System

Abstract

An object of the system according to the embodiment is to analyze a game video and propose and execute a training menu or a strategy based on a result of the analysis.SOLUTION: A system includes an analysis unit, a result providing unit, a proposal unit, and an execution unit. The analysis unit analyzes the game video. The result providing unit provides a result analyzed by the analysis unit. The suggestion unit suggests a training menu or a strategy on the basis of the result provided by the result providing unit. The execution unit executes the training menu or the strategy proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not effectively analyze game footage and suggest training menus and tactics based on the results, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze game footage and, based on the results, propose and implement training menus and tactics. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a result providing unit, a suggestion unit, and an execution unit. The analysis unit analyzes game footage. The result providing unit provides the results analyzed by the analysis unit. The suggestion unit suggests a training menu or tactics based on the results provided by the result providing unit. The execution unit executes the training menu or tactics suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze game footage and, based on the results, propose and implement training menus and tactics. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) An AI support system according to an embodiment of the present invention automatically analyzes game footage and proposes and executes training programs and tactics based on the analysis results. The AI ​​support system analyzes game footage and evaluates player movements, positioning, and ball movement. For example, in a soccer game, AI can analyze each player's pass success rate, shooting accuracy, defensive positioning, and other factors. The AI ​​support system then recommends training programs and tactics based on the analysis results. For example, for a player with a low pass success rate, it can suggest a passing practice program, and for a team with poor defensive positioning, it can suggest defensive formation practice. Furthermore, the AI ​​support system proposes tactics for the next game based on the analysis results of the game footage. For example, it can analyze the opposing team's attacking patterns and propose defensive tactics to counter them. This allows coaches to hold effective pre-game strategy meetings. This contributes to alleviating coach shortages and developing coaches. For example, for club activities or local teams with few coaches, AI can propose training programs and tactics on behalf of the coach, supporting the improvement of team skills. Professional sports teams can also develop more advanced tactics based on the detailed analysis results provided by AI. This allows the AI ​​support system to support analysis and strategy meetings in team sports, contributing to the alleviation of coaching shortages and the development of new coaches. For example, professional sports teams, club activities, and local teams can use this service to improve their team's skills and tactics.

[0029] The AI ​​support system according to the embodiment includes an analysis unit, a result providing unit, a suggestion unit, and an execution unit. The analysis unit analyzes game footage. Game footage includes, but is not limited to, soccer, basketball, and baseball. The analysis unit, for example, uses video analysis technology to analyze player movements and positions and ball movement. The analysis unit can also evaluate the team's overall tactics and individual player skills using data analysis techniques. For example, the analysis unit analyzes a player's pass success rate, shooting accuracy, defensive positioning, and the like. The result providing unit provides the results of the analysis by the analysis unit. The result providing unit provides the analysis results, for example, in report format. The result providing unit can also display the analysis results in real time. For example, the result providing unit displays the analysis results in real time during a game and provides feedback to the coach. The suggestion unit suggests practice menus and tactics based on the results provided by the result providing unit. For example, the suggestion unit can suggest a passing practice menu for a player with a low pass success rate. The suggestion unit can also suggest defensive formation practice for a team with poor defensive positioning. Furthermore, the suggestion unit can analyze the offensive patterns of the opposing team and propose defensive tactics to counter them. For example, the suggestion unit analyzes the offensive patterns of the opposing team and proposes defensive tactics based on the analysis. The execution unit executes the practice menu and tactics proposed by the suggestion unit. The execution unit executes, for example, the proposed practice menu. The execution unit can also execute the proposed tactics. For example, the execution unit executes the proposed defensive tactics. This allows the AI ​​support system according to the embodiment to perform a consistent process from analyzing game footage to proposing and executing practice menus and tactics.

[0030] The analysis unit can analyze player movements, positions, and ball movements using deep learning. Deep learning includes, but is not limited to, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The analysis unit can analyze player movements, for example, using CNNs. The analysis unit can also analyze player positions using RNNs. The analysis unit can also analyze ball movements using deep learning. For example, the analysis unit can analyze player movements, positions, and ball movements using deep learning, and evaluate the team's overall tactics and the skills of individual players based on the results. This improves the accuracy of the analysis by using deep learning. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze player movements, positions, and ball movements using a deep learning model and output the results.

[0031] The suggestion unit can suggest a passing practice menu for a player with a low pass success rate. Calculation of the pass success rate includes, for example, a success rate threshold and a calculation period, but is not limited to these examples. The suggestion unit can suggest a passing practice menu for a player with a pass success rate of less than 50%. The suggestion unit can also suggest a practice menu based on the pass success rate over the past month. Furthermore, the suggestion unit can also suggest a practice menu based on the pass success rate in a specific game. For example, the suggestion unit can suggest a passing practice menu for a player with a low pass success rate and improve the player's skills based on the results. This makes it possible to suggest an appropriate practice menu for a player with a low pass success rate. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit inputs pass success rate data into AI, which then suggests an appropriate practice menu.

[0032] The suggestion unit can suggest defensive formation practice to a team with poor defensive positioning. Evaluation of defensive positioning includes, but is not limited to, positioning evaluation criteria and positioning analysis methods. For example, the suggestion unit can suggest defensive formation practice to a team with poor defensive positioning. The suggestion unit can also suggest a practice menu based on defensive positioning in a specific game. Furthermore, the suggestion unit can evaluate defensive positioning based on past game data and suggest a practice menu. For example, the suggestion unit can suggest defensive formation practice to a team with poor defensive positioning, and improve the defensive ability of the entire team based on the evaluation results. This makes it possible to suggest an appropriate practice menu to a team with poor defensive positioning. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit inputs defensive positioning data into AI, which then suggests an appropriate practice menu.

[0033] The suggestion unit can analyze the opposing team's offensive patterns and propose defensive tactics to counter them. Examples of analysis of offensive patterns include, but are not limited to, pattern recognition algorithms and types of attacks. For example, the suggestion unit can analyze the opposing team's offensive patterns and propose defensive tactics based on the analysis. The suggestion unit can also propose defensive tactics based on the opposing team's offensive patterns in a specific game. Furthermore, the suggestion unit can analyze the opposing team's offensive patterns based on past game data and propose defensive tactics. For example, the suggestion unit can analyze the opposing team's offensive patterns and propose defensive tactics to counter them, improving the team's overall defensive strength based on the results. This allows the suggestion unit to propose defensive tactics to counter the opposing team's offensive patterns. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit inputs data on the opposing team's offensive patterns into AI, which then proposes appropriate defensive tactics.

[0034] The execution unit can execute the proposed practice menu or tactics. Examples of the execution of a practice menu include, but are not limited to, the type of practice and the purpose of the practice. The execution unit, for example, executes the proposed practice menu. The execution unit can also execute the proposed tactics. Furthermore, the execution unit can provide instructions for executing a specific practice menu or tactics. For example, the execution unit executes the proposed practice menu and aims to improve the player's skills based on the results. This allows the proposed practice menu or tactics to be executed. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit inputs data on the proposed practice menu or tactics into AI, which then provides an appropriate execution method.

[0035] When analyzing game footage, the analysis unit can correct the analysis results by taking into account the player's physical strength and fatigue level. Evaluations of physical strength and fatigue level include, but are not limited to, heart rate, endurance tests, lactate levels, and subjective fatigue assessments. For example, the analysis unit acquires the player's heart rate data and corrects the analysis results if the player's fatigue level is high. The analysis unit can also refer to the player's running distance data and correct the analysis results if the player's physical strength is depleted. Furthermore, the analysis unit can take the player's rest time into consideration and correct the analysis results if the player's rest time is short. For example, the analysis unit corrects the analysis results by taking into account the player's physical strength and fatigue level, and provides more accurate analysis results based on the correction results. By taking the player's physical strength and fatigue level into consideration, more accurate analysis results can be provided. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit inputs the player's physical strength and fatigue level data into the generation AI, which corrects the analysis results.

[0036] When analyzing game footage, the analysis unit can correct the analysis results by taking into account the weather and pitch conditions. Evaluations of the weather and pitch conditions include, but are not limited to, sunny, rainy, wind speed, turf condition, and humidity. For example, the analysis unit corrects the analysis results by taking into account the slipperiness of the surface when it is raining. The analysis unit can also correct the analysis results by taking into account the ease with which the ball rolls when the pitch is dry. Furthermore, the analysis unit can correct the analysis results by taking into account changes in the ball's trajectory when there is strong wind. For example, the analysis unit corrects the analysis results by taking into account the weather and pitch conditions and provides more accurate analysis results based on the correction results. In this way, by taking into account the weather and pitch conditions, more accurate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs data on the weather and pitch conditions into the generation AI, and the generation AI corrects the analysis results.

[0037] When analyzing game footage, the analysis unit can improve analysis accuracy by referring to a player's past performance data. Past performance data includes, but is not limited to, game records, training data, and injury history. For example, the analysis unit can improve analysis accuracy by referring to a player's past game data and comparing it with their current performance. The analysis unit can also improve analysis accuracy by referring to a player's past practice data and taking into account technical advances. Furthermore, the analysis unit can improve analysis accuracy by referring to a player's past injury history and taking into account the impact of injuries. For example, the analysis unit can improve analysis accuracy by referring to a player's past performance data and improving analysis accuracy based on the results. Thus, by referring to a player's past performance data, analysis accuracy can be improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit inputs a player's past performance data into a generation AI, which then improves analysis accuracy.

[0038] When analyzing game footage, the analysis unit can apply different analysis algorithms to each player's position. Analysis algorithms applied to each position include, but are not limited to, algorithms for defenders and forwards. For example, the analysis unit can apply an analysis algorithm that emphasizes shooting accuracy and pass success rate to forward players. The analysis unit can also apply an analysis algorithm that emphasizes defensive positioning and tackle success rate to defenders. The analysis unit can also apply an analysis algorithm that emphasizes save rate and positioning to goalkeepers. For example, the analysis unit can apply different analysis algorithms to each player's position and provide more accurate analysis results based on the results. This allows for more accurate analysis results by performing appropriate analysis for each player's position. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit inputs player position data into a generation AI, which then applies an appropriate analysis algorithm.

[0039] When analyzing game footage, the analysis unit can correct the analysis results based on the opposing team's tactics. Analysis of the opposing team's tactics includes, but is not limited to, the type of tactics and evaluation criteria for the tactics. For example, the analysis unit analyzes the opposing team's offensive patterns and corrects the analysis results of the team's defense based on the analysis results. The analysis unit can also analyze the opposing team's defensive patterns and correct the analysis results of the team's offensive patterns based on the analysis results. Furthermore, the analysis unit can analyze the opposing team's formation and correct the analysis results of the team's overall tactics based on the analysis results. For example, the analysis unit analyzes the opposing team's tactics and corrects the analysis results based on the analysis results. This allows for more accurate analysis results to be provided by taking the opposing team's tactics into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the opposing team's tactical data into a generation AI, and the generation AI corrects the analysis results.

[0040] When analyzing game footage, the analysis unit can improve analysis accuracy by referring to the player's physiological data. Physiological data includes, but is not limited to, heart rate, oxygen intake, and muscle fatigue data. For example, the analysis unit can improve analysis accuracy by referring to the player's heart rate data and taking into account the player's fatigue level. The analysis unit can also improve analysis accuracy by referring to the player's oxygen intake data and taking into account the player's physical exhaustion. Furthermore, the analysis unit can improve analysis accuracy by referring to the player's muscle fatigue data and taking into account a decline in performance. For example, the analysis unit can improve analysis accuracy by referring to the player's physiological data and improving analysis accuracy based on the results. Thus, by referring to the player's physiological data, analysis accuracy can be improved. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the player's physiological data into a generation AI, which then improves analysis accuracy.

[0041] When providing a result, the result providing unit can adjust the level of detail of the result depending on the player's level of understanding. Evaluations of the player's level of understanding include, but are not limited to, test results and feedback. For example, the result providing unit can provide detailed analysis results if the player's level of understanding is high. Furthermore, the result providing unit can provide concise, to-the-point analysis results if the player's level of understanding is low. Furthermore, the result providing unit can provide analysis results using diagrams or videos depending on the player's level of understanding. For example, the result providing unit can evaluate the player's level of understanding and adjust the level of detail of the result based on the evaluation result. This allows for more appropriate feedback to be provided by adjusting the level of detail of the result depending on the player's level of understanding. Some or all of the above-described processing in the result providing unit may be performed using, for example, AI, or may be performed without AI. For example, the result providing unit inputs the player's level of understanding data into a generation AI, which then adjusts the level of detail of the result.

[0042] When providing results, the result providing unit can determine the priority of results based on the importance of the match. Evaluation of the importance of a match includes, but is not limited to, league matches and tournament matches. For example, the result providing unit prioritizes the provision of important match results and performs detailed analysis. The result providing unit can also perform simple analysis of less important match results and postpone them. Furthermore, the result providing unit can adjust the order in which analysis results are provided depending on the importance of the match. For example, the result providing unit evaluates the importance of a match and determines the priority of results based on the evaluation result. Thus, by determining the priority of results based on the importance of a match, appropriate feedback can be provided for important matches. Some or all of the above-described processing in the result providing unit may be performed using, or without, AI. For example, the result providing unit inputs match importance data to a generation AI, which then determines the priority of the results.

[0043] When providing results, the result providing unit can refer to past game results to improve the reliability of the results. Past game results include, but are not limited to, scores and game details. For example, the result providing unit can refer to past game results and compare them with current analysis results to improve reliability. The result providing unit can also correct current analysis results based on data obtained from past game results. Furthermore, the result providing unit can refer to past game results and analyze trends to improve the reliability of current analysis results. For example, the result providing unit can refer to past game results and improve the reliability of current analysis results based on the results. In this way, the reliability of results can be improved by referring to past game results. Some or all of the above-described processing in the result providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the result providing unit inputs past game result data into a generation AI, which then improves the reliability of the results.

[0044] When providing results, the result providing unit can apply different result display methods to each player's position. Result display methods applied to each position include, but are not limited to, those for defenders and forwards. For example, the result providing unit can provide a result display method that emphasizes shooting accuracy and pass success rate to forward players. The result providing unit can also provide a result display method that emphasizes defensive positioning and tackle success rate to defenders. The result providing unit can also provide a result display method that emphasizes save rate and positioning to goalkeepers. For example, the result providing unit can apply different result display methods to each player's position and provide more accurate feedback based on the results. This allows for more accurate feedback by providing an appropriate result display method for each player's position. Some or all of the above-described processing by the result providing unit can be performed using, or without, AI. For example, the result providing unit inputs player position data to a generation AI, which then applies an appropriate result display method.

[0045] When providing results, the result providing unit can adjust the display method of the results based on the situation of the game. Examples of the situation of the game include, but are not limited to, home and away. For example, in the case of a home game, the result providing unit displays the analysis results taking into account the influence of spectators. In addition, in the case of an away game, the result providing unit can also display the analysis results taking into account the influence of travel. Furthermore, the result providing unit can adjust the display method of the analysis results according to the situation of the game. For example, the result providing unit evaluates the situation of the game and adjusts the display method of the results based on the evaluation result. In this way, more appropriate feedback can be provided by adjusting the display method of the results according to the situation of the game. Some or all of the above-mentioned processing in the result providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the result providing unit inputs game situation data to a generation AI, and the generation AI adjusts the display method of the results.

[0046] When providing results, the result providing unit can customize the display method of the results by reflecting player feedback. Player feedback includes, but is not limited to, questionnaire results and verbal feedback. The result providing unit, for example, customizes the display method of the analysis results based on the player feedback. The result providing unit can also change the display format of the analysis results according to the player's preferences. Furthermore, the result providing unit can reflect the player's feedback and provide a highly visible display method. For example, the result providing unit evaluates the player's feedback and customizes the display method of the results based on the evaluation result. This allows for a more appropriate display method of the results by reflecting the player's feedback. Some or all of the above-described processing in the result providing unit may be performed using, or without, AI. For example, the result providing unit inputs the player's feedback data into the generation AI, which then customizes the display method of the results.

[0047] When proposing a practice menu, the suggestion unit can adjust the difficulty level of the practice menu according to the player's skill level. Skill level evaluations include, but are not limited to, skill test results and coach evaluations. For example, the suggestion unit can suggest a practice menu for beginner players to master basic skills. For intermediate players, the suggestion unit can also suggest a practice menu aimed at improving skills. For advanced players, the suggestion unit can also suggest a practice menu for honing advanced skills. For example, the suggestion unit can evaluate the player's skill level and adjust the difficulty level of the practice menu based on the evaluation results. This allows for providing a more appropriate practice menu by adjusting the difficulty level of the practice menu according to the player's skill level. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit inputs the player's skill level data into a generation AI, which then adjusts the difficulty level of the practice menu.

[0048] When making a suggestion, the suggestion unit can customize the suggestion content for each player based on the tactics of the entire team. Examples of the tactics of the entire team include, but are not limited to, offensive tactics and defensive tactics. For example, the suggestion unit can suggest an offensive practice menu to a forward player based on the offensive tactics of the team. The suggestion unit can also suggest a defensive practice menu to a defender based on the defensive tactics of the team. Furthermore, the suggestion unit can suggest a practice menu for a goalkeeper that corresponds to a specific situation based on the tactics of the team. For example, the suggestion unit can evaluate the tactics of the entire team and customize the suggestion content for each player based on the evaluation results. This allows for the provision of a more appropriate practice menu. Some or all of the above-described processing by the suggestion unit may be performed using, or without, an AI. For example, the suggestion unit inputs tactical data of the entire team into a generation AI, which then customizes the suggestion content for each player.

[0049] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to past proposal results. Past proposal results include, but are not limited to, examples of the success rate of the proposal and feedback results. For example, the suggestion unit refers to past proposal results and corrects the current proposal content. The suggestion unit can also optimize the current proposal content based on data obtained from the past proposal results. Furthermore, the suggestion unit can analyze past proposal results, grasp trends, and improve the accuracy of the current proposal content. For example, the suggestion unit refers to past proposal results and improves the accuracy of the current proposal content based on the results. In this way, the accuracy of the proposal can be improved by referring to the past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit inputs past proposal result data into a generation AI, which then improves the accuracy of the proposal.

[0050] When making the proposal, the suggestion unit can propose different practice menus for each player's position. Practice menus applicable to each position include, but are not limited to, those for defenders and forwards. For example, the suggestion unit can propose shooting practice and passing practice to forward players. The suggestion unit can also propose defensive positioning and tackling practice to defenders. The suggestion unit can also propose save practice and positioning practice to goalkeepers. For example, the suggestion unit can propose different practice menus for each player's position and provide more accurate feedback based on the results. This allows for more accurate feedback by providing an appropriate practice menu for each player's position. Some or all of the above-described processing by the suggestion unit may be performed using, or without, an AI. For example, the suggestion unit inputs player position data into a generation AI, which then proposes an appropriate practice menu.

[0051] When making a proposal, the suggestion unit can adjust the proposal content based on the situation of the game. Examples of the situation of the game include, but are not limited to, home and away games. For example, in the case of a home game, the suggestion unit can adjust the proposal content taking into account the influence of spectators. Furthermore, in the case of an away game, the suggestion unit can also adjust the proposal content taking into account the influence of travel. Furthermore, the suggestion unit can optimize the proposal content according to the situation of the game. For example, the suggestion unit evaluates the situation of the game and adjusts the proposal content based on the evaluation result. By adjusting the proposal content according to the situation of the game, a more appropriate practice menu can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit inputs game situation data into a generation AI, which then adjusts the proposal content.

[0052] When making a suggestion, the suggestion unit can customize the suggestion content by reflecting the player's feedback. Examples of the player's feedback include, but are not limited to, questionnaire results and verbal feedback. The suggestion unit customizes the suggestion content based on, for example, the player's feedback. The suggestion unit can also change the suggestion content according to the player's preferences. Furthermore, the suggestion unit can reflect the player's feedback and provide optimal suggestion content. For example, the suggestion unit evaluates the player's feedback and customizes the suggestion content based on the evaluation result. This allows the suggestion content to be more appropriate by reflecting the player's feedback. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit inputs the player's feedback data into a generation AI, which then customizes the suggestion content.

[0053] When executing a practice menu, the execution unit can adjust the execution content taking into account the player's physical strength and fatigue level. Evaluations of physical strength and fatigue level include, but are not limited to, heart rate, endurance tests, lactate levels, and subjective fatigue level assessments. For example, the execution unit acquires the player's heart rate data and reduces the practice menu if the player's fatigue level is high. The execution unit can also refer to the player's running distance data and adjust the practice menu if the player's physical strength is depleted. Furthermore, the execution unit can take the player's rest time into consideration and shorten the practice menu if the player's rest time is short. For example, the execution unit adjusts the execution content taking into account the player's physical strength and fatigue level, and provides more appropriate practice based on the results. This allows for more appropriate practice by taking the player's physical strength and fatigue level into consideration. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without AI. For example, the execution unit inputs the player's physical strength and fatigue level data into a generation AI, and the generation AI adjusts the execution content.

[0054] When executing a practice menu, the execution unit can adjust the execution content taking into account the weather and pitch conditions. Evaluations of the weather and pitch conditions include, but are not limited to, sunny, rainy, wind speed, turf condition, and humidity. For example, the execution unit executes an indoor practice menu when it is raining. Furthermore, when the pitch is dry, the execution unit can adjust the practice menu taking into account the ease of ball rolling. Furthermore, when there is strong wind, the execution unit can adjust the practice menu taking into account the influence of wind. For example, the execution unit adjusts the execution content taking into account the weather and pitch conditions and provides more appropriate practice based on the results. In this way, more appropriate practice can be provided by taking the weather and pitch conditions into account. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit inputs data on the weather and pitch conditions into a generation AI, and the generation AI adjusts the execution content.

[0055] When executing a practice menu, the execution unit can optimize the execution content by referring to the player's past performance data. Past performance data includes, but is not limited to, game records, training data, and injury history. For example, the execution unit can optimize the current practice menu by referring to the player's past game data. The execution unit can also optimize the practice menu by referring to the player's past practice data and taking into account technical advances. Furthermore, the execution unit can optimize the practice menu by referring to the player's past injury history and taking into account the impact of the injury. For example, the execution unit can reference the player's past performance data and optimize the practice menu based on the results. In this way, the execution content of the practice menu can be optimized by referring to the player's past performance data. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit inputs the player's past performance data into a generation AI, and the generation AI optimizes the execution content.

[0056] When executing a practice menu, the execution unit can apply different execution methods to each player's position. Execution methods applied to each position include, but are not limited to, for example, defenders and forwards. For example, the execution unit can focus on shooting practice and passing practice for forward players. The execution unit can also focus on defensive positioning and tackling practice for defenders. Furthermore, the execution unit can focus on saving practice and positioning practice for goalkeepers. For example, the execution unit can apply different execution methods to each player's position and provide more accurate feedback based on the results. This allows for more accurate feedback by providing an appropriate execution method for each player's position. Some or all of the above-described processing in the execution unit can be performed using, for example, AI, or without AI. For example, the execution unit inputs player position data into a generation AI, which then applies an appropriate execution method.

[0057] When executing a practice menu, the execution unit can adjust the execution content based on the tactics of the opposing team. Analysis of the opposing team's tactics includes, for example, the type of tactics and tactical evaluation criteria, but is not limited to these examples. For example, the execution unit can analyze the opposing team's offensive patterns and execute a defensive practice menu based on the analysis. The execution unit can also analyze the opposing team's defensive patterns and execute an offensive practice menu based on the analysis. Furthermore, the execution unit can analyze the opposing team's formation and execute a practice menu of overall tactics based on the analysis. For example, the execution unit can analyze the opposing team's tactics and adjust the execution content based on the results. This allows for more appropriate practice by taking the opposing team's tactics into consideration. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit inputs the opposing team's tactical data into a generation AI, and the generation AI adjusts the execution content.

[0058] When executing a practice menu, the execution unit can optimize the execution content by referring to the player's physiological data. Physiological data includes, but is not limited to, heart rate, oxygen intake, and muscle fatigue data. For example, the execution unit can optimize the practice menu by referring to the player's heart rate data and taking into account the player's fatigue level. The execution unit can also optimize the practice menu by referring to the player's oxygen intake data and taking into account the player's physical exhaustion. Furthermore, the execution unit can optimize the practice menu by referring to the player's muscle fatigue data and taking into account a decline in performance. For example, the execution unit can optimize the practice menu by referring to the player's physiological data and optimizing the practice menu based on the results. In this way, the execution content of the practice menu can be optimized by referring to the player's physiological data. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit inputs the player's physiological data into a generation AI, and the generation AI optimizes the execution content.

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

[0060] When analyzing game footage, the analysis unit can also improve the accuracy of the analysis by referring to the player's physiological data. For example, it can refer to heart rate, oxygen intake, muscle fatigue data, etc., and correct the analysis results by taking into account the player's physical strength and fatigue level. It can also refer to the player's past physiological data and compare it with their current performance to improve the accuracy of the analysis. Furthermore, it can obtain the player's physiological data in real time and adjust the analysis results based on that data. This allows for more accurate analysis results to be provided by taking the player's physiological data into account.

[0061] The suggestion unit can also adjust the difficulty of the practice menu according to the skill level of the player. For example, it can suggest a practice menu for beginner players to master basic skills. It can also suggest a practice menu aimed at improving skills to intermediate players. It can also suggest a practice menu for advanced players to hone advanced skills. This makes it possible to provide an appropriate practice menu according to the skill level of the player.

[0062] When executing a practice menu, the execution unit can also adjust the execution content taking into account the weather and pitch conditions. For example, when it rains, the practice menu is executed indoors. Also, when the pitch is dry, the practice menu can be adjusted taking into account the ease with which the ball rolls. Furthermore, when there is strong wind, the practice menu can be adjusted taking into account the influence of wind. In this way, more appropriate practice can be provided by taking into account the weather and pitch conditions.

[0063] The result providing unit can also determine the priority of results based on the importance of the match. For example, the results of important matches can be provided first and analyzed in detail. The results of less important matches can also be analyzed simply and postponed. Furthermore, the order in which analysis results are provided can be adjusted depending on the importance of the match. In this way, by determining the priority of results based on the importance of the match, appropriate feedback can be provided for important matches.

[0064] The proposal unit can also improve the accuracy of proposals by referring to past proposal results. For example, it can refer to past proposal results and correct the current proposal content. It can also optimize the current proposal content based on data obtained from past proposal results. It can also analyze past proposal results, understand trends, and improve the accuracy of the current proposal content. In this way, it is possible to improve the accuracy of proposals by referring to past proposal results.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The analysis unit analyzes game footage. Game footage includes, but is not limited to, soccer, basketball, baseball, and other sports. The analysis unit uses video analysis technology to analyze player movements, positions, and ball movement. It can also use data analysis techniques to evaluate the team's overall tactics and the skills of individual players. For example, it analyzes a player's pass success rate, shooting accuracy, defensive positioning, etc. Step 2: The results provider provides the results analyzed by the analysis unit. The results provider can provide the analysis results in report format or display the analysis results in real time. For example, the results can be displayed in real time during a match to provide feedback to the coach. Step 3: The suggestion unit proposes training menus and tactics based on the results provided by the result providing unit. For example, it can propose a passing training menu for a player with a low pass success rate, or a defensive formation training for a team with poor defensive positioning. It can also analyze the opposing team's attacking patterns and propose defensive tactics to counter them. Step 4: The execution unit executes the training menu or tactics proposed by the proposal unit. For example, the execution unit executes the proposed training menu or executes the proposed tactics. This allows the proposed defensive tactics to be executed.

[0067] (Example 2) An AI support system according to an embodiment of the present invention automatically analyzes game footage and proposes and executes training programs and tactics based on the analysis results. The AI ​​support system analyzes game footage and evaluates player movements, positioning, and ball movement. For example, in a soccer game, AI can analyze each player's pass success rate, shooting accuracy, defensive positioning, and other factors. The AI ​​support system then recommends training programs and tactics based on the analysis results. For example, for a player with a low pass success rate, it can suggest a passing practice program, and for a team with poor defensive positioning, it can suggest defensive formation practice. Furthermore, the AI ​​support system proposes tactics for the next game based on the analysis results of the game footage. For example, it can analyze the opposing team's attacking patterns and propose defensive tactics to counter them. This allows coaches to hold effective pre-game strategy meetings. This contributes to alleviating coach shortages and developing coaches. For example, for club activities or local teams with few coaches, AI can propose training programs and tactics on behalf of the coach, supporting the improvement of team skills. Professional sports teams can also develop more advanced tactics based on the detailed analysis results provided by AI. This allows the AI ​​support system to support analysis and strategy meetings in team sports, contributing to the alleviation of coaching shortages and the development of new coaches. For example, professional sports teams, club activities, and local teams can use this service to improve their team's skills and tactics.

[0068] The AI ​​support system according to the embodiment includes an analysis unit, a result providing unit, a suggestion unit, and an execution unit. The analysis unit analyzes game footage. Game footage includes, but is not limited to, soccer, basketball, and baseball. The analysis unit, for example, uses video analysis technology to analyze player movements and positions and ball movement. The analysis unit can also evaluate the team's overall tactics and individual player skills using data analysis techniques. For example, the analysis unit analyzes a player's pass success rate, shooting accuracy, defensive positioning, and the like. The result providing unit provides the results of the analysis by the analysis unit. The result providing unit provides the analysis results, for example, in report format. The result providing unit can also display the analysis results in real time. For example, the result providing unit displays the analysis results in real time during a game and provides feedback to the coach. The suggestion unit suggests practice menus and tactics based on the results provided by the result providing unit. For example, the suggestion unit can suggest a passing practice menu for a player with a low pass success rate. The suggestion unit can also suggest defensive formation practice for a team with poor defensive positioning. Furthermore, the suggestion unit can analyze the offensive patterns of the opposing team and propose defensive tactics to counter them. For example, the suggestion unit analyzes the offensive patterns of the opposing team and proposes defensive tactics based on the analysis. The execution unit executes the practice menu and tactics proposed by the suggestion unit. The execution unit executes, for example, the proposed practice menu. The execution unit can also execute the proposed tactics. For example, the execution unit executes the proposed defensive tactics. This allows the AI ​​support system according to the embodiment to perform a consistent process from analyzing game footage to proposing and executing practice menus and tactics.

[0069] The analysis unit can analyze player movements, positions, and ball movements using deep learning. Deep learning includes, but is not limited to, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The analysis unit can analyze player movements, for example, using CNNs. The analysis unit can also analyze player positions using RNNs. The analysis unit can also analyze ball movements using deep learning. For example, the analysis unit can analyze player movements, positions, and ball movements using deep learning, and evaluate the team's overall tactics and the skills of individual players based on the results. This improves the accuracy of the analysis by using deep learning. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze player movements, positions, and ball movements using a deep learning model and output the results.

[0070] The suggestion unit can suggest a passing practice menu for a player with a low pass success rate. Calculation of the pass success rate includes, for example, a success rate threshold and a calculation period, but is not limited to these examples. The suggestion unit can suggest a passing practice menu for a player with a pass success rate of less than 50%. The suggestion unit can also suggest a practice menu based on the pass success rate over the past month. Furthermore, the suggestion unit can also suggest a practice menu based on the pass success rate in a specific game. For example, the suggestion unit can suggest a passing practice menu for a player with a low pass success rate and improve the player's skills based on the results. This makes it possible to suggest an appropriate practice menu for a player with a low pass success rate. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit inputs pass success rate data into AI, which then suggests an appropriate practice menu.

[0071] The suggestion unit can suggest defensive formation practice to a team with poor defensive positioning. Evaluation of defensive positioning includes, but is not limited to, positioning evaluation criteria and positioning analysis methods. For example, the suggestion unit can suggest defensive formation practice to a team with poor defensive positioning. The suggestion unit can also suggest a practice menu based on defensive positioning in a specific game. Furthermore, the suggestion unit can evaluate defensive positioning based on past game data and suggest a practice menu. For example, the suggestion unit can suggest defensive formation practice to a team with poor defensive positioning, and improve the defensive ability of the entire team based on the evaluation results. This makes it possible to suggest an appropriate practice menu to a team with poor defensive positioning. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit inputs defensive positioning data into AI, which then suggests an appropriate practice menu.

[0072] The suggestion unit can analyze the opposing team's offensive patterns and propose defensive tactics to counter them. Examples of analysis of offensive patterns include, but are not limited to, pattern recognition algorithms and types of attacks. For example, the suggestion unit can analyze the opposing team's offensive patterns and propose defensive tactics based on the analysis. The suggestion unit can also propose defensive tactics based on the opposing team's offensive patterns in a specific game. Furthermore, the suggestion unit can analyze the opposing team's offensive patterns based on past game data and propose defensive tactics. For example, the suggestion unit can analyze the opposing team's offensive patterns and propose defensive tactics to counter them, improving the team's overall defensive strength based on the results. This allows the suggestion unit to propose defensive tactics to counter the opposing team's offensive patterns. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit inputs data on the opposing team's offensive patterns into AI, which then proposes appropriate defensive tactics.

[0073] The execution unit can execute the proposed practice menu or tactics. Examples of the execution of a practice menu include, but are not limited to, the type of practice and the purpose of the practice. The execution unit, for example, executes the proposed practice menu. The execution unit can also execute the proposed tactics. Furthermore, the execution unit can provide instructions for executing a specific practice menu or tactics. For example, the execution unit executes the proposed practice menu and aims to improve the player's skills based on the results. This allows the proposed practice menu or tactics to be executed. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit inputs data on the proposed practice menu or tactics into AI, which then provides an appropriate execution method.

[0074] The analysis unit can estimate the player's emotions and adjust the accuracy of the analysis based on the estimated player's emotions. Estimation of the player's emotions includes, but is not limited to, facial expression analysis and voice analysis. For example, if the player is nervous, the analysis unit can increase the accuracy of the analysis and provide detailed feedback. Furthermore, if the player is relaxed, the analysis unit can adjust the accuracy of the analysis and evaluate the player's overall performance. Furthermore, if the player is tired, the analysis unit can decrease the accuracy of the analysis and provide simple feedback. For example, the analysis unit can estimate the player's emotions and adjust the accuracy of the analysis based on the estimation result. This allows for more appropriate analysis results to be provided by adjusting the analysis accuracy according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit inputs the player's emotional data into the generation AI, which then estimates the emotion and adjusts the accuracy of the analysis based on the results.

[0075] When analyzing game footage, the analysis unit can correct the analysis results by taking into account the player's physical strength and fatigue level. Evaluations of physical strength and fatigue level include, but are not limited to, heart rate, endurance tests, lactate levels, and subjective fatigue assessments. For example, the analysis unit acquires the player's heart rate data and corrects the analysis results if the player's fatigue level is high. The analysis unit can also refer to the player's running distance data and correct the analysis results if the player's physical strength is depleted. Furthermore, the analysis unit can take the player's rest time into consideration and correct the analysis results if the player's rest time is short. For example, the analysis unit corrects the analysis results by taking into account the player's physical strength and fatigue level, and provides more accurate analysis results based on the correction results. By taking the player's physical strength and fatigue level into consideration, more accurate analysis results can be provided. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit inputs the player's physical strength and fatigue level data into the generation AI, which corrects the analysis results.

[0076] When analyzing game footage, the analysis unit can correct the analysis results by taking into account the weather and pitch conditions. Evaluations of the weather and pitch conditions include, but are not limited to, sunny, rainy, wind speed, turf condition, and humidity. For example, the analysis unit corrects the analysis results by taking into account the slipperiness of the surface when it is raining. The analysis unit can also correct the analysis results by taking into account the ease with which the ball rolls when the pitch is dry. Furthermore, the analysis unit can correct the analysis results by taking into account changes in the ball's trajectory when there is strong wind. For example, the analysis unit corrects the analysis results by taking into account the weather and pitch conditions and provides more accurate analysis results based on the correction results. In this way, by taking into account the weather and pitch conditions, more accurate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs data on the weather and pitch conditions into the generation AI, and the generation AI corrects the analysis results.

[0077] When analyzing game footage, the analysis unit can improve analysis accuracy by referring to a player's past performance data. Past performance data includes, but is not limited to, game records, training data, and injury history. For example, the analysis unit can improve analysis accuracy by referring to a player's past game data and comparing it with their current performance. The analysis unit can also improve analysis accuracy by referring to a player's past practice data and taking into account technical advances. Furthermore, the analysis unit can improve analysis accuracy by referring to a player's past injury history and taking into account the impact of injuries. For example, the analysis unit can improve analysis accuracy by referring to a player's past performance data and improving analysis accuracy based on the results. Thus, by referring to a player's past performance data, analysis accuracy can be improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit inputs a player's past performance data into a generation AI, which then improves analysis accuracy.

[0078] The analysis unit can estimate the player's emotions and adjust the display method of the analysis results based on the estimated player's emotions. Examples of display methods for the analysis results include, but are not limited to, graph display and text display. For example, if the player is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the player is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the player is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, the analysis unit can estimate the player's emotions and adjust the display method of the analysis results based on the estimation result. This allows for more appropriate feedback to be provided by adjusting the display method of the analysis results according to the player's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, for example, 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 processing in the analysis unit may be performed using, for example, an AI, or without an AI. For example, the analysis unit inputs the player's emotional data into the generation AI, which then adjusts how the analysis results are displayed.

[0079] When analyzing game footage, the analysis unit can apply different analysis algorithms to each player's position. Analysis algorithms applied to each position include, but are not limited to, algorithms for defenders and forwards. For example, the analysis unit can apply an analysis algorithm that emphasizes shooting accuracy and pass success rate to forward players. The analysis unit can also apply an analysis algorithm that emphasizes defensive positioning and tackle success rate to defenders. The analysis unit can also apply an analysis algorithm that emphasizes save rate and positioning to goalkeepers. For example, the analysis unit can apply different analysis algorithms to each player's position and provide more accurate analysis results based on the results. This allows for more accurate analysis results by performing appropriate analysis for each player's position. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit inputs player position data into a generation AI, which then applies an appropriate analysis algorithm.

[0080] When analyzing game footage, the analysis unit can correct the analysis results based on the opposing team's tactics. Analysis of the opposing team's tactics includes, but is not limited to, the type of tactics and evaluation criteria for the tactics. For example, the analysis unit analyzes the opposing team's offensive patterns and corrects the analysis results of the team's defense based on the analysis results. The analysis unit can also analyze the opposing team's defensive patterns and correct the analysis results of the team's offensive patterns based on the analysis results. Furthermore, the analysis unit can analyze the opposing team's formation and correct the analysis results of the team's overall tactics based on the analysis results. For example, the analysis unit analyzes the opposing team's tactics and corrects the analysis results based on the analysis results. This allows for more accurate analysis results to be provided by taking the opposing team's tactics into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the opposing team's tactical data into a generation AI, and the generation AI corrects the analysis results.

[0081] When analyzing game footage, the analysis unit can improve analysis accuracy by referring to the player's physiological data. Physiological data includes, but is not limited to, heart rate, oxygen intake, and muscle fatigue data. For example, the analysis unit can improve analysis accuracy by referring to the player's heart rate data and taking into account the player's fatigue level. The analysis unit can also improve analysis accuracy by referring to the player's oxygen intake data and taking into account the player's physical exhaustion. Furthermore, the analysis unit can improve analysis accuracy by referring to the player's muscle fatigue data and taking into account a decline in performance. For example, the analysis unit can improve analysis accuracy by referring to the player's physiological data and improving analysis accuracy based on the results. Thus, by referring to the player's physiological data, analysis accuracy can be improved. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the player's physiological data into a generation AI, which then improves analysis accuracy.

[0082] The result providing unit can estimate the instructor's emotions and adjust the result presentation method based on the estimated instructor's emotions. Estimation of the instructor's emotions includes, but is not limited to, facial expression analysis and voice analysis. For example, if the instructor is nervous, the result providing unit can provide a simple, highly visible result display method. Furthermore, if the instructor is relaxed, the result providing unit can provide a result display method that includes detailed information. Furthermore, if the instructor is in a hurry, the result providing unit can provide a result display method that focuses on the main points. For example, the result providing unit can estimate the instructor's emotions and adjust the result presentation method based on the estimation result. This allows for more appropriate feedback to be provided by adjusting the result presentation method according to the instructor's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, 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 processing in the result providing unit can be performed using, for example, AI, or without AI. For example, the result providing unit inputs the instructor's emotional data into the generation AI, which then adjusts how the results are provided.

[0083] When providing a result, the result providing unit can adjust the level of detail of the result depending on the player's level of understanding. Evaluations of the player's level of understanding include, but are not limited to, test results and feedback. For example, the result providing unit can provide detailed analysis results if the player's level of understanding is high. Furthermore, the result providing unit can provide concise, to-the-point analysis results if the player's level of understanding is low. Furthermore, the result providing unit can provide analysis results using diagrams or videos depending on the player's level of understanding. For example, the result providing unit can evaluate the player's level of understanding and adjust the level of detail of the result based on the evaluation result. This allows for more appropriate feedback to be provided by adjusting the level of detail of the result depending on the player's level of understanding. Some or all of the above-described processing in the result providing unit may be performed using, for example, AI, or may be performed without AI. For example, the result providing unit inputs the player's level of understanding data into a generation AI, which then adjusts the level of detail of the result.

[0084] When providing results, the result providing unit can determine the priority of results based on the importance of the match. Evaluation of the importance of a match includes, but is not limited to, league matches and tournament matches. For example, the result providing unit prioritizes the provision of important match results and performs detailed analysis. The result providing unit can also perform simple analysis of less important match results and postpone them. Furthermore, the result providing unit can adjust the order in which analysis results are provided depending on the importance of the match. For example, the result providing unit evaluates the importance of a match and determines the priority of results based on the evaluation result. Thus, by determining the priority of results based on the importance of a match, appropriate feedback can be provided for important matches. Some or all of the above-described processing in the result providing unit may be performed using, or without, AI. For example, the result providing unit inputs match importance data to a generation AI, which then determines the priority of the results.

[0085] When providing results, the result providing unit can refer to past game results to improve the reliability of the results. Past game results include, but are not limited to, scores and game details. For example, the result providing unit can refer to past game results and compare them with current analysis results to improve reliability. The result providing unit can also correct current analysis results based on data obtained from past game results. Furthermore, the result providing unit can refer to past game results and analyze trends to improve the reliability of current analysis results. For example, the result providing unit can refer to past game results and improve the reliability of current analysis results based on the results. In this way, the reliability of results can be improved by referring to past game results. Some or all of the above-described processing in the result providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the result providing unit inputs past game result data into a generation AI, which then improves the reliability of the results.

[0086] The result providing unit can estimate the instructor's emotions and adjust the display order of the results based on the estimated emotions of the instructor. The display order of the results can include, but is not limited to, order of importance or chronological order. For example, if the instructor is nervous, the result providing unit can display important results first. Furthermore, if the instructor is relaxed, the result providing unit can sequentially display detailed results. Furthermore, if the instructor is in a hurry, the result providing unit can display results that emphasize the main points first. For example, the result providing unit can estimate the instructor's emotions and adjust the display order of the results based on the estimation result. This allows for more appropriate feedback to be provided by adjusting the display order of the results according to the instructor's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, 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 processing in the result providing unit can be performed using, for example, an AI, or without an AI. For example, the result providing unit inputs the instructor's emotional data into the generation AI, which then adjusts the display order of the results.

[0087] When providing results, the result providing unit can apply different result display methods to each player's position. Result display methods applied to each position include, but are not limited to, those for defenders and forwards. For example, the result providing unit can provide a result display method that emphasizes shooting accuracy and pass success rate to forward players. The result providing unit can also provide a result display method that emphasizes defensive positioning and tackle success rate to defenders. The result providing unit can also provide a result display method that emphasizes save rate and positioning to goalkeepers. For example, the result providing unit can apply different result display methods to each player's position and provide more accurate feedback based on the results. This allows for more accurate feedback by providing an appropriate result display method for each player's position. Some or all of the above-described processing by the result providing unit can be performed using, or without, AI. For example, the result providing unit inputs player position data to a generation AI, which then applies an appropriate result display method.

[0088] When providing results, the result providing unit can adjust the display method of the results based on the situation of the game. Examples of the situation of the game include, but are not limited to, home and away. For example, in the case of a home game, the result providing unit displays the analysis results taking into account the influence of spectators. In addition, in the case of an away game, the result providing unit can also display the analysis results taking into account the influence of travel. Furthermore, the result providing unit can adjust the display method of the analysis results according to the situation of the game. For example, the result providing unit evaluates the situation of the game and adjusts the display method of the results based on the evaluation result. In this way, more appropriate feedback can be provided by adjusting the display method of the results according to the situation of the game. Some or all of the above-mentioned processing in the result providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the result providing unit inputs game situation data to a generation AI, and the generation AI adjusts the display method of the results.

[0089] When providing results, the result providing unit can customize the display method of the results by reflecting player feedback. Player feedback includes, but is not limited to, questionnaire results and verbal feedback. The result providing unit, for example, customizes the display method of the analysis results based on the player feedback. The result providing unit can also change the display format of the analysis results according to the player's preferences. Furthermore, the result providing unit can reflect the player's feedback and provide a highly visible display method. For example, the result providing unit evaluates the player's feedback and customizes the display method of the results based on the evaluation result. This allows for a more appropriate display method of the results by reflecting the player's feedback. Some or all of the above-described processing in the result providing unit may be performed using, or without, AI. For example, the result providing unit inputs the player's feedback data into the generation AI, which then customizes the display method of the results.

[0090] The suggestion unit can estimate the player's emotions and adjust the content of the suggestions based on the estimated player's emotions. The content of the suggestions includes, but is not limited to, practice menus and tactical suggestions. For example, if the player is nervous, the suggestion unit can suggest a relaxing practice menu. If the player is relaxed, the suggestion unit can also suggest a challenging practice menu. If the player is tired, the suggestion unit can also suggest a lighter practice menu. For example, the suggestion unit can estimate the player's emotions and adjust the content of the suggestions based on the results. By adjusting the content of the suggestions according to the player's emotions, a more appropriate practice menu can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 processing in the suggestion unit may be performed using, for example, an AI. For example, the suggestion unit inputs the player's emotion data into the generation AI, which then adjusts the content of the suggestions.

[0091] When proposing a practice menu, the suggestion unit can adjust the difficulty level of the practice menu according to the player's skill level. Skill level evaluations include, but are not limited to, skill test results and coach evaluations. For example, the suggestion unit can suggest a practice menu for beginner players to master basic skills. For intermediate players, the suggestion unit can also suggest a practice menu aimed at improving skills. For advanced players, the suggestion unit can also suggest a practice menu for honing advanced skills. For example, the suggestion unit can evaluate the player's skill level and adjust the difficulty level of the practice menu based on the evaluation results. This allows for providing a more appropriate practice menu by adjusting the difficulty level of the practice menu according to the player's skill level. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit inputs the player's skill level data into a generation AI, which then adjusts the difficulty level of the practice menu.

[0092] When making a suggestion, the suggestion unit can customize the suggestion content for each player based on the tactics of the entire team. Examples of the tactics of the entire team include, but are not limited to, offensive tactics and defensive tactics. For example, the suggestion unit can suggest an offensive practice menu to a forward player based on the offensive tactics of the team. The suggestion unit can also suggest a defensive practice menu to a defender based on the defensive tactics of the team. Furthermore, the suggestion unit can suggest a practice menu for a goalkeeper that corresponds to a specific situation based on the tactics of the team. For example, the suggestion unit can evaluate the tactics of the entire team and customize the suggestion content for each player based on the evaluation results. This allows for the provision of a more appropriate practice menu. Some or all of the above-described processing by the suggestion unit may be performed using, or without, an AI. For example, the suggestion unit inputs tactical data of the entire team into a generation AI, which then customizes the suggestion content for each player.

[0093] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to past proposal results. Past proposal results include, but are not limited to, examples of the success rate of the proposal and feedback results. For example, the suggestion unit refers to past proposal results and corrects the current proposal content. The suggestion unit can also optimize the current proposal content based on data obtained from the past proposal results. Furthermore, the suggestion unit can analyze past proposal results, grasp trends, and improve the accuracy of the current proposal content. For example, the suggestion unit refers to past proposal results and improves the accuracy of the current proposal content based on the results. In this way, the accuracy of the proposal can be improved by referring to the past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit inputs past proposal result data into a generation AI, which then improves the accuracy of the proposal.

[0094] The suggestion unit can estimate the player's emotions and prioritize suggestions based on the estimated player's emotions. Examples of suggestion priorities include, but are not limited to, importance and urgency. For example, if the player is nervous, the suggestion unit prioritizes relaxing suggestions. Furthermore, if the player is relaxed, the suggestion unit can prioritize challenging suggestions. Furthermore, if the player is tired, the suggestion unit can prioritize lighter suggestions. For example, the suggestion unit estimates the player's emotions and prioritizes suggestions based on the results. By prioritizing suggestions based on the player's emotions, a more appropriate practice menu can be provided. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI. For example, the suggestion unit inputs the player's emotion data into the generation AI, which then prioritizes the suggestions.

[0095] When making the proposal, the suggestion unit can propose different practice menus for each player's position. Practice menus applicable to each position include, but are not limited to, those for defenders and forwards. For example, the suggestion unit can propose shooting practice and passing practice to forward players. The suggestion unit can also propose defensive positioning and tackling practice to defenders. The suggestion unit can also propose save practice and positioning practice to goalkeepers. For example, the suggestion unit can propose different practice menus for each player's position and provide more accurate feedback based on the results. This allows for more accurate feedback by providing an appropriate practice menu for each player's position. Some or all of the above-described processing by the suggestion unit may be performed using, or without, an AI. For example, the suggestion unit inputs player position data into a generation AI, which then proposes an appropriate practice menu.

[0096] When making a proposal, the suggestion unit can adjust the proposal content based on the situation of the game. Examples of the situation of the game include, but are not limited to, home and away games. For example, in the case of a home game, the suggestion unit can adjust the proposal content taking into account the influence of spectators. Furthermore, in the case of an away game, the suggestion unit can also adjust the proposal content taking into account the influence of travel. Furthermore, the suggestion unit can optimize the proposal content according to the situation of the game. For example, the suggestion unit evaluates the situation of the game and adjusts the proposal content based on the evaluation result. By adjusting the proposal content according to the situation of the game, a more appropriate practice menu can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit inputs game situation data into a generation AI, which then adjusts the proposal content.

[0097] When making a suggestion, the suggestion unit can customize the suggestion content by reflecting the player's feedback. Examples of the player's feedback include, but are not limited to, questionnaire results and verbal feedback. The suggestion unit customizes the suggestion content based on, for example, the player's feedback. The suggestion unit can also change the suggestion content according to the player's preferences. Furthermore, the suggestion unit can reflect the player's feedback and provide optimal suggestion content. For example, the suggestion unit evaluates the player's feedback and customizes the suggestion content based on the evaluation result. This allows the suggestion content to be more appropriate by reflecting the player's feedback. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit inputs the player's feedback data into a generation AI, which then customizes the suggestion content.

[0098] The execution unit can estimate the player's emotions and adjust the execution method of the practice menu based on the estimated player's emotions. Examples of the execution method of the practice menu include, but are not limited to, execution procedures and execution environments. For example, if the player is nervous, the execution unit executes a relaxing practice menu. Furthermore, if the player is relaxed, the execution unit can execute a challenging practice menu. Furthermore, if the player is tired, the execution unit can execute a lighter practice menu. For example, the execution unit estimates the player's emotions and adjusts the execution method of the practice menu based on the result. This allows for more appropriate practice by adjusting the execution method of the practice menu according to the player's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 processing in the execution unit may be performed using, for example, an AI. For example, the execution unit inputs the player's emotion data into the generation AI, and the generation AI adjusts the execution method of the practice menu.

[0099] When executing a practice menu, the execution unit can adjust the execution content taking into account the player's physical strength and fatigue level. Evaluations of physical strength and fatigue level include, but are not limited to, heart rate, endurance tests, lactate levels, and subjective fatigue level assessments. For example, the execution unit acquires the player's heart rate data and reduces the practice menu if the player's fatigue level is high. The execution unit can also refer to the player's running distance data and adjust the practice menu if the player's physical strength is depleted. Furthermore, the execution unit can take the player's rest time into consideration and shorten the practice menu if the player's rest time is short. For example, the execution unit adjusts the execution content taking into account the player's physical strength and fatigue level, and provides more appropriate practice based on the results. This allows for more appropriate practice by taking the player's physical strength and fatigue level into consideration. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without AI. For example, the execution unit inputs the player's physical strength and fatigue level data into a generation AI, and the generation AI adjusts the execution content.

[0100] When executing a practice menu, the execution unit can adjust the execution content taking into account the weather and pitch conditions. Evaluations of the weather and pitch conditions include, but are not limited to, sunny, rainy, wind speed, turf condition, and humidity. For example, the execution unit executes an indoor practice menu when it is raining. Furthermore, when the pitch is dry, the execution unit can adjust the practice menu taking into account the ease of ball rolling. Furthermore, when there is strong wind, the execution unit can adjust the practice menu taking into account the influence of wind. For example, the execution unit adjusts the execution content taking into account the weather and pitch conditions and provides more appropriate practice based on the results. In this way, more appropriate practice can be provided by taking the weather and pitch conditions into account. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit inputs data on the weather and pitch conditions into a generation AI, and the generation AI adjusts the execution content.

[0101] When executing a practice menu, the execution unit can optimize the execution content by referring to the player's past performance data. Past performance data includes, but is not limited to, game records, training data, and injury history. For example, the execution unit can optimize the current practice menu by referring to the player's past game data. The execution unit can also optimize the practice menu by referring to the player's past practice data and taking into account technical advances. Furthermore, the execution unit can optimize the practice menu by referring to the player's past injury history and taking into account the impact of the injury. For example, the execution unit can reference the player's past performance data and optimize the practice menu based on the results. In this way, the execution content of the practice menu can be optimized by referring to the player's past performance data. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit inputs the player's past performance data into a generation AI, and the generation AI optimizes the execution content.

[0102] The execution unit can estimate the player's emotions and adjust the execution order of the practice menus based on the estimated player's emotions. The execution order of the practice menus can include, but is not limited to, order of importance or chronological order. For example, if the player is nervous, the execution unit can execute a relaxing practice menu first. Furthermore, if the player is relaxed, the execution unit can execute a challenging practice menu first. Furthermore, if the player is tired, the execution unit can execute a lighter practice menu first. For example, the execution unit can estimate the player's emotions and adjust the execution order of the practice menus based on the result. This allows for more appropriate practice by adjusting the execution order of the practice menus according to the player's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the execution unit can be performed using, for example, an AI, or without an AI. For example, the execution unit inputs the player's emotional data into the generation AI, which then adjusts the execution order of the practice menu.

[0103] When executing a practice menu, the execution unit can apply different execution methods to each player's position. Execution methods applied to each position include, but are not limited to, for example, defenders and forwards. For example, the execution unit can focus on shooting practice and passing practice for forward players. The execution unit can also focus on defensive positioning and tackling practice for defenders. Furthermore, the execution unit can focus on saving practice and positioning practice for goalkeepers. For example, the execution unit can apply different execution methods to each player's position and provide more accurate feedback based on the results. This allows for more accurate feedback by providing an appropriate execution method for each player's position. Some or all of the above-described processing in the execution unit can be performed using, for example, AI, or without AI. For example, the execution unit inputs player position data into a generation AI, which then applies an appropriate execution method.

[0104] When executing a practice menu, the execution unit can adjust the execution content based on the tactics of the opposing team. Analysis of the opposing team's tactics includes, for example, the type of tactics and tactical evaluation criteria, but is not limited to these examples. For example, the execution unit can analyze the opposing team's offensive patterns and execute a defensive practice menu based on the analysis. The execution unit can also analyze the opposing team's defensive patterns and execute an offensive practice menu based on the analysis. Furthermore, the execution unit can analyze the opposing team's formation and execute a practice menu of overall tactics based on the analysis. For example, the execution unit can analyze the opposing team's tactics and adjust the execution content based on the results. This allows for more appropriate practice by taking the opposing team's tactics into consideration. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit inputs the opposing team's tactical data into a generation AI, and the generation AI adjusts the execution content.

[0105] When executing a practice menu, the execution unit can optimize the execution content by referring to the player's physiological data. Physiological data includes, but is not limited to, heart rate, oxygen intake, and muscle fatigue data. For example, the execution unit can optimize the practice menu by referring to the player's heart rate data and taking into account the player's fatigue level. The execution unit can also optimize the practice menu by referring to the player's oxygen intake data and taking into account the player's physical exhaustion. Furthermore, the execution unit can optimize the practice menu by referring to the player's muscle fatigue data and taking into account a decline in performance. For example, the execution unit can optimize the practice menu by referring to the player's physiological data and optimizing the practice menu based on the results. In this way, the execution content of the practice menu can be optimized by referring to the player's physiological data. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit inputs the player's physiological data into a generation AI, and the generation AI optimizes the execution content. === Hard Collateral 1-1 === Each of the multiple elements, including the analysis unit, result providing unit, suggestion unit, and execution unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit can acquire game footage using the camera 42 of the smart device 14, and analyze player movements and positions and ball movement using video analysis technology by the specific processing unit 290 of the data processing device 12. The result providing unit can display the analysis results in real time using, for example, the display 40A of the smart device 14 and provide feedback to the coach. For example, the suggestion unit can suggest practice menus and tactics based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the execution unit can execute the practice menus and tactics suggested by, for example, the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned analysis unit, result providing unit, suggestion unit, and execution unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit can acquire game video using the camera 42 of the smart glasses 214 and analyze player movements and positions and ball movement using video analysis technology by the specific processing unit 290 of the data processing device 12. The result providing unit can display the analysis results in real time using, for example, the display of the smart glasses 214 and provide feedback to the coach. For example, the suggestion unit can suggest a training menu or tactics based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the execution unit can execute the training menu or tactics suggested by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, result providing unit, suggestion unit, and execution unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the analysis unit can acquire game video using the camera 42 of the headset terminal 314 and analyze the movements and positions of players and the movement of the ball using video analysis technology by the specific processing unit 290 of the data processing device 12. The result providing unit can display the analysis results in real time using, for example, the display 343 of the headset terminal 314 and provide feedback to the trainer. For example, the suggestion unit can suggest a practice menu or tactics based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the execution unit can execute the practice menu or tactics suggested by the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, result providing unit, suggestion unit, and execution unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit can acquire game footage using the camera 42 of the robot 414, and analyze the movements and positions of players and the movement of the ball using video analysis technology by the specific processing unit 290 of the data processing device 12. The result providing unit can display the analysis results in real time using the display of the robot 414, for example, and provide feedback to the trainer. The suggestion unit can suggest, for example, a training menu or tactics based on the analysis results by the specific processing unit 290 of the data processing device 12. The execution unit can execute, for example, the training menu or tactics suggested by the control unit 46A of the robot 414.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] When analyzing game footage, the analysis unit can estimate the player's psychological state and correct the analysis results based on the estimated psychological state. For example, if the player is nervous, the analysis unit can correct the analysis results taking that influence into account. Also, if the player is relaxed, the analysis unit can adjust the analysis results to reflect that state. Furthermore, if the player is concentrating, the analysis unit can refine the analysis results by taking that level of concentration into account. This allows for more accurate analysis results to be provided by taking the player's psychological state into account.

[0108] The suggestion unit can also estimate the player's emotions and adjust the suggestion content based on the estimated emotions. For example, if the player is depressed, the suggestion unit can suggest a practice menu to increase motivation. Also, if the player is excited, the suggestion unit can suggest a practice menu to keep the player calm. Furthermore, if the player is tired, the suggestion unit can suggest a recovery menu. In this way, it is possible to provide an appropriate practice menu according to the player's emotions.

[0109] The execution unit can also estimate the player's emotions and adjust the way the practice menu is executed based on the estimated emotions. For example, if the player is nervous, the execution unit executes practice in a relaxing environment. Also, if the player is relaxed, the execution unit can execute a challenging practice menu. Furthermore, if the player is tired, the execution unit can execute a lighter practice menu. In this way, an appropriate practice environment can be provided according to the player's emotions.

[0110] The result providing unit can also estimate the emotions of the instructor and adjust the way in which the results are presented based on the estimated emotions. For example, if the instructor is nervous, the result providing unit can provide a simple, highly visible result display method. If the instructor is relaxed, the result providing unit can provide a result display method that includes detailed information. Furthermore, if the instructor is in a hurry, the result display method can provide a result display method that focuses on the main points. This makes it possible to provide appropriate feedback according to the instructor's emotions.

[0111] The suggestion unit can also estimate the player's emotions and prioritize suggestions based on the estimated emotions. For example, if the player is nervous, it can prioritize relaxing suggestions. Also, if the player is relaxed, it can prioritize challenging suggestions. Furthermore, if the player is tired, it can prioritize lighter suggestions. In this way, it is possible to provide appropriate suggestions according to the player's emotions.

[0112] When analyzing game footage, the analysis unit can also improve the accuracy of the analysis by referring to the player's physiological data. For example, it can refer to heart rate, oxygen intake, muscle fatigue data, etc., and correct the analysis results by taking into account the player's physical strength and fatigue level. It can also refer to the player's past physiological data and compare it with their current performance to improve the accuracy of the analysis. Furthermore, it can obtain the player's physiological data in real time and adjust the analysis results based on that data. This allows for more accurate analysis results to be provided by taking the player's physiological data into account.

[0113] The suggestion unit can also adjust the difficulty of the practice menu according to the skill level of the player. For example, it can suggest a practice menu for beginner players to master basic skills. It can also suggest a practice menu aimed at improving skills to intermediate players. It can also suggest a practice menu for advanced players to hone advanced skills. This makes it possible to provide an appropriate practice menu according to the skill level of the player.

[0114] When executing a practice menu, the execution unit can also adjust the execution content taking into account the weather and pitch conditions. For example, when it rains, the practice menu is executed indoors. Also, when the pitch is dry, the practice menu can be adjusted taking into account the ease with which the ball rolls. Furthermore, when there is strong wind, the practice menu can be adjusted taking into account the influence of wind. In this way, more appropriate practice can be provided by taking into account the weather and pitch conditions.

[0115] The result providing unit can also determine the priority of results based on the importance of the match. For example, the results of important matches can be provided first and analyzed in detail. The results of less important matches can also be analyzed simply and postponed. Furthermore, the order in which analysis results are provided can be adjusted depending on the importance of the match. In this way, by determining the priority of results based on the importance of the match, appropriate feedback can be provided for important matches.

[0116] The proposal unit can also improve the accuracy of proposals by referring to past proposal results. For example, it can refer to past proposal results and correct the current proposal content. It can also optimize the current proposal content based on data obtained from past proposal results. It can also analyze past proposal results, understand trends, and improve the accuracy of the current proposal content. In this way, it is possible to improve the accuracy of proposals by referring to past proposal results.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The analysis unit analyzes game footage. Game footage includes, but is not limited to, soccer, basketball, baseball, and other sports. The analysis unit uses video analysis technology to analyze player movements, positions, and ball movement. It can also use data analysis techniques to evaluate the team's overall tactics and the skills of individual players. For example, it analyzes a player's pass success rate, shooting accuracy, defensive positioning, etc. Step 2: The results provider provides the results analyzed by the analysis unit. The results provider can provide the analysis results in report format or display the analysis results in real time. For example, the results can be displayed in real time during a match to provide feedback to the coach. Step 3: The suggestion unit proposes training menus and tactics based on the results provided by the result providing unit. For example, it can propose a passing training menu for a player with a low pass success rate, or a defensive formation training for a team with poor defensive positioning. It can also analyze the opposing team's attacking patterns and propose defensive tactics to counter them. Step 4: The execution unit executes the training menu or tactics proposed by the proposal unit. For example, the execution unit executes the proposed training menu or executes the proposed tactics. This allows the proposed defensive tactics to be executed.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis unit that analyzes game footage; a result providing unit that provides the results analyzed by the analysis unit; a suggestion unit that suggests a training menu or tactics based on the results provided by the result providing unit; an implementation unit that implements the practice menu or tactics suggested by the suggestion unit; A system characterized by:

2. The analysis unit Using deep learning to analyze player movements, positions, and ball movement 2. The system of claim 1.

3. The proposal unit Propose a passing practice menu for players with low pass success rates 2. The system of claim 1.

4. The proposal unit Suggest defensive formation drills for teams with poor defensive positioning 2. The system of claim 1.

5. The proposal unit Analyze the opposing team's attacking patterns and propose defensive tactics to counter them 2. The system of claim 1.

6. The execution unit: Implement the suggested training and tactics 2. The system of claim 1.

7. The analysis unit Estimate the player's emotions and adjust the accuracy of the analysis based on the estimated player's emotions.

2. The system of claim 1.

8. The analysis unit When analyzing game footage, the analysis results are adjusted to take into account the player's physical strength or fatigue level.

2. The system of claim 1.

Images