning unit can input data on the athlete's goals and preferences into a generating AI and have the generating AI generate the training plan.

[0055] The training planning unit can consider an athlete's lifestyle when generating an optimal training plan for each individual athlete. For example, the training planning unit can propose a training plan that matches the athlete's daily rhythm. The training planning unit can also adjust the training plan considering the athlete's work or academic schedule. For example, the training planning unit can propose a training plan that matches the athlete's daily rhythm. The training planning unit can also adjust the training plan considering the athlete's work or academic schedule. The training planning unit can also propose a training plan considering the athlete's home environment and hobbies. For example, the training planning unit can propose a training plan considering the athlete's home environment and hobbies. By considering the athlete's lifestyle, a more effective training plan can be generated. Some or all of the above processes in the training planning unit may be performed using AI, for example, or not. For example, the training planning unit can input data on the athlete's lifestyle into a generating AI and have the generating AI perform the generation of the training plan.

[0056] The training planning unit can consider the player's role within the team when generating a training plan. For example, the training planning unit can propose a training plan based on the player's position. The training planning unit can also adjust the training plan based on the player's role within the team. For example, the training planning unit can propose a training plan based on the player's position. The training planning unit can also adjust the training plan based on the player's role within the team. The training planning unit can also propose a training plan that aligns with the team's tactics. For example, the training planning unit can propose a training plan that aligns with the team's tactics. By considering the player's role within the team, a more effective training plan can be generated. Some or all of the above processing in the training planning unit may be performed using AI, for example, or without AI. For example, the training planning unit can input data on the player's role within the team into a generating AI and have the generating AI generate the training plan.

[0057] The match simulation unit can incorporate past match results when performing match simulations that combine video and text data. For example, the match simulation unit can create simulation scenarios based on past match results. The match simulation unit can also analyze past match data and incorporate it into the simulation. For example, the match simulation unit can create simulation scenarios based on past match results. The match simulation unit can also analyze past match data and incorporate it into the simulation. Furthermore, the match simulation unit can adjust the simulation tactics considering past match results. For example, the match simulation unit adjusts the simulation tactics considering past match results. By incorporating past match results, a more realistic simulation becomes possible. Some or all of the above processing in the match simulation unit may be performed using AI, for example, or without AI. For example, the match simulation unit can input past match result data into a generating AI and have the generating AI create the simulation scenario.

[0058] The match simulation unit can reflect the individual characteristics of players when conducting match simulations. For example, the match simulation unit can perform simulations considering the skills and abilities of the players. It can also perform simulations considering the physical strength and stamina of the players. For example, the match simulation unit can perform simulations considering the skills and abilities of the players. It can also perform simulations considering the physical strength and stamina of the players. It can also perform simulations considering the positions and roles of the players. For example, the match simulation unit can perform simulations considering the positions and roles of the players. By reflecting the individual characteristics of players, a more realistic simulation becomes possible. Some or all of the above processing in the match simulation unit may be performed using AI, for example, or without AI. For example, the match simulation unit can input player characteristic data into a generating AI and have the generating AI perform the simulation.

[0059] The match simulation unit can reflect the environmental conditions of a match when performing a match simulation that combines video data and text data. For example, the match simulation unit can perform a simulation considering the weather conditions of the match. The match simulation unit can also perform a simulation considering the characteristics of the match venue. For example, the match simulation unit can perform a simulation considering the weather conditions of the match. The match simulation unit can also perform a simulation considering the characteristics of the match venue. The match simulation unit can also perform a simulation considering the influence of the time of day of the match. For example, the match simulation unit can perform a simulation considering the influence of the time of day of the match. By reflecting the environmental conditions of the match, a more realistic simulation becomes possible. Some or all of the above processing in the match simulation unit may be performed using AI, for example, or without using AI. For example, the match simulation unit can input data on the environmental conditions of the match into a generating AI and have the generating AI perform the simulation.

[0060] The match simulation unit can reflect changes in the opposing team's tactics when conducting a match simulation. For example, the match simulation unit can reflect changes in the opposing team's tactics in the simulation. The match simulation unit can also reflect substitutions made by the opposing team in the simulation. For example, the match simulation unit can reflect changes in the opposing team's tactics in the simulation. The match simulation unit can also reflect substitutions made by the opposing team in the simulation. The match simulation unit can also reflect changes in the opposing team's formation in the simulation. For example, the match simulation unit can reflect changes in the opposing team's formation in the simulation. By reflecting changes in the opposing team's tactics, a more realistic simulation becomes possible. Some or all of the above processing in the match simulation unit may be performed using AI, for example, or without AI. For example, the match simulation unit can input data on the opposing team's tactical changes into a generating AI and have the generating AI perform the simulation.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] A sports team support system can monitor athletes' nutritional status and suggest optimal meal plans. For example, it can measure an athlete's weight and body fat percentage and adjust their calorie intake accordingly. It can also suggest meal plans containing necessary nutrients based on the athlete's training regimen. Furthermore, it can adjust the timing of athletes' meals and provide advice to maximize their performance in training and games. This optimizes athletes' nutritional status and improves their performance.

[0063] A sports team support system can monitor athletes' sleep patterns and suggest optimal sleep environments. For example, it can measure athletes' sleep duration and quality and provide advice on improving their sleep environment based on that data. It can also suggest optimal sleep durations according to athletes' training schedules. Furthermore, it can provide advice on relaxation methods and stress management to improve athletes' sleep quality. This can improve athletes' sleep quality and maximize their performance.

[0064] Sports team support systems can monitor athletes' health and prevent or detect injuries early. For example, they can measure athletes' body temperature and heart rate and address any abnormalities promptly. They can also monitor athletes' training content and load, adjusting it to prevent excessive strain. Furthermore, they can analyze athletes' body movements, identify high-risk actions, and provide advice for improvement. This helps maintain athletes' health and minimize the risk of injury.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The tactical analysis unit analyzes the opposing team's match data. This data includes scoring patterns, player movements, and tactical tendencies. For example, the tactical analysis unit analyzes the opposing team's scoring patterns to understand their offensive tendencies. It can also analyze player movements to identify defensive weaknesses. Furthermore, it can analyze tactical tendencies to predict changes in the opposing team's tactics. Step 2: The real-time adjustment unit adjusts tactics based on the data analyzed by the tactical analysis unit. Tactical adjustments include changes to formations and changes in player positions. For example, formations can be changed to respond to the opposing team's attack, or player positions can be changed to strengthen the defense. It can also analyze data during the match and propose tactical adjustments in real time. Step 3: The performance prediction unit predicts player performance based on the tactics adjusted by the real-time adjustment unit. Performance prediction includes the use of historical data analysis and statistical models. For example, player performance can be predicted based on past match data, or player development can be predicted using statistical models. It is also possible to analyze data during a match and predict player performance in real time. Step 4: The training planning unit generates a training plan based on the data predicted by the performance prediction unit. The training plan includes training menus, training frequency and intensity, etc. For example, it can propose a training plan tailored to the athlete's fitness level, skill level, and goals. Step 5: The match simulation department conducts match simulations based on the plans generated by the training planning department. Match simulations include the simulation scenarios and the types of data used. For example, they can perform match simulations that combine video and text data, simulations based on past match data, and simulations that reflect the individual characteristics of the players.

[0067] (Example of form 2) The sports team support system according to an embodiment of the present invention is a system that utilizes an AI agent to disseminate tactics and training methods that enable amateur sports teams to defeat professional teams. This system integrates tactical analysis, real-time adjustments, player performance prediction, training plans, and match simulations, aiming to create strong teams even without excellent coaches. For example, the sports team support system analyzes the opposing team's match data and extracts tactical patterns. This allows it to propose optimal countermeasures for the team. For example, it analyzes the opposing team's offensive patterns and defensive weaknesses and adjusts the team's tactics based on that. Next, the sports team support system analyzes the players' movements during practice in real time and provides technical advice. The AI ​​agent monitors the players' movements and suggests areas for improvement in form and ways to make movements more efficient. This helps improve the players' skills. Furthermore, the sports team support system analyzes match data in real time and suggests tactical adjustments. The AI ​​agent grasps the situation during the match and changes tactics as needed to gain an advantage in the flow of the game. For example, it instantly adjusts the team's tactics in response to changes in the opposing team's tactics. In addition, the sports team support system predicts player performance based on past data and generates an optimal training plan. The AI ​​agent analyzes a player's past performance data and predicts their future growth. Based on this, it proposes an optimal training plan for each individual player. Finally, the sports team support system performs match simulations combining video and text data to verify the effectiveness of tactics in advance. Through the match simulation, the AI ​​agent confirms the effectiveness of tactics and modifies them as needed. This allows for improved tactical precision before the match. This system enables amateur sports teams to acquire tactics and training methods comparable to professional teams, even without top-tier coaches. This significantly improves the team's competitive level and increases the likelihood of winning matches. In this way, the sports team support system can disseminate tactics and training methods that allow amateur sports teams to beat professional teams.

[0068] The sports team support system according to this embodiment comprises a tactical analysis unit, a real-time adjustment unit, a performance prediction unit, a training planning unit, and a match simulation unit. The tactical analysis unit analyzes the opposing team's match data. The opposing team's match data includes, but is not limited to, scoring patterns, player movements, and tactical tendencies. For example, the tactical analysis unit analyzes the opposing team's scoring patterns to understand their offensive tendencies. The tactical analysis unit can also analyze player movements to identify defensive weaknesses. Furthermore, the tactical analysis unit can analyze tactical tendencies to predict changes in the opposing team's tactics. For example, the tactical analysis unit detects changes in tactics based on the opposing team's past match data. The real-time adjustment unit adjusts tactics based on the data analyzed by the tactical analysis unit. Tactical adjustments include, but are not limited to, changes in formation and changes in player positioning. For example, the real-time adjustment unit changes the formation to respond to the opposing team's attacks. The real-time adjustment unit can also strengthen defenses by changing player positioning. Furthermore, the real-time adjustment unit can also make tactical changes in real time. For example, the real-time adjustment unit analyzes data during a match and proposes tactical adjustments. The performance prediction unit predicts player performance based on the tactics adjusted by the real-time adjustment unit. Performance prediction includes, but is not limited to, analysis of historical data and use of statistical models. For example, the performance prediction unit predicts player performance based on past match data. The performance prediction unit can also predict player growth using statistical models. Furthermore, the performance prediction unit can predict player performance in real time. For example, the performance prediction unit analyzes data during a match and predicts player performance. The training planning unit generates a training plan based on the data predicted by the performance prediction unit. The training plan includes, but is not limited to, practice menus, training frequency and intensity. For example, the training planning unit proposes a training plan tailored to the player's fitness level.Furthermore, the training planning unit can propose training plans tailored to the skill level of the players. It can also propose training plans tailored to the players' goals. For example, the training planning unit adjusts the training plan based on the players' goals. The match simulation unit performs match simulations based on the plans generated by the training planning unit. Match simulations include, but are not limited to, the simulation scenario and the types of data used. For example, the match simulation unit performs match simulations combining video and text data. It can also perform simulations based on past match data. Furthermore, the match simulation unit can perform simulations that reflect the individual characteristics of the players. For example, it performs simulations considering the players' skills and abilities. As a result, the sports team support system according to this embodiment can disseminate tactics and training methods that enable amateur sports teams to defeat professional teams.

[0069] The tactical analysis unit analyzes the opposing team's match data. This data includes, but is not limited to, scoring patterns, player movements, and tactical tendencies. For example, the tactical analysis unit analyzes the opposing team's scoring patterns to understand their offensive tendencies. It can also analyze player movements to identify defensive weaknesses. Furthermore, it can analyze tactical tendencies to predict tactical changes by the opposing team. For example, the tactical analysis unit detects tactical changes based on the opposing team's past match data. The tactical analysis unit uses AI to quickly and accurately analyze large amounts of match data. The AI ​​uses deep learning technology to analyze match videos and player movement data to extract the opposing team's tactical patterns. For example, the AI ​​learns the opposing team's offensive formations and player movement characteristics to predict scoring patterns under specific circumstances. The AI ​​also analyzes player positioning and movement tendencies during defense to identify defensive weaknesses. In addition, the AI ​​detects signs of tactical changes by the opposing team and provides information to quickly respond to tactical changes during a match. This allows the tactical analysis department to gain a detailed understanding of the opposing team's tactics and provide crucial information for formulating match strategies.

[0070] The Real-Time Adjustment Unit adjusts tactics based on data analyzed by the Tactical Analysis Unit. These adjustments include, but are not limited to, changes in formation and player positioning. For example, the Real-Time Adjustment Unit might change the formation to counter the opposing team's attack. It can also strengthen defenses by changing player positioning. Furthermore, the Real-Time Adjustment Unit can make tactical changes in real time. For example, it might analyze in-game data and propose tactical adjustments. The Real-Time Adjustment Unit uses AI to analyze in-game data in real time and propose optimal tactical changes. The AI ​​monitors player movements and opposing team tactical changes in real time, quickly proposing tactical changes appropriate to the situation. For example, the AI ​​might detect the opposing team's attack patterns and change the defensive formation to neutralize their attack. The AI ​​also monitors player fatigue and performance, and optimizes overall team performance by proposing substitutions and positioning changes. Finally, the Real-Time Adjustment Unit evaluates the effectiveness of tactical changes based on in-game data and makes further adjustments as needed. This allows the real-time adjustment unit to respond quickly and flexibly to the situation during a match, maximizing the team's performance.

[0071] The performance prediction unit predicts player performance based on tactics adjusted by the real-time adjustment unit. Performance prediction includes, but is not limited to, analysis of historical data and the use of statistical models. For example, the performance prediction unit predicts player performance based on past match data. The performance prediction unit can also predict player growth using statistical models. Furthermore, the performance prediction unit can predict player performance in real time. For example, the performance prediction unit analyzes data during a match to predict player performance. The performance prediction unit uses AI to predict player performance with high accuracy. The AI ​​learns from the player's past match data and training data to build a model for predicting player performance. For example, the AI ​​analyzes data such as a player's scoring patterns, number of assists, and defensive success rate to predict player performance. The AI ​​also predicts player performance in real time, taking into account the player's physical condition, fatigue level, and injury risk. In addition, the AI ​​can predict player growth and propose training plans that anticipate future performance improvements. As a result, the performance prediction unit can predict player performance with high accuracy and contribute to team strategy planning and training plan development.

[0072] The training planning unit generates a training plan based on data predicted by the performance prediction unit. The training plan includes, but is not limited to, practice menus, training frequency, and intensity. For example, the training planning unit can propose a training plan tailored to the athlete's fitness level. It can also propose a training plan tailored to the athlete's skill level. Furthermore, it can propose a training plan tailored to the athlete's goals. For example, the training planning unit adjusts the training plan based on the athlete's goals. The training planning unit uses AI to analyze athlete data and generate an optimal training plan. The AI ​​proposes training menus tailored to the athlete's fitness level, skill level, and goals. For example, the AI ​​proposes training menus to improve endurance based on the athlete's fitness data. The AI ​​also analyzes the athlete's skill data and proposes practice menus to improve skill. In addition, the AI ​​adjusts the training frequency and intensity, taking the athlete's goals into consideration. For example, it adjusts the training plan so that the athlete can achieve peak performance for a specific match. This allows the training planning unit to provide an optimal training plan tailored to the individual needs of each athlete, supporting their performance improvement.

[0073] The match simulation unit performs match simulations based on plans generated by the training planning unit. Match simulations include, but are not limited to, the simulation scenario and the types of data used. For example, the match simulation unit can perform match simulations combining video and text data. It can also perform simulations based on past match data. Furthermore, it can perform simulations that reflect the individual characteristics of players. For example, it can perform simulations considering the skills and abilities of players. The match simulation unit uses AI to perform match simulations with high accuracy. The AI ​​learns from past match data and player characteristic data to simulate match scenarios. For example, the AI ​​simulates the opposing team's tactics and player movements to predict the flow of the match. The AI ​​also simulates in-game play considering the skills and abilities of players. Furthermore, the AI ​​updates the simulation in real time in response to changes in the match situation and proposes optimal tactics. This allows the match simulation unit to predict the flow of matches with high accuracy and provide information useful for team strategy planning and player training.

[0074] The tactical analysis unit can analyze the opposing team's attack patterns and defensive weaknesses. For example, the tactical analysis unit can analyze the opposing team's attack patterns to understand scoring methods and attack timing. It can also analyze the opposing team's defensive weaknesses to identify defensive positioning and player movements. For example, the tactical analysis unit can analyze the opposing team's attack patterns to identify scoring methods. It can also analyze the opposing team's defensive weaknesses to identify defensive positioning. Furthermore, the tactical analysis unit can comprehensively analyze the opposing team's attack patterns and defensive weaknesses and propose tactical adjustments. For example, the tactical analysis unit can analyze the opposing team's attack patterns and defensive weaknesses and propose tactical adjustments. In this way, by analyzing the opposing team's attack patterns and defensive weaknesses, it can propose effective tactics. Some or all of the above processing in the tactical analysis unit may be performed using AI, for example, or not. For example, the tactical analysis unit can input the opposing team's match data into a generating AI and have the generating AI perform the analysis of attack patterns and defensive weaknesses.

[0075] The real-time adjustment unit can analyze the movements of players during practice in real time and provide technical advice. For example, the real-time adjustment unit can monitor players' movements and suggest corrections to their form or ways to improve the efficiency of their movements. The real-time adjustment unit can also analyze players' movements and provide tactical instructions. For example, the real-time adjustment unit can monitor players' movements and suggest corrections to their form. The real-time adjustment unit can also analyze players' movements and suggest ways to improve the efficiency of their movements. The real-time adjustment unit can also analyze players' movements and provide tactical instructions. For example, the real-time adjustment unit can monitor players' movements and provide tactical instructions. This allows for the improvement of players' skills by analyzing their movements in real time during practice and providing technical advice. Some or all of the above processing in the real-time adjustment unit may be performed using AI, for example, or without AI. For example, the real-time adjustment unit can input player movement data into a generating AI and have the generating AI provide technical advice.

[0076] The real-time adjustment unit can analyze match data in real time and propose tactical adjustments. For example, the real-time adjustment unit can analyze match data and respond to changes in the opposing team's tactics. The real-time adjustment unit can also analyze match data and instantly adjust its own team's tactics. For example, the real-time adjustment unit can analyze match data and respond to changes in the opposing team's tactics. The real-time adjustment unit can also analyze match data and instantly adjust its own team's tactics. The real-time adjustment unit can also analyze match data and propose tactical adjustments. For example, the real-time adjustment unit can analyze match data and propose tactical adjustments. By analyzing match data in real time and proposing tactical adjustments, it is possible to gain an advantage in the flow of the match. Some or all of the above processing in the real-time adjustment unit may be performed using AI, for example, or without AI. For example, the real-time adjustment unit can input match data into a generating AI and have the generating AI perform tactical adjustments.

[0077] The performance prediction unit can predict a player's performance based on past data. For example, the performance prediction unit can analyze past match data to predict a player's performance. The performance prediction unit can also predict a player's growth using statistical models. For example, the performance prediction unit can analyze past match data to predict a player's performance. The performance prediction unit can also predict a player's growth using statistical models. Furthermore, the performance prediction unit can predict a player's performance in real time. For example, the performance prediction unit can analyze data during a match to predict a player's performance. This allows for the generation of an optimal training plan by predicting a player's performance based on past data. Some or all of the above processing in the performance prediction unit may be performed using AI, for example, or without AI. For example, the performance prediction unit can input past match data into a generating AI and have the generating AI perform the prediction of a player's performance.

[0078] The training planning unit can generate an optimal training plan for each individual athlete. For example, the training planning unit can propose a training plan that is appropriate for the athlete's physical fitness level. It can also propose a training plan that is appropriate for the athlete's skill level. For example, the training planning unit can propose a training plan that is appropriate for the athlete's physical fitness level. It can also propose a training plan that is appropriate for the athlete's skill level. Furthermore, the training planning unit can propose a training plan that is appropriate for the athlete's goals. For example, the training planning unit can adjust the training plan based on the athlete's goals. This allows for the generation of an optimal training plan for each individual athlete, thereby promoting their growth. Some or all of the above processes in the training planning unit may be performed using AI, for example, or without AI. For example, the training planning unit can input data on the athlete's physical fitness level and skill level into a generating AI and have the generating AI generate the training plan.

[0079] The match simulation unit can perform match simulations by combining video data and text data, allowing for the prior verification of tactical effectiveness. For example, the match simulation unit can perform match simulations by combining video data and text data. The match simulation unit can also perform simulations based on past match data. For example, the match simulation unit can perform match simulations by combining video data and text data. The match simulation unit can also perform simulations based on past match data. Furthermore, the match simulation unit can perform simulations that reflect the individual characteristics of players. For example, the match simulation unit can perform simulations considering the skills and abilities of players. This allows for the confirmation of the effectiveness of tactics through match simulations and the improvement of tactical accuracy before the match by modifying tactics as needed. Some or all of the above-described processes in the match simulation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the match simulation unit can input video data and text data into a generative AI and have the generative AI perform the match simulation.

[0080] The tactical analysis unit can estimate the emotions of players and improve the accuracy of tactical analysis based on the estimated emotions. For example, the tactical analysis unit can estimate a player's emotions and suggest tactics to help them relax if they are tense. It can also estimate a player's emotions and suggest tactics to help them stay calm if they are excited. For example, the tactical analysis unit can estimate a player's emotions and suggest tactics to help them relax if they are tense. It can also estimate a player's emotions and suggest tactics to help them stay calm if they are excited. It can also estimate a player's emotions and suggest tactics to help them rest if they are tired. For example, the tactical analysis unit can estimate a player's emotions and suggest tactics to help them rest if they are tired. By improving the accuracy of tactical analysis based on players' emotions, it is possible to propose more effective tactics. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the tactical analysis unit may be performed using AI, for example, or without AI. For example, the tactical analysis unit can input player emotion data into a generating AI and have the generating AI perform emotion estimation.

[0081] The tactical analysis unit can detect changes in tactics by comparing the opponent team's match data with past match data. For example, the tactical analysis unit can analyze the data from the opponent team's last five matches and detect changes in tactics. The tactical analysis unit can also detect changes in the movements of specific players on the opponent team. For example, the tactical analysis unit can analyze the data from the opponent team's last five matches and detect changes in tactics. The tactical analysis unit can also detect changes in the movements of specific players on the opponent team. The tactical analysis unit can also detect changes in the tactics of the opponent team's coach. For example, the tactical analysis unit can detect changes in the tactics of the opponent team's coach. This allows the system to respond to changes in the opponent team's tactics by detecting changes in tactics by comparing them with past match data. Some or all of the above processing in the tactical analysis unit may be performed using AI, for example, or without AI. For example, the tactical analysis unit can input the opponent team's past match data into a generating AI and have the generating AI perform the detection of changes in tactics.

[0082] The tactical analysis unit can analyze the movements and performance of individual players on the opposing team and propose individual countermeasures. For example, the tactical analysis unit can analyze the movements of the opposing team's ace player and propose countermeasures. The tactical analysis unit can also analyze the weaknesses in the opposing team's defense and propose offensive tactics. For example, the tactical analysis unit can analyze the movements of the opposing team's ace player and propose countermeasures. The tactical analysis unit can also analyze the weaknesses in the opposing team's defense and propose offensive tactics. Furthermore, the tactical analysis unit can analyze the fatigue levels of the opposing team's players and predict the flow of the game. For example, the tactical analysis unit can analyze the fatigue levels of the opposing team's players and predict the flow of the game. This allows for the proposal of individual countermeasures by analyzing the movements and performance of individual players on the opposing team. Some or all of the above processing in the tactical analysis unit may be performed using AI, for example, or without AI. For example, the tactical analysis unit can input data on the movements and performance of opposing team players into a generating AI and have the generating AI execute the proposal of individual countermeasures.

[0083] The tactical analysis unit can estimate a player's emotions and determine the priority of tactical analysis based on the estimated emotions. For example, if a player is nervous, the tactical analysis unit can prioritize tactics to help them relax. It can also prioritize tactics to help them stay calm if they are excited. By determining the priority of tactical analysis based on the player's emotions, it can propose more effective tactics. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, a text-generating AI (e.g., LLM) or a multimodal generative AI. Some or all of the above-described processes in the tactical analysis unit may be performed using AI, for example, or not using AI. For example, the tactical analysis unit may input player emotion data into the generative AI and have the generative AI perform emotion estimation.

[0084] The tactical analysis unit can consider external factors such as weather and match location when analyzing the opposing team's match data. For example, the tactical analysis unit can analyze match data from rainy weather and propose tactics. The tactical analysis unit can also propose tactics considering the characteristics of the match location. For example, the tactical analysis unit can analyze match data from rainy weather and propose tactics. The tactical analysis unit can also propose tactics considering the characteristics of the match location. The tactical analysis unit can also propose tactics considering the influence of the time of day. For example, the tactical analysis unit can propose tactics considering the influence of the time of day. By considering external factors such as weather and match location, it is possible to propose more realistic tactics. Some or all of the above processing in the tactical analysis unit may be performed using AI, for example, or without AI. For example, the tactical analysis unit can input weather and match location data into a generating AI and have the generating AI perform the consideration of external factors.

[0085] The tactical analysis unit can consider the tactical tendencies of the opposing team's coach when analyzing the opposing team's match data. For example, the tactical analysis unit can analyze the opposing team's coach's past tactical tendencies and propose tactics. The tactical analysis unit can also analyze patterns of tactical changes by the opposing team's coach. For example, the tactical analysis unit can analyze the opposing team's coach's past tactical tendencies and propose tactics. The tactical analysis unit can also analyze patterns of tactical changes by the opposing team's coach. Furthermore, the tactical analysis unit can predict the flow of the game based on the opposing team's coach's tactical tendencies. For example, the tactical analysis unit predicts the flow of the game based on the opposing team's coach's tactical tendencies. By considering the opposing team's coach's tactical tendencies, more effective tactics can be proposed. Some or all of the above processing in the tactical analysis unit may be performed using AI, for example, or without AI. For example, the tactical analysis unit can input data on the opposing team's coach's tactical tendencies into a generating AI and have the generating AI consider the tactical tendencies.

[0086] The real-time adjustment unit can estimate the emotions of players and make real-time tactical adjustments based on the estimated emotions. For example, the real-time adjustment unit can estimate the emotions of players and suggest tactics to help them relax if they are tense. It can also estimate the emotions of players and suggest tactics to help them stay calm if they are excited. For example, the real-time adjustment unit can estimate the emotions of players and suggest tactics to help them relax if they are tense. It can also estimate the emotions of players and suggest tactics to help them stay calm if they are excited. It can also estimate the emotions of players and suggest tactics to help them rest if they are tired. For example, the real-time adjustment unit can estimate the emotions of players and suggest tactics to help them rest if they are tired. This allows for the suggestion of more effective tactics by making real-time tactical adjustments based on the emotions of players. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the real-time adjustment unit may be performed using AI, for example, or without AI. For example, the real-time adjustment unit can input player emotion data into a generating AI and have the generating AI perform emotion estimation.

[0087] The real-time adjustment unit can analyze the movements of athletes during practice and provide technical advice while considering the athlete's fatigue level. For example, the real-time adjustment unit can monitor the athlete's fatigue level and suggest appropriate rest. The real-time adjustment unit can also adjust the practice menu according to the athlete's fatigue level. For example, the real-time adjustment unit can monitor the athlete's fatigue level and suggest appropriate rest. The real-time adjustment unit can also adjust the practice menu according to the athlete's fatigue level. The real-time adjustment unit can also suggest areas for improvement in form, taking the athlete's fatigue level into consideration. For example, the real-time adjustment unit can suggest areas for improvement in form, taking the athlete's fatigue level into consideration. In this way, by providing technical advice while considering the athlete's fatigue level, the athlete's skills can be improved. Some or all of the above processing in the real-time adjustment unit may be performed using AI, for example, or without AI. For example, the real-time adjustment unit can input athlete fatigue data into a generating AI and have the generating AI provide technical advice.

[0088] The real-time adjustment unit can provide alerts to immediately respond to changes in the opposing team's tactics when analyzing data during a match. For example, the real-time adjustment unit can detect changes in the opposing team's tactics and provide an alert. The real-time adjustment unit can also detect substitutions by the opposing team and provide an alert. For example, the real-time adjustment unit can detect changes in the opposing team's tactics and provide an alert. The real-time adjustment unit can also detect substitutions by the opposing team and provide an alert. The real-time adjustment unit can also detect changes in the opposing team's formation and provide an alert. For example, the real-time adjustment unit can detect changes in the opposing team's formation and provide an alert. By providing alerts to immediately respond to changes in the opposing team's tactics, it is possible to gain an advantage in the flow of the match. Some or all of the above processing in the real-time adjustment unit may be performed using AI, for example, or without AI. For example, the real-time adjustment unit can input data on changes in the opposing team's tactics into a generating AI and have the generating AI provide the alert.

[0089] The real-time adjustment unit can estimate the athlete's emotions and prioritize real-time advice based on the estimated emotions. For example, if the athlete is nervous, the real-time adjustment unit can prioritize advice to help them relax. It can also estimate the athlete's emotions and prioritize advice to help them stay calm if they are excited. For example, if the athlete is nervous, the real-time adjustment unit can prioritize advice to help them relax. It can also estimate the athlete's emotions and prioritize advice to help them stay calm if they are excited. It can also estimate the athlete's emotions and prioritize advice to help them rest if they are tired. For example, if the athlete is tired, the real-time adjustment unit can prioritize advice to help them rest. This allows for more effective advice to be provided by prioritizing real-time advice based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the real-time adjustment unit may be performed using AI, or not using AI. For example, the real-time adjustment unit can input player emotion data into the generative AI and have the generative AI perform emotion estimation.

[0090] The real-time adjustment unit can monitor the athlete's health status and suggest appropriate rest when analyzing the athlete's movements during practice. For example, the real-time adjustment unit can monitor the athlete's heart rate and suggest appropriate rest. It can also monitor the athlete's body temperature and suggest appropriate rest. For example, the real-time adjustment unit can monitor the athlete's heart rate and suggest appropriate rest. It can also monitor the athlete's body temperature and suggest appropriate rest. It can also monitor the athlete's fatigue level and suggest appropriate rest. For example, the real-time adjustment unit can monitor the athlete's fatigue level and suggest appropriate rest. In this way, the athlete's health can be maintained by monitoring their health status and suggesting appropriate rest. Some or all of the above processing in the real-time adjustment unit may be performed using AI, for example, or without AI. For example, the real-time adjustment unit can input data on the athlete's health status into a generating AI and have the generating AI suggest appropriate rest.

[0091] The real-time adjustment unit can adjust tactics by considering the reactions of the audience when analyzing data during a match. For example, the real-time adjustment unit can analyze the volume of the audience's cheers and adjust tactics. The real-time adjustment unit can also analyze the reactions of the audience and adjust tactics. For example, the real-time adjustment unit can analyze the volume of the audience's cheers and adjust tactics. The real-time adjustment unit can also analyze the trends in the cheering of the audience and adjust tactics. For example, the real-time adjustment unit can analyze the trends in the cheering of the audience and adjust tactics. By adjusting tactics while considering the reactions of the audience, it is possible to gain an advantage in the flow of the match. Some or all of the above processing in the real-time adjustment unit may be performed using AI, for example, or without AI. For example, the real-time adjustment unit can input data on the audience's reactions into a generating AI and have the generating AI perform tactical adjustments.

[0092] The performance prediction unit can estimate the athlete's emotions and improve the accuracy of performance predictions based on the estimated emotions. For example, the performance prediction unit can estimate the athlete's emotions and make predictions to help them relax if they are nervous. It can also estimate the athlete's emotions and make predictions to help them stay calm if they are excited. For example, the performance prediction unit can estimate the athlete's emotions and make predictions to help them relax if they are nervous. It can also estimate the athlete's emotions and make predictions to help them stay calm if they are excited. It can also estimate the athlete's emotions and make predictions to help them rest if they are tired. For example, the performance prediction unit can estimate the athlete's emotions and make predictions to help them rest if they are tired. By improving the accuracy of performance predictions based on the athlete's emotions, more accurate predictions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the performance prediction unit may be performed using AI, for example, or without AI. For example, the performance prediction unit can input player emotion data into a generating AI and have the generating AI perform emotion estimation.

[0093] The performance prediction unit can consider the player's growth curve when predicting the player's performance based on past data. For example, the performance prediction unit predicts future growth based on the player's past growth data. The performance prediction unit can also analyze the player's growth curve and predict performance. For example, the performance prediction unit predicts future growth based on the player's past growth data. The performance prediction unit can also analyze the player's growth curve and predict performance. The performance prediction unit can also analyze the player's growth pattern and predict performance. For example, the performance prediction unit analyzes the player's growth pattern and predicts performance. By considering the player's growth curve, more accurate performance prediction becomes possible. Some or all of the above processing in the performance prediction unit may be performed using AI, for example, or without AI. For example, the performance prediction unit can input the player's growth data into a generating AI and have the generating AI perform the analysis of the growth curve.

[0094] The performance prediction unit can consider the athlete's nutritional status and sleep patterns when predicting the athlete's performance. For example, the performance prediction unit can monitor the athlete's nutritional status and predict performance. The performance prediction unit can also monitor the athlete's sleep patterns and predict performance. For example, the performance prediction unit can monitor the athlete's nutritional status and predict performance. The performance prediction unit can also monitor the athlete's sleep patterns and predict performance. The performance prediction unit can also analyze the athlete's nutritional status and sleep patterns and predict performance. For example, the performance prediction unit can analyze the athlete's nutritional status and sleep patterns and predict performance. This makes it possible to make more accurate performance predictions by considering the athlete's nutritional status and sleep patterns. Some or all of the above processing in the performance prediction unit may be performed using AI, for example, or without AI. For example, the performance prediction unit can input data on the athlete's nutritional status and sleep patterns into a generating AI and have the generating AI perform the performance prediction.

[0095] The performance prediction unit can estimate the athlete's emotions and determine the priority of performance predictions based on the estimated emotions. For example, if the athlete is nervous, the performance prediction unit can prioritize predictions that promote relaxation. Alternatively, if the athlete is excited, the performance prediction unit can prioritize predictions that promote calmness. For example, if the athlete is nervous, the performance prediction unit can prioritize predictions that promote relaxation. Alternatively, if the athlete is excited, the performance prediction unit can prioritize predictions that promote calmness. Alternatively, if the athlete is tired, the performance prediction unit can prioritize predictions that promote rest. For example, if the athlete is tired, the performance prediction unit can prioritize predictions that promote rest. By determining the priority of performance predictions based on the athlete's emotions, more effective predictions become possible. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the performance prediction unit may be performed using AI, for example, or not using AI. For example, the performance prediction unit may input player emotion data into the generative AI and have the generative AI perform emotion estimation.

[0096] The performance prediction unit can consider the athlete's training history when predicting an athlete's performance based on past data. For example, the performance prediction unit predicts performance based on the athlete's past training data. The performance prediction unit can also analyze the athlete's training history and predict performance. For example, the performance prediction unit predicts performance based on the athlete's past training data. The performance prediction unit can also analyze the athlete's training history and predict performance. The performance prediction unit can also analyze the athlete's training patterns and predict performance. For example, the performance prediction unit analyzes the athlete's training patterns and predicts performance. By considering the athlete's training history, more accurate performance predictions become possible. Some or all of the above processing in the performance prediction unit may be performed using AI, for example, or without AI. For example, the performance prediction unit can input the athlete's training history data into a generating AI and have the generating AI perform the performance prediction.

[0097] The performance prediction unit can consider the psychological state of an athlete when predicting their performance. For example, the performance prediction unit can monitor the athlete's psychological state and predict their performance. The performance prediction unit can also analyze the athlete's psychological state and predict their performance. For example, the performance prediction unit can monitor the athlete's psychological state and predict their performance. The performance prediction unit can also analyze the athlete's psychological state and predict their performance. The performance prediction unit can also analyze the relationship between the athlete's psychological state and their performance and predict their performance. For example, the performance prediction unit can analyze the relationship between the athlete's psychological state and their performance and predict their performance. By considering the athlete's psychological state, more accurate performance predictions become possible. Some or all of the above processing in the performance prediction unit may be performed using AI, for example, or without AI. For example, the performance prediction unit can input data on the athlete's psychological state into a generating AI and have the generating AI perform the performance prediction.

[0098] The training planning unit can estimate an athlete's emotions and adjust the training plan based on those emotions. For example, the training planning unit can estimate an athlete's emotions and suggest training to help them relax if they are tense. It can also estimate an athlete's emotions and suggest training to help them stay calm if they are excited. For example, the training planning unit can estimate an athlete's emotions and suggest training to help them relax if they are tense. It can also estimate an athlete's emotions and suggest training to help them stay calm if they are excited. It can also estimate an athlete's emotions and suggest training to help them rest if they are tired. For example, the training planning unit can estimate an athlete's emotions and suggest training to help them rest if they are tired. By adjusting the training plan based on the athlete's emotions, more effective training becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the training planning department may be performed using AI, for example, or without AI. For example, the training planning department may input athlete emotional data into a generating AI and have the generating AI perform emotional estimation.

[0099] The training planning unit can consider an athlete's past training data when generating an optimal training plan for each individual athlete. For example, the training planning unit can propose an optimal training plan based on the athlete's past training data. The training planning unit can also analyze an athlete's training history and propose an optimal training plan. For example, the training planning unit can propose an optimal training plan based on the athlete's past training data. The training planning unit can also analyze an athlete's training history and propose an optimal training plan. The training planning unit can also analyze an athlete's training pattern and propose an optimal training plan. For example, the training planning unit can analyze an athlete's training pattern and propose an optimal training plan. By considering an athlete's past training data, a more effective training plan can be generated. Some or all of the above processes in the training planning unit may be performed using AI, for example, or without AI. For example, the training planning unit can input an athlete's past training data into a generating AI and have the generating AI perform the generation of the training plan.

[0100] The training planning unit can incorporate the athlete's goals and preferences when generating training plans. For example, the training planning unit can interview the athlete to understand their goals and propose a training plan based on those goals. The training planning unit can also generate a plan that reflects the athlete's desired training content. For example, the training planning unit can interview the athlete to understand their goals and propose a training plan based on those goals. The training planning unit can also generate a plan that reflects the athlete's desired training content. Furthermore, the training planning unit can adjust the training plan considering the athlete's short-term and long-term goals. For example, the training planning unit can adjust the training plan considering the athlete's short-term and long-term goals. This allows for the generation of more effective training plans by incorporating the athlete's goals and preferences. Some or all of the above processes in the training planning unit may be performed using AI, for example, or without AI. For example, the training planning unit can input data on the athlete's goals and preferences into a generating AI and have the generating AI generate the training plan.

[0101] The training planning department can estimate an athlete's emotions and prioritize training plans based on those estimates. For example, if an athlete is nervous, the training planning department can prioritize training to help them relax. It can also prioritize training to help them stay calm if they are excited. This allows for more effective training by prioritizing training plans based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processes described above in the training planning unit may be performed using AI, for example, or not using AI. For example, the training planning unit may input athlete emotion data into the generative AI and have the generative AI perform emotion estimation.

[0102] The training planning unit can consider an athlete's lifestyle when generating an optimal training plan for each individual athlete. For example, the training planning unit can propose a training plan that matches the athlete's daily rhythm. The training planning unit can also adjust the training plan considering the athlete's work or academic schedule. For example, the training planning unit can propose a training plan that matches the athlete's daily rhythm. The training planning unit can also adjust the training plan considering the athlete's work or academic schedule. The training planning unit can also propose a training plan considering the athlete's home environment and hobbies. For example, the training planning unit can propose a training plan considering the athlete's home environment and hobbies. By considering the athlete's lifestyle, a more effective training plan can be generated. Some or all of the above processes in the training planning unit may be performed using AI, for example, or not. For example, the training planning unit can input data on the athlete's lifestyle into a generating AI and have the generating AI perform the generation of the training plan.

[0103] The training planning unit can consider the player's role within the team when generating a training plan. For example, the training planning unit can propose a training plan based on the player's position. The training planning unit can also adjust the training plan based on the player's role within the team. For example, the training planning unit can propose a training plan based on the player's position. The training planning unit can also adjust the training plan based on the player's role within the team. The training planning unit can also propose a training plan that aligns with the team's tactics. For example, the training planning unit can propose a training plan that aligns with the team's tactics. By considering the player's role within the team, a more effective training plan can be generated. Some or all of the above processing in the training planning unit may be performed using AI, for example, or without AI. For example, the training planning unit can input data on the player's role within the team into a generating AI and have the generating AI generate the training plan.

[0104] The match simulation unit can estimate the emotions of the players and adjust the match simulation scenario based on the estimated emotions. For example, the match simulation unit can estimate the emotions of the players and suggest a scenario to help them relax if they are nervous. It can also estimate the emotions of the players and suggest a scenario to help them stay calm if they are excited. For example, the match simulation unit can estimate the emotions of the players and suggest a scenario to help them relax if they are nervous. It can also estimate the emotions of the players and suggest a scenario to help them stay calm if they are excited. It can also estimate the emotions of the players and suggest a scenario to help them rest if they are tired. For example, the match simulation unit can estimate the emotions of the players and suggest a scenario to help them rest if they are tired. By adjusting the match simulation scenario based on the emotions of the players, a more realistic simulation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the match simulation unit may be performed using AI, for example, or without AI. For example, the match simulation unit can input player emotion data into a generating AI and have the generating AI perform emotion estimation.

[0105] The match simulation unit can incorporate past match results when performing match simulations that combine video and text data. For example, the match simulation unit can create simulation scenarios based on past match results. The match simulation unit can also analyze past match data and incorporate it into the simulation. For example, the match simulation unit can create simulation scenarios based on past match results. The match simulation unit can also analyze past match data and incorporate it into the simulation. Furthermore, the match simulation unit can adjust the simulation tactics considering past match results. For example, the match simulation unit adjusts the simulation tactics considering past match results. By incorporating past match results, a more realistic simulation becomes possible. Some or all of the above processing in the match simulation unit may be performed using AI, for example, or without AI. For example, the match simulation unit can input past match result data into a generating AI and have the generating AI create the simulation scenario.

[0106] The match simulation unit can reflect the individual characteristics of players when conducting match simulations. For example, the match simulation unit can perform simulations considering the skills and abilities of the players. It can also perform simulations considering the physical strength and stamina of the players. For example, the match simulation unit can perform simulations considering the skills and abilities of the players. It can also perform simulations considering the physical strength and stamina of the players. It can also perform simulations considering the positions and roles of the players. For example, the match simulation unit can perform simulations considering the positions and roles of the players. By reflecting the individual characteristics of players, a more realistic simulation becomes possible. Some or all of the above processing in the match simulation unit may be performed using AI, for example, or without AI. For example, the match simulation unit can input player characteristic data into a generating AI and have the generating AI perform the simulation.

[0107] The match simulation unit can estimate the emotions of the players and determine the priority of match simulations based on the estimated emotions. For example, the match simulation unit can estimate the emotions of the players and prioritize simulations to help them relax if they are nervous. It can also estimate the emotions of the players and prioritize simulations to help them stay calm if they are excited. For example, the match simulation unit can estimate the emotions of the players and prioritize simulations to help them relax if they are nervous. It can also estimate the emotions of the players and prioritize simulations to help them stay calm if they are excited. It can also estimate the emotions of the players and prioritize simulations to help them rest if they are tired. For example, the match simulation unit can estimate the emotions of the players and prioritize simulations to help them rest if they are tired. This allows for more effective simulations by determining the priority of match simulations based on the emotions of the players. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI may be, but is not limited to, a text-generating AI (e.g., LLM) or a multimodal generative AI. Some or all of the processing described above in the match simulation unit may be performed using AI, for example, or not using AI. For example, the match simulation unit may input player emotion data into the generative AI and have the generative AI perform emotion estimation.

[0108] The match simulation unit can reflect the environmental conditions of a match when performing a match simulation that combines video data and text data. For example, the match simulation unit can perform a simulation considering the weather conditions of the match. The match simulation unit can also perform a simulation considering the characteristics of the match venue. For example, the match simulation unit can perform a simulation considering the weather conditions of the match. The match simulation unit can also perform a simulation considering the characteristics of the match venue. The match simulation unit can also perform a simulation considering the influence of the time of day of the match. For example, the match simulation unit can perform a simulation considering the influence of the time of day of the match. By reflecting the environmental conditions of the match, a more realistic simulation becomes possible. Some or all of the above processing in the match simulation unit may be performed using AI, for example, or without using AI. For example, the match simulation unit can input data on the environmental conditions of the match into a generating AI and have the generating AI perform the simulation.

[0109] The match simulation unit can reflect changes in the opposing team's tactics when conducting a match simulation. For example, the match simulation unit can reflect changes in the opposing team's tactics in the simulation. The match simulation unit can also reflect substitutions made by the opposing team in the simulation. For example, the match simulation unit can reflect changes in the opposing team's tactics in the simulation. The match simulation unit can also reflect substitutions made by the opposing team in the simulation. The match simulation unit can also reflect changes in the opposing team's formation in the simulation. For example, the match simulation unit can reflect changes in the opposing team's formation in the simulation. By reflecting changes in the opposing team's tactics, a more realistic simulation becomes possible. Some or all of the above processing in the match simulation unit may be performed using AI, for example, or without AI. For example, the match simulation unit can input data on the opposing team's tactical changes into a generating AI and have the generating AI perform the simulation.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] A sports team support system can monitor the psychological state of athletes and provide psychological support. For example, if an athlete is nervous before a game, it can suggest mental training to help them relax. It can also provide advice to improve concentration if an athlete lacks focus during a game. Furthermore, if an athlete is depressed after a game, it can provide counseling to help them regain their motivation. This helps maintain the psychological well-being of athletes and maximizes their performance.

[0112] A sports team support system can monitor athletes' nutritional status and suggest optimal meal plans. For example, it can measure an athlete's weight and body fat percentage and adjust their calorie intake accordingly. It can also suggest meal plans containing necessary nutrients based on the athlete's training regimen. Furthermore, it can adjust the timing of athletes' meals and provide advice to maximize their performance in training and games. This optimizes athletes' nutritional status and improves their performance.

[0113] A sports team support system can monitor athletes' sleep patterns and suggest optimal sleep environments. For example, it can measure athletes' sleep duration and quality and provide advice on improving their sleep environment based on that data. It can also suggest optimal sleep durations according to athletes' training schedules. Furthermore, it can provide advice on relaxation methods and stress management to improve athletes' sleep quality. This can improve athletes' sleep quality and maximize their performance.

[0114] Sports team support systems can monitor athletes' health and prevent or detect injuries early. For example, they can measure athletes' body temperature and heart rate and address any abnormalities promptly. They can also monitor athletes' training content and load, adjusting it to prevent excessive strain. Furthermore, they can analyze athletes' body movements, identify high-risk actions, and provide advice for improvement. This helps maintain athletes' health and minimize the risk of injury.

[0115] A sports team support system can estimate an athlete's emotions and adjust training content based on those emotions. For example, if an athlete is feeling stressed, it can suggest training to help them relax. If an athlete is unmotivated, it can suggest training to boost their motivation. Furthermore, if an athlete is tired, it can suggest training to help them rest. This allows for training content to be adjusted based on the athlete's emotions, maximizing their performance.

[0116] The sports team support system can estimate the emotions of the players and adjust tactics during a match based on those emotions. For example, if a player is nervous, it can suggest tactics to help them relax. If a player is excited, it can suggest tactics to help them stay calm. Furthermore, if a player is tired, it can suggest tactics to help them rest. This allows the team to adjust tactics during a match based on the players' emotions and gain an advantage in the flow of the game.

[0117] A sports team support system can estimate a player's emotions and provide post-game feedback based on those emotions. For example, if a player is down after a game, it can provide feedback to help them regain their motivation. If a player is excited after a game, it can provide feedback to help them stay calm. Furthermore, if a player is tired after a game, it can provide feedback to encourage them to rest. This allows for post-game feedback tailored to the player's emotions, helping them prepare for the next game.

[0118] A sports team support system can estimate an athlete's emotions and prioritize training based on those emotions. For example, if an athlete is nervous, training to help them relax can be prioritized. If an athlete is excited, training to help them stay calm can be prioritized. Furthermore, if an athlete is tired, training to help them rest can be prioritized. This allows for more effective training by prioritizing training based on the athlete's emotions.

[0119] The sports team support system can estimate the emotions of the players and adjust pre-game preparations based on those emotions. For example, if a player is nervous, it can suggest preparations to help them relax. If a player is excited, it can suggest preparations to help them stay calm. Furthermore, if a player is tired, it can suggest preparations to help them rest. This allows the system to adjust pre-game preparations based on the players' emotions, ensuring they are in optimal condition for the game.

[0120] A sports team support system can estimate players' emotions and adjust in-game communication based on those emotions. For example, if a player is nervous, it can suggest communication to help them relax. If a player is excited, it can suggest communication to help them stay calm. Furthermore, if a player is tired, it can suggest communication to encourage them to rest. This allows the system to adjust in-game communication based on players' emotions and maximize team performance.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The tactical analysis unit analyzes the opposing team's match data. This data includes scoring patterns, player movements, and tactical tendencies. For example, the tactical analysis unit analyzes the opposing team's scoring patterns to understand their offensive tendencies. It can also analyze player movements to identify defensive weaknesses. Furthermore, it can analyze tactical tendencies to predict changes in the opposing team's tactics. Step 2: The real-time adjustment unit adjusts tactics based on the data analyzed by the tactical analysis unit. Tactical adjustments include changes to formations and changes in player positions. For example, formations can be changed to respond to the opposing team's attack, or player positions can be changed to strengthen the defense. It can also analyze data during the match and propose tactical adjustments in real time. Step 3: The performance prediction unit predicts player performance based on the tactics adjusted by the real-time adjustment unit. Performance prediction includes the use of historical data analysis and statistical models. For example, player performance can be predicted based on past match data, or player development can be predicted using statistical models. It is also possible to analyze data during a match and predict player performance in real time. Step 4: The training planning unit generates a training plan based on the data predicted by the performance prediction unit. The training plan includes training menus, training frequency and intensity, etc. For example, it can propose a training plan tailored to the athlete's fitness level, skill level, and goals. Step 5: The match simulation department conducts match simulations based on the plans generated by the training planning department. Match simulations include the simulation scenarios and the types of data used. For example, they can perform match simulations that combine video and text data, simulations based on past match data, and simulations that reflect the individual characteristics of the players.

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0126] Each of the multiple elements described above, including the tactical analysis unit, real-time adjustment unit, performance prediction unit, training planning unit, and match simulation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the tactical analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the opponent team's match data. The real-time adjustment unit is implemented by the control unit 46A of the smart device 14 and adjusts tactics. The performance prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the performance of players. The training planning unit is implemented by the control unit 46A of the smart device 14 and generates a training plan. The match simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs a match simulation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 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.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the tactical analysis unit, real-time adjustment unit, performance prediction unit, training planning unit, and match simulation unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the tactical analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the opponent team's match data. The real-time adjustment unit is implemented by, for example, the control unit 46A of the smart glasses 214 and adjusts tactics. The performance prediction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and predicts the performance of players. The training planning unit is implemented by, for example, the control unit 46A of the smart glasses 214 and generates a training plan. The match simulation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs a match simulation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the tactical analysis unit, real-time adjustment unit, performance prediction unit, training planning unit, and match simulation unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the tactical analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the opposing team's match data. The real-time adjustment unit is implemented by the control unit 46A of the headset terminal 314 and adjusts tactics. The performance prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the performance of players. The training planning unit is implemented by the control unit 46A of the headset terminal 314 and generates a training plan. The match simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs a match simulation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the tactical analysis unit, real-time adjustment unit, performance prediction unit, training planning unit, and match simulation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the tactical analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the opponent team's match data. The real-time adjustment unit is implemented by, for example, the control unit 46A of the robot 414 and adjusts tactics. The performance prediction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and predicts the player's performance. The training planning unit is implemented by, for example, the control unit 46A of the robot 414 and generates a training plan. The match simulation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs a match simulation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) The tactical analysis department analyzes the opposing team's match data, A real-time adjustment unit that adjusts tactics based on data analyzed by the aforementioned tactical analysis unit, A performance prediction unit that predicts a player's performance based on tactics adjusted by the real-time adjustment unit, A training planning unit generates a training plan based on the data predicted by the performance prediction unit, The system includes a match simulation unit that performs a match simulation based on a plan generated by the training planning unit. A system characterized by the following features. (Note 2) The aforementioned tactical analysis unit, Analyze the opposing team's offensive patterns and defensive weaknesses. The system described in Appendix 1, characterized by the features described herein. (Note 3) The real-time adjustment unit is, It analyzes the movements of players during practice in real time and provides technical advice. The system described in Appendix 1, characterized by the features described herein. (Note 4) The real-time adjustment unit is, We analyze in-game data in real time and suggest tactical adjustments. The system described in Appendix 1, characterized by the features described herein. (Note 5) The performance prediction unit, Predicting player performance based on past data The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned training planning department, Generate appropriate training plans for each individual athlete. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned match simulation unit, By combining video and text data, we conduct match simulations to verify the effectiveness of tactics in advance. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned tactical analysis unit, It estimates the emotions of the players and improves the accuracy of tactical analysis based on the estimated emotions of the players. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned tactical analysis unit, When analyzing the opposing team's match data, we detect changes in tactics by comparing it with past match data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned tactical analysis unit, We analyze the individual movements and performance of opposing team players and propose individual countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned tactical analysis unit, The system estimates the players' emotions and determines the priority of tactical analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned tactical analysis unit, When analyzing the opposing team's match data, based on external factors such as weather and match location The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned tactical analysis unit, When analyzing the opposing team's match data, consider the tactical tendencies of the opposing team's coach. The system described in Appendix 1, characterized by the features described herein. (Note 14) The real-time adjustment unit is, It estimates the players' emotions and makes real-time tactical adjustments based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The real-time adjustment unit is, When analyzing the movements of athletes during practice, we provide technical advice while taking into account the athletes' fatigue levels. The system described in Appendix 1, characterized by the features described herein. (Note 16) The real-time adjustment unit is, When analyzing data during a match, it provides alerts to immediately respond to changes in the opposing team's tactics. The system described in Appendix 1, characterized by the features described herein. (Note 17) The real-time adjustment unit is, It estimates the player's emotions and prioritizes advice in real time based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The real-time adjustment unit is, When analyzing the movements of athletes during practice, we monitor their health and suggest appropriate rest periods. The system described in Appendix 1, characterized by the features described herein. (Note 19) The real-time adjustment unit is, When analyzing data during a match, tactical adjustments are made while taking into account the reactions of the audience. The system described in Appendix 1, characterized by the features described herein. (Note 20) The performance prediction unit, We estimate the emotions of the players and improve the accuracy of performance predictions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The performance prediction unit, When predicting player performance based on past data, the player's growth curve should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The performance prediction unit, When predicting an athlete's performance, their nutritional status and sleep patterns should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The performance prediction unit, The system estimates the players' emotions and prioritizes performance predictions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The performance prediction unit, When predicting a player's performance based on past data, their training history should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The performance prediction unit, When predicting an athlete's performance, their psychological state should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned training planning department, The system estimates the athlete's emotions and adjusts the training plan based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned training planning department, When generating an optimal training plan for each individual athlete, we take into account the athlete's past training data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned training planning department, When generating a training plan, reflect the athlete's goals and aspirations. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned training planning department, The system estimates the players' emotions and prioritizes training plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned training planning department, When creating an optimal training plan for each individual athlete, we take their lifestyle into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned training planning department, When generating a training plan, consider the player's role within the team. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned match simulation unit, The system estimates the players' emotions and adjusts the match simulation scenario based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned match simulation unit, When conducting match simulations that combine video and text data, past match results should be reflected. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned match simulation unit, When conducting match simulations, reflect the individual characteristics of the players. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned match simulation unit, The system estimates the players' emotions and determines the priority of match simulations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned match simulation unit, When conducting a match simulation that combines video data and text data, the environmental conditions of the match should be reflected. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned match simulation unit, When conducting a match simulation, reflect the opposing team's tactical changes. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The tactical analysis department analyzes the opposing team's match data, A real-time adjustment unit that adjusts tactics based on data analyzed by the aforementioned tactical analysis unit, A performance prediction unit that predicts a player's performance based on tactics adjusted by the real-time adjustment unit, A training planning unit generates a training plan based on the data predicted by the performance prediction unit, The system includes a match simulation unit that performs a match simulation based on a plan generated by the training planning unit. A system characterized by the following features.

2. The aforementioned tactical analysis unit, Analyze the opposing team's offensive patterns and defensive weaknesses. The system according to feature 1.

3. The real-time adjustment unit is, It analyzes the movements of players during practice in real time and provides technical advice. The system according to feature 1.

4. The real-time adjustment unit is, We analyze in-game data in real time and suggest tactical adjustments. The system according to feature 1.

5. The performance prediction unit, Predicting player performance based on past data The system according to feature 1.

6. The aforementioned training planning department, Generate appropriate training plans for each individual athlete. The system according to feature 1.

7. The aforementioned match simulation unit, By combining video and text data, we conduct match simulations to verify the effectiveness of tactics in advance. The system according to feature 1.

8. The aforementioned tactical analysis unit, It estimates the emotions of the players and improves the accuracy of tactical analysis based on the estimated emotions of the players. The system according to feature 1.

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