system

Abstract

The system according to the embodiment aims to individually suggest optimal forms and training methods to athletes. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a presentation unit, and a training unit. The collection unit collects data on athletes. The analysis unit analyzes the data collected by the collection unit. The presentation unit presents a form based on the analysis results obtained by the analysis unit. The training unit presents a training method for achieving the form presented by the presentation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to individually suggest optimal form and training methods to athletes.

[0005] The system according to the embodiment aims to individually suggest optimal forms and training methods to athletes. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a presentation unit, and a training unit. The collection unit collects data on athletes. The analysis unit analyzes the data collected by the collection unit. The presentation unit presents a form based on the analysis results obtained by the analysis unit. The training unit presents a training method for achieving the form presented by the presentation unit. [Effects of the Invention]

[0007] The system according to the embodiment can individually suggest optimal form and training methods to athletes. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An athlete support system according to an embodiment of the present invention collects and analyzes athlete data, suggests optimal form, and suggests training methods. This athlete support system collects an athlete's current form and performance data, and uses AI to analyze it to suggest the optimal form for each athlete. For example, it analyzes the athlete's muscle strength, flexibility, balance, smoothness of movement, etc., and suggests the optimal form for each individual athlete, while also referring to data from other professional athletes. Furthermore, the AI ​​suggests steps and training methods for changing from current form to optimal form. This includes specific training menus and practice methods. For example, it suggests strength training, stretching, and repeated practice of specific movements. This allows athletes to aim for their optimal form and train efficiently. This allows the athlete support system to improve athletes' performance and prevent injuries.

[0029] The athlete support system according to the embodiment includes a collection unit, an analysis unit, a presentation unit, and a training unit. The collection unit collects athlete data. The athlete data includes, but is not limited to, movement data, heart rate, and muscle strength data. The collection unit collects athlete data using, for example, video analysis or sensors. Video analysis is performed using, for example, frame rate and analysis software. The sensors collect data using, for example, an acceleration sensor or a heart rate sensor. The analysis unit analyzes the athlete's movement patterns and muscle strength balance using a machine learning algorithm. Machine learning algorithms include, for example, deep learning and support vector machines. The analysis unit analyzes, for example, the athlete's movement patterns and evaluates muscle strength balance. The presentation unit presents the optimal form for each athlete based on data from other professional athletes. The presentation unit suggests the optimal form for the athlete, for example, by referring to the movement data and performance data of other professional athletes. The training unit presents a specific training menu of at least one of muscle training, stretching, and repetitive practice of a specific movement. The training unit may, for example, suggest weight training or resistance training as strength training, suggest dynamic stretching or static stretching as stretching, or suggest running form practice or swing practice as repetitive practice of a specific movement. This allows the athlete support system according to the embodiment to efficiently collect, analyze, and present athlete data and suggest training methods.

[0030] The collection unit can collect athlete data using video analysis or sensors. The collection unit, for example, collects athlete movement data using video analysis. Video analysis is performed using, for example, a frame rate and analysis software. The collection unit can also collect athlete data using sensors. Examples of sensors include an acceleration sensor and a heart rate sensor. The acceleration sensor measures the acceleration of the athlete's movement and collects it as data. The heart rate sensor measures the athlete's heart rate and collects it as data. In this way, the use of video analysis and sensors improves the accuracy of athlete data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input video analysis data to a generation AI and have the generation AI analyze the movement data.

[0031] The analysis unit can analyze an athlete's movement patterns and muscle strength balance using a machine learning algorithm. The analysis unit can analyze an athlete's movement patterns using, for example, deep learning. Deep learning learns large amounts of data and has advanced pattern recognition capabilities. The analysis unit can also analyze muscle strength balance using a support vector machine. A support vector machine is a machine learning algorithm that excels at data classification and regression analysis. Furthermore, the analysis unit can comprehensively analyze an athlete's movement patterns and muscle strength balance. For example, deep learning and a support vector machine can be combined to perform more accurate analysis. This improves the accuracy of analysis of an athlete's movement patterns and muscle strength balance by using a machine learning algorithm. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input movement pattern data to a generation AI and have the generation AI analyze the movement pattern.

[0032] The presentation unit can present forms to individual athletes based on data of other professional athletes. The presentation unit, for example, refers to the movement data of other professional athletes to suggest the optimal form for the athlete. The data of other professional athletes includes, for example, movement data and performance data. The presentation unit presents the optimal form for the athlete based on this data. For example, it presents an optimal form that partially incorporates the form of a specific professional athlete while matching the athlete's own characteristics. The presentation unit can also analyze the athlete's movement data and present specific improvements to the form. In this way, the optimal form can be presented to each individual athlete by referring to the data of other professional athletes. Some or all of the above-described processing by the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the professional athlete's data into a generation AI and have the generation AI suggest the optimal form.

[0033] The training unit can present at least one specific training menu from among strength training, stretching, and repetitive practice of a specific movement. The training unit, for example, suggests weight training or resistance training as strength training. Weight training is a method of building muscle strength using weights, and resistance training is a method of building muscle strength using resistance. The training unit can also suggest dynamic stretching or static stretching as stretching. Dynamic stretching is stretching that involves movement, and static stretching is stretching performed in a stationary state. Furthermore, the training unit can also suggest running form practice or swing practice as repetitive practice of a specific movement. By presenting a specific training menu, the athlete's training effect is improved. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input a training menu into a generation AI and have the generation AI suggest an optimal training menu.

[0034] The collection unit may use a motion capture system or a wearable sensor. The collection unit may collect athlete movement data using, for example, a motion capture system. Examples of motion capture systems include optical motion capture and inertial motion capture. Optical motion capture is a method of capturing and analyzing movements using a camera, while inertial motion capture is a method of measuring and analyzing movements using a sensor. The collection unit may also collect athlete data using a wearable sensor. Examples of wearable sensors include smartwatches and fitness trackers. Smartwatches measure heart rate and movement data and collect the data. Fitness trackers measure steps and calories burned and collect the data. This improves the accuracy of athlete data collection by using a motion capture system or a wearable sensor. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit may input motion capture data to a generation AI and have the generation AI analyze the movement data.

[0035] The analysis unit can build a database and compare data of athletes with similar characteristics. For example, the analysis unit builds a database of athletes and compares data of athletes with similar characteristics. The database includes, for example, the athletes' movement data and performance data. The analysis unit compares data of athletes with similar characteristics based on this data. For example, the analysis unit compares data of athletes with specific movement patterns or muscle strength balances to identify similarities and differences. The analysis unit can also use the database to provide information useful for improving athletes' performance. Thus, building a database makes it easier to compare data of athletes with similar characteristics. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data from the database into a generation AI and have the generation AI compare the data.

[0036] The training unit can present a method for providing feedback on training progress. The training unit, for example, evaluates training progress and provides feedback. Training progress includes, for example, training achievement, training frequency, and training intensity. The training unit provides feedback to the athlete based on this progress data. For example, if training achievement is high, advice for moving on to the next step is provided, and if achievement is low, points out areas for improvement. The training unit can also adjust the training menu based on the training frequency and intensity. This makes it easier to confirm the training effect of the athlete by providing feedback on training progress. Some or all of the above-mentioned processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input progress data into a generation AI and have the generation AI provide feedback.

[0037] The collection unit can analyze the athlete's past performance data and select the optimal data collection method. For example, the collection unit can select the most effective camera angle based on past video analysis data. The past video analysis data includes, for example, the athlete's movement data and performance data. The collection unit can also analyze past sensor data and determine the optimal sensor placement. The past sensor data includes, for example, heart rate data and muscle strength data. Furthermore, the collection unit can adjust the frequency of data collection by referring to past training data. The past training data includes, for example, training records and feedback. This allows the optimal data collection method to be selected by analyzing the past performance data. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input past performance data into a generation AI and have the generation AI select the optimal data collection method.

[0038] When collecting data, the collection unit can filter the data based on the athlete's current training status and physical condition. For example, when the athlete is performing high-intensity training, the collection unit prioritizes collection of heart rate data. The heart rate data is collected by measuring the athlete's heart rate. The collection unit can also monitor the muscle recovery status when the athlete is in a recovery period. The muscle recovery status is measured, for example, by measuring muscle stiffness and flexibility and collecting the data. Furthermore, when the athlete is trying a new training menu, the collection unit can collect data to evaluate its effectiveness. In this way, by filtering the data based on the athlete's current training status and physical condition, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input training status and physical condition data into the generation AI and have the generation AI perform data filtering.

[0039] When collecting data, the collection unit can select the optimal collection means depending on the athlete's input method. For example, if the athlete uses voice input, the collection unit collects data using voice recognition technology. Voice recognition technology is technology that converts the athlete's voice into text data. Furthermore, if the athlete uses text input, the collection unit can also analyze the input text data. Text data is collected as data containing character information entered by the athlete. Furthermore, if the athlete uses image input, the collection unit can also collect form data using image analysis technology. Image analysis technology is technology that collects and analyzes the athlete's movements as image data. This improves the efficiency of data collection by selecting the optimal collection means depending on the input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input voice data, text data, and image data into a generation AI and have the generation AI collect the data.

[0040] When collecting data, the collection unit can prioritize collection of highly relevant data based on the athlete's geographical location information. For example, if an athlete is training at high altitude, the collection unit collects data specific to high altitude. Data specific to high altitude includes, for example, oxygen concentration data and heart rate data. The collection unit can also collect data to evaluate the impact of training under different climatic conditions if the athlete is training under those conditions. Climate condition data includes, for example, temperature data and humidity data. Furthermore, if an athlete is training at a specific stadium, the collection unit can collect data specific to that environment. Stadium data includes, for example, track condition and wind speed data. This allows highly relevant data to be collected preferentially by taking the geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input geographical location data into a generation AI and cause the generation AI to collect data.

[0041] During data collection, the collection unit can analyze the athlete's social media activity and collect related data. The collection unit can collect additional data, for example, based on training content shared by the athlete on social media. Social media data can include, for example, training records and feedback. The collection unit can also analyze the athlete's social media feedback to collect data evaluating the effectiveness of the training. Feedback data can include, for example, comments and ratings. The collection unit can also refer to data on other athletes the athlete follows on social media. The data on other athletes can include, for example, training menus and performance data. This allows related data to be collected by analyzing social media activity. Some or all of the above-mentioned processing by the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can input social media data into a generation AI and have the generation AI collect data.

[0042] When collecting data, the collection unit can customize the collection method by reflecting the athlete's past feedback. The collection unit, for example, adjusts the frequency of data collection based on feedback provided by the athlete in the past. Feedback includes, for example, questionnaire results and training records. The collection unit can also select the type of data to collect by referring to the athlete's past feedback. Furthermore, the collection unit can prioritize collecting data to solve problems previously pointed out by the athlete. In this way, the collection method can be customized by reflecting past feedback. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into the generation AI and have the generation AI customize the collection method.

[0043] During analysis, the analysis unit can adjust the level of detail of the analysis based on the athlete's important movements. For example, the analysis unit can focus on the athlete's main movements and perform a detailed analysis. Major movements include, for example, running form and swing movements. The analysis unit can also perform a simplified analysis of the athlete's secondary movements. Secondary movements include, for example, warm-up and cool-down movements. Furthermore, the analysis unit can provide analysis results that emphasize particularly important parts of the athlete's movements. In this way, adjusting the level of detail of the analysis based on important movements can provide more relevant analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input movement data to a generation AI and have the generation AI adjust the level of detail of the analysis.

[0044] During analysis, the analysis unit can apply different analysis algorithms depending on the athlete's category. For example, the analysis unit applies an analysis algorithm focusing on speed and power to a sprinter. Analysis of a sprinter includes, for example, speed data and power data. The analysis unit can also apply an analysis algorithm focusing on endurance and pace to a marathon runner. Analysis of a marathon runner includes, for example, endurance data and pace data. The analysis unit can also apply an analysis algorithm focusing on flexibility and balance to a gymnast. Analysis of a gymnast includes, for example, flexibility data and balance data. This allows for applying an analysis algorithm according to the category to provide more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data to a generation AI and cause the generation AI to apply an analysis algorithm.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the athlete's past analysis results. The analysis unit, for example, compares the current analysis results with the athlete's past analysis results to improve accuracy. Past analysis results include, for example, analysis results of movement patterns and muscle strength balance. The analysis unit can also adjust the analysis algorithm by referring to the athlete's past training data. Training data includes, for example, training records and feedback. Furthermore, the analysis unit can improve the reliability of the analysis results based on the athlete's past performance data. Performance data includes, for example, game results and training results. In this way, by referring to the past analysis results, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0046] During analysis, the analysis unit can determine analysis priorities based on the time the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data and provides real-time feedback. The most recent data includes, for example, the most recent training data or match data. The analysis unit can also analyze long-term trends by referring to past data. Past data includes, for example, training data or match data from the past few months. Furthermore, the analysis unit can prioritize analysis of data from a specific training period and evaluate its effectiveness. Data from a specific training period includes, for example, data from a training camp or an intensive training period. In this way, by determining analysis priorities based on the time the data was collected, more important data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input collection time data to the generation AI and have the generation AI determine the analysis priorities.

[0047] During analysis, the analysis unit can adjust the order of analysis based on the relevance of data. For example, the analysis unit prioritizes analysis of data related to the athlete's main movements. Examples of main movements include running form and swing movements. The analysis unit can also postpone analysis of data related to the athlete's auxiliary movements. Examples of auxiliary movements include warm-up and cool-down movements. The analysis unit can also prioritize analysis of data that directly affects the athlete's performance. Examples of data that directly affect performance include muscle strength data and speed data. By adjusting the order of analysis based on the relevance of data, more relevant data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0048] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the athlete's level of expertise. For example, if the athlete is a beginner, the analysis unit can explain the analysis results in simple terms. Simple terms include, for example, general terms and everyday expressions. Furthermore, if the athlete is an intermediate athlete, the analysis unit can explain the analysis results using appropriate technical terminology. Appropriate technical terminology includes, for example, basic technical terms and sports terms. Furthermore, if the athlete is an advanced athlete, the analysis unit can explain the analysis results using detailed technical terminology. Detailed technical terminology includes, for example, advanced technical terms and specialized sports terms. By adjusting the use of technical terminology in the analysis according to the athlete's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input expertise level data into a generation AI and have the generation AI use technical terminology.

[0049] When presenting form, the presentation unit can adjust the level of detail of the presentation based on the athlete's important movements. For example, the presentation unit can provide a detailed explanation of the athlete's form, focusing on the athlete's main movements. Main movements include, for example, running form and swing movements. The presentation unit can also provide a simplified explanation of the athlete's secondary movements. Secondary movements include, for example, warm-up and cool-down movements. Furthermore, the presentation unit can provide an explanation of the athlete's form by emphasizing particularly important parts of the athlete's movements. In this way, by adjusting the level of detail of the presentation based on important movements, more relevant form can be presented. Some or all of the above-mentioned processing in the presentation unit may be performed using, or without, AI. For example, the presentation unit can input movement data to a generation AI and have the generation AI adjust the level of detail of the presentation.

[0050] When presenting a form, the presentation unit can apply different presentation algorithms depending on the athlete's category. For example, the presentation unit applies a form presentation algorithm focusing on speed and power to a sprinter. The sprinter's form includes, for example, speed data and power data. The presentation unit can also apply a form presentation algorithm focusing on endurance and pace to a marathon runner. The marathon runner's form includes, for example, endurance data and pace data. The presentation unit can also apply a form presentation algorithm focusing on flexibility and balance to a gymnast. The gymnast's form includes, for example, flexibility data and balance data. This allows a more appropriate form to be presented by applying a presentation algorithm according to the category. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input category data to a generation AI and cause the generation AI to apply a presentation algorithm.

[0051] When presenting form, the presentation unit can improve the accuracy of the presentation by referring to the athlete's past presentation results. The presentation unit, for example, adjusts the current form presentation based on the athlete's past form presentation results. Past form presentation results include, for example, presentation results of movement patterns and presentation results of muscle strength balance. The presentation unit can also improve the form presentation algorithm by referring to the athlete's past training data. Training data includes, for example, training records and feedback. Furthermore, the presentation unit can improve the reliability of the form presentation based on the athlete's past performance data. Performance data includes, for example, game results and training results. In this way, by referring to the past presentation results, the accuracy of the presentation is improved. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input past presentation result data into the generation AI and cause the generation AI to improve the accuracy of the presentation.

[0052] When presenting forms, the presentation unit can determine the presentation priority based on the time when the data was collected. The presentation unit, for example, prioritizes the presentation of the most important forms based on the most recent data. The most recent data includes, for example, the most recent training data or match data. The presentation unit can also present forms that take long-term trends into consideration by referring to past data. The past data includes, for example, training data or match data from the past few months. Furthermore, the presentation unit can present the optimal form based on data from a specific training period. The data from a specific training period includes, for example, data from a training camp or an intensive training period. In this way, by determining the presentation priority based on the time when the data was collected, more important forms can be presented preferentially. Some or all of the above-described processing by the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input collection time data to the generation AI and cause the generation AI to determine the presentation priority.

[0053] When presenting forms, the presentation unit can adjust the order of presentation based on the relevance of the data. For example, the presentation unit prioritizes presentation of forms related to the athlete's main movements. Examples of main movements include running form and swing movements. The presentation unit can also postpone presentation of forms related to the athlete's auxiliary movements. Examples of auxiliary movements include warm-up and cool-down movements. Furthermore, the presentation unit can prioritize presentation of forms that directly affect the athlete's performance. Examples of forms that directly affect performance include muscle strength data and speed data. In this way, by adjusting the presentation order based on the relevance of the data, more relevant forms can be presented preferentially. Some or all of the above-described processing by the presentation unit may be performed using, or without, AI. For example, the presentation unit can input the relevance data to the generation AI and cause the generation AI to adjust the presentation order.

[0054] When presenting the form, the presentation unit can adjust the use of technical terminology in the presentation depending on the athlete's level of expertise. For example, if the athlete is a beginner, the presentation unit can explain the form in simple terms. Simple terms include, for example, general terms and everyday expressions. Furthermore, if the athlete is an intermediate athlete, the presentation unit can explain the form using appropriate technical terminology. Appropriate technical terminology includes, for example, basic technical terms and sports terms. Furthermore, if the athlete is an advanced athlete, the presentation unit can explain the form using detailed technical terminology. Detailed technical terminology includes, for example, advanced technical terms and specialized sports terms. In this way, by adjusting the use of technical terminology in the presentation depending on the athlete's level of expertise, a more understandable form can be presented. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without AI. For example, the presentation unit can input expertise level data to the generation AI and cause the generation AI to use technical terminology.

[0055] When presenting a training method, the training unit can analyze the athlete's past training data to select the optimal method. The training unit, for example, selects the most effective training method based on the athlete's past training data. Past training data includes, for example, training records and feedback. The training unit can also analyze the athlete's past training data to adjust the frequency of training. Furthermore, the training unit can also adjust the intensity of training with reference to the athlete's past training data. In this way, the optimal training method can be selected by analyzing the past training data. Some or all of the above-mentioned processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input past training data into the generation AI and have the generation AI select the optimal training method.

[0056] When presenting a training method, the training unit can customize the method based on the athlete's current physical condition and training status. For example, if the athlete is tired, the training unit can suggest recovery training. Recovery training includes, for example, light exercise and stretching. Furthermore, if the athlete is performing high-intensity training, the training unit can also provide a training menu that includes appropriate rest. Furthermore, if the athlete is trying a new training menu, the training unit can also suggest a training method to evaluate its effectiveness. In this way, by customizing the method based on the athlete's current physical condition and training status, a more appropriate training method can be provided. Some or all of the above-mentioned processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input physical condition data and training status data into the generation AI and have the generation AI customize the training method.

[0057] When presenting a training method, the training unit can improve the method by reflecting the athlete's feedback. The training unit, for example, adjusts the training menu based on the feedback provided by the athlete. The feedback includes, for example, questionnaire results and training records. The training unit can also adjust the frequency of training based on the athlete's feedback. Furthermore, the training unit can adjust the intensity of training based on the athlete's feedback. In this way, the training method can be improved by reflecting the feedback. Some or all of the above-mentioned processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input feedback data into a generation AI and have the generation AI improve the training method.

[0058] When presenting a training method, the training unit can select the optimal method based on the athlete's geographical location information. For example, if the athlete is training at high altitude, the training unit can propose a training method specific to high altitude. Examples of high altitude training methods include training in a low-oxygen environment and high altitude-specific stretching. Furthermore, if the athlete is training under different climatic conditions, the training unit can also propose a training method that takes those conditions into account. Examples of climatic conditions include temperature and humidity. Furthermore, if the athlete is training at a specific stadium, the training unit can also propose a training method specific to that environment. Examples of stadium environments include track conditions and wind speed. This allows the optimal training method to be provided by taking the geographical location information into consideration. Some or all of the above-described processing in the training unit may be performed using, or without, AI. For example, the training unit can input geographical location data into the generation AI and have the generation AI select a training method.

[0059] When presenting a training method, the training department can analyze the athlete's social media activity to suggest a method. For example, the training department can suggest additional training methods based on the training content the athlete has shared on social media. Social media data includes, for example, training records and feedback. The training department can also analyze the athlete's social media feedback to suggest a method for evaluating the effectiveness of the training. Feedback data includes, for example, comments and ratings. Furthermore, the training department can refer to the training methods of other athletes the athlete follows on social media. Data from other athletes includes, for example, training menus and performance data. This allows for the provision of more relevant training methods by analyzing social media activity. Some or all of the above-described processing in the training department may be performed using, for example, AI, or may be performed without using AI. For example, the training department can input social media data into a generation AI and have the generation AI execute training method suggestions.

[0060] When presenting a training method, the training unit can customize the method by reflecting the athlete's past feedback. The training unit, for example, adjusts the training menu based on feedback provided by the athlete in the past. Feedback includes, for example, questionnaire results and training records. The training unit can also adjust the frequency of training by referring to the athlete's past feedback. Furthermore, the training unit can prioritize providing training methods that solve problems previously pointed out by the athlete. In this way, the training method can be customized by reflecting past feedback. Some or all of the above-mentioned processing in the training unit may be performed, for example, using AI, or may be performed without using AI. For example, the training unit can input past feedback data into a generation AI and have the generation AI customize the training method.

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

[0062] The collection unit can analyze an athlete's past performance data and select the optimal data collection method. For example, the most effective camera angle can be selected based on past video analysis data. Past video analysis data includes the athlete's movement data and performance data. The collection unit can also analyze past sensor data to determine the optimal sensor placement. Past sensor data includes heart rate data and muscle strength data. Furthermore, the frequency of data collection can be adjusted based on past training data. Past training data includes training records and feedback. This allows the optimal data collection method to be selected by analyzing past performance data.

[0063] When collecting data, the collection unit can filter the data based on the athlete's current training status and physical condition. For example, if an athlete is undergoing high-intensity training, heart rate data can be collected as a priority. Heart rate data is collected by measuring the athlete's heart rate. In addition, if an athlete is in a recovery period, muscle recovery status can be monitored by measuring muscle stiffness and flexibility and collecting data. Furthermore, if an athlete is trying a new training menu, data can be collected to evaluate its effectiveness. This allows more relevant data to be collected by filtering data based on the athlete's current training status and physical condition.

[0064] During analysis, the analysis unit can adjust the level of detail of the analysis based on the athlete's important movements. For example, it can perform a detailed analysis focusing on the athlete's main movements. Main movements include running form and swing movements. It can also perform a simplified analysis of the athlete's secondary movements. Secondary movements include warm-up and cool-down movements. It can also provide analysis results that emphasize particularly important parts of the athlete's movements. By adjusting the level of detail of the analysis based on important movements, it is possible to provide more relevant analysis results.

[0065] When presenting form, the presentation unit can apply different presentation algorithms depending on the athlete's category. For example, a form presentation algorithm focusing on speed and power can be applied to a sprinter. A sprinter's form includes speed data and power data. A form presentation algorithm focusing on endurance and pace can also be applied to a marathon runner. A marathon runner's form includes endurance data and pace data. A form presentation algorithm focusing on flexibility and balance can also be applied to a gymnast. A gymnast's form includes flexibility data and balance data. This makes it possible to present a more appropriate form by applying a presentation algorithm according to the category.

[0066] When presenting a training method, the training department can select the optimal method based on the athlete's geographical location information. For example, if an athlete is training at high altitude, the training department can suggest a training method specific to high altitude. High altitude training methods include training in an environment with low oxygen concentration and stretches specific to high altitude. In addition, if an athlete is training in different climatic conditions, the training department can suggest a training method that takes those effects into consideration. Climatic conditions include temperature and humidity. Furthermore, if an athlete is training at a specific stadium, the training department can suggest a training method specific to that environment. Stadium environments include track conditions and wind speed. This makes it possible to provide the optimal training method by taking geographical location information into consideration.

[0067] When presenting training methods, the training department can analyze an athlete's social media activity to suggest methods. For example, additional training methods can be suggested based on the training content the athlete has shared on social media. Social media data includes training records and feedback. The training department can also suggest methods for evaluating the effectiveness of training by analyzing the athlete's social media feedback. Feedback data includes comments and ratings. Furthermore, the training methods of other athletes the athlete follows on social media can be used as reference. Data from other athletes includes training menus and performance data. This allows the training department to provide more relevant training methods by analyzing social media activity.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The collection unit collects athlete data. The athlete data includes movement data, heart rate, muscle strength data, etc. The collection unit collects data using video analysis and sensors. Video analysis is performed using frame rate and analysis software, and sensors collect data using acceleration sensors and heart rate sensors. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses machine learning algorithms to analyze the athlete's movement patterns and muscle strength balance. Machine learning algorithms include deep learning and support vector machines. Step 3: The suggestion unit suggests a form based on the analysis results obtained by the analysis unit. The suggestion unit refers to the movement data and performance data of other professional athletes and suggests the optimal form for each individual athlete. Step 4: The training unit suggests a training method for achieving the form suggested by the suggestion unit. The training unit suggests at least one specific training menu from the following: strength training, stretching, and repetitive practice of a specific movement. For example, it suggests weight training, resistance training, dynamic stretching, static stretching, running form practice, and swing practice.

[0070] (Example 2) An athlete support system according to an embodiment of the present invention collects and analyzes athlete data, suggests optimal form, and suggests training methods. This athlete support system collects an athlete's current form and performance data, and uses AI to analyze it to suggest the optimal form for each athlete. For example, it analyzes the athlete's muscle strength, flexibility, balance, smoothness of movement, etc., and suggests the optimal form for each individual athlete, while also referring to data from other professional athletes. Furthermore, the AI ​​suggests steps and training methods for changing from current form to optimal form. This includes specific training menus and practice methods. For example, it suggests strength training, stretching, and repeated practice of specific movements. This allows athletes to aim for their optimal form and train efficiently. This allows the athlete support system to improve athletes' performance and prevent injuries.

[0071] The athlete support system according to the embodiment includes a collection unit, an analysis unit, a presentation unit, and a training unit. The collection unit collects athlete data. The athlete data includes, but is not limited to, movement data, heart rate, and muscle strength data. The collection unit collects athlete data using, for example, video analysis or sensors. Video analysis is performed using, for example, frame rate and analysis software. The sensors collect data using, for example, an acceleration sensor or a heart rate sensor. The analysis unit analyzes the athlete's movement patterns and muscle strength balance using a machine learning algorithm. Machine learning algorithms include, for example, deep learning and support vector machines. The analysis unit analyzes, for example, the athlete's movement patterns and evaluates muscle strength balance. The presentation unit presents the optimal form for each athlete based on data from other professional athletes. The presentation unit suggests the optimal form for the athlete, for example, by referring to the movement data and performance data of other professional athletes. The training unit presents a specific training menu of at least one of muscle training, stretching, and repetitive practice of a specific movement. The training unit may, for example, suggest weight training or resistance training as strength training, suggest dynamic stretching or static stretching as stretching, or suggest running form practice or swing practice as repetitive practice of a specific movement. This allows the athlete support system according to the embodiment to efficiently collect, analyze, and present athlete data and suggest training methods.

[0072] The collection unit can collect athlete data using video analysis or sensors. The collection unit, for example, collects athlete movement data using video analysis. Video analysis is performed using, for example, a frame rate and analysis software. The collection unit can also collect athlete data using sensors. Examples of sensors include an acceleration sensor and a heart rate sensor. The acceleration sensor measures the acceleration of the athlete's movement and collects it as data. The heart rate sensor measures the athlete's heart rate and collects it as data. In this way, the use of video analysis and sensors improves the accuracy of athlete data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input video analysis data to a generation AI and have the generation AI analyze the movement data.

[0073] The analysis unit can analyze an athlete's movement patterns and muscle strength balance using a machine learning algorithm. The analysis unit can analyze an athlete's movement patterns using, for example, deep learning. Deep learning learns large amounts of data and has advanced pattern recognition capabilities. The analysis unit can also analyze muscle strength balance using a support vector machine. A support vector machine is a machine learning algorithm that excels at data classification and regression analysis. Furthermore, the analysis unit can comprehensively analyze an athlete's movement patterns and muscle strength balance. For example, deep learning and a support vector machine can be combined to perform more accurate analysis. This improves the accuracy of analysis of an athlete's movement patterns and muscle strength balance by using a machine learning algorithm. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input movement pattern data to a generation AI and have the generation AI analyze the movement pattern.

[0074] The presentation unit can present forms to individual athletes based on data of other professional athletes. The presentation unit, for example, refers to the movement data of other professional athletes to suggest the optimal form for the athlete. The data of other professional athletes includes, for example, movement data and performance data. The presentation unit presents the optimal form for the athlete based on this data. For example, it presents an optimal form that partially incorporates the form of a specific professional athlete while matching the athlete's own characteristics. The presentation unit can also analyze the athlete's movement data and present specific improvements to the form. In this way, the optimal form can be presented to each individual athlete by referring to the data of other professional athletes. Some or all of the above-described processing by the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the professional athlete's data into a generation AI and have the generation AI suggest the optimal form.

[0075] The training unit can present at least one specific training menu from among strength training, stretching, and repetitive practice of a specific movement. The training unit, for example, suggests weight training or resistance training as strength training. Weight training is a method of building muscle strength using weights, and resistance training is a method of building muscle strength using resistance. The training unit can also suggest dynamic stretching or static stretching as stretching. Dynamic stretching is stretching that involves movement, and static stretching is stretching performed in a stationary state. Furthermore, the training unit can also suggest running form practice or swing practice as repetitive practice of a specific movement. By presenting a specific training menu, the athlete's training effect is improved. Some or all of the above-described processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input a training menu into a generation AI and have the generation AI suggest an optimal training menu.

[0076] The collection unit may use a motion capture system or a wearable sensor. The collection unit may collect athlete movement data using, for example, a motion capture system. Examples of motion capture systems include optical motion capture and inertial motion capture. Optical motion capture is a method of capturing and analyzing movements using a camera, while inertial motion capture is a method of measuring and analyzing movements using a sensor. The collection unit may also collect athlete data using a wearable sensor. Examples of wearable sensors include smartwatches and fitness trackers. Smartwatches measure heart rate and movement data and collect the data. Fitness trackers measure steps and calories burned and collect the data. This improves the accuracy of athlete data collection by using a motion capture system or a wearable sensor. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit may input motion capture data to a generation AI and have the generation AI analyze the movement data.

[0077] The analysis unit can build a database and compare data of athletes with similar characteristics. For example, the analysis unit builds a database of athletes and compares data of athletes with similar characteristics. The database includes, for example, the athletes' movement data and performance data. The analysis unit compares data of athletes with similar characteristics based on this data. For example, the analysis unit compares data of athletes with specific movement patterns or muscle strength balances to identify similarities and differences. The analysis unit can also use the database to provide information useful for improving athletes' performance. Thus, building a database makes it easier to compare data of athletes with similar characteristics. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data from the database into a generation AI and have the generation AI compare the data.

[0078] The training unit can present a method for providing feedback on training progress. The training unit, for example, evaluates training progress and provides feedback. Training progress includes, for example, training achievement, training frequency, and training intensity. The training unit provides feedback to the athlete based on this progress data. For example, if training achievement is high, advice for moving on to the next step is provided, and if achievement is low, points out areas for improvement. The training unit can also adjust the training menu based on the training frequency and intensity. This makes it easier to confirm the training effect of the athlete by providing feedback on training progress. Some or all of the above-mentioned processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input progress data into a generation AI and have the generation AI provide feedback.

[0079] The collection unit can estimate the athlete's emotions and adjust the timing of data collection based on the estimated emotions. For example, the collection unit captures the athlete's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expressions. The collection unit can also record the athlete's voice and estimate the emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice and calculates an emotion score. The collection unit can also collect the athlete's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations. This allows for adjusting the timing of data collection based on the athlete's emotions, thereby collecting more natural data. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative 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 collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input emotion data to the generation AI and cause the generation AI to adjust the timing of data collection.

[0080] The collection unit can analyze the athlete's past performance data and select the optimal data collection method. For example, the collection unit can select the most effective camera angle based on past video analysis data. The past video analysis data includes, for example, the athlete's movement data and performance data. The collection unit can also analyze past sensor data and determine the optimal sensor placement. The past sensor data includes, for example, heart rate data and muscle strength data. Furthermore, the collection unit can adjust the frequency of data collection by referring to past training data. The past training data includes, for example, training records and feedback. This allows the optimal data collection method to be selected by analyzing the past performance data. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input past performance data into a generation AI and have the generation AI select the optimal data collection method.

[0081] When collecting data, the collection unit can filter the data based on the athlete's current training status and physical condition. For example, when the athlete is performing high-intensity training, the collection unit prioritizes collection of heart rate data. The heart rate data is collected by measuring the athlete's heart rate. The collection unit can also monitor the muscle recovery status when the athlete is in a recovery period. The muscle recovery status is measured, for example, by measuring muscle stiffness and flexibility and collecting the data. Furthermore, when the athlete is trying a new training menu, the collection unit can collect data to evaluate its effectiveness. In this way, by filtering the data based on the athlete's current training status and physical condition, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input training status and physical condition data into the generation AI and have the generation AI perform data filtering.

[0082] When collecting data, the collection unit can select the optimal collection means depending on the athlete's input method. For example, if the athlete uses voice input, the collection unit collects data using voice recognition technology. Voice recognition technology is technology that converts the athlete's voice into text data. Furthermore, if the athlete uses text input, the collection unit can also analyze the input text data. Text data is collected as data containing character information entered by the athlete. Furthermore, if the athlete uses image input, the collection unit can also collect form data using image analysis technology. Image analysis technology is technology that collects and analyzes the athlete's movements as image data. This improves the efficiency of data collection by selecting the optimal collection means depending on the input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input voice data, text data, and image data into a generation AI and have the generation AI collect the data.

[0083] The collection unit can estimate the athlete's emotions and prioritize data to be collected based on the estimated emotions. For example, if the athlete is tense, the collection unit prioritizes collecting data for relaxation. Examples of data for relaxation include heart rate data and breathing data. The collection unit can also prioritize collecting performance data if the athlete is concentrating. Examples of performance data include movement data and muscle strength data. Furthermore, if the athlete is tired, the collection unit can prioritize collecting recovery data. Examples of recovery data include muscle recovery status and sleep data. This allows important data to be collected preferentially by prioritizing data based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative 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 collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input emotion data into the generation AI and have the generation AI determine the priority of the data.

[0084] When collecting data, the collection unit can prioritize collection of highly relevant data based on the athlete's geographical location information. For example, if an athlete is training at high altitude, the collection unit collects data specific to high altitude. Data specific to high altitude includes, for example, oxygen concentration data and heart rate data. The collection unit can also collect data to evaluate the impact of training under different climatic conditions if the athlete is training under those conditions. Climate condition data includes, for example, temperature data and humidity data. Furthermore, if an athlete is training at a specific stadium, the collection unit can collect data specific to that environment. Stadium data includes, for example, track condition and wind speed data. This allows highly relevant data to be collected preferentially by taking the geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input geographical location data into a generation AI and cause the generation AI to collect data.

[0085] During data collection, the collection unit can analyze the athlete's social media activity and collect related data. The collection unit can collect additional data, for example, based on training content shared by the athlete on social media. Social media data can include, for example, training records and feedback. The collection unit can also analyze the athlete's social media feedback to collect data evaluating the effectiveness of the training. Feedback data can include, for example, comments and ratings. The collection unit can also refer to data on other athletes the athlete follows on social media. The data on other athletes can include, for example, training menus and performance data. This allows related data to be collected by analyzing social media activity. Some or all of the above-mentioned processing by the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can input social media data into a generation AI and have the generation AI collect data.

[0086] When collecting data, the collection unit can customize the collection method by reflecting the athlete's past feedback. The collection unit, for example, adjusts the frequency of data collection based on feedback provided by the athlete in the past. Feedback includes, for example, questionnaire results and training records. The collection unit can also select the type of data to collect by referring to the athlete's past feedback. Furthermore, the collection unit can prioritize collecting data to solve problems previously pointed out by the athlete. In this way, the collection method can be customized by reflecting past feedback. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into the generation AI and have the generation AI customize the collection method.

[0087] The analysis unit can estimate the athlete's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the athlete is relaxed, the analysis unit provides detailed analysis results. Detailed analysis results include, for example, a detailed analysis of movement patterns and a detailed assessment of muscle balance. Furthermore, if the athlete is tense, the analysis unit can provide simple, to-the-point analysis results. Simple analysis results include, for example, an overview of key movement patterns and muscle balance. Furthermore, if the athlete is excited, the analysis unit can provide visually stimulating analysis results. Visual analysis results include, for example, analysis results using 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, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input emotion data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0088] During analysis, the analysis unit can adjust the level of detail of the analysis based on the athlete's important movements. For example, the analysis unit can focus on the athlete's main movements and perform a detailed analysis. Major movements include, for example, running form and swing movements. The analysis unit can also perform a simplified analysis of the athlete's secondary movements. Secondary movements include, for example, warm-up and cool-down movements. Furthermore, the analysis unit can provide analysis results that emphasize particularly important parts of the athlete's movements. In this way, adjusting the level of detail of the analysis based on important movements can provide more relevant analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input movement data to a generation AI and have the generation AI adjust the level of detail of the analysis.

[0089] During analysis, the analysis unit can apply different analysis algorithms depending on the athlete's category. For example, the analysis unit applies an analysis algorithm focusing on speed and power to a sprinter. Analysis of a sprinter includes, for example, speed data and power data. The analysis unit can also apply an analysis algorithm focusing on endurance and pace to a marathon runner. Analysis of a marathon runner includes, for example, endurance data and pace data. The analysis unit can also apply an analysis algorithm focusing on flexibility and balance to a gymnast. Analysis of a gymnast includes, for example, flexibility data and balance data. This allows for applying an analysis algorithm according to the category to provide more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data to a generation AI and cause the generation AI to apply an analysis algorithm.

[0090] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the athlete's past analysis results. The analysis unit, for example, compares the current analysis results with the athlete's past analysis results to improve accuracy. Past analysis results include, for example, analysis results of movement patterns and muscle strength balance. The analysis unit can also adjust the analysis algorithm by referring to the athlete's past training data. Training data includes, for example, training records and feedback. Furthermore, the analysis unit can improve the reliability of the analysis results based on the athlete's past performance data. Performance data includes, for example, game results and training results. In this way, by referring to the past analysis results, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0091] The analysis unit can estimate the athlete's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the athlete is in a hurry, the analysis unit can provide short, concise analysis results. Short analysis results can include, for example, an overview of key movement patterns and muscle balance. The analysis unit can also provide detailed analysis results if the athlete is relaxed. Detailed analysis results can include, for example, a detailed analysis of movement patterns and a detailed assessment of muscle balance. The analysis unit can also provide visually stimulating analysis results if the athlete is excited. Visual analysis results can include, for example, analysis results using graphs and charts. This allows for more appropriate analysis results to be provided by adjusting the length of the analysis based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input emotion data to the generation AI and have the generation AI adjust the length of the analysis.

[0092] During analysis, the analysis unit can determine analysis priorities based on the time the data was collected. The analysis unit, for example, prioritizes analysis of the most recent data and provides real-time feedback. The most recent data includes, for example, the most recent training data or match data. The analysis unit can also analyze long-term trends by referring to past data. Past data includes, for example, training data or match data from the past few months. Furthermore, the analysis unit can prioritize analysis of data from a specific training period and evaluate its effectiveness. Data from a specific training period includes, for example, data from a training camp or an intensive training period. In this way, by determining analysis priorities based on the time the data was collected, more important data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input collection time data to the generation AI and have the generation AI determine the analysis priorities.

[0093] During analysis, the analysis unit can adjust the order of analysis based on the relevance of data. For example, the analysis unit prioritizes analysis of data related to the athlete's main movements. Examples of main movements include running form and swing movements. The analysis unit can also postpone analysis of data related to the athlete's auxiliary movements. Examples of auxiliary movements include warm-up and cool-down movements. The analysis unit can also prioritize analysis of data that directly affects the athlete's performance. Examples of data that directly affect performance include muscle strength data and speed data. By adjusting the order of analysis based on the relevance of data, more relevant data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0094] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the athlete's level of expertise. For example, if the athlete is a beginner, the analysis unit can explain the analysis results in simple terms. Simple terms include, for example, general terms and everyday expressions. Furthermore, if the athlete is an intermediate athlete, the analysis unit can explain the analysis results using appropriate technical terminology. Appropriate technical terminology includes, for example, basic technical terms and sports terms. Furthermore, if the athlete is an advanced athlete, the analysis unit can explain the analysis results using detailed technical terminology. Detailed technical terminology includes, for example, advanced technical terms and specialized sports terms. By adjusting the use of technical terminology in the analysis according to the athlete's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input expertise level data into a generation AI and have the generation AI use technical terminology.

[0095] The presentation unit can estimate the athlete's emotions and adjust the presentation method of form based on the estimated emotions of the athlete. For example, if the athlete is relaxed, the presentation unit provides a detailed explanation of form. The detailed explanation of form includes, for example, each step of the movement and important points. Furthermore, if the athlete is nervous, the presentation unit can provide a simple, concise explanation of form. The simple explanation of form includes, for example, key movements and basic points. Furthermore, if the athlete is excited, the presentation unit can provide a visually stimulating explanation of form. The visual explanation of form includes, for example, video presentation or animation. This allows for the presentation of more appropriate form by adjusting the presentation method of form based on the athlete's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. Generative 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-mentioned processing in the presentation unit can be performed using, for example, AI, or without AI. For example, the presentation unit can input emotion data into the generation AI and cause the generation AI to adjust the presentation method of the form.

[0096] When presenting form, the presentation unit can adjust the level of detail of the presentation based on the athlete's important movements. For example, the presentation unit can provide a detailed explanation of the athlete's form, focusing on the athlete's main movements. Main movements include, for example, running form and swing movements. The presentation unit can also provide a simplified explanation of the athlete's secondary movements. Secondary movements include, for example, warm-up and cool-down movements. Furthermore, the presentation unit can provide an explanation of the athlete's form by emphasizing particularly important parts of the athlete's movements. In this way, by adjusting the level of detail of the presentation based on important movements, more relevant form can be presented. Some or all of the above-mentioned processing in the presentation unit may be performed using, or without, AI. For example, the presentation unit can input movement data to a generation AI and have the generation AI adjust the level of detail of the presentation.

[0097] When presenting a form, the presentation unit can apply different presentation algorithms depending on the athlete's category. For example, the presentation unit applies a form presentation algorithm focusing on speed and power to a sprinter. The sprinter's form includes, for example, speed data and power data. The presentation unit can also apply a form presentation algorithm focusing on endurance and pace to a marathon runner. The marathon runner's form includes, for example, endurance data and pace data. The presentation unit can also apply a form presentation algorithm focusing on flexibility and balance to a gymnast. The gymnast's form includes, for example, flexibility data and balance data. This allows a more appropriate form to be presented by applying a presentation algorithm according to the category. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input category data to a generation AI and cause the generation AI to apply a presentation algorithm.

[0098] When presenting form, the presentation unit can improve the accuracy of the presentation by referring to the athlete's past presentation results. The presentation unit, for example, adjusts the current form presentation based on the athlete's past form presentation results. Past form presentation results include, for example, presentation results of movement patterns and presentation results of muscle strength balance. The presentation unit can also improve the form presentation algorithm by referring to the athlete's past training data. Training data includes, for example, training records and feedback. Furthermore, the presentation unit can improve the reliability of the form presentation based on the athlete's past performance data. Performance data includes, for example, game results and training results. In this way, by referring to the past presentation results, the accuracy of the presentation is improved. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input past presentation result data into the generation AI and cause the generation AI to improve the accuracy of the presentation.

[0099] The presentation unit can estimate the athlete's emotions and adjust the length of the form based on the estimated emotions. For example, if the athlete is in a hurry, the presentation unit can provide a short, concise explanation of the form. The short explanation can include, for example, key movements and basic points. The presentation unit can also provide a detailed explanation of the form if the athlete is relaxed. The detailed explanation can include, for example, each step of the movement and important points. The presentation unit can also provide a visually stimulating explanation of the form if the athlete is excited. The visual explanation can include, for example, video presentation or animation. This allows the presentation of a more appropriate form by adjusting the length of the form based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI 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 presentation unit can be performed using, for example, AI, or without AI. For example, the presentation unit can input emotion data to the generation AI and cause the generation AI to adjust the length of the form.

[0100] When presenting forms, the presentation unit can determine the presentation priority based on the time when the data was collected. The presentation unit, for example, prioritizes the presentation of the most important forms based on the most recent data. The most recent data includes, for example, the most recent training data or match data. The presentation unit can also present forms that take long-term trends into consideration by referring to past data. The past data includes, for example, training data or match data from the past few months. Furthermore, the presentation unit can present the optimal form based on data from a specific training period. The data from a specific training period includes, for example, data from a training camp or an intensive training period. In this way, by determining the presentation priority based on the time when the data was collected, more important forms can be presented preferentially. Some or all of the above-described processing by the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input collection time data to the generation AI and cause the generation AI to determine the presentation priority.

[0101] When presenting forms, the presentation unit can adjust the order of presentation based on the relevance of the data. For example, the presentation unit prioritizes presentation of forms related to the athlete's main movements. Examples of main movements include running form and swing movements. The presentation unit can also postpone presentation of forms related to the athlete's auxiliary movements. Examples of auxiliary movements include warm-up and cool-down movements. Furthermore, the presentation unit can prioritize presentation of forms that directly affect the athlete's performance. Examples of forms that directly affect performance include muscle strength data and speed data. In this way, by adjusting the presentation order based on the relevance of the data, more relevant forms can be presented preferentially. Some or all of the above-described processing by the presentation unit may be performed using, or without, AI. For example, the presentation unit can input the relevance data to the generation AI and cause the generation AI to adjust the presentation order.

[0102] When presenting the form, the presentation unit can adjust the use of technical terminology in the presentation depending on the athlete's level of expertise. For example, if the athlete is a beginner, the presentation unit can explain the form in simple terms. Simple terms include, for example, general terms and everyday expressions. Furthermore, if the athlete is an intermediate athlete, the presentation unit can explain the form using appropriate technical terminology. Appropriate technical terminology includes, for example, basic technical terms and sports terms. Furthermore, if the athlete is an advanced athlete, the presentation unit can explain the form using detailed technical terminology. Detailed technical terminology includes, for example, advanced technical terms and specialized sports terms. In this way, by adjusting the use of technical terminology in the presentation depending on the athlete's level of expertise, a more understandable form can be presented. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without AI. For example, the presentation unit can input expertise level data to the generation AI and cause the generation AI to use technical terminology.

[0103] The training unit can estimate the athlete's emotions and adjust the presentation of the training method based on the estimated emotions of the athlete. For example, if the athlete is relaxed, the training unit can provide a detailed training menu. The detailed training menu can include, for example, steps and points to note for each training. Furthermore, if the athlete is nervous, the training unit can provide a simple training menu that focuses on the main points. The simple training menu can include, for example, key training exercises and basic points. Furthermore, if the athlete is excited, the training unit can provide a visually stimulating training menu. The visual training menu can include, for example, video presentations and animations. This allows for adjusting the presentation of the training method based on the athlete's emotions, thereby providing a more appropriate training method. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the training unit can be performed, for example, using AI or without AI. For example, the training unit can input emotion data into the generation AI and have the generation AI execute a training method suggestion.

[0104] When presenting a training method, the training unit can analyze the athlete's past training data to select the optimal method. The training unit, for example, selects the most effective training method based on the athlete's past training data. Past training data includes, for example, training records and feedback. The training unit can also analyze the athlete's past training data to adjust the frequency of training. Furthermore, the training unit can also adjust the intensity of training with reference to the athlete's past training data. In this way, the optimal training method can be selected by analyzing the past training data. Some or all of the above-mentioned processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input past training data into the generation AI and have the generation AI select the optimal training method.

[0105] When presenting a training method, the training unit can customize the method based on the athlete's current physical condition and training status. For example, if the athlete is tired, the training unit can suggest recovery training. Recovery training includes, for example, light exercise and stretching. Furthermore, if the athlete is performing high-intensity training, the training unit can also provide a training menu that includes appropriate rest. Furthermore, if the athlete is trying a new training menu, the training unit can also suggest a training method to evaluate its effectiveness. In this way, by customizing the method based on the athlete's current physical condition and training status, a more appropriate training method can be provided. Some or all of the above-mentioned processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input physical condition data and training status data into the generation AI and have the generation AI customize the training method.

[0106] When presenting a training method, the training unit can improve the method by reflecting the athlete's feedback. The training unit, for example, adjusts the training menu based on the feedback provided by the athlete. The feedback includes, for example, questionnaire results and training records. The training unit can also adjust the frequency of training based on the athlete's feedback. Furthermore, the training unit can adjust the intensity of training based on the athlete's feedback. In this way, the training method can be improved by reflecting the feedback. Some or all of the above-mentioned processing in the training unit may be performed using, for example, AI, or may be performed without using AI. For example, the training unit can input feedback data into a generation AI and have the generation AI improve the training method.

[0107] The training unit can estimate the athlete's emotions and prioritize training methods based on the estimated emotions. For example, if the athlete is relaxed, the training unit can prioritize providing a detailed training menu. The detailed training menu can include, for example, each training step and points to note. Furthermore, if the athlete is nervous, the training unit can prioritize providing a simple, to-the-point training menu. The simple training menu can include, for example, key training exercises and basic points. Furthermore, if the athlete is excited, the training unit can prioritize providing a visually stimulating training menu. The visual training menu can include, for example, video presentations and animations. This allows for prioritizing training methods based on the athlete's emotions, thereby providing a more appropriate training method. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative 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 training unit can be performed, for example, using AI, or without AI. For example, the training unit can input emotion data into the generation AI and have the generation AI determine the priority of training methods.

[0108] When presenting a training method, the training unit can select the optimal method based on the athlete's geographical location information. For example, if the athlete is training at high altitude, the training unit can propose a training method specific to high altitude. Examples of high altitude training methods include training in a low-oxygen environment and high altitude-specific stretching. Furthermore, if the athlete is training under different climatic conditions, the training unit can also propose a training method that takes those conditions into account. Examples of climatic conditions include temperature and humidity. Furthermore, if the athlete is training at a specific stadium, the training unit can also propose a training method specific to that environment. Examples of stadium environments include track conditions and wind speed. This allows the optimal training method to be provided by taking the geographical location information into consideration. Some or all of the above-described processing in the training unit may be performed using, or without, AI. For example, the training unit can input geographical location data into the generation AI and have the generation AI select a training method.

[0109] When presenting a training method, the training department can analyze the athlete's social media activity to suggest a method. For example, the training department can suggest additional training methods based on the training content the athlete has shared on social media. Social media data includes, for example, training records and feedback. The training department can also analyze the athlete's social media feedback to suggest a method for evaluating the effectiveness of the training. Feedback data includes, for example, comments and ratings. Furthermore, the training department can refer to the training methods of other athletes the athlete follows on social media. Data from other athletes includes, for example, training menus and performance data. This allows for the provision of more relevant training methods by analyzing social media activity. Some or all of the above-described processing in the training department may be performed using, for example, AI, or may be performed without using AI. For example, the training department can input social media data into a generation AI and have the generation AI execute training method suggestions.

[0110] When presenting a training method, the training unit can customize the method by reflecting the athlete's past feedback. The training unit, for example, adjusts the training menu based on feedback provided by the athlete in the past. Feedback includes, for example, questionnaire results and training records. The training unit can also adjust the frequency of training by referring to the athlete's past feedback. Furthermore, the training unit can prioritize providing training methods that solve problems previously pointed out by the athlete. In this way, the training method can be customized by reflecting past feedback. Some or all of the above-mentioned processing in the training unit may be performed, for example, using AI, or may be performed without using AI. For example, the training unit can input past feedback data into a generation AI and have the generation AI customize the training method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, presentation unit, and training 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 data on the athlete using the camera 42 and sensors of the smart device 14 and processes the data using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the athlete's movement patterns and muscle strength balance using a machine learning algorithm. The presentation unit presents the athlete with the optimal form using the display 40A and speaker 40B of the smart device 14. The training unit is realized by the control unit 46A of the smart device 14 and presents a specific training menu. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, presentation unit, and training 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 and sensors of the smart glasses 214 and processes the data using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the athlete's movement patterns and muscle strength balance using machine learning algorithms. The presentation unit presents the athlete with the optimal form using the display and speakers of the smart glasses 214. The training unit is realized by the control unit 46A of the smart glasses 214 and presents a specific training menu. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, presentation unit, and training 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 data on the athlete using the camera 42 and sensors of the headset-type device 314, and processes the data using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the athlete's movement patterns and muscle strength balance using a machine learning algorithm. The presentation unit presents the athlete with the optimal form using the display and speaker of the headset-type device 314. The training unit is realized by the control unit 46A of the headset-type device 314, and presents a specific training menu. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, presentation unit, and training 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 and sensors of the robot 414, and processes the data using the control unit 46A. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the athlete's movement patterns and muscle strength balance using a machine learning algorithm. The presentation unit presents the athlete with the optimal form using the display and speaker of the robot 414. The training unit is realized by the control unit 46A of the robot 414, and presents a specific training menu.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The analysis unit can estimate the athlete's emotions and determine the priorities of analysis based on the estimated emotions of the athlete. For example, if the athlete is relaxed, detailed analysis can be prioritized. Detailed analysis includes a detailed analysis of movement patterns and a detailed evaluation of muscle strength balance. Alternatively, if the athlete is tense, simple and to-the-point analysis can be prioritized. Simple analysis includes an overview of key movement patterns and muscle strength balance. Furthermore, if the athlete is excited, visually stimulating analysis can be prioritized. Visual analysis includes analysis using graphs and charts. This allows for more appropriate analysis results to be provided by prioritizing analysis based on the athlete's emotions.

[0113] The collection unit can analyze an athlete's past performance data and select the optimal data collection method. For example, the most effective camera angle can be selected based on past video analysis data. Past video analysis data includes the athlete's movement data and performance data. The collection unit can also analyze past sensor data to determine the optimal sensor placement. Past sensor data includes heart rate data and muscle strength data. Furthermore, the frequency of data collection can be adjusted based on past training data. Past training data includes training records and feedback. This allows the optimal data collection method to be selected by analyzing past performance data.

[0114] The training department can estimate the athlete's emotions and adjust the training method presentation based on the estimated athlete's emotions. For example, if the athlete is relaxed, a detailed training menu can be provided. The detailed training menu includes steps and points to note for each training. Alternatively, if the athlete is nervous, a simple training menu can be provided that covers the main points. The simple training menu includes key training exercises and basic points. Furthermore, if the athlete is excited, a visually stimulating training menu can be provided. The visual training menu includes video presentations and animations. This allows the athlete to be provided with a more appropriate training method by adjusting the training method presentation based on the athlete's emotions.

[0115] The analysis unit can estimate the athlete's emotions and adjust the way the analysis is presented based on the estimated emotions of the athlete. For example, if the athlete is relaxed, detailed analysis results can be provided. Detailed analysis results include a detailed analysis of movement patterns and a detailed evaluation of muscle balance. Alternatively, if the athlete is tense, simple and to-the-point analysis results can be provided. Simple analysis results include an overview of key movement patterns and muscle balance. Furthermore, if the athlete is excited, visually stimulating analysis results can be provided. Visual analysis results include analysis results using graphs and charts. This allows the analysis unit to provide more appropriate analysis results by adjusting the way the analysis is presented based on the athlete's emotions.

[0116] The collection unit can estimate the athlete's emotions and determine the priority of data to be collected based on the estimated emotions of the athlete. For example, if the athlete is nervous, data for relaxation can be collected with priority. Data for relaxation includes heart rate data and breathing data. Also, if the athlete is concentrating, performance data can be collected with priority. Performance data includes movement data and muscle strength data. Furthermore, if the athlete is tired, recovery data can be collected with priority. Recovery data includes muscle recovery status and sleep data. In this way, by determining the priority of data based on the athlete's emotions, important data can be collected with priority.

[0117] When collecting data, the collection unit can filter the data based on the athlete's current training status and physical condition. For example, if an athlete is undergoing high-intensity training, heart rate data can be collected as a priority. Heart rate data is collected by measuring the athlete's heart rate. In addition, if an athlete is in a recovery period, muscle recovery status can be monitored by measuring muscle stiffness and flexibility and collecting data. Furthermore, if an athlete is trying a new training menu, data can be collected to evaluate its effectiveness. This allows more relevant data to be collected by filtering data based on the athlete's current training status and physical condition.

[0118] During analysis, the analysis unit can adjust the level of detail of the analysis based on the athlete's important movements. For example, it can perform a detailed analysis focusing on the athlete's main movements. Main movements include running form and swing movements. It can also perform a simplified analysis of the athlete's secondary movements. Secondary movements include warm-up and cool-down movements. It can also provide analysis results that emphasize particularly important parts of the athlete's movements. By adjusting the level of detail of the analysis based on important movements, it is possible to provide more relevant analysis results.

[0119] When presenting form, the presentation unit can apply different presentation algorithms depending on the athlete's category. For example, a form presentation algorithm focusing on speed and power can be applied to a sprinter. A sprinter's form includes speed data and power data. A form presentation algorithm focusing on endurance and pace can also be applied to a marathon runner. A marathon runner's form includes endurance data and pace data. A form presentation algorithm focusing on flexibility and balance can also be applied to a gymnast. A gymnast's form includes flexibility data and balance data. This makes it possible to present a more appropriate form by applying a presentation algorithm according to the category.

[0120] When presenting a training method, the training department can select the optimal method based on the athlete's geographical location information. For example, if an athlete is training at high altitude, the training department can suggest a training method specific to high altitude. High altitude training methods include training in an environment with low oxygen concentration and stretches specific to high altitude. In addition, if an athlete is training in different climatic conditions, the training department can suggest a training method that takes those effects into consideration. Climatic conditions include temperature and humidity. Furthermore, if an athlete is training at a specific stadium, the training department can suggest a training method specific to that environment. Stadium environments include track conditions and wind speed. This makes it possible to provide the optimal training method by taking geographical location information into consideration.

[0121] When presenting training methods, the training department can analyze an athlete's social media activity to suggest methods. For example, additional training methods can be suggested based on the training content the athlete has shared on social media. Social media data includes training records and feedback. The training department can also suggest methods for evaluating the effectiveness of training by analyzing the athlete's social media feedback. Feedback data includes comments and ratings. Furthermore, the training methods of other athletes the athlete follows on social media can be used as reference. Data from other athletes includes training menus and performance data. This allows the training department to provide more relevant training methods by analyzing social media activity.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The collection unit collects athlete data. The athlete data includes movement data, heart rate, muscle strength data, etc. The collection unit collects data using video analysis and sensors. Video analysis is performed using frame rate and analysis software, and sensors collect data using acceleration sensors and heart rate sensors. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses machine learning algorithms to analyze the athlete's movement patterns and muscle strength balance. Machine learning algorithms include deep learning and support vector machines. Step 3: The suggestion unit suggests a form based on the analysis results obtained by the analysis unit. The suggestion unit refers to the movement data and performance data of other professional athletes and suggests the optimal form for each individual athlete. Step 4: The training unit suggests a training method for achieving the form suggested by the suggestion unit. The training unit suggests at least one specific training menu from the following: strength training, stretching, and repetitive practice of a specific movement. For example, it suggests weight training, resistance training, dynamic stretching, static stretching, running form practice, and swing practice.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] [Explanation of symbols]

[0196] 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 presentation unit that presents a form based on the analysis result obtained by the analysis unit; a training unit that presents a training method for achieving the form presented by the presentation unit. A system characterized by:

2. The collecting unit Collecting athlete data using video analysis or sensors 2. The system of claim 1.

3. The analysis unit Analyzing athletes' movement patterns and muscle strength balance using machine learning algorithms 2. The system of claim 1.

4. The presentation unit Present individual athletes with forms based on data from other professional athletes 2. The system of claim 1.

5. The training section Presents a specific training menu of at least one of the following: strength training, stretching, and repetitive practice of specific movements 2. The system of claim 1.

6. The collecting unit Using a motion capture system or wearable sensors 2. The system of claim 1.

7. The analysis unit Build a database and compare data from athletes with similar characteristics 2. The system of claim 1.

8. The training section Provide feedback on training progress 2. The system of claim 1.

Images