System

Abstract

An object of a system according to an embodiment is to analyze data of an athlete and provide a personalized training program.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data of an athlete. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a training program based on the analysis result obtained by the analysis unit. The providing unit provides feedback based on the training program generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not yet effectively utilized athlete data to provide personalized training programs, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze athlete data and provide a personalized training program. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data on the athlete. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a training program based on the analysis results obtained by the analysis unit. The provision unit provides feedback based on the training program generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the data of the athlete and provide a personalized training program. [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 support system according to an embodiment of the present invention collects athlete data, analyzes it using a generation AI, generates a training program, and provides feedback. The sports support system collects athletes' physical characteristics, performance data, health status, and other information, and then generates an optimal training program for each individual athlete using a generation AI. For example, the sports support system collects data on athletes' muscle strength, endurance, flexibility, and other factors, and then analyzes the data using a generation AI to create an optimal training menu for each individual athlete. Next, the sports support system uses a generation AI to analyze the athletes' motion analysis data, identify movements and postures that pose a high risk of injury, and propose improvements. Furthermore, the sports support system uses a generation AI to analyze in-game play data and propose tactical improvements and new strategies. This allows the sports support system to optimize athletes' performance and support injury prevention and rehabilitation. This allows the sports support system to efficiently collect and analyze athletes' data and provide individually optimized training programs. For example, it can provide feedback and strategic advice to help athletes grow and improve. It can also provide support focused on athletes' psychological aspects, helping them maintain motivation and manage stress.

[0029] A sports support system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects athlete data. Examples of athlete data include, but are not limited to, physical characteristics, performance data, and health status. The collection unit collects data such as the athlete's muscle strength, endurance, and flexibility. The collection unit can also collect health status data such as the athlete's heart rate, blood pressure, and sleep patterns. The collection unit can also collect play data from the athlete's game. For example, the collection unit measures the athlete's movements using a sensor and collects motion analysis data. The analysis unit uses a generation AI to analyze the data collected by the collection unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit uses the generation AI to obtain analysis results based on the athlete's characteristics and goals. The analysis unit can also use the generation AI to analyze the athlete's motion analysis data and identify movements and postures that pose a high risk of injury. The analysis unit can also use the generation AI to analyze play data from the game and suggest tactical improvements or new strategies. The generation unit uses the generation AI to generate a training program based on the analysis results obtained by the analysis unit. The training program includes, for example, but is not limited to, the type, frequency, and intensity of exercises. For example, the generation unit uses the generation AI to generate an optimal training program for an individual athlete based on the athlete's characteristics and goals. The generation unit can also use the generation AI to generate a program for injury prevention and rehabilitation. Furthermore, the generation unit can also use the generation AI to generate a program for providing tactical advice and strategy. The provision unit provides feedback to the athlete based on the generated training program. The feedback can be provided, for example, in the form of real-time feedback or a report, but is not limited to these examples. For example, the provision unit provides feedback to the athlete based on the generated training program using the generation AI.The providing unit can also use the generating AI to provide information and instructions to support injury treatment and recovery. Furthermore, the providing unit can also use the generating AI to provide tactical advice and strategies. This allows the sports support system according to the embodiment to efficiently collect and analyze athlete data and provide individually optimized training programs. For example, it can provide feedback and strategic advice for the athlete's growth and improvement. It can also provide support focusing on the athlete's psychological aspects, helping them maintain motivation and manage stress.

[0030] The collection unit can collect data on the athlete's physical characteristics, performance data, and health status. Physical characteristics include, but are not limited to, for example, height, weight, muscle mass, etc. For example, the collection unit can measure the athlete's height and collect data. The collection unit can also measure the athlete's weight and collect data. The collection unit can also measure the athlete's muscle mass and collect data. Performance data includes, but is not limited to, for example, speed, endurance, reaction time, etc. For example, the collection unit can measure the athlete's speed and collect data. The collection unit can also measure the athlete's endurance and collect data. The collection unit can also measure the athlete's reaction time and collect data. Health status includes, but is not limited to, for example, heart rate, blood pressure, sleep patterns, etc. For example, the collection unit can measure the athlete's heart rate and collect data. The collection unit can also measure the athlete's blood pressure and collect data. The collection unit can also monitor the athlete's sleep patterns and collect data. This allows for more accurate analysis by collecting a variety of data on athletes. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the athlete's physical characteristics, performance data, and health status data into the generation AI, which then collects the data.

[0031] The analysis unit analyzes the collected data and obtains analysis results based on the athlete's characteristics and goals. Characteristics include, but are not limited to, physical fitness level, technical skill level, and psychological state. For example, the analysis unit analyzes the athlete's physical fitness level to identify the characteristics. The analysis unit can also analyze the athlete's technical level to identify the characteristics. The analysis unit can also analyze the athlete's psychological state to identify the characteristics. Goals include, but are not limited to, improving athletic performance, preventing injuries, and mastering a specific skill. For example, the analysis unit performs analysis with the goal of improving the athlete's athletic performance. The analysis unit can also perform analysis with the goal of preventing injuries. The analysis unit can also perform analysis with the goal of mastering a specific skill. By obtaining analysis results based on the athlete's characteristics and goals, an individually optimized training program can be provided. 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 the collected data into the generation AI, which can then perform analysis of the data.

[0032] The generation unit can generate an appropriate training program for each individual athlete based on the analysis results. An appropriate training program may include, but is not limited to, the type, frequency, and intensity of exercises. For example, the generation unit can generate a strength training program based on the athlete's characteristics and goals. The generation unit can also generate an endurance training program based on the athlete's characteristics and goals. The generation unit can also generate a flexibility training program based on the athlete's characteristics and goals. This maximizes the training effect by generating an optimal training program for each individual athlete. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the analysis results into the generation AI, which then generates the training program.

[0033] The providing unit can provide feedback to the athlete based on the generated training program. Feedback includes, for example, real-time feedback, report format, and the like, but is not limited to these examples. For example, the providing unit can provide feedback in real time based on the generated training program. The providing unit can also provide feedback in report format based on the generated training program. The providing unit can also provide feedback for the athlete's growth and improvement based on the generated training program. In this way, providing feedback based on the generated training program improves the athlete's training effect. Some or all of the above-described processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the providing unit can input the generated training program into the generation AI, which can then provide the feedback.

[0034] The analysis unit can analyze the athlete's motion analysis data to identify movements or postures that pose an injury risk. The motion analysis data includes, but is not limited to, movement speed, angle, and force distribution. For example, the analysis unit can analyze the athlete's motion speed to identify movements that pose an injury risk. The analysis unit can also analyze the angle of the athlete's motion to identify movements that pose an injury risk. The analysis unit can also analyze the force distribution of the athlete's motion to identify movements that pose an injury risk. Movements or postures that pose an injury risk include, but are not limited to, movements that put strain on the knees and postures that put strain on the lower back. For example, the analysis unit can identify movements that put strain on the knees and suggest improvements. The analysis unit can also identify postures that put strain on the lower back and suggest improvements. The analysis unit can also identify movements that put strain on the shoulders and suggest improvements. This helps prevent injuries by identifying movements and postures that pose a high risk of injury. 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 the generation AI. For example, the analysis unit may input motion analysis data to the generation AI, which may then identify motions and postures that pose a risk of injury.

[0035] The generation unit can generate a program for injury prevention and rehabilitation. Examples of programs for injury prevention and rehabilitation include, but are not limited to, stretching, strength training, and rehabilitation exercises. For example, the generation unit can generate a stretching program based on the athlete's characteristics and goals. The generation unit can also generate a strength training program based on the athlete's characteristics and goals. The generation unit can also generate a rehabilitation exercise program based on the athlete's characteristics and goals. This supports the athlete's health management by generating a program for injury prevention and rehabilitation. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input analysis results into the generation AI, which then generates a program for injury prevention and rehabilitation.

[0036] The providing unit can provide information and instructions to support injury treatment and recovery. Information and instructions to support injury treatment and recovery include, but are not limited to, rest instructions, rehabilitation methods, and nutritional guidance. For example, the providing unit provides rest instructions to the athlete. The providing unit can also provide rehabilitation methods to the athlete. The providing unit can also provide nutritional guidance to the athlete. This promotes early recovery of the athlete by providing information and instructions to support injury treatment and recovery. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the generated information and instructions into the generation AI, which then provides the information and instructions.

[0037] The analysis unit can analyze play data during a match and propose tactical improvements and new strategies. Examples of tactical improvements and new strategies include, but are not limited to, changes in positioning, improvements to attacking patterns, and strengthening of defensive tactics. For example, the analysis unit can analyze play data during a match and propose changes to positioning. The analysis unit can also analyze play data during a match and propose improvements to attacking patterns. The analysis unit can also analyze play data during a match and propose strengthening of defensive tactics. Thus, by analyzing play data during a match, tactical improvements and new strategies can be proposed. Some or all of the above-described processing by the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input play data during a match into a generation AI, which then proposes tactical improvements and new strategies.

[0038] The providing unit can provide tactical advice and strategies. Examples of tactical advice and strategies include, but are not limited to, real-time advice during a game and strategy reviews after a game. For example, the providing unit can provide real-time advice during a game. The providing unit can also provide strategy reviews after a game. The providing unit can also provide a pre-game strategic plan. This improves the performance of athletes and teams by providing tactical advice and strategies. Some or all of the above-described processing by the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the generated tactical advice and strategies into the generation AI, which can then provide the advice and strategies.

[0039] The analysis unit can monitor the athlete's psychological state and suggest advice to increase motivation and stress management methods. Examples of psychological state include, but are not limited to, stress level, motivation, and concentration. For example, the analysis unit can monitor the athlete's stress level and suggest stress management methods. The analysis unit can also monitor the athlete's motivation and suggest advice to increase motivation. The analysis unit can also monitor the athlete's concentration and suggest advice to improve concentration. Examples of advice to increase motivation and stress management methods include, but are not limited to, mental training, relaxation techniques, and goal setting methods. For example, the analysis unit can suggest mental training methods to the athlete. The analysis unit can also suggest relaxation techniques to the athlete. The analysis unit can also suggest goal setting methods to the athlete. In this way, by monitoring the athlete's psychological state, advice to increase motivation and stress management methods can be provided. 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 the athlete's psychological state data into the generation AI, which can then provide advice and suggest stress management methods.

[0040] The collection unit can analyze the athlete's past performance data and select the optimal data collection method. Past performance data includes, but is not limited to, past game results, training records, and feedback results. For example, the collection unit can analyze the athlete's past training data and suggest the most effective data collection method. The collection unit can also select a data collection method for a specific situation based on the athlete's past game data. The collection unit can also analyze the athlete's past health data and select a data collection method according to the athlete's health condition. In this way, the optimal data collection method can be selected by analyzing the athlete's past performance data. Some or all of the above-described processing in the collection unit can be performed using, or without, the generation AI. For example, the collection unit can input the athlete's past performance data into the generation AI, which can then select the data collection method.

[0041] When collecting data, the collection unit can filter the data based on the athlete's current training status and health condition. Examples of training status include, but are not limited to, the type, intensity, and frequency of training. For example, if the athlete is performing high-intensity training, the collection unit prioritizes collecting data on that type of training. Furthermore, if the athlete is in a recovery period, the collection unit can collect low-intensity data to monitor the athlete's health condition. Furthermore, if the athlete is injured, the collection unit can filter and collect data affected by the injury. Examples of health conditions include, but are not limited to, heart rate, blood pressure, and sleep patterns. For example, the collection unit can monitor the athlete's heart rate and filter the data if an abnormality is detected. Furthermore, the collection unit can monitor the athlete's blood pressure and filter the data if an abnormality is detected. Furthermore, the collection unit can monitor the athlete's sleep pattern and filter the data if an abnormality is detected. This allows for more accurate data collection by filtering data based on the athlete's current training status and health condition. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input data on the athlete's training status and health condition into the generation AI, and the generation AI may perform data filtering.

[0042] When collecting data, the collection unit can select the optimal collection means depending on the athlete's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the athlete uses voice input, the collection unit prioritizes collecting voice data. Furthermore, if the athlete uses text input, the collection unit can also collect text data and use it for analysis. Furthermore, if the athlete provides image data, the collection unit can perform image analysis and extract necessary data. This enables efficient data collection by selecting the optimal collection means depending on the athlete's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the athlete's input data into the generation AI, which then selects the optimal collection means.

[0043] When collecting data, the collection unit can prioritize collection of highly relevant data based on the athlete's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and the use of location information services. For example, if the athlete is training at high altitude, the collection unit can prioritize collection of that data. Furthermore, if the athlete is training at a specific stadium, the collection unit can also collect data on that location. Furthermore, if the athlete is training in different climatic conditions, the collection unit can collect that data. In this way, by taking the athlete's geographical location information into consideration, highly relevant data can be collected preferentially. Some or all of the above-described processing by the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the athlete's geographical location information into the generation AI, which can then collect the highly relevant data.

[0044] During data collection, the collection unit can analyze the athlete's social media activity and collect related data. Social media activity includes, but is not limited to, for example, the content of posts, follower reactions, and activity frequency. For example, the collection unit can collect training content shared by the athlete on social media. The collection unit can also collect health conditions mentioned by the athlete on social media. The collection unit can also collect dietary information shared by the athlete on social media and reflect this in the nutrition plan. In this way, related data can be collected by analyzing the athlete's social media activity. Some or all of the above-described processing in the collection unit can be performed, for example, using or without the generation AI. For example, the collection unit can input the athlete's social media data into the generation AI, which can then collect the related data.

[0045] When collecting data, the collection unit can customize the collection method by reflecting the athlete's past feedback. Past feedback includes, but is not limited to, examples of training effects, areas for improvement, and the athlete's thoughts. The collection unit, for example, adjusts the data collection method based on feedback provided by the athlete in the past. The collection unit can also prioritize the use of data collection methods that the athlete has previously preferred. The collection unit can also adjust to avoid data collection methods that the athlete has previously dissatisfied with. In this way, by reflecting the athlete's past feedback, a more appropriate data collection method can be selected. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the athlete's past feedback data into the generation AI, which then customizes the collection method.

[0046] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. Examples of the importance of data include, but are not limited to, the impact of training and the urgency of a health condition. For example, the analysis unit performs a detailed analysis of important performance data. The analysis unit can also perform a detailed analysis of data related to a health condition. The analysis unit can also perform a concise analysis of training data to provide results that focus on the main points. By adjusting the level of detail of the analysis based on the importance of the data, important data can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the data into the generation AI, which then adjusts the level of detail of the analysis.

[0047] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. Data categories include, but are not limited to, physical data, performance data, and psychological data. For example, the analysis unit can analyze performance data using a specific algorithm. The analysis unit can also analyze health data using a different algorithm. The analysis unit can also select an optimal algorithm for training data and perform analysis. This enables more accurate analysis by applying different analysis algorithms depending on the data category. 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 data category into the generation AI, which can then apply the optimal analysis algorithm.

[0048] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the athlete's past analysis results. Past analysis results include, but are not limited to, past training effects, performance changes, and feedback results. The analysis unit improves the accuracy of the analysis, for example, based on the athlete's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the athlete's past training data. The analysis unit can also improve the accuracy of the analysis by referring to the athlete's past health data. In this way, the accuracy of the analysis can be improved by referring to the athlete's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the athlete's past analysis results into the generation AI, which then improves the accuracy of the analysis.

[0049] During analysis, the analysis unit can determine analysis priorities based on the time when data was collected. Examples of data collection times include, but are not limited to, before and after training, during a match, and during rest. The analysis unit, for example, prioritizes analysis of the most recent data and provides real-time feedback. The analysis unit can also prioritize analysis of data collected before an important event and provide strategic advice. The analysis unit can also prioritize analysis of past data to understand long-term trends. In this way, by determining the analysis priorities based on the time when data was collected, important data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time when data was collected into the generation AI, and the generation AI can determine the analysis priorities.

[0050] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. Examples of data relevance include, but are not limited to, the relevance between training data and health data, and the relevance between performance data and psychological data. For example, the analysis unit can prioritize analysis of data with high relevance to provide detailed results. Alternatively, the analysis unit can analyze data with medium relevance next to provide results that focus on the main points. Alternatively, the analysis unit can analyze data with low relevance last to provide concise results. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of the data into the generation AI, which can then adjust the order of analysis.

[0051] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the athlete's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the athlete has specialized knowledge, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the athlete has general knowledge, the analysis unit can provide analysis results that use easy-to-understand terminology. Furthermore, if the athlete is a beginner, the analysis unit can provide analysis results that use simple terminology. Thus, by adjusting the use of technical terminology in the analysis according to the athlete's level of expertise, it is possible to provide analysis results that are easy to understand. 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 the athlete's level of expertise into the generation AI, which can then adjust the use of technical terminology.

[0052] The generation unit can adjust the level of detail of the generated program based on the importance of the analysis results during generation. The importance of the analysis results includes, but is not limited to, the impact of training and the urgency of a health condition. For example, the generation unit provides a detailed training program based on important analysis results. The generation unit can also provide a detailed training program based on analysis results related to a health condition. The generation unit can also provide a concise training program based on training data. By adjusting the level of detail of the generated program based on the importance of the analysis results, a detailed training program based on important analysis results can be provided. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the importance of the analysis results into the generation AI, which then adjusts the level of detail of the program.

[0053] During generation, the generation unit can apply different generation algorithms depending on the athlete's characteristics and goals. Examples of the athlete's characteristics and goals include, but are not limited to, physical strength level, technical level, improvement of athletic performance, and injury prevention. For example, when aiming to improve the athlete's muscle strength, the generation unit can apply an algorithm specialized for muscle training. Furthermore, when aiming to improve the athlete's endurance, the generation unit can apply an algorithm specialized for endurance training. Furthermore, when aiming to improve the athlete's flexibility, the generation unit can apply an algorithm specialized for flexibility training. In this way, by applying different generation algorithms depending on the athlete's characteristics and goals, it is possible to provide an individually optimized training program. Some or all of the above-described 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 the athlete's characteristics and goals into the generation AI, which then applies the optimal generation algorithm.

[0054] During generation, the generation unit can improve the accuracy of generation by referring to the athlete's past training programs. Past training programs include, but are not limited to, past training menus, training effects, and feedback results. For example, the generation unit optimizes a new program based on the athlete's past training programs. The generation unit can also improve the accuracy of the training program by referring to the athlete's past training data. The generation unit can also adjust the training program based on the athlete's past feedback. In this way, the accuracy of generation can be improved by referring to the athlete's past training programs. 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 athlete's past training programs into the generation AI, which then performs the improvement of the generation accuracy.

[0055] At the time of generation, the generation unit can determine the priority of the programs to be generated based on the time when the analysis results were collected. Examples of the time when the analysis results were collected include, but are not limited to, before and after training, during a match, and during rest. The generation unit, for example, prioritizes generating training programs based on the latest analysis results. The generation unit can also generate strategic training programs based on analysis results obtained before an important event. The generation unit can also generate training programs based on past analysis results to grasp long-term trends. Thus, by determining the priority of the programs to be generated based on the time when the analysis results were collected, training programs based on important analysis results can be provided preferentially. Some or all of the above-described 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 the time when the analysis results were collected into the generation AI, and the generation AI can determine the priority of the programs.

[0056] During generation, the generation unit can adjust the order of the programs to be generated based on the relevance of the analysis results. Examples of the relevance of the analysis results include, but are not limited to, the relevance between training data and health data, and the relevance between performance data and psychological data. For example, the generation unit prioritizes generating training programs based on analysis results with high relevance. The generation unit can also generate training programs based on analysis results with medium relevance next. The generation unit can also generate training programs based on analysis results with low relevance last. This allows for the provision of efficient training programs by adjusting the order of the programs to be generated based on the relevance of the analysis results. Some or all of the above-described 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 the relevance of the analysis results into the generation AI, which then adjusts the order of the programs.

[0057] During generation, the generation unit can adjust the use of technical terminology in the generated program according to the athlete's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the athlete has specialized knowledge, the generation unit can provide a training program that uses a lot of technical terminology. Furthermore, if the athlete has general knowledge, the generation unit can also provide a training program that uses easy-to-understand terminology. Furthermore, if the athlete is a beginner, the generation unit can also provide a training program that uses simple terminology. In this way, by adjusting the use of technical terminology in the generated program according to the athlete's level of expertise, an easy-to-understand training program can be provided. Some or all of the above-described 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 the athlete's level of expertise into the generation AI, which can then adjust the use of technical terminology.

[0058] When providing feedback, the providing unit can adjust the level of detail of the feedback based on the importance of the training program. The importance of the training program includes, but is not limited to, for example, the impact of the training and the urgency of the health condition. For example, the providing unit provides detailed feedback for an important training program. The providing unit can also provide detailed feedback for a training program related to a health condition. The providing unit can also provide brief feedback for a general training program. By adjusting the level of detail of the feedback based on the importance of the training program, detailed feedback can be provided for an important training program. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the importance of the training program to the generation AI, which can then adjust the level of detail of the feedback.

[0059] When providing feedback, the providing unit can apply different feedback algorithms depending on the athlete's characteristics and goals. Examples of the athlete's characteristics and goals include, but are not limited to, physical strength level, technical level, improvement of athletic performance, and injury prevention. For example, when aiming to improve the athlete's muscle strength, the providing unit can provide feedback specialized for muscle training. Furthermore, when aiming to improve the athlete's endurance, the providing unit can provide feedback specialized for endurance training. Furthermore, when aiming to improve the athlete's flexibility, the providing unit can provide feedback specialized for flexibility training. In this way, by applying different feedback algorithms depending on the athlete's characteristics and goals, individually optimized feedback can be provided. Some or all of the above-described processing by the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the athlete's characteristics and goals into the generation AI, which can then apply the optimal feedback algorithm.

[0060] When providing feedback, the providing unit can improve the accuracy of the feedback by referring to the athlete's past feedback results. Past feedback results include, but are not limited to, for example, training effects, areas for improvement, and the athlete's thoughts. The providing unit, for example, optimizes new feedback based on the athlete's past feedback results. The providing unit can also improve the accuracy of the feedback by referring to the athlete's past training data. The providing unit can also adjust the feedback based on the athlete's past feedback. In this way, the accuracy of the feedback can be improved by referring to the athlete's past feedback results. Some or all of the above-mentioned processing by the providing unit may be performed, for example, using or without the generation AI. For example, the providing unit can input the athlete's past feedback results into the generation AI, which can then improve the accuracy of the feedback.

[0061] When providing feedback, the providing unit can determine the priority of feedback based on the collection time of the training program. Examples of the collection time of the training program include, but are not limited to, before and after training, during a match, and during rest. For example, the providing unit can prioritize feedback based on the most recent training program. The providing unit can also provide strategic feedback based on a training program before an important event. The providing unit can also provide feedback based on past training programs to grasp long-term trends. Thus, by determining the priority of feedback based on the collection time of the training program, feedback can be prioritized for important training programs. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the collection time of the training program to the generation AI, which can then determine the priority of the feedback.

[0062] When providing feedback, the providing unit can adjust the order of feedback based on the relevance of the training programs. Examples of the relevance of the training programs include, but are not limited to, the relevance between training data and health data, and the relevance between performance data and psychological data. For example, the providing unit can prioritize providing feedback based on training programs with high relevance. The providing unit can also provide feedback based on training programs with medium relevance next. The providing unit can also provide feedback based on training programs with low relevance last. This enables efficient feedback provision by adjusting the order of feedback based on the relevance of the training programs. Some or all of the above-described processing by the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the relevance of the training programs to the generation AI, and the generation AI can adjust the order of feedback.

[0063] When providing feedback, the providing unit can adjust the use of technical terminology in the feedback depending on the athlete's expertise level. Examples of expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the athlete has specialized knowledge, the providing unit can provide feedback that uses a lot of technical terminology. Furthermore, if the athlete has general knowledge, the providing unit can also provide feedback that uses easy-to-understand terminology. Furthermore, if the athlete is a beginner, the providing unit can also provide feedback that uses simple terminology. Thus, by adjusting the use of technical terminology in the feedback depending on the athlete's expertise level, it is possible to provide feedback that is easy to understand. Some or all of the above-described processing by the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the athlete's expertise level into the generation AI, which can then adjust the use of technical terminology.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] When collecting athlete data, the collection unit can also collect environmental data about the athlete. Environmental data includes, but is not limited to, temperature, humidity, and wind speed. For example, if the athlete is training outdoors, the collection unit collects temperature and humidity data. Furthermore, if the athlete is training indoors, the collection unit can also collect indoor temperature and humidity data. Furthermore, if the athlete is training at high altitude, the collection unit can also collect altitude data. By collecting data based on the athlete's training environment, more accurate analysis is possible.

[0066] When analyzing the athlete's data, the analysis unit can also analyze the athlete's dietary data. Dietary data includes, but is not limited to, calorie intake, nutrient balance, and meal timing. For example, the analysis unit analyzes the athlete's calorie intake and evaluates the balance with energy expenditure. The analysis unit can also analyze the athlete's nutrient balance and suggest supplementing with necessary nutrients. The analysis unit can also analyze the athlete's meal timing and suggest a meal schedule to maximize training effects. In this way, by analyzing the athlete's dietary data, a more effective training program can be provided.

[0067] The providing unit can provide the athlete with feedback that visualizes the progress of the training program. Examples of feedback include, but are not limited to, visual displays using graphs and charts, progress reports, and goal achievement evaluations. For example, the providing unit can display the athlete's training progress in a graph to visually indicate the degree of goal achievement. The providing unit can also provide the athlete's training progress in report format. The providing unit can also evaluate the athlete's degree of goal achievement and suggest next steps. This makes it easier to maintain motivation by visualizing the athlete's training progress.

[0068] When collecting athlete data, the collection unit may also collect social media activity data of the athlete. Examples of social media activity data include, but are not limited to, the content of posts, follower reactions, and activity frequency. For example, the collection unit may collect training content shared by the athlete on social media. The collection unit may also collect health conditions mentioned by the athlete on social media. The collection unit may also collect dietary information shared by the athlete on social media and reflect this information in the athlete's nutrition plan. This allows related data to be collected by analyzing the athlete's social media activity.

[0069] When analyzing the athlete's data, the analysis unit can also analyze the athlete's sleep data. Sleep data includes, but is not limited to, sleep duration, sleep quality, and sleep cycle. For example, the analysis unit analyzes the athlete's sleep duration and evaluates the correlation with training effects. The analysis unit can also analyze the athlete's sleep quality and suggest improvements to improve performance. The analysis unit can also analyze the athlete's sleep cycle and suggest optimal training times. In this way, by analyzing the athlete's sleep data, a more effective training program can be provided.

[0070] The providing unit can provide the athlete with feedback evaluating the effectiveness of the training program. Examples of feedback include, but are not limited to, an evaluation of the training effect, suggestions for improvement, and instructions for the next step. For example, the providing unit can evaluate the athlete's training effect and indicate the degree of achievement. The providing unit can also suggest improvements to the athlete's training program. The providing unit can also instruct the athlete on the next step and support the athlete in continuing the training. In this way, the quality of training can be improved by evaluating the effectiveness of the athlete's training program.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The collection unit collects athlete data. Athlete data includes physical characteristics, performance data, and health status. For example, the collection unit collects the athlete's muscle strength, endurance, flexibility, heart rate, blood pressure, sleep patterns, and play data during a game. The collection unit can also use sensors to measure the athlete's movements and collect motion analysis data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, the analysis unit uses generative AI to obtain analysis results based on the athlete's characteristics and goals. It can also analyze motion analysis data to identify movements and postures that pose a high risk of injury. It can also analyze play data during a match to suggest areas for tactical improvement and new strategies. Step 3: The generator generates a training program based on the analysis results obtained by the analyzer. The generator uses the generative AI to generate a training program that includes the type, frequency, and intensity of exercises. For example, it generates an optimal training program for each individual athlete based on the athlete's characteristics and goals. It can also generate programs for injury prevention and rehabilitation, as well as programs that provide tactical advice and strategies. Step 4: The provider provides feedback to the athlete based on the generated training program. The feedback may be provided in the form of real-time feedback or a report. For example, feedback may be provided based on the training program generated using generative AI. The provider may also provide information, instructions, tactical advice, and strategies to support injury treatment and recovery.

[0073] (Example 2) A sports support system according to an embodiment of the present invention collects athlete data, analyzes it using a generation AI, generates a training program, and provides feedback. The sports support system collects athletes' physical characteristics, performance data, health status, and other information, and then generates an optimal training program for each individual athlete using a generation AI. For example, the sports support system collects data on athletes' muscle strength, endurance, flexibility, and other factors, and then analyzes the data using a generation AI to create an optimal training menu for each individual athlete. Next, the sports support system uses a generation AI to analyze the athletes' motion analysis data, identify movements and postures that pose a high risk of injury, and propose improvements. Furthermore, the sports support system uses a generation AI to analyze in-game play data and propose tactical improvements and new strategies. This allows the sports support system to optimize athletes' performance and support injury prevention and rehabilitation. This allows the sports support system to efficiently collect and analyze athletes' data and provide individually optimized training programs. For example, it can provide feedback and strategic advice to help athletes grow and improve. It can also provide support focused on athletes' psychological aspects, helping them maintain motivation and manage stress.

[0074] A sports support system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects athlete data. Examples of athlete data include, but are not limited to, physical characteristics, performance data, and health status. The collection unit collects data such as the athlete's muscle strength, endurance, and flexibility. The collection unit can also collect health status data such as the athlete's heart rate, blood pressure, and sleep patterns. The collection unit can also collect play data from the athlete's game. For example, the collection unit measures the athlete's movements using a sensor and collects motion analysis data. The analysis unit uses a generation AI to analyze the data collected by the collection unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit uses the generation AI to obtain analysis results based on the athlete's characteristics and goals. The analysis unit can also use the generation AI to analyze the athlete's motion analysis data and identify movements and postures that pose a high risk of injury. The analysis unit can also use the generation AI to analyze play data from the game and suggest tactical improvements or new strategies. The generation unit uses the generation AI to generate a training program based on the analysis results obtained by the analysis unit. The training program includes, for example, but is not limited to, the type, frequency, and intensity of exercises. For example, the generation unit uses the generation AI to generate an optimal training program for an individual athlete based on the athlete's characteristics and goals. The generation unit can also use the generation AI to generate a program for injury prevention and rehabilitation. Furthermore, the generation unit can also use the generation AI to generate a program for providing tactical advice and strategy. The provision unit provides feedback to the athlete based on the generated training program. The feedback can be provided, for example, in the form of real-time feedback or a report, but is not limited to these examples. For example, the provision unit provides feedback to the athlete based on the generated training program using the generation AI.The providing unit can also use the generating AI to provide information and instructions to support injury treatment and recovery. Furthermore, the providing unit can also use the generating AI to provide tactical advice and strategies. This allows the sports support system according to the embodiment to efficiently collect and analyze athlete data and provide individually optimized training programs. For example, it can provide feedback and strategic advice for the athlete's growth and improvement. It can also provide support focusing on the athlete's psychological aspects, helping them maintain motivation and manage stress.

[0075] The collection unit can collect data on the athlete's physical characteristics, performance data, and health status. Physical characteristics include, but are not limited to, for example, height, weight, muscle mass, etc. For example, the collection unit can measure the athlete's height and collect data. The collection unit can also measure the athlete's weight and collect data. The collection unit can also measure the athlete's muscle mass and collect data. Performance data includes, but is not limited to, for example, speed, endurance, reaction time, etc. For example, the collection unit can measure the athlete's speed and collect data. The collection unit can also measure the athlete's endurance and collect data. The collection unit can also measure the athlete's reaction time and collect data. Health status includes, but is not limited to, for example, heart rate, blood pressure, sleep patterns, etc. For example, the collection unit can measure the athlete's heart rate and collect data. The collection unit can also measure the athlete's blood pressure and collect data. The collection unit can also monitor the athlete's sleep patterns and collect data. This allows for more accurate analysis by collecting a variety of data on athletes. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input the athlete's physical characteristics, performance data, and health status data into the generation AI, which then collects the data.

[0076] The analysis unit analyzes the collected data and obtains analysis results based on the athlete's characteristics and goals. Characteristics include, but are not limited to, physical fitness level, technical skill level, and psychological state. For example, the analysis unit analyzes the athlete's physical fitness level to identify the characteristics. The analysis unit can also analyze the athlete's technical level to identify the characteristics. The analysis unit can also analyze the athlete's psychological state to identify the characteristics. Goals include, but are not limited to, improving athletic performance, preventing injuries, and mastering a specific skill. For example, the analysis unit performs analysis with the goal of improving the athlete's athletic performance. The analysis unit can also perform analysis with the goal of preventing injuries. The analysis unit can also perform analysis with the goal of mastering a specific skill. By obtaining analysis results based on the athlete's characteristics and goals, an individually optimized training program can be provided. 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 the collected data into the generation AI, which can then perform analysis of the data.

[0077] The generation unit can generate an appropriate training program for each individual athlete based on the analysis results. An appropriate training program may include, but is not limited to, the type, frequency, and intensity of exercises. For example, the generation unit can generate a strength training program based on the athlete's characteristics and goals. The generation unit can also generate an endurance training program based on the athlete's characteristics and goals. The generation unit can also generate a flexibility training program based on the athlete's characteristics and goals. This maximizes the training effect by generating an optimal training program for each individual athlete. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the analysis results into the generation AI, which then generates the training program.

[0078] The providing unit can provide feedback to the athlete based on the generated training program. Feedback includes, for example, real-time feedback, report format, and the like, but is not limited to these examples. For example, the providing unit can provide feedback in real time based on the generated training program. The providing unit can also provide feedback in report format based on the generated training program. The providing unit can also provide feedback for the athlete's growth and improvement based on the generated training program. In this way, providing feedback based on the generated training program improves the athlete's training effect. Some or all of the above-described processing in the providing unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the providing unit can input the generated training program into the generation AI, which can then provide the feedback.

[0079] The analysis unit can analyze the athlete's motion analysis data to identify movements or postures that pose an injury risk. The motion analysis data includes, but is not limited to, movement speed, angle, and force distribution. For example, the analysis unit can analyze the athlete's motion speed to identify movements that pose an injury risk. The analysis unit can also analyze the angle of the athlete's motion to identify movements that pose an injury risk. The analysis unit can also analyze the force distribution of the athlete's motion to identify movements that pose an injury risk. Movements or postures that pose an injury risk include, but are not limited to, movements that put strain on the knees and postures that put strain on the lower back. For example, the analysis unit can identify movements that put strain on the knees and suggest improvements. The analysis unit can also identify postures that put strain on the lower back and suggest improvements. The analysis unit can also identify movements that put strain on the shoulders and suggest improvements. This helps prevent injuries by identifying movements and postures that pose a high risk of injury. 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 the generation AI. For example, the analysis unit may input motion analysis data to the generation AI, which may then identify motions and postures that pose a risk of injury.

[0080] The generation unit can generate a program for injury prevention and rehabilitation. Examples of programs for injury prevention and rehabilitation include, but are not limited to, stretching, strength training, and rehabilitation exercises. For example, the generation unit can generate a stretching program based on the athlete's characteristics and goals. The generation unit can also generate a strength training program based on the athlete's characteristics and goals. The generation unit can also generate a rehabilitation exercise program based on the athlete's characteristics and goals. This supports the athlete's health management by generating a program for injury prevention and rehabilitation. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input analysis results into the generation AI, which then generates a program for injury prevention and rehabilitation.

[0081] The providing unit can provide information and instructions to support injury treatment and recovery. Information and instructions to support injury treatment and recovery include, but are not limited to, rest instructions, rehabilitation methods, and nutritional guidance. For example, the providing unit provides rest instructions to the athlete. The providing unit can also provide rehabilitation methods to the athlete. The providing unit can also provide nutritional guidance to the athlete. This promotes early recovery of the athlete by providing information and instructions to support injury treatment and recovery. Some or all of the above-described processing by the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the generated information and instructions into the generation AI, which then provides the information and instructions.

[0082] The analysis unit can analyze play data during a match and propose tactical improvements and new strategies. Examples of tactical improvements and new strategies include, but are not limited to, changes in positioning, improvements to attacking patterns, and strengthening of defensive tactics. For example, the analysis unit can analyze play data during a match and propose changes to positioning. The analysis unit can also analyze play data during a match and propose improvements to attacking patterns. The analysis unit can also analyze play data during a match and propose strengthening of defensive tactics. Thus, by analyzing play data during a match, tactical improvements and new strategies can be proposed. Some or all of the above-described processing by the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input play data during a match into a generation AI, which then proposes tactical improvements and new strategies.

[0083] The providing unit can provide tactical advice and strategies. Examples of tactical advice and strategies include, but are not limited to, real-time advice during a game and strategy reviews after a game. For example, the providing unit can provide real-time advice during a game. The providing unit can also provide strategy reviews after a game. The providing unit can also provide a pre-game strategic plan. This improves the performance of athletes and teams by providing tactical advice and strategies. Some or all of the above-described processing by the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the generated tactical advice and strategies into the generation AI, which can then provide the advice and strategies.

[0084] The analysis unit can monitor the athlete's psychological state and suggest advice to increase motivation and stress management methods. Examples of psychological state include, but are not limited to, stress level, motivation, and concentration. For example, the analysis unit can monitor the athlete's stress level and suggest stress management methods. The analysis unit can also monitor the athlete's motivation and suggest advice to increase motivation. The analysis unit can also monitor the athlete's concentration and suggest advice to improve concentration. Examples of advice to increase motivation and stress management methods include, but are not limited to, mental training, relaxation techniques, and goal setting methods. For example, the analysis unit can suggest mental training methods to the athlete. The analysis unit can also suggest relaxation techniques to the athlete. The analysis unit can also suggest goal setting methods to the athlete. In this way, by monitoring the athlete's psychological state, advice to increase motivation and stress management methods can be provided. 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 the athlete's psychological state data into the generation AI, which can then provide advice and suggest stress management methods.

[0085] The collection unit can estimate the athlete's emotions and adjust the timing of data collection based on the estimated athlete's emotions. Examples of emotions include, but are not limited to, relaxation, concentration, fatigue, etc. The collection unit can, for example, start data collection when the athlete is relaxed and prioritize data collection when the athlete is in a low-stress state. The collection unit can also collect data when the athlete is concentrated and collect data when the athlete is performing at their best. The collection unit can also temporarily stop data collection when the athlete is tired and resume it after a rest. This allows for more appropriate data collection by adjusting the timing of data collection based on the athlete'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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, the generation AI. For example, the collection unit can input the athlete's emotion data into the generation AI, and the generation AI can adjust the timing of data collection.

[0086] The collection unit can analyze the athlete's past performance data and select the optimal data collection method. Past performance data includes, but is not limited to, past game results, training records, and feedback results. For example, the collection unit can analyze the athlete's past training data and suggest the most effective data collection method. The collection unit can also select a data collection method for a specific situation based on the athlete's past game data. The collection unit can also analyze the athlete's past health data and select a data collection method according to the athlete's health condition. In this way, the optimal data collection method can be selected by analyzing the athlete's past performance data. Some or all of the above-described processing in the collection unit can be performed using, or without, the generation AI. For example, the collection unit can input the athlete's past performance data into the generation AI, which can then select the data collection method.

[0087] When collecting data, the collection unit can filter the data based on the athlete's current training status and health condition. Examples of training status include, but are not limited to, the type, intensity, and frequency of training. For example, if the athlete is performing high-intensity training, the collection unit prioritizes collecting data on that type of training. Furthermore, if the athlete is in a recovery period, the collection unit can collect low-intensity data to monitor the athlete's health condition. Furthermore, if the athlete is injured, the collection unit can filter and collect data affected by the injury. Examples of health conditions include, but are not limited to, heart rate, blood pressure, and sleep patterns. For example, the collection unit can monitor the athlete's heart rate and filter the data if an abnormality is detected. Furthermore, the collection unit can monitor the athlete's blood pressure and filter the data if an abnormality is detected. Furthermore, the collection unit can monitor the athlete's sleep pattern and filter the data if an abnormality is detected. This allows for more accurate data collection by filtering data based on the athlete's current training status and health condition. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input data on the athlete's training status and health condition into the generation AI, and the generation AI may perform data filtering.

[0088] When collecting data, the collection unit can select the optimal collection means depending on the athlete's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the athlete uses voice input, the collection unit prioritizes collecting voice data. Furthermore, if the athlete uses text input, the collection unit can also collect text data and use it for analysis. Furthermore, if the athlete provides image data, the collection unit can perform image analysis and extract necessary data. This enables efficient data collection by selecting the optimal collection means depending on the athlete's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the athlete's input data into the generation AI, which then selects the optimal collection means.

[0089] The collection unit can estimate the athlete's emotions and prioritize the data to be collected based on the estimated emotions. Examples of emotions include, but are not limited to, stress, relaxation, and concentration. For example, if the athlete is feeling stressed, the collection unit can prioritize collecting stress-related data. Furthermore, if the athlete is relaxed, the collection unit can prioritize collecting performance data. Furthermore, if the athlete is focused, the collection unit can prioritize collecting training data. By prioritizing the data to be collected based on the athlete's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit can be performed using, for example, the generation AI. For example, the collection unit can input the athlete's emotion data into the generation AI, which can then prioritize the data.

[0090] When collecting data, the collection unit can prioritize collection of highly relevant data based on the athlete's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and the use of location information services. For example, if the athlete is training at high altitude, the collection unit can prioritize collection of that data. Furthermore, if the athlete is training at a specific stadium, the collection unit can also collect data on that location. Furthermore, if the athlete is training in different climatic conditions, the collection unit can collect that data. In this way, by taking the athlete's geographical location information into consideration, highly relevant data can be collected preferentially. Some or all of the above-described processing by the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the athlete's geographical location information into the generation AI, which can then collect the highly relevant data.

[0091] During data collection, the collection unit can analyze the athlete's social media activity and collect related data. Social media activity includes, but is not limited to, for example, the content of posts, follower reactions, and activity frequency. For example, the collection unit can collect training content shared by the athlete on social media. The collection unit can also collect health conditions mentioned by the athlete on social media. The collection unit can also collect dietary information shared by the athlete on social media and reflect this in the nutrition plan. In this way, related data can be collected by analyzing the athlete's social media activity. Some or all of the above-described processing in the collection unit can be performed, for example, using or without the generation AI. For example, the collection unit can input the athlete's social media data into the generation AI, which can then collect the related data.

[0092] When collecting data, the collection unit can customize the collection method by reflecting the athlete's past feedback. Past feedback includes, but is not limited to, examples of training effects, areas for improvement, and the athlete's thoughts. The collection unit, for example, adjusts the data collection method based on feedback provided by the athlete in the past. The collection unit can also prioritize the use of data collection methods that the athlete has previously preferred. The collection unit can also adjust to avoid data collection methods that the athlete has previously dissatisfied with. In this way, by reflecting the athlete's past feedback, a more appropriate data collection method can be selected. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the athlete's past feedback data into the generation AI, which then customizes the collection method.

[0093] The analysis unit can estimate the athlete's emotions and adjust the presentation of the analysis based on the estimated emotions. Examples of emotions include, but are not limited to, relaxation, stress, and concentration. For example, if the athlete is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the athlete is stressed, the analysis unit can provide concise and concise analysis results. Furthermore, if the athlete is focused, the analysis unit can provide analysis results using visually easy-to-understand graphs and charts. This allows for more appropriate analysis results to be provided by adjusting the presentation of the analysis based on the athlete'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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI. For example, the analysis unit can input the athlete's emotion data into the generation AI, which can then adjust the presentation of the analysis.

[0094] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. Examples of the importance of data include, but are not limited to, the impact of training and the urgency of a health condition. For example, the analysis unit performs a detailed analysis of important performance data. The analysis unit can also perform a detailed analysis of data related to a health condition. The analysis unit can also perform a concise analysis of training data to provide results that focus on the main points. By adjusting the level of detail of the analysis based on the importance of the data, important data can be analyzed in detail. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the data into the generation AI, which then adjusts the level of detail of the analysis.

[0095] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. Data categories include, but are not limited to, physical data, performance data, and psychological data. For example, the analysis unit can analyze performance data using a specific algorithm. The analysis unit can also analyze health data using a different algorithm. The analysis unit can also select an optimal algorithm for training data and perform analysis. This enables more accurate analysis by applying different analysis algorithms depending on the data category. 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 data category into the generation AI, which can then apply the optimal analysis algorithm.

[0096] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the athlete's past analysis results. Past analysis results include, but are not limited to, past training effects, performance changes, and feedback results. The analysis unit improves the accuracy of the analysis, for example, based on the athlete's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the athlete's past training data. The analysis unit can also improve the accuracy of the analysis by referring to the athlete's past health data. In this way, the accuracy of the analysis can be improved by referring to the athlete's past analysis results. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the athlete's past analysis results into the generation AI, which then improves the accuracy of the analysis.

[0097] The analysis unit can estimate the athlete's emotions and adjust the length of the analysis based on the estimated athlete's emotions. Examples of emotions include, but are not limited to, relaxation, stress, and concentration. For example, the analysis unit can provide a detailed analysis when the athlete is relaxed. Alternatively, the analysis unit can provide a brief analysis when the athlete is stressed. Alternatively, the analysis unit can provide a medium level of detail when the athlete is focused. This allows for more appropriate analysis results by adjusting the length of the analysis based on the athlete's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI. For example, the analysis unit can input the athlete's emotion data into the generation AI, which can then adjust the length of the analysis.

[0098] During analysis, the analysis unit can determine analysis priorities based on the time when data was collected. Examples of data collection times include, but are not limited to, before and after training, during a match, and during rest. The analysis unit, for example, prioritizes analysis of the most recent data and provides real-time feedback. The analysis unit can also prioritize analysis of data collected before an important event and provide strategic advice. The analysis unit can also prioritize analysis of past data to understand long-term trends. In this way, by determining the analysis priorities based on the time when data was collected, important data can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time when data was collected into the generation AI, and the generation AI can determine the analysis priorities.

[0099] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. Examples of data relevance include, but are not limited to, the relevance between training data and health data, and the relevance between performance data and psychological data. For example, the analysis unit can prioritize analysis of data with high relevance to provide detailed results. Alternatively, the analysis unit can analyze data with medium relevance next to provide results that focus on the main points. Alternatively, the analysis unit can analyze data with low relevance last to provide concise results. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of the data into the generation AI, which can then adjust the order of analysis.

[0100] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the athlete's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the athlete has specialized knowledge, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the athlete has general knowledge, the analysis unit can provide analysis results that use easy-to-understand terminology. Furthermore, if the athlete is a beginner, the analysis unit can provide analysis results that use simple terminology. Thus, by adjusting the use of technical terminology in the analysis according to the athlete's level of expertise, it is possible to provide analysis results that are easy to understand. 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 the athlete's level of expertise into the generation AI, which can then adjust the use of technical terminology.

[0101] The generation unit can estimate the athlete's emotions and adjust the presentation of the training program to be generated based on the estimated emotions. Emotions include, but are not limited to, relaxation, stress, and concentration. For example, if the athlete is relaxed, the generation unit can provide a detailed training program. Furthermore, if the athlete is stressed, the generation unit can provide a concise and to-the-point training program. Furthermore, if the athlete is focused, the generation unit can provide a training program using visually easy-to-understand graphs and charts. This allows for a more appropriate training program to be provided by adjusting the presentation of the training program based on the athlete's emotions. The 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 generation unit can be performed using, for example, the generation AI. For example, the generation unit can input the athlete's emotion data into the generation AI, which can then adjust the presentation of the training program.

[0102] The generation unit can adjust the level of detail of the generated program based on the importance of the analysis results during generation. The importance of the analysis results includes, but is not limited to, the impact of training and the urgency of a health condition. For example, the generation unit provides a detailed training program based on important analysis results. The generation unit can also provide a detailed training program based on analysis results related to a health condition. The generation unit can also provide a concise training program based on training data. By adjusting the level of detail of the generated program based on the importance of the analysis results, a detailed training program based on important analysis results can be provided. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the importance of the analysis results into the generation AI, which then adjusts the level of detail of the program.

[0103] During generation, the generation unit can apply different generation algorithms depending on the athlete's characteristics and goals. Examples of the athlete's characteristics and goals include, but are not limited to, physical strength level, technical level, improvement of athletic performance, and injury prevention. For example, when aiming to improve the athlete's muscle strength, the generation unit can apply an algorithm specialized for muscle training. Furthermore, when aiming to improve the athlete's endurance, the generation unit can apply an algorithm specialized for endurance training. Furthermore, when aiming to improve the athlete's flexibility, the generation unit can apply an algorithm specialized for flexibility training. In this way, by applying different generation algorithms depending on the athlete's characteristics and goals, it is possible to provide an individually optimized training program. Some or all of the above-described 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 the athlete's characteristics and goals into the generation AI, which then applies the optimal generation algorithm.

[0104] During generation, the generation unit can improve the accuracy of generation by referring to the athlete's past training programs. Past training programs include, but are not limited to, past training menus, training effects, and feedback results. For example, the generation unit optimizes a new program based on the athlete's past training programs. The generation unit can also improve the accuracy of the training program by referring to the athlete's past training data. The generation unit can also adjust the training program based on the athlete's past feedback. In this way, the accuracy of generation can be improved by referring to the athlete's past training programs. 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 athlete's past training programs into the generation AI, which then performs the improvement of the generation accuracy.

[0105] The generation unit can estimate the athlete's emotions and adjust the length of the generated program based on the estimated athlete's emotions. Emotions include, but are not limited to, relaxation, stress, and concentration. For example, if the athlete is relaxed, the generation unit can provide a detailed and long training program. Furthermore, if the athlete is stressed, the generation unit can provide a concise and short training program. Furthermore, if the athlete is focused, the generation unit can provide a medium-length training program. Thus, by adjusting the length of the generated program based on the athlete's emotions, a more appropriate training program can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, 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 generation unit can be performed using, for example, the generation AI. For example, the generation unit can input the athlete's emotion data into the generation AI, which can then adjust the program length.

[0106] At the time of generation, the generation unit can determine the priority of the programs to be generated based on the time when the analysis results were collected. Examples of the time when the analysis results were collected include, but are not limited to, before and after training, during a match, and during rest. The generation unit, for example, prioritizes generating training programs based on the latest analysis results. The generation unit can also generate strategic training programs based on analysis results obtained before an important event. The generation unit can also generate training programs based on past analysis results to grasp long-term trends. Thus, by determining the priority of the programs to be generated based on the time when the analysis results were collected, training programs based on important analysis results can be provided preferentially. Some or all of the above-described 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 the time when the analysis results were collected into the generation AI, and the generation AI can determine the priority of the programs.

[0107] During generation, the generation unit can adjust the order of the programs to be generated based on the relevance of the analysis results. Examples of the relevance of the analysis results include, but are not limited to, the relevance between training data and health data, and the relevance between performance data and psychological data. For example, the generation unit prioritizes generating training programs based on analysis results with high relevance. The generation unit can also generate training programs based on analysis results with medium relevance next. The generation unit can also generate training programs based on analysis results with low relevance last. This allows for the provision of efficient training programs by adjusting the order of the programs to be generated based on the relevance of the analysis results. Some or all of the above-described 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 the relevance of the analysis results into the generation AI, which then adjusts the order of the programs.

[0108] During generation, the generation unit can adjust the use of technical terminology in the generated program according to the athlete's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the athlete has specialized knowledge, the generation unit can provide a training program that uses a lot of technical terminology. Furthermore, if the athlete has general knowledge, the generation unit can also provide a training program that uses easy-to-understand terminology. Furthermore, if the athlete is a beginner, the generation unit can also provide a training program that uses simple terminology. In this way, by adjusting the use of technical terminology in the generated program according to the athlete's level of expertise, an easy-to-understand training program can be provided. Some or all of the above-described 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 the athlete's level of expertise into the generation AI, which can then adjust the use of technical terminology.

[0109] The providing unit can estimate the athlete's emotions and adjust the way feedback is presented based on the estimated emotions. Examples of emotions include, but are not limited to, relaxation, stress, and concentration. For example, if the athlete is relaxed, the providing unit can provide detailed feedback. Furthermore, if the athlete is stressed, the providing unit can provide concise and to-the-point feedback. Furthermore, if the athlete is focused, the providing unit can provide feedback using visually easy-to-understand graphs or charts. This allows for more appropriate feedback to be provided by adjusting the way feedback is presented based on the athlete's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, the generation AI. For example, the providing unit can input the athlete's emotion data into the generation AI, which can then adjust the way feedback is presented.

[0110] When providing feedback, the providing unit can adjust the level of detail of the feedback based on the importance of the training program. The importance of the training program includes, but is not limited to, for example, the impact of the training and the urgency of the health condition. For example, the providing unit provides detailed feedback for an important training program. The providing unit can also provide detailed feedback for a training program related to a health condition. The providing unit can also provide brief feedback for a general training program. By adjusting the level of detail of the feedback based on the importance of the training program, detailed feedback can be provided for an important training program. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the importance of the training program to the generation AI, which can then adjust the level of detail of the feedback.

[0111] When providing feedback, the providing unit can apply different feedback algorithms depending on the athlete's characteristics and goals. Examples of the athlete's characteristics and goals include, but are not limited to, physical strength level, technical level, improvement of athletic performance, and injury prevention. For example, when aiming to improve the athlete's muscle strength, the providing unit can provide feedback specialized for muscle training. Furthermore, when aiming to improve the athlete's endurance, the providing unit can provide feedback specialized for endurance training. Furthermore, when aiming to improve the athlete's flexibility, the providing unit can provide feedback specialized for flexibility training. In this way, by applying different feedback algorithms depending on the athlete's characteristics and goals, individually optimized feedback can be provided. Some or all of the above-described processing by the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the athlete's characteristics and goals into the generation AI, which can then apply the optimal feedback algorithm.

[0112] When providing feedback, the providing unit can improve the accuracy of the feedback by referring to the athlete's past feedback results. Past feedback results include, but are not limited to, for example, training effects, areas for improvement, and the athlete's thoughts. The providing unit, for example, optimizes new feedback based on the athlete's past feedback results. The providing unit can also improve the accuracy of the feedback by referring to the athlete's past training data. The providing unit can also adjust the feedback based on the athlete's past feedback. In this way, the accuracy of the feedback can be improved by referring to the athlete's past feedback results. Some or all of the above-mentioned processing by the providing unit may be performed, for example, using or without the generation AI. For example, the providing unit can input the athlete's past feedback results into the generation AI, which can then improve the accuracy of the feedback.

[0113] The providing unit can estimate the athlete's emotion and adjust the length of the feedback based on the estimated emotion. Emotions include, but are not limited to, relaxation, stress, and concentration. For example, if the athlete is relaxed, the providing unit can provide detailed and longer feedback. Furthermore, if the athlete is stressed, the providing unit can provide concise and shorter feedback. Furthermore, if the athlete is focused, the providing unit can provide medium-length feedback. This allows for more appropriate feedback by adjusting the length of the feedback based on the athlete's emotion. The 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 providing unit can be performed using, for example, the generation AI. For example, the providing unit can input the athlete's emotion data into the generation AI, which can then adjust the length of the feedback.

[0114] When providing feedback, the providing unit can determine the priority of feedback based on the collection time of the training program. Examples of the collection time of the training program include, but are not limited to, before and after training, during a match, and during rest. For example, the providing unit can prioritize feedback based on the most recent training program. The providing unit can also provide strategic feedback based on a training program before an important event. The providing unit can also provide feedback based on past training programs to grasp long-term trends. Thus, by determining the priority of feedback based on the collection time of the training program, feedback can be prioritized for important training programs. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the collection time of the training program to the generation AI, which can then determine the priority of the feedback.

[0115] When providing feedback, the providing unit can adjust the order of feedback based on the relevance of the training programs. Examples of the relevance of the training programs include, but are not limited to, the relevance between training data and health data, and the relevance between performance data and psychological data. For example, the providing unit can prioritize providing feedback based on training programs with high relevance. The providing unit can also provide feedback based on training programs with medium relevance next. The providing unit can also provide feedback based on training programs with low relevance last. This enables efficient feedback provision by adjusting the order of feedback based on the relevance of the training programs. Some or all of the above-described processing by the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the relevance of the training programs to the generation AI, and the generation AI can adjust the order of feedback.

[0116] When providing feedback, the providing unit can adjust the use of technical terminology in the feedback depending on the athlete's expertise level. Examples of expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the athlete has specialized knowledge, the providing unit can provide feedback that uses a lot of technical terminology. Furthermore, if the athlete has general knowledge, the providing unit can also provide feedback that uses easy-to-understand terminology. Furthermore, if the athlete is a beginner, the providing unit can also provide feedback that uses simple terminology. Thus, by adjusting the use of technical terminology in the feedback depending on the athlete's expertise level, it is possible to provide feedback that is easy to understand. Some or all of the above-described processing by the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the athlete's expertise level into the generation AI, which can then adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision 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 collection unit collects athlete data using the camera 42 or sensors of the smart device 14 and processes the data using the control unit 46A. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI. The generation unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and generates a training program based on the analysis results. The provision unit, for example, is realized by the control unit 46A of the smart device 14 and provides feedback to the athlete based on the generated training program. The collection unit, for example, is also realized by the specific processing unit 290 of the data processing device 12 and can estimate the athlete's emotions and adjust the timing of data collection. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision 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 collection unit collects athlete data using the camera 42 or sensor of the smart glasses 214 and processes the data using the control unit 46A. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI. The generation unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and generates a training program based on the analysis results. The provision unit, for example, is realized by the control unit 46A of the smart glasses 214 and provides feedback to the athlete based on the generated training program. The collection unit, for example, is also realized by the specific processing unit 290 of the data processing device 12 and can estimate the athlete's emotions and adjust the timing of data collection. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the headset-type device 314 and the data processing device 12. For example, the collection unit collects athlete data using the camera 42 or sensors of the headset-type device 314 and processes the data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using a generation AI. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates a training program based on the analysis results. The provision unit, realized, for example, by the control unit 46A of the headset-type device 314, provides feedback to the athlete based on the generated training program. The collection unit, realized, for example, by the specific processing unit 290 of the data processing device 12, can also estimate the athlete's emotions and adjust the timing of data collection. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects athlete data using the camera 42 or sensors of the robot 414 and processes the data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using a generation AI. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates a training program based on the analysis results. The provision unit, realized, for example, by the control unit 46A of the robot 414, provides feedback to the athlete based on the generated training program. The collection unit, realized, for example, by the specific processing unit 290 of the data processing device 12, can also estimate the athlete's emotions and adjust the timing of data collection.

[0117] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0118] When collecting athlete data, the collection unit can also collect environmental data about the athlete. Environmental data includes, but is not limited to, temperature, humidity, and wind speed. For example, if the athlete is training outdoors, the collection unit collects temperature and humidity data. Furthermore, if the athlete is training indoors, the collection unit can also collect indoor temperature and humidity data. Furthermore, if the athlete is training at high altitude, the collection unit can also collect altitude data. By collecting data based on the athlete's training environment, more accurate analysis is possible.

[0119] When analyzing the athlete's data, the analysis unit can also analyze the athlete's dietary data. Dietary data includes, but is not limited to, calorie intake, nutrient balance, and meal timing. For example, the analysis unit analyzes the athlete's calorie intake and evaluates the balance with energy expenditure. The analysis unit can also analyze the athlete's nutrient balance and suggest supplementing with necessary nutrients. The analysis unit can also analyze the athlete's meal timing and suggest a meal schedule to maximize training effects. In this way, by analyzing the athlete's dietary data, a more effective training program can be provided.

[0120] The generation unit can generate a mental training program based on the athlete's data. Examples of mental training programs include, but are not limited to, exercises to improve concentration, techniques for stress management, and methods for maintaining motivation. For example, the generation unit can generate meditation exercises to improve the athlete's concentration. The generation unit can also generate relaxation techniques to reduce the athlete's stress. The generation unit can also generate goal setting methods to maintain the athlete's motivation. This can maximize the training effect by supporting the athlete's mental state.

[0121] The providing unit can provide the athlete with feedback that visualizes the progress of the training program. Examples of feedback include, but are not limited to, visual displays using graphs and charts, progress reports, and goal achievement evaluations. For example, the providing unit can display the athlete's training progress in a graph to visually indicate the degree of goal achievement. The providing unit can also provide the athlete's training progress in report format. The providing unit can also evaluate the athlete's degree of goal achievement and suggest next steps. This makes it easier to maintain motivation by visualizing the athlete's training progress.

[0122] The analysis unit can estimate the athlete's emotions and adjust the method of providing feedback on the analysis results based on the estimated emotions of the athlete. Examples of emotions include, but are not limited to, relaxation, stress, and concentration. For example, if the athlete is relaxed, the analysis unit can provide detailed analysis results. If the athlete is stressed, the analysis unit can provide concise and to-the-point analysis results. If the athlete is concentrated, the analysis unit can provide analysis results using easy-to-understand graphs and charts. This allows for more appropriate feedback to be provided by adjusting the method of providing feedback on the analysis results based on the athlete's emotions.

[0123] When collecting athlete data, the collection unit may also collect social media activity data of the athlete. Examples of social media activity data include, but are not limited to, the content of posts, follower reactions, and activity frequency. For example, the collection unit may collect training content shared by the athlete on social media. The collection unit may also collect health conditions mentioned by the athlete on social media. The collection unit may also collect dietary information shared by the athlete on social media and reflect this information in the athlete's nutrition plan. This allows related data to be collected by analyzing the athlete's social media activity.

[0124] When analyzing the athlete's data, the analysis unit can also analyze the athlete's sleep data. Sleep data includes, but is not limited to, sleep duration, sleep quality, and sleep cycle. For example, the analysis unit analyzes the athlete's sleep duration and evaluates the correlation with training effects. The analysis unit can also analyze the athlete's sleep quality and suggest improvements to improve performance. The analysis unit can also analyze the athlete's sleep cycle and suggest optimal training times. In this way, by analyzing the athlete's sleep data, a more effective training program can be provided.

[0125] The generation unit can estimate the athlete's emotions and adjust the content of the training program to be generated based on the estimated emotions of the athlete. Emotions include, but are not limited to, for example, relaxation, stress, and concentration. For example, if the athlete is relaxed, the generation unit can generate a training program that includes exercises that have a relaxing effect. Furthermore, if the athlete is feeling stressed, the generation unit can generate a training program that includes exercises to reduce stress. Furthermore, if the athlete is concentrating, the generation unit can generate a training program that includes exercises to improve concentration. In this way, by adjusting the content of the training program based on the athlete's emotions, a more appropriate training program can be provided.

[0126] The providing unit can provide the athlete with feedback evaluating the effectiveness of the training program. Examples of feedback include, but are not limited to, an evaluation of the training effect, suggestions for improvement, and instructions for the next step. For example, the providing unit can evaluate the athlete's training effect and indicate the degree of achievement. The providing unit can also suggest improvements to the athlete's training program. The providing unit can also instruct the athlete on the next step and support the athlete in continuing the training. In this way, the quality of training can be improved by evaluating the effectiveness of the athlete's training program.

[0127] The analysis unit can estimate the athlete's emotions and determine the priorities of analysis based on the estimated emotions of the athlete. Emotions include, but are not limited to, relaxation, stress, and concentration. For example, if the athlete is relaxed, the analysis unit can prioritize detailed analysis. Furthermore, if the athlete is stressed, the analysis unit can prioritize brief analysis. Furthermore, if the athlete is concentrated, the analysis unit can prioritize visually easy-to-understand analysis. In this way, by determining the priorities of analysis based on the athlete's emotions, more appropriate analysis results can be provided.

[0128] The processing flow of the second embodiment will be briefly explained below.

[0129] Step 1: The collection unit collects athlete data. Athlete data includes physical characteristics, performance data, and health status. For example, the collection unit collects the athlete's muscle strength, endurance, flexibility, heart rate, blood pressure, sleep patterns, and play data during a game. The collection unit can also use sensors to measure the athlete's movements and collect motion analysis data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, the analysis unit uses generative AI to obtain analysis results based on the athlete's characteristics and goals. It can also analyze motion analysis data to identify movements and postures that pose a high risk of injury. It can also analyze play data during a match to suggest areas for tactical improvement and new strategies. Step 3: The generator generates a training program based on the analysis results obtained by the analyzer. The generator uses the generative AI to generate a training program that includes the type, frequency, and intensity of exercises. For example, it generates an optimal training program for each individual athlete based on the athlete's characteristics and goals. It can also generate programs for injury prevention and rehabilitation, as well as programs that provide tactical advice and strategies. Step 4: The provider provides feedback to the athlete based on the generated training program. The feedback may be provided in the form of real-time feedback or a report. For example, feedback may be provided based on the training program generated using generative AI. The provider may also provide information, instructions, tactical advice, and strategies to support injury treatment and recovery.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] 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.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0200] 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.

[0201] [Explanation of symbols]

[0202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects athlete data; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates a training program based on the analysis result obtained by the analysis unit; a providing unit that provides feedback based on the training program generated by the generating unit. A system characterized by:

2. The collecting unit Collecting data on athletes' physical characteristics, performance data, and health status 2. The system of claim 1.

3. The analysis unit Analyze the collected data and obtain analytical results based on the athlete's characteristics and goals.

2. The system of claim 1.

4. The generation unit Generate an appropriate training program for each individual athlete based on the analysis results 2. The system of claim 1.

5. The providing unit Provide feedback to athletes based on the generated training program 2. The system of claim 1.

6. The analysis unit Analyzing athletes' motion analysis data to identify movements or postures that pose a risk of injury 2. The system of claim 1.

7. The generation unit Generate programs for injury prevention and rehabilitation 2. The system of claim 1.

8. The providing unit Providing information and instructions to support injury treatment and recovery 2. The system of claim 1.

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