System

Abstract

An object of a system according to an embodiment is to make an optimal training plan for each player and provide a customized training menu.SOLUTION: A system according to an embodiment includes a training plan drafting unit, a training menu providing unit, and a performance monitoring unit. The training plan creation unit analyzes the player data and creates an optimal training plan for each player. The training menu providing unit provides a training menu customized for each player based on the training plan drafted by the training plan drafting unit. The performance monitoring unit monitors the performance of the player based on the training menu provided by the training menu providing unit and provides feedback in real time.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 has the problem that player development plans are uniform and do not provide optimal development for individual players.

[0005] The system according to the embodiment aims to create an optimal development plan for each individual player and provide a customized training menu. [Means for solving the problem]

[0006] The system according to the embodiment includes a development plan creation unit, a training menu providing unit, and a performance monitoring unit. The development plan creation unit analyzes player data and creates an optimal development plan for each player. The training menu providing unit provides a training menu customized for each player based on the development plan created by the development plan creation unit. The performance monitoring unit monitors the player's performance based on the training menu provided by the training menu providing unit and provides feedback in real time. [Effects of the Invention]

[0007] The system according to the embodiment can create an optimal development plan for each individual player and provide a customized training menu. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The training system according to an embodiment of the present invention analyzes player data, uses a generation AI to create a training plan, provides training menus, and monitors performance, thereby enabling the training system to effectively support the growth of players.

[0029] A development system according to an embodiment includes a development plan creation unit, a training menu provision unit, and a performance monitoring unit. The development plan creation unit analyzes player data and creates a development plan optimized for each individual player. For example, the development plan creation unit creates a training menu and practice schedule based on the player's physical ability, technical level, past performance, etc. The development plan creation unit also supports the player's mental health by using a generation AI to create a development plan that takes into account the player's psychological state and motivation. The development plan creation unit also uses a generation AI to create a comprehensive development plan, including the player's diet and nutritional management, to promote both internal and external physical growth. The training menu provision unit provides a customized training menu for each player based on the development plan created by the development plan creation unit. For example, the training menu provision unit suggests specific batting drills and practice methods to improve the player's batting technique. The training menu provision unit also suggests specific pitching drills and practice methods to improve the player's pitching technique. Furthermore, the training menu providing unit measures the player's swing speed and pitching speed and points out areas for improvement. The performance monitoring unit monitors the player's performance based on the training menu provided by the training menu providing unit and provides feedback in real time. For example, the performance monitoring unit measures the player's swing speed and pitching speed and points out areas for improvement. The performance monitoring unit also analyzes play during a game and provides tactical advice. Furthermore, the performance monitoring unit collects player feedback in real time, and the generation AI dynamically adjusts the training menu based on that feedback. This allows the development system according to the embodiment to effectively support the growth of players.

[0030] The development plan creation unit can create training menus and practice schedules based on the player's physical ability, technical level, and past performance. For example, the development plan creation unit collects the player's physical ability data, and the generation AI creates an optimal training menu based on that data. For example, it suggests strength training and endurance training. The development plan creation unit also evaluates the player's technical level, and the generation AI creates a practice schedule to improve that skill based on that. For example, it suggests batting drills and pitching drills. Furthermore, the development plan creation unit analyzes the player's past performance data, and the generation AI creates a training menu to improve performance based on that data. For example, it provides a review of the game and tactical advice. In this way, by creating training menus and practice schedules based on the player's physical ability, technical level, and past performance, it is possible to provide an optimal development plan for each individual player.

[0031] The training menu providing unit can suggest specific batting drills or practice methods aimed at improving a player's batting technique. For example, the training menu providing unit evaluates the player's batting technique, and the generation AI uses that to suggest specific batting drills. For example, it can suggest tee batting or live pitching. The training menu providing unit can also analyze the player's swing form, and the generation AI can use that to point out areas for improvement in the swing. For example, it can correct the swing timing or the bat trajectory. The training menu providing unit can also analyze the player's batting data, and the generation AI can use that to suggest practice methods aimed at improving the player's batting technique. For example, it can suggest how to respond to a specific pitch or training to increase the distance the ball travels. This can support the player's improvement by suggesting specific batting drills or practice methods aimed at improving the player's batting technique.

[0032] The performance monitoring unit can measure a player's swing speed or pitching speed and point out areas for improvement. For example, the performance monitoring unit measures a player's swing speed in real time, and the generating AI points out areas for improvement based on that data. For example, it suggests modifications to swing timing or form. The performance monitoring unit also measures pitching speed, and the generating AI points out areas for improvement in pitching form based on that data. For example, it suggests adjustments to arm swing or weight transfer. The performance monitoring unit also analyzes swing speed and pitching speed data, and the generating AI provides specific advice to improve the player's performance. For example, it suggests exercises for strength training and flexibility improvement. In this way, by measuring a player's swing speed and pitching speed and pointing out areas for improvement, it is possible to support the player's performance improvement.

[0033] The development plan creation unit generates a comprehensive development plan that includes a player's diet and nutritional management, promoting the player's internal and external physical growth. For example, the development plan creation unit collects a player's nutritional data, and the generation AI incorporates that data into the development plan to create an optimal meal plan. For example, a high-protein diet is suggested for a player aiming to build muscle. The development plan creation unit also considers the balance between diet and training, and the generation AI creates a nutritional supplementation plan based on the player's physical condition and training content. For example, it adjusts the meal content before and after a game. The development plan creation unit also monitors a player's dietary records in real time, and the generation AI dynamically adjusts the development plan based on that data. For example, if a nutritional deficiency is detected, it suggests the intake of supplements. This allows the system to generate a comprehensive development plan that includes a player's diet and nutritional management, promoting internal and external growth and supporting the player's overall development.

[0034] The development plan creation unit can incorporate development methods from other sports or the entertainment industry to generate development plans that utilize knowledge from different fields. For example, the development plan creation unit incorporates training methods from other sports, and the generation AI creates a development plan based on that. For example, soccer fitness training is applied to baseball player development. The development plan creation unit also incorporates performance improvement methods from the entertainment industry, and the generation AI creates a development plan based on that. For example, mental training for stage actors is applied to player development. The development plan creation unit also creates a database of knowledge from experts in different fields, and the generation AI creates a development plan based on that. For example, a method for improving musicians' concentration is applied to player development. In this way, by incorporating development methods from other sports or the entertainment industry and generating a development plan that utilizes knowledge from different fields, it is possible to promote the multifaceted growth of players.

[0035] The development plan creation unit can incorporate feedback from the player's family and coach and optimize the development plan from multiple angles. The development plan creation unit, for example, collects feedback from the player's family, and the generation AI optimizes the development plan based on that. For example, it proposes a training menu that suits the player's home environment and lifestyle. The development plan creation unit also collects feedback from coaches in real time, and the generation AI dynamically adjusts the development plan based on that. For example, it adds a training menu that suits the coach's instruction content. The development plan creation unit also integrates feedback from the player's family and coach, and the generation AI optimizes the development plan from multiple angles based on that. For example, it proposes a training menu that can be carried out with the support of the family. In this way, by incorporating feedback from the player's family and coach and optimizing the development plan from multiple angles, it is possible to support the player's overall growth.

[0036] The training menu providing unit can analyze the genetic information of an athlete and generate a development plan based on the athlete's genetic characteristics. For example, the training menu providing unit collects the athlete's genetic information, and the generation AI uses that information to create an optimal training menu. For example, it suggests training based on muscle growth rate and recovery ability. The training menu providing unit also analyzes the genetic characteristics, and the generation AI uses that information to create a training menu that maximizes the athlete's strengths. For example, it suggests long training sessions for athletes with high endurance. The training menu providing unit also uses the genetic information to create a training menu that compensates for the athlete's weaknesses. For example, it may strengthen stretching for athletes with genetically low flexibility. In this way, by analyzing the athlete's genetic information and generating a development plan based on their genetic characteristics, the athlete's strengths can be maximized.

[0037] The training menu providing unit can analyze an athlete's sleep patterns and stress levels and suggest optimal training timing. For example, the training menu providing unit collects the athlete's sleep data, and the generation AI uses that data to suggest optimal training timing. For example, training requiring concentration can be performed the day after a deep sleep. The training menu providing unit also analyzes stress levels, and the generation AI uses that data to adjust the intensity and timing of training. For example, lighter training is suggested when stress is high. The training menu providing unit also performs an integrated analysis of sleep patterns and stress levels, and the generation AI uses that data to plan an optimal training schedule. For example, if sleep deprivation continues, rest can be prioritized. This allows the athlete's performance to be maximized by analyzing their sleep patterns and stress levels and suggesting optimal training timing.

[0038] The training menu providing unit can incorporate training menus from other sports or fitness and recommend cross-training. For example, the training menu providing unit incorporates training menus from other sports, and the generation AI suggests cross-training based on that. For example, soccer fitness training can be applied to developing baseball players. The training menu providing unit can also incorporate fitness training menus, and the generation AI can use that to improve a player's physical strength. For example, weight training and aerobic exercise can be combined. The training menu providing unit can also integrate training menus from different sports or fitness, and the generation AI can recommend cross-training based on that. For example, agility training for basketball can be incorporated. In this way, by incorporating training menus from other sports and fitness and recommending cross-training, a player's overall physical strength can be improved.

[0039] The training menu providing unit can gamify the training menu, allowing players to train while competing against each other. For example, the training menu providing unit gamifies the training menu, and the generation AI uses it to allow players to train while competing against each other. For example, scores and rankings can be introduced to stimulate a competitive spirit. The training menu providing unit also provides a training menu that incorporates game elements, and the generation AI uses it to increase the players' motivation. For example, rewards can be set according to the level of training achievement. The training menu providing unit also gamifies the training menu, and the generation AI uses it to allow players to train while having fun. For example, training can be simulated using virtual reality. In this way, gamifying the training menu and allowing players to train while competing against each other can increase the players' motivation and improve the training effect.

[0040] The performance monitoring unit can analyze plays during a game and provide tactical advice. For example, the performance monitoring unit collects play data during a game in real time, and the generating AI provides tactical advice based on that data. For example, it suggests adjustments to defensive positions or changes to batting strategies. The performance monitoring unit also analyzes players' movements during a game, and the generating AI uses that data to point out areas for tactical improvement. For example, it suggests base running timing and defensive positioning. The performance monitoring unit also analyzes play data during a game, and the generating AI provides specific tactical advice to improve players' performance. For example, it suggests strategies to exploit the opposing team's weaknesses. In this way, by analyzing plays during a game and providing tactical advice, players' performance can be improved.

[0041] The performance monitoring unit collects player feedback in real time, and the generation AI can dynamically adjust the training menu based on that. For example, the performance monitoring unit collects player feedback in real time, and the generation AI dynamically adjusts the training menu based on that. For example, the difficulty and content of training may be changed based on the player's opinion. The performance monitoring unit also analyzes the feedback data, and the generation AI proposes a specific training menu to improve the player's performance. For example, training may be added to strengthen areas in which the player is weak. The performance monitoring unit also optimizes the training menu in real time based on the player's feedback. For example, the training content may be adjusted according to the player's physical condition and fatigue level. In this way, the performance monitoring unit collects player feedback in real time, and the generation AI dynamically adjusts the training menu based on that, thereby optimizing the player's performance.

[0042] The performance monitoring unit can compare a player's performance data with other players and set standards. For example, the performance monitoring unit compares a player's performance data with other players, and the generation AI sets standards based on that. For example, a goal is set based on the data of the top player in the same position. The performance monitoring unit also analyzes the performance data, and the generation AI proposes specific standards to promote the player's growth. For example, the degree of growth is evaluated by comparing with past data. The performance monitoring unit also sets standards for improving a player's performance based on the performance data of other players. For example, a goal is set to encourage competition within the same team. In this way, the player's growth can be promoted by comparing a player's performance data with other players and setting standards.

[0043] The performance monitoring unit can analyze a player's performance data and predict long-term growth trends. For example, the performance monitoring unit collects a player's performance data over the long term, and the generation AI predicts growth trends based on that data. For example, it predicts future performance based on past data. The performance monitoring unit also analyzes growth trends, and the generation AI creates a development plan that takes the player's entire career into consideration. For example, it proposes a plan to concentrate training at a specific period. The performance monitoring unit also proposes a specific training menu based on the performance data to promote the player's long-term growth. For example, it introduces new training during a period when growth is stagnating. In this way, by analyzing a player's performance data and predicting long-term growth trends, it is possible to create a development plan that takes the player's entire career into consideration.

[0044] The development plan creation unit can set the steps and goals necessary for a player to reach a professional level and adjust training and match schedules based on those steps. For example, the development plan creation unit sets specific steps and goals for a player to reach a professional level, and the generation AI creates a training schedule based on those steps. For example, it sets step-by-step goals for mastering a specific skill. The development plan creation unit also adjusts the player's match schedule based on a long-term development plan. For example, it adjusts the intensity of training leading up to an important match. The development plan creation unit also creates a development plan that takes the player's entire career into account, and the generation AI dynamically adjusts training and match schedules based on that plan. For example, it introduces new training when growth is stagnating. This makes it possible to effectively support a player's growth by setting the steps and goals necessary for a player to reach a professional level and adjusting training and match schedules based on those steps and goals.

[0045] The development plan creation unit creates a development plan that takes into account the player's entire career, and the generation AI can dynamically adjust training and match schedules based on that. For example, the development plan creation unit creates a development plan that takes into account the player's entire career, and the generation AI can dynamically adjust training and match schedules based on that. For example, new training can be introduced during periods when growth is stagnating. The development plan creation unit also adjusts the player's match schedule based on the long-term development plan. For example, it adjusts the intensity of training leading up to important matches. The development plan creation unit also sets specific steps and goals for the player to reach a professional level, and the generation AI can create a training schedule based on that. For example, it sets step-by-step goals for mastering a specific skill. In this way, the development plan creation unit creates a development plan that takes into account the player's entire career, and the generation AI can dynamically adjust training and match schedules based on that, thereby supporting the player's long-term growth.

[0046] The development plan creation unit can incorporate development methods from other sports or the entertainment industry to generate long-term development plans that utilize knowledge from different fields. For example, the development plan creation unit incorporates development methods from other sports, and the generation AI creates a long-term development plan based on that. For example, soccer fitness training is applied to baseball player development. The development plan creation unit also incorporates performance improvement methods from the entertainment industry, and the generation AI creates a long-term development plan based on that. For example, mental training for stage actors is applied to player development. The development plan creation unit also compiles a database of knowledge from experts in different fields, and the generation AI creates a long-term development plan based on that. For example, a method for improving musicians' concentration is applied to player development. In this way, by incorporating development methods from other sports and the entertainment industry and generating a long-term development plan that utilizes knowledge from different fields, it is possible to promote the multifaceted growth of players.

[0047] The development plan creation unit incorporates feedback from the player's family and coach, and can optimize the long-term development plan from multiple angles. The development plan creation unit, for example, collects feedback from the player's family, and the generation AI optimizes the long-term development plan based on that. For example, it proposes training menus that suit the player's home environment and lifestyle. The development plan creation unit also collects feedback from coaches in real time, and the generation AI dynamically adjusts the long-term development plan based on that. For example, it adds training menus that match the coach's instruction content. The development plan creation unit also integrates feedback from the player's family and coach, and the generation AI uses that to optimize the long-term development plan from multiple angles. For example, it proposes training menus that can be carried out with the support of the family. In this way, by incorporating feedback from the player's family and coach and optimizing the long-term development plan from multiple angles, it is possible to support the player's overall growth.

[0048] The development plan formulation unit can identify the team's strengths and weaknesses and adjust player development plans based on that. For example, the development plan formulation unit analyzes the performance data of the entire team, and the generation AI identifies the team's strengths and weaknesses. For example, if the team's defensive ability is weak, defensive training will be strengthened. Furthermore, the development plan formulation unit adjusts the development plans for individual players based on the team's strengths and weaknesses. For example, it suggests focused training for players in specific positions. Furthermore, the development plan formulation unit analyzes the performance data of the entire team in real time, and the generation AI dynamically adjusts the development plan based on that. For example, it changes the training content depending on the results of a match. In this way, the team's strengths and weaknesses can be identified and the player development plans adjusted based on that, thereby improving the performance of the entire team.

[0049] The development plan formulation unit can propose tactics and training methods to improve the performance of the entire team. For example, the development plan formulation unit analyzes the performance data of the entire team, and the generation AI proposes tactics and training methods based on that. For example, it proposes tactics to increase offensive power. In addition, the development plan formulation unit proposes specific training methods to improve the team's performance. For example, it proposes fitness training to be conducted by the entire team. In addition, the development plan formulation unit analyzes the performance data of the entire team in real time, and the generation AI dynamically adjusts the tactics and training methods based on that. For example, it proposes tactical changes during a game. In this way, by proposing tactics and training methods to improve the performance of the entire team, the team's competitiveness can be increased.

[0050] The development plan formulation unit can incorporate development methods from other sports or the entertainment industry and propose tactics and training methods that utilize knowledge from different fields. For example, the development plan formulation unit incorporates development methods from other sports, and the generation AI proposes tactics and training methods based on that. For example, soccer fitness training is applied to the development of a baseball team. The development plan formulation unit also incorporates performance improvement methods from the entertainment industry, and the generation AI proposes tactics and training methods based on that. For example, mental training for stage actors is applied to team development. The development plan formulation unit also creates a database of knowledge from experts in different fields, and the generation AI proposes tactics and training methods based on that. For example, methods for improving musicians' concentration are applied to team development. In this way, team performance can be improved by incorporating development methods from other sports or the entertainment industry and proposing tactics and training methods that utilize knowledge from different fields.

[0051] The development plan creation unit incorporates feedback from the player's family and coach, and can optimize tactics and training methods from multiple angles. The development plan creation unit, for example, collects feedback from the player's family, and the generation AI optimizes tactics and training methods based on that. For example, it proposes training menus that suit the player's home environment and lifestyle. The development plan creation unit also collects feedback from coaches in real time, and the generation AI dynamically adjusts tactics and training methods based on that. For example, it adds training menus that suit the coach's instruction content. The development plan creation unit also integrates feedback from the player's family and coach, and the generation AI optimizes tactics and training methods from multiple angles based on that. For example, it proposes training menus that can be carried out with the support of the family. In this way, by incorporating feedback from the player's family and coach, and optimizing tactics and training methods from multiple angles, it is possible to improve team performance.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The development plan creation unit can also analyze a player's genetic information and generate a development plan based on their genetic characteristics. For example, the genetic information of the player is collected and the generation AI uses it to create an optimal training menu. For example, it can suggest training based on muscle growth rate and recovery ability. The genetic characteristics can also be analyzed and the generation AI can use that information to create a training menu that maximizes the player's strengths. For example, it can suggest long training sessions for players with high endurance. The generation AI can also use the genetic information to create a training menu to compensate for a player's weaknesses. For example, it can strengthen stretching for players with genetically low flexibility. In this way, by analyzing a player's genetic information and generating a development plan based on their genetic characteristics, it is possible to maximize a player's strengths.

[0054] The training menu provider can also analyze an athlete's sleep patterns and stress levels to suggest optimal training timing. For example, the AI ​​collects the athlete's sleep data and uses it to suggest optimal training timing. For example, the AI ​​could perform training requiring concentration the day after a deep sleep. The AI ​​can also analyze stress levels and use that information to adjust the intensity and timing of training. For example, it could suggest lighter training when stress is high. The AI ​​can also perform an integrated analysis of sleep patterns and stress levels and use that information to create an optimal training schedule. For example, it could prioritize rest if sleep deprivation persists. This allows the AI ​​to analyze a player's sleep patterns and stress levels and suggest optimal training timing, thereby maximizing the athlete's performance.

[0055] The development plan creation unit can also incorporate development methods from other sports or the entertainment industry to generate development plans that utilize knowledge from other fields. For example, training methods from other sports are incorporated, and the generation AI creates a development plan based on that. For example, soccer fitness training can be applied to baseball player development. Performance improvement methods from the entertainment industry can also be incorporated, and the generation AI can create a development plan based on that. For example, mental training for stage actors can be applied to player development. The knowledge of experts from other fields can also be compiled into a database, and the generation AI can create a development plan based on that. For example, methods for improving musicians' concentration can be applied to player development. In this way, by incorporating development methods from other sports and the entertainment industry and generating development plans that utilize knowledge from other fields, it is possible to promote the multifaceted growth of players.

[0056] The development plan planning unit can also incorporate feedback from players' families and coaches to optimize the development plan from multiple angles. For example, feedback from players' families is collected, and the generation AI uses this information to optimize the development plan. For example, it can suggest training menus that suit the player's home environment and lifestyle. Feedback from coaches is also collected in real time, and the generation AI uses this information to dynamically adjust the development plan. For example, it can add training menus that match the coach's instruction content. Feedback from families and coaches is also integrated, and the generation AI uses this information to optimize the development plan from multiple angles. For example, it can suggest training menus that can be carried out with the support of family members. In this way, by incorporating feedback from players' families and coaches and optimizing the development plan from multiple angles, it is possible to support the player's overall growth.

[0057] The development plan planning unit can also identify the team's strengths and weaknesses and adjust player development plans based on that. For example, the generation AI analyzes the performance data of the entire team and identifies the team's strengths and weaknesses. For example, if the team's defensive strength is weak, it will strengthen defensive training. The generation AI also adjusts individual player development plans based on the team's strengths and weaknesses. For example, it suggests focused training for players in specific positions. The generation AI also analyzes the performance data of the entire team in real time and dynamically adjusts the development plan based on that. For example, it changes the training content depending on the results of a match. In this way, the team's strengths and weaknesses can be identified and the player development plans adjusted based on that, thereby improving the performance of the entire team.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The development plan planning unit analyzes player data and creates the optimal development plan for each individual player. For example, it creates training menus and practice schedules based on the player's physical ability, technical level, past performance, etc. The AI ​​also creates development plans that take into account the player's psychological state and motivation, providing mental support for the player. The AI ​​also creates comprehensive development plans that include diet and nutritional management, promoting growth both inside and outside the body. Step 2: The training menu provision department provides a customized training menu for each player based on the development plan drawn up by the development plan planning department. For example, it proposes specific batting drills and practice methods to improve batting technique, and specific pitching drills and practice methods to improve pitching technique. It also measures swing speed and pitching speed and points out areas for improvement. Step 3: The performance monitoring unit monitors the player's performance based on the training menu provided by the training menu provider and provides feedback in real time. For example, it measures swing speed and pitching speed and points out areas for improvement. It also analyzes play during games and provides tactical advice. Furthermore, it collects player feedback in real time, and the generation AI dynamically adjusts the training menu based on that feedback.

[0060] (Example 2) The training system according to an embodiment of the present invention analyzes player data, uses a generation AI to create a training plan, provides training menus, and monitors performance, thereby enabling the training system to effectively support the growth of players.

[0061] A development system according to an embodiment includes a development plan creation unit, a training menu provision unit, and a performance monitoring unit. The development plan creation unit analyzes player data and creates a development plan optimized for each individual player. For example, the development plan creation unit creates a training menu and practice schedule based on the player's physical ability, technical level, past performance, etc. The development plan creation unit also supports the player's mental health by using a generation AI to create a development plan that takes into account the player's psychological state and motivation. The development plan creation unit also uses a generation AI to create a comprehensive development plan, including the player's diet and nutritional management, to promote both internal and external physical growth. The training menu provision unit provides a customized training menu for each player based on the development plan created by the development plan creation unit. For example, the training menu provision unit suggests specific batting drills and practice methods to improve the player's batting technique. The training menu provision unit also suggests specific pitching drills and practice methods to improve the player's pitching technique. Furthermore, the training menu providing unit measures the player's swing speed and pitching speed and points out areas for improvement. The performance monitoring unit monitors the player's performance based on the training menu provided by the training menu providing unit and provides feedback in real time. For example, the performance monitoring unit measures the player's swing speed and pitching speed and points out areas for improvement. The performance monitoring unit also analyzes play during a game and provides tactical advice. Furthermore, the performance monitoring unit collects player feedback in real time, and the generation AI dynamically adjusts the training menu based on that feedback. This allows the development system according to the embodiment to effectively support the growth of players.

[0062] The development plan creation unit can create training menus and practice schedules based on the player's physical ability, technical level, and past performance. For example, the development plan creation unit collects the player's physical ability data, and the generation AI creates an optimal training menu based on that data. For example, it suggests strength training and endurance training. The development plan creation unit also evaluates the player's technical level, and the generation AI creates a practice schedule to improve that skill based on that. For example, it suggests batting drills and pitching drills. Furthermore, the development plan creation unit analyzes the player's past performance data, and the generation AI creates a training menu to improve performance based on that data. For example, it provides a review of the game and tactical advice. In this way, by creating training menus and practice schedules based on the player's physical ability, technical level, and past performance, it is possible to provide an optimal development plan for each individual player.

[0063] The training menu providing unit can suggest specific batting drills or practice methods aimed at improving a player's batting technique. For example, the training menu providing unit evaluates the player's batting technique, and the generation AI uses that to suggest specific batting drills. For example, it can suggest tee batting or live pitching. The training menu providing unit can also analyze the player's swing form, and the generation AI can use that to point out areas for improvement in the swing. For example, it can correct the swing timing or the bat trajectory. The training menu providing unit can also analyze the player's batting data, and the generation AI can use that to suggest practice methods aimed at improving the player's batting technique. For example, it can suggest how to respond to a specific pitch or training to increase the distance the ball travels. This can support the player's improvement by suggesting specific batting drills or practice methods aimed at improving the player's batting technique.

[0064] The performance monitoring unit can measure a player's swing speed or pitching speed and point out areas for improvement. For example, the performance monitoring unit measures a player's swing speed in real time, and the generating AI points out areas for improvement based on that data. For example, it suggests modifications to swing timing or form. The performance monitoring unit also measures pitching speed, and the generating AI points out areas for improvement in pitching form based on that data. For example, it suggests adjustments to arm swing or weight transfer. The performance monitoring unit also analyzes swing speed and pitching speed data, and the generating AI provides specific advice to improve the player's performance. For example, it suggests exercises for strength training and flexibility improvement. In this way, by measuring a player's swing speed and pitching speed and pointing out areas for improvement, it is possible to support the player's performance improvement.

[0065] The development plan creation unit generates development plans that take into account the player's psychological state and motivation, thereby supporting the player's mental health. For example, the development plan creation unit collects data on the player's psychological test results and daily mental health, and the generation AI analyzes the data and reflects it in the development plan. For example, for a player with a high stress level, the generation AI incorporates relaxation training. To maintain the player's motivation, the development plan creation unit periodically incorporates goal setting and tasks that give the player a sense of accomplishment into the development plan. For example, setting short-term goals and achieving them helps the player gain confidence. The development plan creation unit also monitors the player's psychological state in real time, and the generation AI dynamically adjusts the development plan based on that data. For example, for a player who is nervous before a game, mental training to help them relax is added. This allows the creation of development plans that take into account the player's psychological state and motivation, and by providing mental support, the overall development of the player can be promoted.

[0066] The development plan creation unit generates a comprehensive development plan that includes a player's diet and nutritional management, promoting the player's internal and external physical growth. For example, the development plan creation unit collects a player's nutritional data, and the generation AI incorporates that data into the development plan to create an optimal meal plan. For example, a high-protein diet is suggested for a player aiming to build muscle. The development plan creation unit also considers the balance between diet and training, and the generation AI creates a nutritional supplementation plan based on the player's physical condition and training content. For example, it adjusts the meal content before and after a game. The development plan creation unit also monitors a player's dietary records in real time, and the generation AI dynamically adjusts the development plan based on that data. For example, if a nutritional deficiency is detected, it suggests the intake of supplements. This allows the system to generate a comprehensive development plan that includes a player's diet and nutritional management, promoting internal and external growth and supporting the player's overall development.

[0067] The development plan creation unit can monitor a player's emotional state in real time using the emotion estimation function and dynamically adjust the development plan according to the player's emotional state. The development plan creation unit, for example, analyzes a player's facial expressions and voice and monitors the player's emotional state in real time using the emotion estimation function. For example, it detects tension or anxiety during a game and suggests training to help them relax. The development plan creation unit also uses the emotion estimation data to dynamically adjust the development plan according to the player's emotional state. For example, when positive emotions are strong, it adds challenging training. The development plan creation unit also provides feedback on the player's emotional state in real time, and the generation AI optimizes the development plan based on that data. For example, when emotions are stable, it suggests training to improve concentration. In this way, by monitoring a player's emotional state in real time and dynamically adjusting the development plan according to their emotions, it is possible to support the player's mental health and achieve optimal development.

[0068] The development plan creation unit can incorporate development methods from other sports or the entertainment industry to generate development plans that utilize knowledge from different fields. For example, the development plan creation unit incorporates training methods from other sports, and the generation AI creates a development plan based on that. For example, soccer fitness training is applied to baseball player development. The development plan creation unit also incorporates performance improvement methods from the entertainment industry, and the generation AI creates a development plan based on that. For example, mental training for stage actors is applied to player development. The development plan creation unit also creates a database of knowledge from experts in different fields, and the generation AI creates a development plan based on that. For example, a method for improving musicians' concentration is applied to player development. In this way, by incorporating development methods from other sports or the entertainment industry and generating a development plan that utilizes knowledge from different fields, it is possible to promote the multifaceted growth of players.

[0069] The development plan creation unit can incorporate feedback from the player's family and coach and optimize the development plan from multiple angles. The development plan creation unit, for example, collects feedback from the player's family, and the generation AI optimizes the development plan based on that. For example, it proposes a training menu that suits the player's home environment and lifestyle. The development plan creation unit also collects feedback from coaches in real time, and the generation AI dynamically adjusts the development plan based on that. For example, it adds a training menu that suits the coach's instruction content. The development plan creation unit also integrates feedback from the player's family and coach, and the generation AI optimizes the development plan from multiple angles based on that. For example, it proposes a training menu that can be carried out with the support of the family. In this way, by incorporating feedback from the player's family and coach and optimizing the development plan from multiple angles, it is possible to support the player's overall growth.

[0070] The development plan creation unit can use the emotion estimation function to consider the emotions of the player's family and coaches and develop a development plan that strengthens the overall support system. For example, the development plan creation unit monitors the emotional state of the player's family and coaches using the emotion estimation function, and the generation AI uses that information to create a development plan. For example, if the family is feeling stressed, support is strengthened. The development plan creation unit also uses the emotional data of the family and coaches to create a development plan that strengthens the overall support system. For example, when the family's emotions are stable, the generation AI suggests challenging training for the player. The development plan creation unit also uses the emotion estimation function to provide real-time feedback on the emotional states of the family and coaches, and the generation AI uses that feedback to optimize the development plan. For example, when the coach is feeling positive, the generation AI teaches the player new techniques. This allows the development plan to strengthen the overall support system by considering the emotions of the player's family and coaches, thereby supporting the player's overall growth.

[0071] The training menu providing unit can analyze the genetic information of an athlete and generate a development plan based on the athlete's genetic characteristics. For example, the training menu providing unit collects the athlete's genetic information, and the generation AI uses that information to create an optimal training menu. For example, it suggests training based on muscle growth rate and recovery ability. The training menu providing unit also analyzes the genetic characteristics, and the generation AI uses that information to create a training menu that maximizes the athlete's strengths. For example, it suggests long training sessions for athletes with high endurance. The training menu providing unit also uses the genetic information to create a training menu that compensates for the athlete's weaknesses. For example, it may strengthen stretching for athletes with genetically low flexibility. In this way, by analyzing the athlete's genetic information and generating a development plan based on their genetic characteristics, the athlete's strengths can be maximized.

[0072] The training menu providing unit can analyze an athlete's sleep patterns and stress levels and suggest optimal training timing. For example, the training menu providing unit collects the athlete's sleep data, and the generation AI uses that data to suggest optimal training timing. For example, training requiring concentration can be performed the day after a deep sleep. The training menu providing unit also analyzes stress levels, and the generation AI uses that data to adjust the intensity and timing of training. For example, lighter training is suggested when stress is high. The training menu providing unit also performs an integrated analysis of sleep patterns and stress levels, and the generation AI uses that data to plan an optimal training schedule. For example, if sleep deprivation continues, rest can be prioritized. This allows the athlete's performance to be maximized by analyzing their sleep patterns and stress levels and suggesting optimal training timing.

[0073] The training menu providing unit can use the emotion estimation function to analyze the player's emotional data and provide a training menu that corresponds to their emotional fluctuations. For example, the training menu providing unit collects the player's emotional data in real time, and the generation AI adjusts the training menu based on that data. For example, when positive emotions are strong, it suggests challenging training. The training menu providing unit also uses the emotion estimation function to analyze the player's emotional fluctuations, and the generation AI dynamically adjusts the training menu based on that. For example, when negative emotions are strong, it suggests training to help players relax. The training menu providing unit also uses the emotion data to provide a training menu that corresponds to the player's emotional state. For example, when emotions are stable, it suggests training to improve concentration. In this way, by analyzing the player's emotional data and providing a training menu that corresponds to their emotional fluctuations, it is possible to maintain the player's motivation and achieve optimal training.

[0074] The training menu providing unit can incorporate training menus from other sports or fitness and recommend cross-training. For example, the training menu providing unit incorporates training menus from other sports, and the generation AI suggests cross-training based on that. For example, soccer fitness training can be applied to developing baseball players. The training menu providing unit can also incorporate fitness training menus, and the generation AI can use that to improve a player's physical strength. For example, weight training and aerobic exercise can be combined. The training menu providing unit can also integrate training menus from different sports or fitness, and the generation AI can recommend cross-training based on that. For example, agility training for basketball can be incorporated. In this way, by incorporating training menus from other sports and fitness and recommending cross-training, a player's overall physical strength can be improved.

[0075] The training menu providing unit can gamify the training menu, allowing players to train while competing against each other. For example, the training menu providing unit gamifies the training menu, and the generation AI uses it to allow players to train while competing against each other. For example, scores and rankings can be introduced to stimulate a competitive spirit. The training menu providing unit also provides a training menu that incorporates game elements, and the generation AI uses it to increase the players' motivation. For example, rewards can be set according to the level of training achievement. The training menu providing unit also gamifies the training menu, and the generation AI uses it to allow players to train while having fun. For example, training can be simulated using virtual reality. In this way, gamifying the training menu and allowing players to train while competing against each other can increase the players' motivation and improve the training effect.

[0076] The training menu providing unit uses the emotion estimation function to adjust the difficulty of the training menu based on the player's emotions, thereby maintaining motivation. The training menu providing unit, for example, uses the emotion estimation function to adjust the difficulty of the training menu according to the player's emotional state. For example, the difficulty is increased when positive emotions are strong. Furthermore, the training menu providing unit uses the emotion estimation function to analyze the player's emotional fluctuations, and the generation AI dynamically adjusts the difficulty of the training menu based on that. For example, the difficulty is increased when emotions are stable. In this way, the difficulty of the training menu based on the player's emotions using the emotion estimation function to maintain motivation, thereby maximizing the training effect of the player.

[0077] The performance monitoring unit can analyze plays during a game and provide tactical advice. For example, the performance monitoring unit collects play data during a game in real time, and the generating AI provides tactical advice based on that data. For example, it suggests adjustments to defensive positions or changes to batting strategies. The performance monitoring unit also analyzes players' movements during a game, and the generating AI uses that data to point out areas for tactical improvement. For example, it suggests base running timing and defensive positioning. The performance monitoring unit also analyzes play data during a game, and the generating AI provides specific tactical advice to improve players' performance. For example, it suggests strategies to exploit the opposing team's weaknesses. In this way, by analyzing plays during a game and providing tactical advice, players' performance can be improved.

[0078] The performance monitoring unit collects player feedback in real time, and the generation AI can dynamically adjust the training menu based on that. For example, the performance monitoring unit collects player feedback in real time, and the generation AI dynamically adjusts the training menu based on that. For example, the difficulty and content of training may be changed based on the player's opinion. The performance monitoring unit also analyzes the feedback data, and the generation AI proposes a specific training menu to improve the player's performance. For example, training may be added to strengthen areas in which the player is weak. The performance monitoring unit also optimizes the training menu in real time based on the player's feedback. For example, the training content may be adjusted according to the player's physical condition and fatigue level. In this way, the performance monitoring unit collects player feedback in real time, and the generation AI dynamically adjusts the training menu based on that, thereby optimizing the player's performance.

[0079] The performance monitoring unit can compare a player's performance data with other players and set standards. For example, the performance monitoring unit compares a player's performance data with other players, and the generation AI sets standards based on that. For example, a goal is set based on the data of the top player in the same position. The performance monitoring unit also analyzes the performance data, and the generation AI proposes specific standards to promote the player's growth. For example, the degree of growth is evaluated by comparing with past data. The performance monitoring unit also sets standards for improving a player's performance based on the performance data of other players. For example, a goal is set to encourage competition within the same team. In this way, the player's growth can be promoted by comparing a player's performance data with other players and setting standards.

[0080] The performance monitoring unit can analyze a player's performance data and predict long-term growth trends. For example, the performance monitoring unit collects a player's performance data over the long term, and the generation AI predicts growth trends based on that data. For example, it predicts future performance based on past data. The performance monitoring unit also analyzes growth trends, and the generation AI creates a development plan that takes the player's entire career into consideration. For example, it proposes a plan to concentrate training at a specific period. The performance monitoring unit also proposes a specific training menu based on the performance data to promote the player's long-term growth. For example, it introduces new training during a period when growth is stagnating. In this way, by analyzing a player's performance data and predicting long-term growth trends, it is possible to create a development plan that takes the player's entire career into consideration.

[0081] The performance monitoring unit can use the emotion estimation function to analyze the player's emotional state and provide feedback according to the emotion. For example, the performance monitoring unit uses the emotion estimation function to analyze the player's emotional state in real time, and the generation AI provides feedback based on that. For example, when positive emotions are strong, it provides words of praise. The performance monitoring unit also uses the emotion data to enable the generation AI to provide specific feedback according to the player's emotional state. For example, when negative emotions are strong, it provides words of encouragement. The performance monitoring unit also uses the emotion estimation function to analyze the player's emotional fluctuations, and the generation AI dynamically adjusts the feedback based on that. For example, when emotions are stable, it provides challenging feedback. In this way, by using the emotion estimation function to analyze the player's emotional state and providing feedback according to their emotions, it is possible to support the player's mental state and improve their performance.

[0082] The development plan creation unit can set the steps and goals necessary for a player to reach a professional level and adjust training and match schedules based on those steps. For example, the development plan creation unit sets specific steps and goals for a player to reach a professional level, and the generation AI creates a training schedule based on those steps. For example, it sets step-by-step goals for mastering a specific skill. The development plan creation unit also adjusts the player's match schedule based on a long-term development plan. For example, it adjusts the intensity of training leading up to an important match. The development plan creation unit also creates a development plan that takes the player's entire career into account, and the generation AI dynamically adjusts training and match schedules based on that plan. For example, it introduces new training when growth is stagnating. This makes it possible to effectively support a player's growth by setting the steps and goals necessary for a player to reach a professional level and adjusting training and match schedules based on those steps and goals.

[0083] The development plan creation unit creates a development plan that takes into account the player's entire career, and the generation AI can dynamically adjust training and match schedules based on that. For example, the development plan creation unit creates a development plan that takes into account the player's entire career, and the generation AI can dynamically adjust training and match schedules based on that. For example, new training can be introduced during periods when growth is stagnating. The development plan creation unit also adjusts the player's match schedule based on the long-term development plan. For example, it adjusts the intensity of training leading up to important matches. The development plan creation unit also sets specific steps and goals for the player to reach a professional level, and the generation AI can create a training schedule based on that. For example, it sets step-by-step goals for mastering a specific skill. In this way, the development plan creation unit creates a development plan that takes into account the player's entire career, and the generation AI can dynamically adjust training and match schedules based on that, thereby supporting the player's long-term growth.

[0084] The development plan creation unit can use the emotion estimation function to analyze the player's emotional state and provide a long-term development plan based on the player's emotions. For example, the development plan creation unit uses the emotion estimation function to analyze the player's emotional state in real time, and the generation AI provides a long-term development plan based on that. For example, when positive emotions are strong, a challenging goal is set. The development plan creation unit also uses the emotion data to create a specific long-term development plan based on the player's emotional state. For example, when negative emotions are strong, relaxation training is added. The development plan creation unit also uses the emotion estimation function to analyze the player's emotional fluctuations, and the generation AI dynamically adjusts the long-term development plan based on that. For example, when emotions are stable, training to improve concentration is suggested. In this way, by using the emotion estimation function to analyze the player's emotional state and providing a long-term development plan based on emotions, it is possible to support the player's mental health and promote long-term growth.

[0085] The development plan creation unit can incorporate development methods from other sports or the entertainment industry to generate long-term development plans that utilize knowledge from different fields. For example, the development plan creation unit incorporates development methods from other sports, and the generation AI creates a long-term development plan based on that. For example, soccer fitness training is applied to baseball player development. The development plan creation unit also incorporates performance improvement methods from the entertainment industry, and the generation AI creates a long-term development plan based on that. For example, mental training for stage actors is applied to player development. The development plan creation unit also compiles a database of knowledge from experts in different fields, and the generation AI creates a long-term development plan based on that. For example, a method for improving musicians' concentration is applied to player development. In this way, by incorporating development methods from other sports and the entertainment industry and generating a long-term development plan that utilizes knowledge from different fields, it is possible to promote the multifaceted growth of players.

[0086] The development plan creation unit incorporates feedback from the player's family and coach, and can optimize the long-term development plan from multiple angles. The development plan creation unit, for example, collects feedback from the player's family, and the generation AI optimizes the long-term development plan based on that. For example, it proposes training menus that suit the player's home environment and lifestyle. The development plan creation unit also collects feedback from coaches in real time, and the generation AI dynamically adjusts the long-term development plan based on that. For example, it adds training menus that match the coach's instruction content. The development plan creation unit also integrates feedback from the player's family and coach, and the generation AI uses that to optimize the long-term development plan from multiple angles. For example, it proposes training menus that can be carried out with the support of the family. In this way, by incorporating feedback from the player's family and coach and optimizing the long-term development plan from multiple angles, it is possible to support the player's overall growth.

[0087] The development plan formulation unit can use the emotion estimation function to consider the emotions of the player's family and coaches and formulate long-term development plans that strengthen the overall support system. For example, the development plan formulation unit monitors the emotional state of the player's family and coaches using the emotion estimation function, and the generation AI formulates a long-term development plan based on that information. For example, if the family is feeling stressed, the generation AI strengthens support. Furthermore, the development plan formulation unit formulates a long-term development plan that strengthens the overall support system based on the emotional data of the family and coaches. For example, when the family's emotions are stable, the generation AI suggests challenging training for the player. Furthermore, the development plan formulation unit uses the emotion estimation function to provide real-time feedback on the emotional state of the family and coaches, and the generation AI optimizes the long-term development plan based on that feedback. For example, when the coach is feeling positive, the generation AI teaches the player new techniques. This allows the overall development of the player to be supported by formulating a long-term development plan that strengthens the overall support system, taking into account the emotions of the player's family and coaches.

[0088] The development plan formulation unit can identify the team's strengths and weaknesses and adjust player development plans based on that. For example, the development plan formulation unit analyzes the performance data of the entire team, and the generation AI identifies the team's strengths and weaknesses. For example, if the team's defensive ability is weak, defensive training will be strengthened. Furthermore, the development plan formulation unit adjusts the development plans for individual players based on the team's strengths and weaknesses. For example, it suggests focused training for players in specific positions. Furthermore, the development plan formulation unit analyzes the performance data of the entire team in real time, and the generation AI dynamically adjusts the development plan based on that. For example, it changes the training content depending on the results of a match. In this way, the team's strengths and weaknesses can be identified and the player development plans adjusted based on that, thereby improving the performance of the entire team.

[0089] The development plan formulation unit can propose tactics and training methods to improve the performance of the entire team. For example, the development plan formulation unit analyzes the performance data of the entire team, and the generation AI proposes tactics and training methods based on that. For example, it proposes tactics to increase offensive power. In addition, the development plan formulation unit proposes specific training methods to improve the team's performance. For example, it proposes fitness training to be conducted by the entire team. In addition, the development plan formulation unit analyzes the performance data of the entire team in real time, and the generation AI dynamically adjusts the tactics and training methods based on that. For example, it proposes tactical changes during a game. In this way, by proposing tactics and training methods to improve the performance of the entire team, the team's competitiveness can be increased.

[0090] The development plan formulation unit can use the emotion estimation function to analyze the emotional state of the entire team and propose tactics and training methods according to the emotions. For example, the development plan formulation unit uses the emotion estimation function to analyze the emotional state of the entire team in real time, and the generation AI proposes tactics and training methods based on that. For example, when positive emotions are strong, an aggressive tactic is proposed. Furthermore, the development plan formulation unit uses the emotion data to have the generation AI propose specific tactics and training methods according to the emotional state of the entire team. For example, when negative emotions are strong, training to relax is proposed. Furthermore, the development plan formulation unit uses the emotion estimation function to analyze emotional fluctuations of the entire team, and the generation AI dynamically adjusts tactics and training methods based on that. For example, when emotions are stable, training to improve concentration is proposed. In this way, team performance can be optimized by using the emotion estimation function to analyze the emotional state of the entire team and proposing tactics and training methods according to emotions.

[0091] The development plan formulation unit can incorporate development methods from other sports or the entertainment industry and propose tactics and training methods that utilize knowledge from different fields. For example, the development plan formulation unit incorporates development methods from other sports, and the generation AI proposes tactics and training methods based on that. For example, soccer fitness training is applied to the development of a baseball team. The development plan formulation unit also incorporates performance improvement methods from the entertainment industry, and the generation AI proposes tactics and training methods based on that. For example, mental training for stage actors is applied to team development. The development plan formulation unit also creates a database of knowledge from experts in different fields, and the generation AI proposes tactics and training methods based on that. For example, methods for improving musicians' concentration are applied to team development. In this way, team performance can be improved by incorporating development methods from other sports or the entertainment industry and proposing tactics and training methods that utilize knowledge from different fields.

[0092] The development plan creation unit incorporates feedback from the player's family and coach, and can optimize tactics and training methods from multiple angles. The development plan creation unit, for example, collects feedback from the player's family, and the generation AI optimizes tactics and training methods based on that. For example, it proposes training menus that suit the player's home environment and lifestyle. The development plan creation unit also collects feedback from coaches in real time, and the generation AI dynamically adjusts tactics and training methods based on that. For example, it adds training menus that suit the coach's instruction content. The development plan creation unit also integrates feedback from the player's family and coach, and the generation AI optimizes tactics and training methods from multiple angles based on that. For example, it proposes training menus that can be carried out with the support of the family. In this way, by incorporating feedback from the player's family and coach, and optimizing tactics and training methods from multiple angles, it is possible to improve team performance.

[0093] The development plan formulation unit can use the emotion estimation function to analyze the emotional state of the entire team and propose tactics and training methods according to the emotions. For example, the development plan formulation unit uses the emotion estimation function to analyze the emotional state of the entire team in real time, and the generation AI proposes tactics and training methods based on that. For example, when positive emotions are strong, an aggressive tactic is proposed. Furthermore, the development plan formulation unit uses the emotion data to have the generation AI propose specific tactics and training methods according to the emotional state of the entire team. For example, when negative emotions are strong, training to relax is proposed. Furthermore, the development plan formulation unit uses the emotion estimation function to analyze emotional fluctuations of the entire team, and the generation AI dynamically adjusts tactics and training methods based on that. For example, when emotions are stable, training to improve concentration is proposed. In this way, team performance can be optimized by using the emotion estimation function to analyze the emotional state of the entire team and proposing tactics and training methods according to emotions.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The development plan creation unit can also analyze a player's genetic information and generate a development plan based on their genetic characteristics. For example, the genetic information of the player is collected and the generation AI uses it to create an optimal training menu. For example, it can suggest training based on muscle growth rate and recovery ability. The genetic characteristics can also be analyzed and the generation AI can use that information to create a training menu that maximizes the player's strengths. For example, it can suggest long training sessions for players with high endurance. The generation AI can also use the genetic information to create a training menu to compensate for a player's weaknesses. For example, it can strengthen stretching for players with genetically low flexibility. In this way, by analyzing a player's genetic information and generating a development plan based on their genetic characteristics, it is possible to maximize a player's strengths.

[0096] The training menu provider can also analyze an athlete's sleep patterns and stress levels to suggest optimal training timing. For example, the AI ​​collects the athlete's sleep data and uses it to suggest optimal training timing. For example, the AI ​​could perform training requiring concentration the day after a deep sleep. The AI ​​can also analyze stress levels and use that information to adjust the intensity and timing of training. For example, it could suggest lighter training when stress is high. The AI ​​can also perform an integrated analysis of sleep patterns and stress levels and use that information to create an optimal training schedule. For example, it could prioritize rest if sleep deprivation persists. This allows the AI ​​to analyze a player's sleep patterns and stress levels and suggest optimal training timing, thereby maximizing the athlete's performance.

[0097] The development plan creation unit can also incorporate development methods from other sports or the entertainment industry to generate development plans that utilize knowledge from other fields. For example, training methods from other sports are incorporated, and the generation AI creates a development plan based on that. For example, soccer fitness training can be applied to baseball player development. Performance improvement methods from the entertainment industry can also be incorporated, and the generation AI can create a development plan based on that. For example, mental training for stage actors can be applied to player development. The knowledge of experts from other fields can also be compiled into a database, and the generation AI can create a development plan based on that. For example, methods for improving musicians' concentration can be applied to player development. In this way, by incorporating development methods from other sports and the entertainment industry and generating development plans that utilize knowledge from other fields, it is possible to promote the multifaceted growth of players.

[0098] The development plan planning unit can also incorporate feedback from players' families and coaches to optimize the development plan from multiple angles. For example, feedback from players' families is collected, and the generation AI uses this information to optimize the development plan. For example, it can suggest training menus that suit the player's home environment and lifestyle. Feedback from coaches is also collected in real time, and the generation AI uses this information to dynamically adjust the development plan. For example, it can add training menus that match the coach's instruction content. Feedback from families and coaches is also integrated, and the generation AI uses this information to optimize the development plan from multiple angles. For example, it can suggest training menus that can be carried out with the support of family members. In this way, by incorporating feedback from players' families and coaches and optimizing the development plan from multiple angles, it is possible to support the player's overall growth.

[0099] The development plan planning unit can also identify the team's strengths and weaknesses and adjust player development plans based on that. For example, the generation AI analyzes the performance data of the entire team and identifies the team's strengths and weaknesses. For example, if the team's defensive strength is weak, it will strengthen defensive training. The generation AI also adjusts individual player development plans based on the team's strengths and weaknesses. For example, it suggests focused training for players in specific positions. The generation AI also analyzes the performance data of the entire team in real time and dynamically adjusts the development plan based on that. For example, it changes the training content depending on the results of a match. In this way, the team's strengths and weaknesses can be identified and the player development plans adjusted based on that, thereby improving the performance of the entire team.

[0100] The development plan planning unit can also use the emotion estimation function to monitor a player's emotional state in real time and dynamically adjust the development plan according to their emotional state. For example, it can analyze a player's facial expressions and voice and use the emotion estimation function to monitor their emotional state in real time. For example, it can detect tension or anxiety during a game and suggest training to help them relax. Furthermore, based on the emotion estimation data, the generation AI dynamically adjusts the development plan according to the player's emotional state. For example, it can add challenging training when positive emotions are strong. It also provides feedback on the player's emotional state in real time, and the generation AI can optimize the development plan based on that data. For example, it can suggest training to improve concentration when emotions are stable. In this way, by monitoring a player's emotional state in real time and dynamically adjusting the development plan according to their emotions, it is possible to support the player's mental health and achieve optimal development.

[0101] The development plan planning unit can also use the emotion estimation function to consider the emotions of the player's family and coaches and develop development plans that strengthen the overall support system. For example, the emotion estimation function monitors the emotional state of the player's family and coaches, and the generation AI uses that information to develop a development plan. For example, if the family is feeling stressed, support will be strengthened. The generation AI also develops development plans that strengthen the overall support system based on the emotional data of the family and coaches. For example, when the family's emotions are stable, it suggests challenging training to the player. The emotion estimation function also provides real-time feedback on the emotional state of the family and coaches, and the generation AI uses that information to optimize the development plan. For example, when the coach is feeling positive, it will teach the player new techniques. This allows the system to develop development plans that strengthen the overall support system by considering the emotions of the player's family and coaches, thereby supporting the player's overall growth.

[0102] The training menu provision unit can also use the emotion estimation function to analyze the player's emotional data and provide a training menu that corresponds to their emotional fluctuations. For example, the player's emotional data is collected in real time, and the generation AI adjusts the training menu based on that. For example, when positive emotions are strong, it suggests challenging training. The emotion estimation function can also be used to analyze the player's emotional fluctuations, and the generation AI can dynamically adjust the training menu based on that. For example, when negative emotions are strong, it can suggest training to help them relax. The generation AI can also provide a training menu that corresponds to the player's emotional state based on the emotional data. For example, when emotions are stable, it can suggest training to improve concentration. In this way, by analyzing the player's emotional data and providing a training menu that corresponds to their emotional fluctuations, it is possible to maintain the player's motivation and achieve optimal training.

[0103] The development planning department can also use the emotion estimation function to analyze the emotional state of the entire team and propose tactics and training methods according to the emotions. For example, the emotion estimation function can be used to analyze the emotional state of the entire team in real time, and the generation AI can propose tactics and training methods based on that. For example, when positive emotions are strong, aggressive tactics can be proposed. Furthermore, based on the emotion data, the generation AI can propose specific tactics and training methods according to the emotional state of the entire team. For example, when negative emotions are strong, training to relax can be proposed. Furthermore, the emotion estimation function can be used to analyze the emotional fluctuations of the entire team, and the generation AI can dynamically adjust tactics and training methods based on that. For example, when emotions are stable, training to improve concentration can be proposed. In this way, by using the emotion estimation function to analyze the emotional state of the entire team and proposing tactics and training methods according to emotions, team performance can be optimized.

[0104] The development plan planning unit can also use the emotion estimation function to analyze a player's emotional state and provide a long-term development plan tailored to their emotions. For example, the emotion estimation function can be used to analyze a player's emotional state in real time, and the generation AI can then provide a long-term development plan based on that. For example, when positive emotions are strong, a challenging goal can be set. The generation AI can also provide a specific long-term development plan tailored to the player's emotional state based on the emotional data. For example, when negative emotions are strong, relaxation training can be added. The emotion estimation function can also be used to analyze a player's emotional fluctuations, and the generation AI can dynamically adjust the long-term development plan based on that. For example, when emotions are stable, training to improve concentration can be suggested. In this way, by using the emotion estimation function to analyze a player's emotional state and providing a long-term development plan tailored to their emotions, it is possible to support the player's mental health and promote their long-term growth.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The development plan planning unit analyzes player data and creates the optimal development plan for each individual player. For example, it creates training menus and practice schedules based on the player's physical ability, technical level, past performance, etc. The AI ​​also creates development plans that take into account the player's psychological state and motivation, providing mental support for the player. The AI ​​also creates comprehensive development plans that include diet and nutritional management, promoting growth both inside and outside the body. Step 2: The training menu provision department provides a customized training menu for each player based on the development plan drawn up by the development plan planning department. For example, it proposes specific batting drills and practice methods to improve batting technique, and specific pitching drills and practice methods to improve pitching technique. It also measures swing speed and pitching speed and points out areas for improvement. Step 3: The performance monitoring unit monitors the player's performance based on the training menu provided by the training menu provider and provides feedback in real time. For example, it measures swing speed and pitching speed and points out areas for improvement. It also analyzes play during games and provides tactical advice. Furthermore, it collects player feedback in real time, and the generation AI dynamically adjusts the training menu based on that feedback.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0151] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0174] 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. The Development Planning Department analyzes player data and creates the optimal development plan for each individual player. a training menu providing unit that provides a training menu customized for each player based on the development plan formulated by the development plan formulating unit; a performance monitoring unit that monitors the performance of the player based on the training menu provided by the training menu providing unit and provides feedback in real time. A system characterized by:

2. The training menu providing unit Suggest specific batting drills or practice methods to improve the batting technique of said player.

2. The system of claim 1.

3. The performance monitoring unit Measure the player's swing speed or pitching speed and point out areas for improvement 2. The system of claim 1.

4. The training menu providing unit Analyzing the genetic information of the player and generating the development plan based on the genetic characteristics 2. The system of claim 1.

5. The development plan formulation unit Set the necessary steps and goals for the athlete to reach a professional level and adjust training and match schedules accordingly 2. The system of claim 1.

6. The development plan formulation unit Monitoring the emotional state of the player in real time and dynamically adjusting the development plan according to the emotional state.

2. The system of claim 1.

7. The training menu providing unit Analyzing the emotional data of the player and providing the training menu according to the emotional fluctuations 2. The system of claim 1.

8. The performance monitoring unit Analyzing the emotional state of the player and providing the feedback according to the emotion.

2. The system of claim 1.

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