System

Abstract

Embodiments of the system are directed to analyzing player performance data and / or video data to provide customized training programs and / or tactical advice.SOLUTION: A system includes an input unit, a generation unit, a training unit, a video input unit, an analysis unit, and an advice unit. The input unit inputs past performance data or physical data of a player. The generation unit analyzes the data input by the input unit and generates a customized training program. The training unit performs training based on the training program generated by the generation unit. The video input unit inputs video data of a game. The analysis unit analyzes the video data input by the video input unit and specifies a tactical strength and an improvement point. The adviser provides tactical advice based on the tactical strengths and improvements identified by the analyzer.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 technologies have not yet effectively utilized player performance data and video data to provide customized training programs and tactical advice, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze performance data and video data of players and provide customized training programs and tactical advice. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a generation unit, a training unit, a video input unit, an analysis unit, and an advice unit. The input unit inputs a player's past performance data or physical data. The generation unit analyzes the data input by the input unit and generates a customized training program. The training unit performs training based on the training program generated by the generation unit. The video input unit inputs game video data. The analysis unit analyzes the video data input by the video input unit and identifies tactical strengths and areas for improvement. The advice unit provides tactical advice based on the tactical strengths and areas for improvement identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the performance data and video data of the players and provide customized training programs and tactical advice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A sports performance improvement system according to an embodiment of the present invention utilizes generative AI to improve the performance of athletes and teams. The sports performance improvement system inputs a player's past performance data and physical data, which the generative AI analyzes to generate a customized training program. Furthermore, the sports performance improvement system inputs game video data, which the generative AI analyzes to identify tactical strengths and areas for improvement, and provides specific tactical advice. For example, the sports performance improvement system provides support focused on the psychological aspects of athletes, offering advice and practice on improving self-confidence, maintaining motivation, and dealing with pressure. The sports performance improvement system also provides athletes with advice on proper nutritional intake and meal plans to help improve performance and prevent injuries. Furthermore, the sports performance improvement system analyzes team performance data, tracks the individual performance of athletes, identifies areas of improvement and potential, and proposes data-based improvement measures and strategies. This allows athletes and teams to receive more effective training, tactical modifications, psychological support, and nutritional management, potentially improving overall performance. This allows the sports performance improvement system to improve the performance of athletes and teams. For example, it can propose training focused on improving specific athletes' techniques and tactics. It can also identify players' and teams' tactical strengths and areas for improvement, and provide advice on optimal tactics and tactical modifications. Furthermore, it can provide support focused on the psychological aspects of players, offering advice and practice on improving self-confidence, maintaining motivation, and dealing with pressure. This allows players and teams to receive more effective training, tactical modifications, psychological support, and nutritional management, which can lead to improved overall performance.

[0029] A sports performance improvement system according to an embodiment includes an input unit, a generation unit, a training unit, a video input unit, an analysis unit, and an advice unit. The input unit inputs a player's past performance data or physical data. The player's past performance data includes, but is not limited to, game points, assists, and playing time. The physical data includes, but is not limited to, weight, height, and muscle strength measurement results. The generation unit analyzes the data input by the input unit using a generation AI to generate a customized training program. The generation unit generates, for example, a training program focused on improving a player's specific skills or tactics. The generation unit can also provide support focused on the player's psychological aspects. The generation unit can also provide players with advice on appropriate nutritional intake and meal plans. The training unit conducts training based on the training program generated by the generation unit. The training unit conducts, for example, physical training, technical training, and the like. The video input unit inputs video data of a game. The video data includes, for example, but is not limited to, overall game footage and specific play scenes. The analysis unit uses a generation AI to analyze the video data input by the video input unit and identify tactical strengths and areas for improvement. The analysis unit identifies, for example, defensive positioning, attacking patterns, etc. The advice unit provides tactical advice based on the tactical strengths and areas for improvement identified by the analysis unit. The advice unit provides advice such as improving positioning and recommending specific plays. In this way, the sports performance improvement system according to the embodiment can improve a player's performance by analyzing the player's performance data and physical data and generating a customized training program.

[0030] The generation unit can generate a training program focused on improving a player's specific technique or tactic. For example, the generation unit can generate a training program focused on improving a player's dribbling technique. The generation unit can also generate a training program focused on improving zone defense tactics. The generation unit can also generate a training program focused on improving shooting technique. In this way, by generating a training program focused on improving a player's specific technique or tactic, it is possible to effectively improve a player's specific skill. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input technical data of a player into the generation AI and cause the generation AI to generate a training program for improving the technique.

[0031] The analysis unit can analyze game video data and identify tactical strengths and areas for improvement. The analysis unit can, for example, analyze game video data and identify strengths and areas for improvement in defensive positioning. The analysis unit can also identify strengths and areas for improvement in attacking patterns. The analysis unit can also identify strengths and areas for improvement in cooperation between players. In this way, analyzing game video data can identify tactical strengths and areas for improvement, which can be used to improve tactics. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input video data to a generation AI and cause the generation AI to identify tactical strengths and areas for improvement.

[0032] The advice unit can provide tactical advice based on tactical strengths and areas for improvement. For example, the advice unit can provide advice regarding improving defensive positioning. The advice unit can also provide advice regarding improving attacking patterns. The advice unit can also provide advice regarding improving cooperation between players. In this way, by providing tactical advice based on tactical strengths and areas for improvement, it is possible to effectively improve the tactics of players and teams. Some or all of the above-described processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the advice unit can input data on tactical strengths and areas for improvement into the generation AI and cause the generation AI to generate tactical advice.

[0033] The generation unit can provide support focusing on the psychological aspects of the player. For example, the generation unit can provide support related to improving the player's confidence. The generation unit can also provide support related to maintaining motivation. The generation unit can also provide support related to dealing with pressure. In this way, by providing support focusing on the psychological aspects of the player, it is possible to strengthen the player's mental state and contribute to improving performance. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI, for example. For example, the generation unit can input the player's psychological data into the generation AI and cause the generation AI to provide psychological support.

[0034] The generation unit can provide players with advice regarding appropriate nutritional intake and meal plans. For example, the generation unit can provide advice regarding the players' protein intake. The generation unit can also provide advice regarding meal timing. The generation unit can also provide advice regarding vitamin and mineral intake. This can help improve players' performance and prevent injuries by providing players with advice regarding appropriate nutritional intake and meal plans. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI, for example. For example, the generation unit can input the player's nutritional data into the generation AI and cause the generation AI to provide nutritional advice.

[0035] The analysis unit can analyze the team's performance data, track the individual performance of players, and identify room for improvement or potential. For example, the analysis unit can analyze game score data and identify room for improvement in players' scoring ability. The analysis unit can also analyze assist data and identify potential for players' assisting ability. The analysis unit can also analyze playing time data and identify room for improvement in players' stamina. In this way, by analyzing the team's performance data and tracking the individual performance of players, room for improvement or potential can be identified and effective improvement measures can be proposed. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input performance data into the generation AI and cause the generation AI to identify room for improvement or potential.

[0036] The input unit can analyze the player's past performance data and select the optimal data input method. For example, the input unit can analyze the player's past performance data and select the most effective input method. The input unit can also suggest the optimal input method based on the player's past data input history. The input unit can also analyze the player's performance data in real time and select the optimal input method. In this way, by analyzing the player's past performance data, the optimal data input method can be selected and the accuracy and efficiency of data input can be improved. Some or all of the above-mentioned processing in the input unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the input unit can input past performance data to the generation AI and have the generation AI select the optimal data input method.

[0037] The input unit can perform filtering based on the player's current physical condition when inputting data. The input unit, for example, monitors the player's current physical condition in real time and performs filtering when inputting data. The input unit can also temporarily stop data input when the player's physical condition is poor. The input unit can also quickly input data when the player's physical condition is good. This allows for filtering data input based on the player's current physical condition, thereby improving data accuracy. Some or all of the above-described processing in the input unit can be performed using, or without, a generation AI. For example, the input unit can input the player's physical condition data to the generation AI and have the generation AI perform filtering.

[0038] When inputting data, the input unit can select the optimal input means depending on the input method of the player. For example, if the player prefers voice input, the input unit can preferentially select voice input. Furthermore, if the player prefers text input, the input unit can also preferentially select text input. Furthermore, if the player prefers image input, the input unit can also preferentially select image input. In this way, by selecting the optimal input means depending on the input method of the player, the efficiency and accuracy of data input can be improved. Some or all of the above-mentioned processing in the input unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the input unit can input the player's input method data to the generation AI and have the generation AI select the optimal input means.

[0039] When inputting data, the input unit can prioritize inputting highly relevant data in consideration of the geographical location information of the player. For example, when a player is in a specific area, the input unit can prioritize inputting data related to that area. Furthermore, when a player is traveling, the input unit can also prioritize inputting data related to his / her destination. Furthermore, when a player is in a specific location, the input unit can also prioritize inputting data related to that location. In this way, by prioritizing input of highly relevant data in consideration of the geographical location information of the player, it is possible to improve the relevance and accuracy of the data. Some or all of the above-described processing in the input unit may be performed using, or without, the generation AI. For example, the input unit can input geographical location information data of the player to the generation AI and cause the generation AI to prioritize input of highly relevant data.

[0040] The input unit can analyze the player's social media activity and input relevant data when inputting data. For example, the input unit analyzes the player's social media activity and inputs relevant data preferentially. The input unit can also input relevant data based on the content of the player's social media posts. The input unit can also input relevant data with reference to the activity of the player's friends on social media. In this way, by analyzing the player's social media activity, relevant data can be input preferentially, improving the accuracy of data input. Some or all of the above-described processing in the input unit can be performed using, or without, a generation AI. For example, the input unit can input the player's social media data to the generation AI and cause the generation AI to input relevant data.

[0041] The input unit can customize the input method by reflecting the player's past feedback when inputting data. The input unit, for example, suggests an optimal input method based on the player's past feedback. The input unit can also customize the input method by reflecting the player's past feedback. The input unit can also analyze the player's past feedback and select the optimal input method. In this way, customizing the input method by reflecting the player's past feedback can improve the accuracy and efficiency of data input. Some or all of the above-described processing in the input unit may be performed using, or without, the generation AI, for example. For example, the input unit can input the player's feedback data to the generation AI and cause the generation AI to customize the input method.

[0042] When generating a training program, the generation unit can adjust the level of detail of the program based on the player's important skills. For example, the generation unit can adjust the level of detail of the training program based on the player's dribbling technique. The generation unit can also adjust the level of detail of the training program based on the player's shooting technique. The generation unit can also adjust the level of detail of the training program based on the player's passing technique. In this way, by adjusting the level of detail of the training program based on the player's important skills, it is possible to effectively support the improvement of a player's skills. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the player's technical data into the generation AI and cause the generation AI to adjust the level of detail of the training program.

[0043] When generating a training program, the generation unit can apply different generation algorithms depending on the player's category. The generation unit can apply different generation algorithms depending on the player's position, for example. The generation unit can also apply different generation algorithms depending on the player's age. The generation unit can also apply different generation algorithms depending on the player's experience level. In this way, by applying different generation algorithms depending on the player's category, it is possible to provide an optimal training program for each player. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input player category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0044] When generating a training program, the generation unit can improve the accuracy of the program by referring to the player's past training results. The generation unit, for example, analyzes the player's past training results and improves the accuracy of the training program. The generation unit can also improve the accuracy of the training program based on the player's past training data. The generation unit can also improve the accuracy of the training program by referring to the player's past training results. In this way, by referring to the player's past training results, the accuracy of the training program can be improved and more effective training can be provided. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past training result data into the generation AI and cause the generation AI to improve the accuracy of the training program.

[0045] When generating training programs, the generation unit can determine the priority of the programs based on the training period of the player. The generation unit determines the priority of the training programs based on, for example, the training period of the player. The generation unit can also adjust the priority of the training programs according to the training schedule of the player. The generation unit can also determine the priority of the training programs based on the training goals of the player. In this way, by determining the priority of the programs based on the training period of the player, the effectiveness of the training can be maximized. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input the training period data of the player into the generation AI and cause the generation AI to determine the priority of the programs.

[0046] When generating training programs, the generation unit can adjust the order of the programs based on the relevance of the players. The generation unit can adjust the order of the training programs based on, for example, the positions of the players. The generation unit can also adjust the order of the training programs based on the roles of the players. The generation unit can also adjust the order of the training programs based on the playing styles of the players. In this way, by adjusting the order of the programs based on the relevance of the players, the effectiveness of the training can be maximized. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input player relevance data into the generation AI and cause the generation AI to adjust the order of the programs.

[0047] When generating a training program, the generation unit can adjust the use of technical terminology in the program according to the player's level of expertise. The generation unit can adjust the use of technical terminology in the training program according to, for example, the player's years of experience. The generation unit can also adjust the use of technical terminology in the training program according to the player's past training content. The generation unit can also adjust the use of technical terminology in the training program according to the player's level of understanding. In this way, by adjusting the use of technical terminology in the program according to the player's level of expertise, it is possible to provide a training program that is easy for the player to understand. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the player's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0048] During training, the training unit can analyze the player's past training data and select the optimal training method. For example, the training unit analyzes the player's past training data and selects the optimal training method. The training unit can also suggest the optimal training method based on the player's past training history. The training unit can also analyze the player's training data in real time and select the optimal training method. In this way, by analyzing the player's past training data, the optimal training method can be selected and the training effect can be maximized. Some or all of the above-mentioned processing in the training unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the training unit can input past training data into the generation AI and have the generation AI select the optimal training method.

[0049] The training unit can customize the training method based on the player's current physical condition during training. For example, the training unit monitors the player's current physical condition in real time and customizes the training method. The training unit can also lower the training intensity if the player's physical condition is poor. The training unit can also increase the training intensity if the player's physical condition is good. This allows the training effect to be maximized by customizing the training method based on the player's current physical condition. Some or all of the above-mentioned processing in the training unit may be performed using, or without, the generation AI, for example. For example, the training unit can input the player's physical condition data into the generation AI and have the generation AI customize the training method.

[0050] The training unit can improve the training method by reflecting the player's feedback during training. The training unit improves the training method based on, for example, the player's feedback. The training unit can also adjust the training method by reflecting the player's feedback in real time. The training unit can also analyze the player's feedback and propose an optimal training method. In this way, the training effect can be maximized by improving the training method by reflecting the player's feedback. Some or all of the above-mentioned processing in the training unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the training unit can input the player's feedback data into the generation AI and have the generation AI improve the training method.

[0051] During training, the training department can select the optimal training method by taking into account the geographical location information of the player. For example, if the player is in a specific area, the training department selects a training method appropriate for that area. Furthermore, if the player is traveling, the training department can select a training method appropriate for the destination. Furthermore, if the player is in a specific location, the training department can select a training method appropriate for that location. In this way, by selecting the optimal training method by taking into account the geographical location information of the player, the training effect can be maximized. Some or all of the above-mentioned processing in the training department may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the training department can input the geographical location information data of the player into the generation AI and have the generation AI select the optimal training method.

[0052] The training department can analyze the player's social media activity during training and suggest training methods. For example, the training department can analyze the player's social media activity and suggest relevant training methods. The training department can also suggest relevant training methods based on the content of the player's social media posts. The training department can also suggest relevant training methods by referring to the activity of the player's friends on social media. In this way, by analyzing the player's social media activity, relevant training methods can be suggested and the training effect can be maximized. Some or all of the above-mentioned processing in the training department can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the training department can input the player's social media data into the generation AI and have the generation AI suggest training methods.

[0053] The training unit can customize the training method by reflecting the player's past feedback during training. For example, the training unit can propose an optimal training method based on the player's past feedback. The training unit can also customize the training method by reflecting the player's past feedback. The training unit can also analyze the player's past feedback and select an optimal training method. In this way, the training effect can be maximized by customizing the training method by reflecting the player's past feedback. Some or all of the above-described processing in the training unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the training unit can input the player's feedback data into the generation AI and have the generation AI customize the training method.

[0054] The video input unit can analyze the player's past video data and select the optimal input method when inputting video data. For example, the video input unit analyzes the player's past video data and selects the optimal input method. The video input unit can also suggest the optimal input method based on the player's past video data input history. The video input unit can also analyze the player's video data in real time and select the optimal input method. This makes it possible to select the optimal input method by analyzing the player's past video data, thereby improving the accuracy and efficiency of video data input. Some or all of the above-described processing in the video input unit can be performed using, or without, a generation AI. For example, the video input unit can input past video data to the generation AI and have the generation AI select the optimal input method.

[0055] The video input unit can perform filtering based on the player's current game situation when inputting video data. The video input unit, for example, monitors the player's current game situation in real time and performs filtering when inputting video data. The video input unit can also temporarily stop video data input when the player's game situation is poor. The video input unit can also quickly input video data when the player's game situation is good. This allows for filtering video data based on the player's current game situation, thereby improving data accuracy. Some or all of the above-described processing in the video input unit can be performed using, or without, a generation AI. For example, the video input unit can input the player's game situation data to the generation AI and have the generation AI perform filtering.

[0056] When inputting video data, the video input unit can select the optimal input means depending on the player's input method. For example, if a player prefers voice input, the video input unit can preferentially select voice input. Furthermore, if a player prefers text input, the video input unit can also preferentially select text input. Furthermore, if a player prefers image input, the video input unit can also preferentially select image input. This allows the optimal input means to be selected depending on the player's input method, thereby improving the efficiency and accuracy of video data input. Some or all of the above-mentioned processing in the video input unit may be performed using, or without, a generation AI. For example, the video input unit can input the player's input method data to the generation AI and have the generation AI select the optimal input means.

[0057] When inputting video data, the video input unit can prioritize inputting highly relevant data by taking into account the geographical location information of the player. For example, if a player is in a specific area, the video input unit can prioritize inputting video data related to that area. Furthermore, if a player is traveling, the video input unit can prioritize inputting video data related to his / her destination. Furthermore, if a player is in a specific location, the video input unit can prioritize inputting video data related to that location. In this way, by prioritizing input of highly relevant data by taking into account the geographical location information of the player, it is possible to improve the relevance and accuracy of the data. Some or all of the above-described processing in the video input unit can be performed using, or without, a generation AI. For example, the video input unit can input geographical location information data of the player to the generation AI and cause the generation AI to prioritize input of highly relevant data.

[0058] The video input unit can analyze the player's social media activity and input related data when inputting video data. For example, the video input unit analyzes the player's social media activity and prioritizes inputting related video data. The video input unit can also input related video data based on the content of the player's social media posts. The video input unit can also input related video data with reference to the activity of the player's friends on social media. In this way, by analyzing the player's social media activity, related data can be prioritized and the accuracy of data input can be improved. Some or all of the above-described processing in the video input unit can be performed using, or without, a generation AI. For example, the video input unit can input the player's social media data to the generation AI and cause the generation AI to input related data.

[0059] The video input unit can customize the input method by reflecting the player's past feedback when inputting video data. The video input unit, for example, suggests an optimal input method based on the player's past feedback. The video input unit can also customize the input method by reflecting the player's past feedback. The video input unit can also analyze the player's past feedback and select the optimal input method. In this way, customizing the input method by reflecting the player's past feedback can improve the accuracy and efficiency of data input. Some or all of the above-described processing in the video input unit may be performed using, or without, a generation AI, for example. For example, the video input unit can input the player's feedback data to the generation AI and cause the generation AI to customize the input method.

[0060] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the video data during analysis. For example, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the video data. The analysis unit can also improve the accuracy of the analysis based on the relevance of the video data. The analysis unit can also analyze the interrelationships of the video data and improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by taking the interrelationships of the video data into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input interrelationship data of the video data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0061] The analysis unit can perform analysis taking into account the attribute information of the players. The analysis unit performs analysis taking into account attribute information such as the player's age, position, and experience. The analysis unit can also improve the accuracy of the analysis based on the player's attribute information. The analysis unit can also analyze the player's attribute information and select the optimal analysis method. In this way, the accuracy of the analysis can be improved by taking into account the player's attribute information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the player's attribute information data into the generation AI and have the generation AI perform the analysis.

[0062] During analysis, the analysis unit can weight the analysis based on the frequency of submission of video data. The analysis unit weights the analysis based on, for example, the frequency of submission of video data. The analysis unit can also improve the accuracy of the analysis by taking the frequency of submission of video data into consideration. The analysis unit can also adjust the weighting of the analysis based on the frequency of submission of video data. In this way, weighting the analysis based on the frequency of submission of video data can improve the accuracy of the analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input video data submission frequency data into the generation AI and have the generation AI perform the weighting of the analysis.

[0063] The analysis unit can perform the analysis taking into account the geographical distribution of the video data. For example, the analysis unit performs the analysis taking into account the geographical distribution of the video data. The analysis unit can also improve the accuracy of the analysis based on the geographical distribution of the video data. The analysis unit can also analyze the geographical distribution of the video data and select an optimal analysis method. In this way, the accuracy of the analysis can be improved by taking the geographical distribution of the video data into consideration. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input geographical distribution data of the video data into the generation AI and have the generation AI perform the analysis.

[0064] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the video data. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the video data. The analysis unit can also improve the accuracy of the analysis based on literature related to the video data. The analysis unit can also analyze literature related to the video data and select an optimal analysis method. In this way, by referring to literature related to the video data, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input related literature data into the generation AI and have the generation AI perform the analysis.

[0065] The analysis unit can perform the analysis taking into account the market value of the video data. For example, the analysis unit performs the analysis taking into account the market value of the video data. The analysis unit can also improve the accuracy of the analysis based on the market value of the video data. The analysis unit can also analyze the market value of the video data and select an optimal analysis method. In this way, the accuracy of the analysis can be improved by taking the market value of the video data into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input market value data into the generation AI and have the generation AI perform the analysis.

[0066] When providing advice, the advice unit can adjust the level of detail of the advice based on the importance of the tactics. The advice unit adjusts the level of detail of the advice based on, for example, the importance of the tactics. The advice unit can also adjust the level of detail of the advice based on the importance of the tactics. The advice unit can also analyze the importance of the tactics and select an optimal level of detail of the advice. In this way, by adjusting the level of detail of the advice based on the importance of the tactics, it is possible to provide optimal advice to a player or a team. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice unit can input tactical importance data into the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0067] The advice unit can apply different advice algorithms depending on the category of tactics when providing advice. The advice unit applies different advice algorithms depending on, for example, the category of tactics (offense, defense, etc.). The advice unit can also select an optimal advice algorithm based on the category of tactics. The advice unit can also customize the advice algorithm depending on the category of tactics. In this way, by applying different advice algorithms depending on the category of tactics, it is possible to provide optimal advice for each tactic. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice unit can input tactical category data into the generation AI and cause the generation AI to apply the advice algorithm.

[0068] When providing advice, the advice unit can improve the accuracy of the advice by referring to the player's past advice results. The advice unit, for example, analyzes the player's past advice results and improves the accuracy of the advice. The advice unit can also improve the accuracy of the advice based on the player's past advice data. The advice unit can also improve the accuracy of the advice by referring to the player's past advice results. In this way, by referring to the player's past advice results, the accuracy of the advice can be improved and more effective advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice unit can input past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0069] When providing advice, the advice unit can determine the priority of the advice based on the time of submission of the tactics. The advice unit can determine the priority of the advice based on, for example, the time of submission of the tactics. The advice unit can also adjust the priority of the advice according to the tactics submission schedule. The advice unit can also determine the priority of the advice based on the tactics submission target. In this way, by determining the priority of the advice based on the time of submission of the tactics, important advice can be provided preferentially. Some or all of the above-mentioned processing in the advice unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the advice unit can input tactics submission time data into the generation AI and cause the generation AI to determine the priority of the advice.

[0070] When giving advice, the advice unit can adjust the order of advice based on the relevance of tactics. The advice unit adjusts the order of advice based on, for example, the relevance of tactics (offense, defense, etc.). The advice unit can also optimize the order of advice according to the relevance of tactics. The advice unit can also adjust the order of advice taking into consideration the relevance of tactics. In this way, by adjusting the order of advice based on the relevance of tactics, it is possible to provide optimal advice to a player or a team. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice unit can input tactical relevance data into the generation AI and cause the generation AI to adjust the order of advice.

[0071] When providing advice, the advising unit can adjust the use of technical terms in the advice according to the player's level of expertise. The advising unit can adjust the use of technical terms in the advice according to, for example, the player's years of experience. The advising unit can also adjust the use of technical terms in the advice according to the player's past training content. The advising unit can also adjust the use of technical terms in the advice according to the player's level of understanding. In this way, by adjusting the use of technical terms in the advice according to the player's level of expertise, advice can be provided in a form that is easy for the player to understand. Some or all of the above-mentioned processing in the advising unit may be performed using, or without, a generation AI, for example. For example, the advising unit can input the player's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The training department can analyze a player's past training data and adjust the frequency of training. For example, if a player has achieved results with high-frequency training in the past, the training frequency can be adjusted at a similar frequency. Conversely, if a player has achieved results with low-frequency training in the past, the training frequency can be reduced. Furthermore, the training frequency can be adjusted according to the player's physical condition and fatigue level. In this way, by adjusting the training frequency based on the player's past training data, the training effect can be maximized and the player's physical condition can be maintained. The training department can input past training data into the generation AI and have the generation AI adjust the training frequency.

[0074] The advice unit can analyze a player's past advice history and adjust the timing of advice. For example, if a player has been more likely to accept advice at a specific timing in the past, it can provide advice at that timing. Also, if a player has been less likely to accept advice at a specific timing in the past, it can avoid that timing. Furthermore, it can adjust the timing of advice according to the player's schedule and the situation of the game. In this way, by adjusting the timing of advice based on the player's past advice history, it is possible to maximize the effectiveness of advice and contribute to improving the player's performance. The advice unit inputs the past advice history into the generation AI and has the generation AI adjust the timing of advice.

[0075] The input unit can analyze the player's past data input history and select the optimal input device. For example, if the player has previously preferred voice input, it can provide a voice input device preferentially. Furthermore, if the player has previously preferred text input, it can provide a text input device preferentially. Furthermore, if the player has previously preferred image input, it can provide an image input device preferentially. In this way, by selecting the optimal input device based on the player's past data input history, it is possible to improve the efficiency and accuracy of data input. The input unit can input the past data input history into the generation AI and cause the generation AI to select the optimal input device.

[0076] The analysis unit can analyze a player's past performance data and determine analysis priorities. For example, if a player has previously performed well at a particular skill, it can prioritize analysis of data related to that skill. Conversely, if a player has previously performed poorly at a particular skill, it can prioritize analysis of data related to that skill. Furthermore, it can adjust analysis priorities according to the player's match situation and training goals. This makes it possible to improve the accuracy and efficiency of analysis by determining analysis priorities based on a player's past performance data. The analysis unit inputs past performance data into the generation AI and has the generation AI determine analysis priorities.

[0077] The training department can adjust the training content taking into account the geographical location information of the player. For example, if the player is at high altitude, high altitude training can be incorporated. If the player is at the seaside, training on the beach can be incorporated. Furthermore, if the player is in an urban area, training suitable for the urban environment can be incorporated. In this way, adjusting the training content taking into account the player's geographical location information can maximize the training effect and contribute to improving the player's performance. The training department can input the geographical location information into the generation AI and have the generation AI adjust the training content.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The input unit inputs the player's past performance data or physical data. The player's past performance data includes, for example, points, assists, playing time, etc. The physical data includes, for example, weight, height, muscle strength measurement results, etc. Step 2: The generator uses the generative AI to analyze the data input by the input unit and generate a customized training program. The generator can provide training programs focused on improving the player's specific techniques and tactics, support focused on psychological aspects, and advice on proper nutritional intake and meal planning. Step 3: The training unit performs training based on the training program generated by the generation unit. The training unit performs physical training, technical training, etc. Step 4: The video input unit inputs video data of the game, including the entire game footage and specific play scenes. Step 5: The analysis unit uses the generative AI to analyze the video data input by the video input unit and identify tactical strengths and areas for improvement. The analysis unit identifies defensive positioning, attacking patterns, etc. Step 6: The Advice Department provides tactical advice based on the tactical strengths and areas for improvement identified by the Analysis Department. The Advice Department provides advice such as improving positioning or recommending specific plays.

[0080] (Example 2) A sports performance improvement system according to an embodiment of the present invention utilizes generative AI to improve the performance of athletes and teams. The sports performance improvement system inputs a player's past performance data and physical data, which the generative AI analyzes to generate a customized training program. Furthermore, the sports performance improvement system inputs game video data, which the generative AI analyzes to identify tactical strengths and areas for improvement, and provides specific tactical advice. For example, the sports performance improvement system provides support focused on the psychological aspects of athletes, offering advice and practice on improving self-confidence, maintaining motivation, and dealing with pressure. The sports performance improvement system also provides athletes with advice on proper nutritional intake and meal plans to help improve performance and prevent injuries. Furthermore, the sports performance improvement system analyzes team performance data, tracks the individual performance of athletes, identifies areas of improvement and potential, and proposes data-based improvement measures and strategies. This allows athletes and teams to receive more effective training, tactical modifications, psychological support, and nutritional management, potentially improving overall performance. This allows the sports performance improvement system to improve the performance of athletes and teams. For example, it can propose training focused on improving specific athletes' techniques and tactics. It can also identify players' and teams' tactical strengths and areas for improvement, and provide advice on optimal tactics and tactical modifications. Furthermore, it can provide support focused on the psychological aspects of players, offering advice and practice on improving self-confidence, maintaining motivation, and dealing with pressure. This allows players and teams to receive more effective training, tactical modifications, psychological support, and nutritional management, which can lead to improved overall performance.

[0081] A sports performance improvement system according to an embodiment includes an input unit, a generation unit, a training unit, a video input unit, an analysis unit, and an advice unit. The input unit inputs a player's past performance data or physical data. The player's past performance data includes, but is not limited to, game points, assists, and playing time. The physical data includes, but is not limited to, weight, height, and muscle strength measurement results. The generation unit analyzes the data input by the input unit using a generation AI to generate a customized training program. The generation unit generates, for example, a training program focused on improving a player's specific skills or tactics. The generation unit can also provide support focused on the player's psychological aspects. The generation unit can also provide players with advice on appropriate nutritional intake and meal plans. The training unit conducts training based on the training program generated by the generation unit. The training unit conducts, for example, physical training, technical training, and the like. The video input unit inputs video data of a game. The video data includes, for example, but is not limited to, overall game footage and specific play scenes. The analysis unit uses a generation AI to analyze the video data input by the video input unit and identify tactical strengths and areas for improvement. The analysis unit identifies, for example, defensive positioning, attacking patterns, etc. The advice unit provides tactical advice based on the tactical strengths and areas for improvement identified by the analysis unit. The advice unit provides advice such as improving positioning and recommending specific plays. In this way, the sports performance improvement system according to the embodiment can improve a player's performance by analyzing the player's performance data and physical data and generating a customized training program.

[0082] The generation unit can generate a training program focused on improving a player's specific technique or tactic. For example, the generation unit can generate a training program focused on improving a player's dribbling technique. The generation unit can also generate a training program focused on improving zone defense tactics. The generation unit can also generate a training program focused on improving shooting technique. In this way, by generating a training program focused on improving a player's specific technique or tactic, it is possible to effectively improve a player's specific skill. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input technical data of a player into the generation AI and cause the generation AI to generate a training program for improving the technique.

[0083] The analysis unit can analyze game video data and identify tactical strengths and areas for improvement. The analysis unit can, for example, analyze game video data and identify strengths and areas for improvement in defensive positioning. The analysis unit can also identify strengths and areas for improvement in attacking patterns. The analysis unit can also identify strengths and areas for improvement in cooperation between players. In this way, analyzing game video data can identify tactical strengths and areas for improvement, which can be used to improve tactics. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input video data to a generation AI and cause the generation AI to identify tactical strengths and areas for improvement.

[0084] The advice unit can provide tactical advice based on tactical strengths and areas for improvement. For example, the advice unit can provide advice regarding improving defensive positioning. The advice unit can also provide advice regarding improving attacking patterns. The advice unit can also provide advice regarding improving cooperation between players. In this way, by providing tactical advice based on tactical strengths and areas for improvement, it is possible to effectively improve the tactics of players and teams. Some or all of the above-described processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the advice unit can input data on tactical strengths and areas for improvement into the generation AI and cause the generation AI to generate tactical advice.

[0085] The generation unit can provide support focusing on the psychological aspects of the player. For example, the generation unit can provide support related to improving the player's confidence. The generation unit can also provide support related to maintaining motivation. The generation unit can also provide support related to dealing with pressure. In this way, by providing support focusing on the psychological aspects of the player, it is possible to strengthen the player's mental state and contribute to improving performance. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI, for example. For example, the generation unit can input the player's psychological data into the generation AI and cause the generation AI to provide psychological support.

[0086] The generation unit can provide players with advice regarding appropriate nutritional intake and meal plans. For example, the generation unit can provide advice regarding the players' protein intake. The generation unit can also provide advice regarding meal timing. The generation unit can also provide advice regarding vitamin and mineral intake. This can help improve players' performance and prevent injuries by providing players with advice regarding appropriate nutritional intake and meal plans. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI, for example. For example, the generation unit can input the player's nutritional data into the generation AI and cause the generation AI to provide nutritional advice.

[0087] The analysis unit can analyze the team's performance data, track the individual performance of players, and identify room for improvement or potential. For example, the analysis unit can analyze game score data and identify room for improvement in players' scoring ability. The analysis unit can also analyze assist data and identify potential for players' assisting ability. The analysis unit can also analyze playing time data and identify room for improvement in players' stamina. In this way, by analyzing the team's performance data and tracking the individual performance of players, room for improvement or potential can be identified and effective improvement measures can be proposed. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input performance data into the generation AI and cause the generation AI to identify room for improvement or potential.

[0088] The input unit can estimate the player's emotions and adjust the timing of data input based on the estimated player's emotions. For example, if the player is relaxed, the input unit can adjust the input timing to match the player's relaxed state to smoothly input data. Furthermore, if the player is feeling stressed, the input unit can temporarily delay data input and wait until the player is relaxed. Furthermore, if the player is concentrating, the input unit can quickly input data to maintain concentration. In this way, by adjusting the timing of data input based on the player's emotions, the player's stress can be reduced and the efficiency of data input can be improved. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the input unit may be performed using, for example, the generation AI. For example, the input unit can input the player's facial expression data to the generation AI and cause the generation AI to estimate the player's emotions.

[0089] The input unit can analyze the player's past performance data and select the optimal data input method. For example, the input unit can analyze the player's past performance data and select the most effective input method. The input unit can also suggest the optimal input method based on the player's past data input history. The input unit can also analyze the player's performance data in real time and select the optimal input method. In this way, by analyzing the player's past performance data, the optimal data input method can be selected and the accuracy and efficiency of data input can be improved. Some or all of the above-mentioned processing in the input unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the input unit can input past performance data to the generation AI and have the generation AI select the optimal data input method.

[0090] The input unit can perform filtering based on the player's current physical condition when inputting data. The input unit, for example, monitors the player's current physical condition in real time and performs filtering when inputting data. The input unit can also temporarily stop data input when the player's physical condition is poor. The input unit can also quickly input data when the player's physical condition is good. This allows for filtering data input based on the player's current physical condition, thereby improving data accuracy. Some or all of the above-described processing in the input unit can be performed using, or without, a generation AI. For example, the input unit can input the player's physical condition data to the generation AI and have the generation AI perform filtering.

[0091] When inputting data, the input unit can select the optimal input means depending on the input method of the player. For example, if the player prefers voice input, the input unit can preferentially select voice input. Furthermore, if the player prefers text input, the input unit can also preferentially select text input. Furthermore, if the player prefers image input, the input unit can also preferentially select image input. In this way, by selecting the optimal input means depending on the input method of the player, the efficiency and accuracy of data input can be improved. Some or all of the above-mentioned processing in the input unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the input unit can input the player's input method data to the generation AI and have the generation AI select the optimal input means.

[0092] The input unit can estimate the player's emotions and determine the priority of data to be input based on the estimated player's emotions. For example, when a player is relaxed, the input unit prioritizes input of important data. Furthermore, when a player is feeling stressed, the input unit can also prioritize input of less important data. Furthermore, when a player is concentrating, the input unit can quickly input important data. Thus, by determining the priority of data based on the player's emotions, important data can be input preferentially, improving the efficiency of data input. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the input unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the input unit can input facial expression data of a player to the generation AI and cause the generation AI to estimate the emotion.

[0093] When inputting data, the input unit can prioritize inputting highly relevant data in consideration of the geographical location information of the player. For example, when a player is in a specific area, the input unit can prioritize inputting data related to that area. Furthermore, when a player is traveling, the input unit can also prioritize inputting data related to his / her destination. Furthermore, when a player is in a specific location, the input unit can also prioritize inputting data related to that location. In this way, by prioritizing input of highly relevant data in consideration of the geographical location information of the player, it is possible to improve the relevance and accuracy of the data. Some or all of the above-described processing in the input unit may be performed using, or without, the generation AI. For example, the input unit can input geographical location information data of the player to the generation AI and cause the generation AI to prioritize input of highly relevant data.

[0094] The input unit can analyze the player's social media activity and input relevant data when inputting data. For example, the input unit analyzes the player's social media activity and inputs relevant data preferentially. The input unit can also input relevant data based on the content of the player's social media posts. The input unit can also input relevant data with reference to the activity of the player's friends on social media. In this way, by analyzing the player's social media activity, relevant data can be input preferentially, improving the accuracy of data input. Some or all of the above-described processing in the input unit can be performed using, or without, a generation AI. For example, the input unit can input the player's social media data to the generation AI and cause the generation AI to input relevant data.

[0095] The input unit can customize the input method by reflecting the player's past feedback when inputting data. The input unit, for example, suggests an optimal input method based on the player's past feedback. The input unit can also customize the input method by reflecting the player's past feedback. The input unit can also analyze the player's past feedback and select the optimal input method. In this way, customizing the input method by reflecting the player's past feedback can improve the accuracy and efficiency of data input. Some or all of the above-described processing in the input unit may be performed using, or without, the generation AI, for example. For example, the input unit can input the player's feedback data to the generation AI and cause the generation AI to customize the input method.

[0096] The generation unit can estimate the player's emotions and adjust the presentation of the training program based on the estimated player's emotions. For example, if the player is relaxed, the generation unit can generate a training program that progresses at a leisurely pace. If the player is in a hurry, the generation unit can generate a training program that emphasizes the shortest route. If the player is excited, the generation unit can generate a training program that adds visually stimulating effects. By adjusting the presentation of the training program based on the player's emotions, the player's motivation can be maintained and the training effect can be maximized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the player's emotion data into the generation AI and cause the generation AI to adjust the presentation of the training program.

[0097] When generating a training program, the generation unit can adjust the level of detail of the program based on the player's important skills. For example, the generation unit can adjust the level of detail of the training program based on the player's dribbling technique. The generation unit can also adjust the level of detail of the training program based on the player's shooting technique. The generation unit can also adjust the level of detail of the training program based on the player's passing technique. In this way, by adjusting the level of detail of the training program based on the player's important skills, it is possible to effectively support the improvement of a player's skills. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the player's technical data into the generation AI and cause the generation AI to adjust the level of detail of the training program.

[0098] When generating a training program, the generation unit can apply different generation algorithms depending on the player's category. The generation unit can apply different generation algorithms depending on the player's position, for example. The generation unit can also apply different generation algorithms depending on the player's age. The generation unit can also apply different generation algorithms depending on the player's experience level. In this way, by applying different generation algorithms depending on the player's category, it is possible to provide an optimal training program for each player. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input player category data into the generation AI and cause the generation AI to apply the generation algorithm.

[0099] When generating a training program, the generation unit can improve the accuracy of the program by referring to the player's past training results. The generation unit, for example, analyzes the player's past training results and improves the accuracy of the training program. The generation unit can also improve the accuracy of the training program based on the player's past training data. The generation unit can also improve the accuracy of the training program by referring to the player's past training results. In this way, by referring to the player's past training results, the accuracy of the training program can be improved and more effective training can be provided. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past training result data into the generation AI and cause the generation AI to improve the accuracy of the training program.

[0100] The generation unit can estimate the player's emotions and adjust the length of the training program based on the estimated player's emotions. For example, if the player is relaxed, the generation unit can generate a longer training program. If the player is in a hurry, the generation unit can also generate a shorter training program. If the player is excited, the generation unit can also generate a training program with visually stimulating effects. This allows the player's motivation to be maintained and the training effect to be maximized by adjusting the length of the training program based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the player's emotion data into the generation AI and cause the generation AI to adjust the length of the training program.

[0101] When generating training programs, the generation unit can determine the priority of the programs based on the training period of the player. The generation unit determines the priority of the training programs based on, for example, the training period of the player. The generation unit can also adjust the priority of the training programs according to the training schedule of the player. The generation unit can also determine the priority of the training programs based on the training goals of the player. In this way, by determining the priority of the programs based on the training period of the player, the effectiveness of the training can be maximized. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input the training period data of the player into the generation AI and cause the generation AI to determine the priority of the programs.

[0102] When generating training programs, the generation unit can adjust the order of the programs based on the relevance of the players. The generation unit can adjust the order of the training programs based on, for example, the positions of the players. The generation unit can also adjust the order of the training programs based on the roles of the players. The generation unit can also adjust the order of the training programs based on the playing styles of the players. In this way, by adjusting the order of the programs based on the relevance of the players, the effectiveness of the training can be maximized. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input player relevance data into the generation AI and cause the generation AI to adjust the order of the programs.

[0103] When generating a training program, the generation unit can adjust the use of technical terminology in the program according to the player's level of expertise. The generation unit can adjust the use of technical terminology in the training program according to, for example, the player's years of experience. The generation unit can also adjust the use of technical terminology in the training program according to the player's past training content. The generation unit can also adjust the use of technical terminology in the training program according to the player's level of understanding. In this way, by adjusting the use of technical terminology in the program according to the player's level of expertise, it is possible to provide a training program that is easy for the player to understand. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input the player's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0104] The training unit can estimate the player's emotions and adjust the training method based on the estimated player's emotions. For example, if the player is relaxed, the training unit can select a training method that proceeds at a leisurely pace. If the player is in a hurry, the training unit can select a training method that emphasizes the shortest route. If the player is excited, the training unit can select a training method that adds visually stimulating effects. By adjusting the training method based on the player's emotions, the player's motivation can be maintained and the training effect can be maximized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the training unit can be performed using, for example, the generation AI. For example, the training unit can input the player's emotion data into the generation AI and have the generation AI adjust the training method.

[0105] During training, the training unit can analyze the player's past training data and select the optimal training method. For example, the training unit analyzes the player's past training data and selects the optimal training method. The training unit can also suggest the optimal training method based on the player's past training history. The training unit can also analyze the player's training data in real time and select the optimal training method. In this way, by analyzing the player's past training data, the optimal training method can be selected and the training effect can be maximized. Some or all of the above-mentioned processing in the training unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the training unit can input past training data into the generation AI and have the generation AI select the optimal training method.

[0106] The training unit can customize the training method based on the player's current physical condition during training. For example, the training unit monitors the player's current physical condition in real time and customizes the training method. The training unit can also lower the training intensity if the player's physical condition is poor. The training unit can also increase the training intensity if the player's physical condition is good. This allows the training effect to be maximized by customizing the training method based on the player's current physical condition. Some or all of the above-mentioned processing in the training unit may be performed using, or without, the generation AI, for example. For example, the training unit can input the player's physical condition data into the generation AI and have the generation AI customize the training method.

[0107] The training unit can improve the training method by reflecting the player's feedback during training. The training unit improves the training method based on, for example, the player's feedback. The training unit can also adjust the training method by reflecting the player's feedback in real time. The training unit can also analyze the player's feedback and propose an optimal training method. In this way, the training effect can be maximized by improving the training method by reflecting the player's feedback. Some or all of the above-mentioned processing in the training unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the training unit can input the player's feedback data into the generation AI and have the generation AI improve the training method.

[0108] The training unit can estimate the player's emotions and determine training priorities based on the estimated player's emotions. For example, if the player is relaxed, the training unit can prioritize important training. Furthermore, if the player is stressed, the training unit can prioritize less important training. Furthermore, if the player is concentrating, the training unit can quickly perform important training. Thus, by determining training priorities based on the player's emotions, important training can be prioritized and the training effect can be maximized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the training unit can be performed using, for example, the generation AI. For example, the training unit can input the player's emotion data into the generation AI and have the generation AI determine the training priorities.

[0109] During training, the training department can select the optimal training method by taking into account the geographical location information of the player. For example, if the player is in a specific area, the training department selects a training method appropriate for that area. Furthermore, if the player is traveling, the training department can select a training method appropriate for the destination. Furthermore, if the player is in a specific location, the training department can select a training method appropriate for that location. In this way, by selecting the optimal training method by taking into account the geographical location information of the player, the training effect can be maximized. Some or all of the above-mentioned processing in the training department may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the training department can input the geographical location information data of the player into the generation AI and have the generation AI select the optimal training method.

[0110] The training department can analyze the player's social media activity during training and suggest training methods. For example, the training department can analyze the player's social media activity and suggest relevant training methods. The training department can also suggest relevant training methods based on the content of the player's social media posts. The training department can also suggest relevant training methods by referring to the activity of the player's friends on social media. In this way, by analyzing the player's social media activity, relevant training methods can be suggested and the training effect can be maximized. Some or all of the above-mentioned processing in the training department can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the training department can input the player's social media data into the generation AI and have the generation AI suggest training methods.

[0111] The training unit can customize the training method by reflecting the player's past feedback during training. For example, the training unit can propose an optimal training method based on the player's past feedback. The training unit can also customize the training method by reflecting the player's past feedback. The training unit can also analyze the player's past feedback and select an optimal training method. In this way, the training effect can be maximized by customizing the training method by reflecting the player's past feedback. Some or all of the above-described processing in the training unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the training unit can input the player's feedback data into the generation AI and have the generation AI customize the training method.

[0112] The video input unit can estimate a player's emotion and adjust the timing of video data input based on the estimated player's emotion. For example, if a player is relaxed, the video input unit can adjust the timing of video data input to match the player's relaxed state. Furthermore, if a player is feeling stressed, the video input unit can temporarily delay the input of video data and wait until the player relaxes. Furthermore, if a player is concentrating, the video input unit can quickly input video data to help the player maintain their concentration. This adjusts the timing of video data input based on the player's emotion, thereby reducing the player's stress and improving data input efficiency. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the video input unit can be performed using, for example, the generation AI. For example, the video input unit can input the player's emotion data to the generation AI and cause the generation AI to adjust the timing of video data input.

[0113] The video input unit can analyze the player's past video data and select the optimal input method when inputting video data. For example, the video input unit analyzes the player's past video data and selects the optimal input method. The video input unit can also suggest the optimal input method based on the player's past video data input history. The video input unit can also analyze the player's video data in real time and select the optimal input method. This makes it possible to select the optimal input method by analyzing the player's past video data, thereby improving the accuracy and efficiency of video data input. Some or all of the above-described processing in the video input unit can be performed using, or without, a generation AI. For example, the video input unit can input past video data to the generation AI and have the generation AI select the optimal input method.

[0114] The video input unit can perform filtering based on the player's current game situation when inputting video data. The video input unit, for example, monitors the player's current game situation in real time and performs filtering when inputting video data. The video input unit can also temporarily stop video data input when the player's game situation is poor. The video input unit can also quickly input video data when the player's game situation is good. This allows for filtering video data based on the player's current game situation, thereby improving data accuracy. Some or all of the above-described processing in the video input unit can be performed using, or without, a generation AI. For example, the video input unit can input the player's game situation data to the generation AI and have the generation AI perform filtering.

[0115] When inputting video data, the video input unit can select the optimal input means depending on the player's input method. For example, if a player prefers voice input, the video input unit can preferentially select voice input. Furthermore, if a player prefers text input, the video input unit can also preferentially select text input. Furthermore, if a player prefers image input, the video input unit can also preferentially select image input. This allows the optimal input means to be selected depending on the player's input method, thereby improving the efficiency and accuracy of video data input. Some or all of the above-mentioned processing in the video input unit may be performed using, or without, a generation AI. For example, the video input unit can input the player's input method data to the generation AI and have the generation AI select the optimal input means.

[0116] The video input unit can estimate the player's emotions and determine the priority of the video data to be input based on the estimated player's emotions. For example, when a player is relaxed, the video input unit prioritizes input of important video data. Furthermore, when a player is stressed, the video input unit can also prioritize input of less important video data. Furthermore, when a player is concentrating, the video input unit can quickly input important video data. Thus, by prioritizing the video data based on the player's emotions, important video data can be input preferentially, improving the efficiency of data input. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the video input unit can be performed using, for example, the generation AI. For example, the video input unit can input the player's emotion data to the generation AI and have the generation AI determine the priority of the video data.

[0117] When inputting video data, the video input unit can prioritize inputting highly relevant data by taking into account the geographical location information of the player. For example, if a player is in a specific area, the video input unit can prioritize inputting video data related to that area. Furthermore, if a player is traveling, the video input unit can prioritize inputting video data related to his / her destination. Furthermore, if a player is in a specific location, the video input unit can prioritize inputting video data related to that location. In this way, by prioritizing input of highly relevant data by taking into account the geographical location information of the player, it is possible to improve the relevance and accuracy of the data. Some or all of the above-described processing in the video input unit can be performed using, or without, a generation AI. For example, the video input unit can input geographical location information data of the player to the generation AI and cause the generation AI to prioritize input of highly relevant data.

[0118] The video input unit can analyze the player's social media activity and input related data when inputting video data. For example, the video input unit analyzes the player's social media activity and prioritizes inputting related video data. The video input unit can also input related video data based on the content of the player's social media posts. The video input unit can also input related video data with reference to the activity of the player's friends on social media. In this way, by analyzing the player's social media activity, related data can be prioritized and the accuracy of data input can be improved. Some or all of the above-described processing in the video input unit can be performed using, or without, a generation AI. For example, the video input unit can input the player's social media data to the generation AI and cause the generation AI to input related data.

[0119] The video input unit can customize the input method by reflecting the player's past feedback when inputting video data. The video input unit, for example, suggests an optimal input method based on the player's past feedback. The video input unit can also customize the input method by reflecting the player's past feedback. The video input unit can also analyze the player's past feedback and select the optimal input method. In this way, customizing the input method by reflecting the player's past feedback can improve the accuracy and efficiency of data input. Some or all of the above-described processing in the video input unit may be performed using, or without, a generation AI, for example. For example, the video input unit can input the player's feedback data to the generation AI and cause the generation AI to customize the input method.

[0120] The analysis unit can estimate the player's emotions and adjust the analysis criteria based on the estimated player's emotions. For example, the analysis unit can perform a detailed analysis when the player is relaxed. The analysis unit can also perform a simplified analysis when the player is stressed. The analysis unit can also quickly perform a detailed analysis when the player is concentrating. By adjusting the analysis criteria based on the player's emotions, the accuracy of the analysis can be improved. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the player's emotion data into the generation AI and have the generation AI adjust the analysis criteria.

[0121] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the video data during analysis. For example, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships of the video data. The analysis unit can also improve the accuracy of the analysis based on the relevance of the video data. The analysis unit can also analyze the interrelationships of the video data and improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by taking the interrelationships of the video data into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input interrelationship data of the video data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0122] The analysis unit can perform analysis taking into account the attribute information of the players. The analysis unit performs analysis taking into account attribute information such as the player's age, position, and experience. The analysis unit can also improve the accuracy of the analysis based on the player's attribute information. The analysis unit can also analyze the player's attribute information and select the optimal analysis method. In this way, the accuracy of the analysis can be improved by taking into account the player's attribute information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the player's attribute information data into the generation AI and have the generation AI perform the analysis.

[0123] During analysis, the analysis unit can weight the analysis based on the frequency of submission of video data. The analysis unit weights the analysis based on, for example, the frequency of submission of video data. The analysis unit can also improve the accuracy of the analysis by taking the frequency of submission of video data into consideration. The analysis unit can also adjust the weighting of the analysis based on the frequency of submission of video data. In this way, weighting the analysis based on the frequency of submission of video data can improve the accuracy of the analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input video data submission frequency data into the generation AI and have the generation AI perform the weighting of the analysis.

[0124] The analysis unit can estimate the player's emotions and adjust the order in which the analysis results are displayed based on the estimated player's emotions. For example, if the player is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, if the player is feeling stressed, the analysis unit can also prioritize displaying simplified analysis results. Furthermore, if the player is concentrating, the analysis unit can quickly display important analysis results. By adjusting the order in which the analysis results are displayed based on the player's emotions, the analysis results can be provided in a format that is easy for the player to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the player's emotion data into the generation AI and have the generation AI adjust the display order of the analysis results.

[0125] The analysis unit can perform the analysis taking into account the geographical distribution of the video data. For example, the analysis unit performs the analysis taking into account the geographical distribution of the video data. The analysis unit can also improve the accuracy of the analysis based on the geographical distribution of the video data. The analysis unit can also analyze the geographical distribution of the video data and select an optimal analysis method. In this way, the accuracy of the analysis can be improved by taking the geographical distribution of the video data into consideration. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input geographical distribution data of the video data into the generation AI and have the generation AI perform the analysis.

[0126] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the video data. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the video data. The analysis unit can also improve the accuracy of the analysis based on literature related to the video data. The analysis unit can also analyze literature related to the video data and select an optimal analysis method. In this way, by referring to literature related to the video data, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input related literature data into the generation AI and have the generation AI perform the analysis.

[0127] The analysis unit can perform the analysis taking into account the market value of the video data. For example, the analysis unit performs the analysis taking into account the market value of the video data. The analysis unit can also improve the accuracy of the analysis based on the market value of the video data. The analysis unit can also analyze the market value of the video data and select an optimal analysis method. In this way, the accuracy of the analysis can be improved by taking the market value of the video data into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input market value data into the generation AI and have the generation AI perform the analysis.

[0128] The advice unit can estimate the player's emotions and adjust the way the advice is presented based on the estimated player's emotions. For example, if the player is relaxed, the advice unit can provide advice to proceed at a leisurely pace. If the player is in a hurry, the advice unit can provide advice that emphasizes the shortest route. If the player is excited, the advice unit can provide advice that adds visually stimulating effects. By adjusting the way the advice is presented based on the player's emotions, the advice can be provided in a form that is easy for the player to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the advice unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the advice unit can input the player's emotion data into the generation AI and cause the generation AI to adjust the way the advice is presented.

[0129] When providing advice, the advice unit can adjust the level of detail of the advice based on the importance of the tactics. The advice unit adjusts the level of detail of the advice based on, for example, the importance of the tactics. The advice unit can also adjust the level of detail of the advice based on the importance of the tactics. The advice unit can also analyze the importance of the tactics and select an optimal level of detail of the advice. In this way, by adjusting the level of detail of the advice based on the importance of the tactics, it is possible to provide optimal advice to a player or a team. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice unit can input tactical importance data into the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0130] The advice unit can apply different advice algorithms depending on the category of tactics when providing advice. The advice unit applies different advice algorithms depending on, for example, the category of tactics (offense, defense, etc.). The advice unit can also select an optimal advice algorithm based on the category of tactics. The advice unit can also customize the advice algorithm depending on the category of tactics. In this way, by applying different advice algorithms depending on the category of tactics, it is possible to provide optimal advice for each tactic. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice unit can input tactical category data into the generation AI and cause the generation AI to apply the advice algorithm.

[0131] When providing advice, the advice unit can improve the accuracy of the advice by referring to the player's past advice results. The advice unit, for example, analyzes the player's past advice results and improves the accuracy of the advice. The advice unit can also improve the accuracy of the advice based on the player's past advice data. The advice unit can also improve the accuracy of the advice by referring to the player's past advice results. In this way, by referring to the player's past advice results, the accuracy of the advice can be improved and more effective advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice unit can input past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0132] The advice unit can estimate the player's emotions and adjust the length of advice based on the estimated player's emotions. For example, if the player is relaxed, the advice unit can provide longer advice. If the player is in a hurry, the advice unit can also provide shorter advice. If the player is excited, the advice unit can also provide advice with visually stimulating effects. By adjusting the length of advice based on the player's emotions, the advice can be provided in a format that is easy for the player to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the advice unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the advice unit can input the player's emotion data into the generation AI and cause the generation AI to adjust the length of the advice.

[0133] When providing advice, the advice unit can determine the priority of the advice based on the time of submission of the tactics. The advice unit can determine the priority of the advice based on, for example, the time of submission of the tactics. The advice unit can also adjust the priority of the advice according to the tactics submission schedule. The advice unit can also determine the priority of the advice based on the tactics submission target. In this way, by determining the priority of the advice based on the time of submission of the tactics, important advice can be provided preferentially. Some or all of the above-mentioned processing in the advice unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the advice unit can input tactics submission time data into the generation AI and cause the generation AI to determine the priority of the advice.

[0134] When giving advice, the advice unit can adjust the order of advice based on the relevance of tactics. The advice unit adjusts the order of advice based on, for example, the relevance of tactics (offense, defense, etc.). The advice unit can also optimize the order of advice according to the relevance of tactics. The advice unit can also adjust the order of advice taking into consideration the relevance of tactics. In this way, by adjusting the order of advice based on the relevance of tactics, it is possible to provide optimal advice to a player or a team. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice unit can input tactical relevance data into the generation AI and cause the generation AI to adjust the order of advice.

[0135] When providing advice, the advising unit can adjust the use of technical terms in the advice according to the player's level of expertise. The advising unit can adjust the use of technical terms in the advice according to, for example, the player's years of experience. The advising unit can also adjust the use of technical terms in the advice according to the player's past training content. The advising unit can also adjust the use of technical terms in the advice according to the player's level of understanding. In this way, by adjusting the use of technical terms in the advice according to the player's level of expertise, advice can be provided in a form that is easy for the player to understand. Some or all of the above-mentioned processing in the advising unit may be performed using, or without, a generation AI, for example. For example, the advising unit can input the player's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements, including the input unit, generation unit, training unit, video input unit, analysis unit, and advice unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit can detect a player's facial expressions and voice using the camera 42 and microphone 38B of the smart device 14, and estimate the player's emotions using the specific processing unit 290 of the data processing device 12. The generation unit analyzes the player's performance data and physical data using the specific processing unit 290 of the data processing device 12, and generates a customized training program. The training unit performs training based on the generated training program using the control unit 46A of the smart device 14. The video input unit inputs game video data using the camera 42 of the smart device 14, and the analysis unit analyzes the video data using the specific processing unit 290 of the data processing device 12. The advice unit provides tactical advice based on the analysis results using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the input unit, generation unit, training unit, video input unit, analysis unit, and advice unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit can detect a player's facial expressions and voice using the camera 42 and microphone 238 of the smart glasses 214 and estimate the player's emotions using the specific processing unit 290 of the data processing device 12. The generation unit analyzes the player's performance data and physical data using the specific processing unit 290 of the data processing device 12 to generate a customized training program. The training unit performs training based on the generated training program using the control unit 46A of the smart glasses 214. The video input unit inputs game video data using the camera 42 of the smart glasses 214, and the analysis unit analyzes the video data using the specific processing unit 290 of the data processing device 12. The advice unit provides tactical advice based on the analysis results using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the input unit, generation unit, training unit, video input unit, analysis unit, and advice unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit can detect a player's facial expressions and voice using the camera 42 and microphone 238 of the headset-type terminal 314 and estimate the player's emotions using the specific processing unit 290 of the data processing device 12. The generation unit analyzes the player's performance data and physical data using the specific processing unit 290 of the data processing device 12 and generates a customized training program. The training unit performs training based on the generated training program using the control unit 46A of the headset-type terminal 314. The video input unit inputs game video data using the camera 42 of the headset-type terminal 314, and the analysis unit analyzes the video data using the specific processing unit 290 of the data processing device 12. The advice unit provides tactical advice based on the analysis results using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the input unit, generation unit, training unit, video input unit, analysis unit, and advice unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit can detect a player's facial expressions and voice using the camera 42 and microphone 238 of the robot 414 and estimate the player's emotions using the specific processing unit 290 of the data processing device 12. The generation unit analyzes the player's performance data and physical data using the specific processing unit 290 of the data processing device 12 and generates a customized training program. The training unit performs training based on the training program generated by the control unit 46A of the robot 414. The video input unit inputs game video data using the camera 42 of the robot 414, and the analysis unit analyzes the video data using the specific processing unit 290 of the data processing device 12. The advice unit provides tactical advice based on the analysis results using the specific processing unit 290 of the data processing device 12.

[0136] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0137] The analysis unit can estimate the player's emotions and adjust the depth of the analysis based on the estimated emotions. For example, if the player is relaxed, a detailed analysis can be performed, and if the player is stressed, a simplified analysis can be performed. Also, if the player is concentrating, a quick but detailed analysis can be performed. In this way, by adjusting the depth of analysis according to the player's emotions, it is possible to reduce the player's psychological burden and improve the player's acceptability of the analysis results. Emotion estimation is achieved using an emotion engine or generation AI. The analysis unit can input the player's emotional data into the generation AI and have the generation AI adjust the depth of the analysis.

[0138] The training department can estimate the player's emotions and adjust the training sequence based on the estimated emotions. For example, if the player is relaxed, more difficult training can be performed first, and if the player is stressed, relaxing training can be performed first. Also, if the player is concentrating, training that requires concentration can be prioritized. In this way, by adjusting the training sequence based on the player's emotions, the training effect can be maximized and the player's motivation can be maintained. Emotion estimation is achieved using an emotion engine or generative AI. The training department can input the player's emotional data into the generative AI and have the generative AI adjust the training sequence.

[0139] The advice unit can estimate the player's emotions and adjust the content of the advice based on the estimated emotions. For example, if the player is relaxed, detailed advice can be provided, and if the player is stressed, concise advice can be provided. Also, if the player is concentrating, specific and practical advice can be provided. In this way, by adjusting the content of advice based on the player's emotions, it becomes easier for the player to accept the advice and put it into practice. Emotion estimation is achieved using an emotion engine and generation AI. The advice unit inputs the player's emotional data into the generation AI and has the generation AI adjust the content of the advice.

[0140] The input unit can estimate the player's emotions and adjust the data input interface based on the estimated emotions. For example, if the player is relaxed, a detailed input interface can be provided, and if the player is stressed, a simplified input interface can be provided. Also, if the player is concentrating, an interface that allows for quick input can be provided. In this way, by adjusting the data input interface based on the player's emotions, the efficiency and accuracy of data input can be improved. Emotion estimation is achieved using an emotion engine or a generation AI. The input unit inputs the player's emotion data into the generation AI and has the generation AI adjust the data input interface.

[0141] The analysis unit can estimate the player's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the player is relaxed, the analysis results can be displayed using detailed graphs and charts, while if the player is stressed, the analysis results can be displayed in simple text. Also, if the player is concentrating, an interactive display method can be used. In this way, by adjusting the display method of the analysis results based on the player's emotions, it is possible to make it easier for the player to understand the analysis results and take the next action. Emotion estimation is achieved using an emotion engine and a generation AI. The analysis unit inputs the player's emotional data into the generation AI and has the generation AI adjust the display method of the analysis results.

[0142] The training department can analyze a player's past training data and adjust the frequency of training. For example, if a player has achieved results with high-frequency training in the past, the training frequency can be adjusted at a similar frequency. Conversely, if a player has achieved results with low-frequency training in the past, the training frequency can be reduced. Furthermore, the training frequency can be adjusted according to the player's physical condition and fatigue level. In this way, by adjusting the training frequency based on the player's past training data, the training effect can be maximized and the player's physical condition can be maintained. The training department can input past training data into the generation AI and have the generation AI adjust the training frequency.

[0143] The advice unit can analyze a player's past advice history and adjust the timing of advice. For example, if a player has been more likely to accept advice at a specific timing in the past, it can provide advice at that timing. Also, if a player has been less likely to accept advice at a specific timing in the past, it can avoid that timing. Furthermore, it can adjust the timing of advice according to the player's schedule and the situation of the game. In this way, by adjusting the timing of advice based on the player's past advice history, it is possible to maximize the effectiveness of advice and contribute to improving the player's performance. The advice unit inputs the past advice history into the generation AI and has the generation AI adjust the timing of advice.

[0144] The input unit can analyze the player's past data input history and select the optimal input device. For example, if the player has previously preferred voice input, it can provide a voice input device preferentially. Furthermore, if the player has previously preferred text input, it can provide a text input device preferentially. Furthermore, if the player has previously preferred image input, it can provide an image input device preferentially. In this way, by selecting the optimal input device based on the player's past data input history, it is possible to improve the efficiency and accuracy of data input. The input unit can input the past data input history into the generation AI and cause the generation AI to select the optimal input device.

[0145] The analysis unit can analyze a player's past performance data and determine analysis priorities. For example, if a player has previously performed well at a particular skill, it can prioritize analysis of data related to that skill. Conversely, if a player has previously performed poorly at a particular skill, it can prioritize analysis of data related to that skill. Furthermore, it can adjust analysis priorities according to the player's match situation and training goals. This makes it possible to improve the accuracy and efficiency of analysis by determining analysis priorities based on a player's past performance data. The analysis unit inputs past performance data into the generation AI and has the generation AI determine analysis priorities.

[0146] The training department can adjust the training content taking into account the geographical location information of the player. For example, if the player is at high altitude, high altitude training can be incorporated. If the player is at the seaside, training on the beach can be incorporated. Furthermore, if the player is in an urban area, training suitable for the urban environment can be incorporated. In this way, adjusting the training content taking into account the player's geographical location information can maximize the training effect and contribute to improving the player's performance. The training department can input the geographical location information into the generation AI and have the generation AI adjust the training content.

[0147] The processing flow of the second embodiment will be briefly explained below.

[0148] Step 1: The input unit inputs the player's past performance data or physical data. The player's past performance data includes, for example, points, assists, playing time, etc. The physical data includes, for example, weight, height, muscle strength measurement results, etc. Step 2: The generator uses the generative AI to analyze the data input by the input unit and generate a customized training program. The generator can provide training programs focused on improving the player's specific techniques and tactics, support focused on psychological aspects, and advice on proper nutritional intake and meal planning. Step 3: The training unit performs training based on the training program generated by the generation unit. The training unit performs physical training, technical training, etc. Step 4: The video input unit inputs video data of the game, including the entire game footage and specific play scenes. Step 5: The analysis unit uses the generative AI to analyze the video data input by the video input unit and identify tactical strengths and areas for improvement. The analysis unit identifies defensive positioning, attacking patterns, etc. Step 6: The Advice Department provides tactical advice based on the tactical strengths and areas for improvement identified by the Analysis Department. The Advice Department provides advice such as improving positioning or recommending specific plays.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0153] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0154] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] 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.

[0165] 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.

[0166] 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 AI 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0169] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0170] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0177] 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.

[0178] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0179] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0180] 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.

[0181] 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.

[0182] 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 AI 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.

[0183] 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.

[0184] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0185] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0186] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0196] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0197] 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.

[0198] 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.

[0199] 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 AI 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.

[0200] 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.

[0201] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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."

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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, in order to avoid confusion and to 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.

[0219] 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.

[0220] [Explanation of symbols]

[0221] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting the player's past performance data or physical data; a generation unit that analyzes the data input by the input unit and generates a customized training program; a training unit that performs training based on the training program generated by the generation unit; a video input unit for inputting video data of a game; an analysis unit that analyzes the video data input by the video input unit and identifies tactical strengths and areas for improvement; an advising unit that provides tactical advice based on the tactical strengths and areas for improvement identified by the analyzing unit; Equipped with A system characterized by:

2. The generation unit Generate training programs focused on improving specific skills and tactics for your players 2. The system of claim 1.

3. The analysis unit Analyze game video data to identify tactical strengths and areas for improvement 2. The system of claim 1.

4. The advice unit Providing tactical advice based on tactical strengths and areas for improvement 2. The system of claim 1.

5. The generation unit Providing support focused on the psychological aspects of athletes 2. The system of claim 1.

6. The generation unit Providing athletes with advice on proper nutrition and meal planning 2. The system of claim 1.

7. The analysis unit Analyze team performance data, track individual player performance and identify areas and potential for improvement 2. The system of claim 1.

8. The input unit Estimate the player's emotions and adjust the timing of data input based on the estimated player's emotions.

2. The system of claim 1.

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