System

Abstract

An object of a system according to an embodiment is to analyze a play of an athlete in real time and provide accurate feedback.SOLUTION: A system includes a camera part, an analysis part, and a feedback part. The camera unit photographs a play of an athlete. The analysis unit analyzes the video captured by the camera unit. The feedback unit provides feedback based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of making it difficult to analyze athletes' play in real time and provide accurate feedback.

[0005] The system according to the embodiment aims to analyze an athlete's play in real time and provide accurate feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera unit, an analysis unit, and a feedback unit. The camera unit captures images of the athlete's play. The analysis unit analyzes the images captured by the camera unit. The feedback unit provides feedback based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze an athlete's play in real time and provide accurate feedback. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A form evaluation system according to an embodiment of the present invention analyzes an athlete's play in real time and provides accurate form evaluation. The form evaluation system films the athlete's play, and a generation AI analyzes the footage to perform a detailed analysis of the athlete's movements. The generation AI compares the athlete's play with past data and ideal form to identify areas for improvement. For example, it analyzes specific movement points, such as running form and swing angle. Based on the analysis results, the generation AI automatically generates an appropriate form for the athlete. This generated form includes specific instructions on how the athlete should correct their movements. For example, specific advice is provided, such as bending the knee angle a little more or swinging the arms more widely. Furthermore, the form evaluation system provides real-time feedback to the athlete as they train. Each time the athlete performs a movement, the generation AI analyzes the movement and provides instant feedback. This allows the athlete to immediately correct their form and conduct effective training. The form evaluation system is applicable to athletes of all levels, from professional to amateur. The accurate form evaluation and real-time feedback provided by the generation AI enable athletes to continuously improve their form and enhance their performance. This allows the form evaluation system to analyze an athlete's play in real time and provide an accurate form evaluation. For example, as an athlete plays, a video is captured on camera. The generative AI then analyzes the video and performs a detailed analysis of the athlete's movements. When analyzing the athlete's movements, the generative AI compares them with past data and ideal form to identify areas that need improvement. For example, it analyzes specific movement points such as running form and swing angle. Based on the analysis results, the generative AI automatically generates an appropriate form for the athlete. This generated form includes specific instructions showing the athlete how to correct their movements.For example, specific advice is provided, such as bending the knee angle a little more or swinging the arms wider. In addition, the form evaluation system provides real-time feedback to athletes as they train. Every time an athlete performs a movement, the generative AI analyzes that movement and provides instant feedback. This allows athletes to correct their form on the spot and train more effectively. The form evaluation system is available to athletes of all levels, from professional athletes to amateurs. The accurate form evaluation and real-time feedback provided by the generative AI allows athletes to continuously improve their form and enhance their performance.

[0029] A form evaluation system according to an embodiment includes a camera unit, an analysis unit, and a feedback unit. The camera unit captures an athlete's play. For example, the camera unit uses a high-resolution camera to capture the athlete's movements in detail. The camera unit can also capture the athlete's movements from different angles using multiple cameras. The camera unit also has the ability to acquire movement speed and position information in real time. For example, the camera unit can capture the athlete's movements at high speed and play them back in slow motion. The camera unit can also track the athlete's position information in real time and capture the athlete's movements from the optimal angle. The analysis unit uses a generation AI to analyze the footage captured by the camera unit. For example, the analysis unit compares the athlete's movements with past data or ideal form. The generation AI analyzes the athlete's movements in detail and identifies areas that need improvement. For example, the generation AI analyzes specific movement points, such as running form and swing angle. The analysis unit can also analyze the athlete's movements in real time and provide immediate feedback. For example, the generation AI analyzes the athlete's movements in real time and provides immediate feedback. The feedback unit provides feedback based on the analysis results obtained by the analysis unit. For example, the feedback unit provides the athlete with specific instructions for correcting their form. The generation AI automatically generates specific instructions showing how the athlete should correct their movements. For example, the generation AI provides specific advice such as bending the knee angle a little more or swinging their arms more widely. The feedback unit can also provide real-time feedback to the athlete while they are training. For example, the generation AI analyzes the athlete's movements every time they perform a movement and returns instant feedback. This allows the form evaluation system according to the embodiment to analyze the athlete's play in real time and provide accurate form evaluation. This allows the athlete to correct their form on the spot and conduct effective training.

[0030] The analysis unit includes a comparison unit that analyzes the athlete's movements by comparing them with past data or an ideal form. The comparison unit uses a generation AI to analyze the athlete's movements by comparing them with past data or an ideal form. Past data includes, for example, past match data and training data. Ideal form includes, for example, the form of professional players and forms recommended by coaches. The comparison unit uses the generation AI to analyze the athlete's movements in detail and identify areas for improvement. For example, the generation AI analyzes specific movement points, such as running form and swing angle. The comparison unit can also analyze the athlete's movements in real time and provide immediate feedback. For example, the generation AI analyzes the athlete's movements in real time and provides immediate feedback. This allows areas for improvement in the athlete's movements to be identified by comparing them with past data and an ideal form. Some or all of the above-described processing in the comparison unit is performed by the generation AI. For example, the comparison unit inputs the athlete's movement data into the generation AI, which then performs analysis by comparing it with past data and an ideal form.

[0031] The feedback unit includes an instruction unit that provides the athlete with specific instructions for correcting their form. The instruction unit uses a generation AI to provide the athlete with specific instructions for correcting their form. The generation AI automatically generates specific instructions showing how the athlete should correct their movements. For example, the generation AI provides specific advice such as bending their knees a little more or swinging their arms more widely. The instruction unit can also provide real-time feedback to the athlete while they are training. For example, the generation AI analyzes the athlete's movements every time they perform a movement and returns instant feedback. This allows the athlete to correct their form on the spot and perform effective training. Some or all of the above-mentioned processing in the instruction unit is performed using the generation AI. For example, the instruction unit inputs the athlete's movement data into the generation AI, which then automatically generates specific instructions for correcting their form.

[0032] The analysis unit includes a real-time analysis unit that analyzes an athlete's movements in real time and provides rapid feedback. The real-time analysis unit uses a generation AI to analyze an athlete's movements in real time and provide rapid feedback. The generation AI analyzes an athlete's movements in real time and provides immediate feedback. For example, the generation AI analyzes an athlete's movements in real time and provides immediate feedback. The real-time analysis unit can also provide real-time feedback when an athlete is training. For example, the generation AI analyzes an athlete's movements every time the athlete performs a movement and provides immediate feedback. This allows the athlete to correct their form on the spot and perform effective training. Some or all of the above-mentioned processing in the real-time analysis unit is performed using the generation AI. For example, the real-time analysis unit inputs the athlete's movement data into the generation AI, which then performs analysis in real time and provides feedback.

[0033] The camera unit automatically adjusts the frame rate according to the athlete's movement speed during filming. The camera unit uses the generation AI to automatically adjust the frame rate according to the athlete's movement speed during filming. The generation AI analyzes the athlete's movement speed and sets the optimal frame rate. For example, the generation AI sets the frame rate high if the athlete is moving fast. The generation AI can also set the frame rate low if the athlete is moving slowly. The generation AI can also adjust the frame rate in real time if the athlete's movement changes. This allows optimal footage to be captured by adjusting the frame rate according to the athlete's movement speed. Some or all of the above-mentioned processing in the camera unit is performed using the generation AI. For example, the camera unit inputs the athlete's movement speed data into the generation AI, which then automatically adjusts the frame rate.

[0034] The camera unit tracks the athlete's location information in real time during filming and shoots from the optimal angle. The camera unit uses the generation AI to track the athlete's location information in real time during filming and shoots from the optimal angle. The generation AI analyzes the athlete's location information and sets the optimal angle. For example, the generation AI automatically tracks the camera as the athlete moves to maintain the optimal angle. The generation AI can also automatically adjust the camera zoom based on the athlete's location information. The generation AI can also adjust the camera position to shoot from the optimal angle when the athlete performs a specific movement. In this way, tracking the athlete's location information makes it possible to shoot from the optimal angle. Some or all of the above-mentioned processing in the camera unit is performed using the generation AI. For example, the camera unit inputs the athlete's location information data into the generation AI, which then sets the optimal angle.

[0035] The camera unit is equipped with an automatic exposure adjustment function to adapt to different lighting conditions when shooting. The camera unit is equipped with an automatic exposure adjustment function to adapt to different lighting conditions when shooting using a generation AI. The generation AI analyzes lighting conditions and sets the optimal exposure. For example, when shooting outdoors, the generation AI automatically adjusts the exposure according to changes in sunlight. When shooting indoors, the generation AI can also automatically adjust the exposure according to the brightness of the lighting. The generation AI can also automatically adjust the exposure to adapt to low-light environments when shooting in the evening or at night. This allows for adapting to different lighting conditions and always enabling shooting with the optimal exposure. Some or all of the above-mentioned processing in the camera unit is performed using the generation AI. For example, the camera unit inputs lighting condition data into the generation AI, which then automatically adjusts the exposure.

[0036] The camera unit selects optimal shooting settings by referring to the athlete's past performance data when shooting. The camera unit uses the generation AI to select optimal shooting settings by referring to the athlete's past performance data when shooting. The generation AI analyzes the athlete's past performance data and selects optimal shooting settings. For example, the generation AI selects the optimal camera angle based on the athlete's past performance data. The generation AI can also set the optimal frame rate based on the athlete's past performance data. The generation AI can also select optimal exposure settings based on the athlete's past performance data. In this way, optimal shooting settings can be selected by referring to past performance data. Some or all of the above-mentioned processing in the camera unit is performed using the generation AI. For example, the camera unit inputs the athlete's past performance data into the generation AI, which selects optimal shooting settings.

[0037] The camera unit is equipped with a zoom function for emphasizing specific parts of the athlete's movements when filming. The camera unit is equipped with a zoom function for emphasizing specific parts of the athlete's movements when filming, using a generation AI. The generation AI analyzes the athlete's movements and sets the zoom to emphasize specific parts. For example, the generation AI zooms in on the movement of the athlete's legs to emphasize the athlete's running form. The generation AI can also zoom in on the movement of the athlete's arms to emphasize the angle of the athlete's swing. The generation AI can also zoom in on the movement of the athlete's entire body to emphasize the height of the athlete's jump. This enables detailed analysis by emphasizing specific parts of the athlete's movements. Some or all of the above-mentioned processing in the camera unit is performed using the generation AI. For example, the camera unit inputs the athlete's movement data into the generation AI, and the generation AI sets the zoom to emphasize specific parts.

[0038] The camera unit has a function to capture the athlete's movements as a 3D model when filming. The camera unit has a function to capture the athlete's movements as a 3D model when filming using a generative AI. The generative AI analyzes the athlete's movements and captures them as a 3D model. For example, the generative AI captures the athlete's movements as a 3D model and performs detailed analysis. The generative AI can also capture the athlete's movements as a 3D model and analyze them from different perspectives. The generative AI can also capture the athlete's movements as a 3D model and detect subtle changes in the movements. This enables detailed analysis by capturing the athlete's movements as a 3D model. Some or all of the above-mentioned processing in the camera unit is performed using the generative AI. For example, the camera unit inputs the athlete's movement data into the generative AI, which then captures it as a 3D model.

[0039] The analysis unit also uses electromyogram data to analyze the athlete's muscle movements during analysis. The analysis unit also uses generation AI to also use electromyogram data to analyze the athlete's muscle movements during analysis. The generation AI analyzes the athlete's electromyogram data and analyzes the muscle movements in detail. For example, the generation AI analyzes the muscle movements in detail based on the athlete's electromyogram data. The generation AI can also analyze the muscle fatigue level based on the athlete's electromyogram data. The generation AI can also analyze the muscle movement patterns based on the athlete's electromyogram data. In this way, by using electromyogram data in combination, muscle movements can be analyzed in detail. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the athlete's electromyogram data into the generation AI, and the generation AI analyzes the muscle movements.

[0040] The analysis unit provides analysis results by taking into account the continuity of the athlete's movements during analysis. The analysis unit uses a generation AI to provide analysis results by taking into account the continuity of the athlete's movements during analysis. The generation AI analyzes the continuity of the athlete's movements and provides analysis results from the start to the end of the movement. For example, the generation AI takes into account the continuity of the athlete's movements and provides analysis results from the start to the end of the movement. The generation AI can also take into account the continuity of the athlete's movements and provide analysis results for the intermediate part of the movement. The generation AI can also take into account the continuity of the athlete's movements and identify points of change in the movement and provide analysis results. In this way, by taking into account the continuity of the movement, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the athlete's movement data into the generation AI, which analyzes the continuity of the movement and provides analysis results.

[0041] The analysis unit applies a highly accurate algorithm to detect subtle changes in the athlete's movements during analysis. The analysis unit uses a generation AI to apply a highly accurate algorithm to detect subtle changes in the athlete's movements during analysis. The generation AI analyzes subtle changes in the athlete's movements and identifies areas for improvement in the movements. For example, the generation AI applies a highly accurate algorithm to detect subtle changes in the athlete's movements. The generation AI can also detect subtle changes in the athlete's movements and reflect them in the analysis results. The generation AI can also detect subtle changes in the athlete's movements and identify areas for improvement in the movements. In this way, by applying a highly accurate algorithm, subtle changes can be detected and reflected in the analysis results. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the athlete's movement data into the generation AI, and the generation AI performs analysis by applying a highly accurate algorithm.

[0042] The analysis unit improves the accuracy of the analysis by referring to the athlete's past training data during analysis. The analysis unit uses the generation AI to improve the accuracy of the analysis by referring to the athlete's past training data during analysis. The generation AI analyzes the athlete's past training data to improve the accuracy of the analysis. For example, the generation AI improves the accuracy of the analysis based on the athlete's past training data. The generation AI can also identify areas for improvement in movement based on the athlete's past training data. The generation AI can also analyze changes in movement based on the athlete's past training data. In this way, by referring to the past training data, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the athlete's past training data into the generation AI, which then performs the analysis.

[0043] The analysis unit analyzes the athlete's movements by comparing them with movements of different sports during analysis. The analysis unit uses the generation AI to analyze the athlete's movements by comparing them with movements of different sports during analysis. The generation AI compares the athlete's movements with movements of different sports and provides analysis results. For example, the generation AI compares the athlete's movements with movements of different sports to identify areas for improvement in the movements. The generation AI can also compare the athlete's movements with movements of different sports and analyze commonalities between the movements. This makes it possible to identify areas for improvement in the movements by comparing them with movements of different sports. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the athlete's movement data into the generation AI, which then performs analysis by comparing the athlete's movement data with movements of different sports.

[0044] The analysis unit analyzes the athlete's movements in synchronization with the audio data during analysis. The analysis unit uses the generation AI to analyze the athlete's movements in synchronization with the audio data during analysis. The generation AI synchronizes the athlete's movements with the audio data and provides the analysis results. For example, the generation AI synchronizes the athlete's movements with the audio data to identify areas for improvement in the movements. The generation AI can also synchronize the athlete's movements with the audio data to analyze changes in the movements. This makes it easier to identify areas for improvement in the movements by synchronizing with the audio data. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the athlete's movement data and audio data into the generation AI, and the generation AI performs the analysis in synchronization.

[0045] The feedback unit generates an animation to visually show areas for improvement in the athlete's movement when feedback is provided. The feedback unit uses a generation AI to generate an animation to visually show areas for improvement in the athlete's movement when feedback is provided. The generation AI analyzes the athlete's movement and generates an animation that visually shows areas for improvement. For example, the generation AI generates an animation to visually show areas for improvement in the athlete's movement. The generation AI can also visually show areas for improvement in the athlete's movement and provide specific methods for correction. The generation AI can also visually show areas for improvement in the athlete's movement and confirm changes in movement. In this way, by generating a visual animation, the athlete can intuitively understand areas for improvement in their movement. Some or all of the above-mentioned processing in the feedback unit is performed using the generation AI. For example, the feedback unit inputs the athlete's movement data into the generation AI, which then generates an animation.

[0046] The feedback unit provides a step-by-step guide on how to correct the athlete's movement when receiving feedback. The feedback unit uses a generation AI to provide a step-by-step guide on how to correct the athlete's movement when receiving feedback. The generation AI analyzes the athlete's movement and provides a step-by-step guide on how to correct it. For example, the generation AI provides a step-by-step guide on how to correct the athlete's movement. The generation AI can also show the athlete how to correct their movement step-by-step and provide specific correction procedures. The generation AI can also show the athlete how to correct their movement step-by-step and support the improvement of their movement. In this way, by providing a step-by-step guide, the athlete can easily understand how to correct their movement. Some or all of the above-mentioned processing in the feedback unit is performed using the generation AI. For example, the feedback unit inputs the athlete's movement data into the generation AI, which then provides a step-by-step guide.

[0047] The feedback unit has a function for comparing the results of corrections to the athlete's movements in real time when feedback is provided. The feedback unit has a function for comparing the results of corrections to the athlete's movements in real time when feedback is provided using the generation AI. The generation AI analyzes the results of corrections to the athlete's movements and compares them in real time. For example, the generation AI compares the results of corrections to the athlete's movements in real time and provides feedback. The generation AI can also compare the results of corrections to the athlete's movements in real time to identify areas for improvement in the movements. The generation AI can also compare the results of corrections to the athlete's movements in real time to confirm changes in the movements. This allows the athlete to immediately confirm areas for improvement in their movements by comparing the results of corrections in real time. Some or all of the above-mentioned processing in the feedback unit is performed using the generation AI. For example, the feedback unit inputs the athlete's movement data into the generation AI, which then performs a comparison in real time.

[0048] The feedback unit provides optimal feedback by referring to the athlete's past feedback history when providing feedback. The feedback unit uses the generation AI to provide optimal feedback by referring to the athlete's past feedback history when providing feedback. The generation AI analyzes the athlete's past feedback history and provides optimal feedback. For example, the generation AI provides optimal feedback based on the athlete's past feedback history. The generation AI can also identify areas for improvement in movements based on the athlete's past feedback history. The generation AI can also analyze changes in movements based on the athlete's past feedback history. This makes it possible to provide optimal feedback by referring to the past feedback history. Some or all of the above-mentioned processing in the feedback unit is performed using the generation AI. For example, the feedback unit inputs the athlete's past feedback history into the generation AI, which then performs the analysis.

[0049] The feedback unit compares the athlete's movements with the movements of other athletes at the time of feedback and provides feedback. The feedback unit uses the generation AI to compare the athlete's movements with the movements of other athletes at the time of feedback and provides feedback. The generation AI compares the athlete's movements with the movements of other athletes and provides feedback. For example, the generation AI compares the athlete's movements with the movements of other athletes to identify areas for improvement in the movements. The generation AI can also compare the athlete's movements with the movements of other athletes and analyze commonalities in the movements. This makes it easier to identify areas for improvement in the movements by comparing them with the movements of other athletes. Some or all of the above-mentioned processing in the feedback unit is performed using the generation AI. For example, the feedback unit inputs the athlete's movement data into the generation AI, which then compares and analyzes the movements of other athletes.

[0050] The feedback unit has a function of explaining, by voice, how to correct the athlete's movement when providing feedback. The feedback unit has a function of using the generation AI to explain, by voice, how to correct the athlete's movement when providing feedback. The generation AI analyzes how to correct the athlete's movement and explains it by voice. For example, the generation AI explains, by voice, how to correct the athlete's movement and provides specific correction steps. The generation AI can also explain, by voice, how to correct the athlete's movement and support the improvement of the movement. The generation AI can also explain, by voice, how to correct the athlete's movement and confirm changes in the movement. This makes it easier for the athlete to understand how to correct their movement by providing explanations by voice. Some or all of the above-mentioned processing in the feedback unit is performed using the generation AI. For example, the feedback unit inputs the athlete's movement data into the generation AI, and the generation AI provides explanations by voice.

[0051] The comparison unit compares and analyzes the athlete's movements at different time periods during the comparison. The comparison unit uses the generation AI to compare and analyze the athlete's movements at different time periods during the comparison. The generation AI compares the athlete's movements at different time periods and provides analysis results. For example, the generation AI compares the athlete's movements in the morning and evening and provides analysis results. The generation AI can also compare the athlete's movements before and after training and provide analysis results. The generation AI can also compare the athlete's movements in different seasons and provide analysis results. This allows for detailed analysis of changes in movements by comparing them at different time periods. Some or all of the above-mentioned processing in the comparison unit is performed using the generation AI. For example, the comparison unit inputs the athlete's movement data into the generation AI, which then compares and analyzes them at different time periods.

[0052] The comparison unit compares and analyzes the athlete's movements under different environmental conditions during the comparison. The comparison unit uses the generation AI to compare and analyze the athlete's movements under different environmental conditions during the comparison. The generation AI compares the athlete's movements under different environmental conditions and provides analysis results. For example, the generation AI compares the athlete's movements indoors and outdoors and provides analysis results. The generation AI can also compare the athlete's movements under different weather conditions and provide analysis results. The generation AI can also compare the athlete's movements under different temperature conditions and provide analysis results. This makes it easier to identify areas for improvement in movements by comparing under different environmental conditions. Some or all of the above-mentioned processing in the comparison unit is performed using the generation AI. For example, the comparison unit inputs the athlete's movement data into the generation AI, which then compares and analyzes under different environmental conditions.

[0053] The comparison unit compares and analyzes the athlete's movements with different training methods during the comparison. The comparison unit uses the generation AI to compare and analyze the athlete's movements with different training methods during the comparison. The generation AI compares the athlete's movements with different training methods and provides analysis results. For example, the generation AI compares the athlete's movements with different training methods to identify areas for improvement in the movements. The generation AI can also compare the athlete's movements with different training methods to analyze commonalities in the movements. This makes it easier to identify areas for improvement in the movements by comparing with different training methods. Some or all of the above-mentioned processing in the comparison unit is performed using the generation AI. For example, the comparison unit inputs the athlete's movement data into the generation AI, which then compares and analyzes the movements with different training methods.

[0054] The comparison unit compares and analyzes the athlete's movements with movements of different sports during the comparison. The comparison unit uses the generation AI to compare and analyze the athlete's movements with movements of different sports during the comparison. The generation AI compares the athlete's movements with movements of different sports and provides analysis results. For example, the generation AI compares the athlete's movements with movements of different sports to identify areas for improvement in the movements. The generation AI can also compare the athlete's movements with movements of different sports to analyze commonalities between the movements. This makes it easier to identify areas for improvement in the movements by comparing them with movements of different sports. Some or all of the above-mentioned processing in the comparison unit is performed using the generation AI. For example, the comparison unit inputs the athlete's movement data into the generation AI, which then compares and analyzes the movements with movements of different sports.

[0055] The comparison unit compares and analyzes the athlete's movements with athletes of different age groups during the comparison. The comparison unit uses the generation AI to compare and analyze the athlete's movements with athletes of different age groups during the comparison. The generation AI compares the athlete's movements with athletes of different age groups and provides analysis results. For example, the generation AI compares the athlete's movements with athletes of different age groups to identify areas for improvement in the movements. The generation AI can also compare the athlete's movements with athletes of different age groups to analyze commonalities in the movements. This makes it easier to identify areas for improvement in the movements by comparing with athletes of different age groups. Some or all of the above-mentioned processing in the comparison unit is performed using the generation AI. For example, the comparison unit inputs the athlete's movement data into the generation AI, and the generation AI performs analysis by comparing with athletes of different age groups.

[0056] The comparison unit compares and analyzes the athlete's movements with athletes of a different gender during the comparison. The comparison unit uses the generation AI to compare and analyze the athlete's movements with athletes of a different gender during the comparison. The generation AI compares the athlete's movements with athletes of a different gender and provides analysis results. For example, the generation AI compares the athlete's movements with athletes of a different gender to identify areas for improvement in the movements. The generation AI can also compare the athlete's movements with athletes of a different gender to analyze commonalities in the movements. This makes it easier to identify areas for improvement in the movements by comparing with athletes of a different gender. Some or all of the above-mentioned processing in the comparison unit is performed using the generation AI. For example, the comparison unit inputs the athlete's movement data into the generation AI, which then performs analysis by comparing with athletes of a different gender.

[0057] The instruction unit generates an animation to visually show how to correct the athlete's movement when instructed. The instruction unit uses a generation AI to generate an animation to visually show how to correct the athlete's movement when instructed. The generation AI analyzes the athlete's movement and generates an animation that visually shows how to correct the movement. For example, the generation AI generates an animation to visually show how to correct the athlete's movement. The generation AI can also visually show how to correct the athlete's movement and provide specific correction steps. The generation AI can also visually show how to correct the athlete's movement and confirm changes in the movement. In this way, by generating a visual animation, the athlete can intuitively understand how to correct the movement. Some or all of the above-mentioned processing in the instruction unit is performed using the generation AI. For example, the instruction unit inputs the athlete's movement data into the generation AI, and the generation AI generates an animation.

[0058] The instruction unit provides a step-by-step guide on how to correct the athlete's movement when instructed. The instruction unit uses a generating AI to provide a step-by-step guide on how to correct the athlete's movement when instructed. The generating AI analyzes the athlete's movement and provides a step-by-step guide on how to correct it. For example, the generating AI provides a step-by-step guide on how to correct the athlete's movement. The generating AI can also show step-by-step how to correct the athlete's movement and provide specific correction procedures. The generating AI can also show step-by-step how to correct the athlete's movement and support the improvement of the movement. In this way, by providing a step-by-step guide, the athlete can easily understand how to correct their movement. Some or all of the above-mentioned processing in the instruction unit is performed using the generating AI. For example, the instruction unit inputs the athlete's movement data into the generating AI, which then provides a step-by-step guide.

[0059] The instruction unit has a function for comparing the results of corrections to the athlete's movements in real time when instructions are given. The instruction unit has a function for using the generation AI to compare the results of corrections to the athlete's movements in real time when instructions are given. The generation AI analyzes the results of corrections to the athlete's movements and compares them in real time. For example, the generation AI compares the results of corrections to the athlete's movements in real time and provides instructions. The generation AI can also compare the results of corrections to the athlete's movements in real time to identify areas for improvement in movements. The generation AI can also compare the results of corrections to the athlete's movements in real time to confirm changes in movements. In this way, by comparing the results of corrections in real time, the athlete can immediately confirm areas for improvement in their movements. Some or all of the above-mentioned processing in the instruction unit is performed using the generation AI. For example, the instruction unit inputs the athlete's movement data into the generation AI, which then performs a comparison in real time.

[0060] The instruction unit provides optimal instructions by referring to the athlete's past instruction history when giving instructions. The instruction unit uses the generation AI to provide optimal instructions by referring to the athlete's past instruction history when giving instructions. The generation AI analyzes the athlete's past instruction history and provides optimal instructions. For example, the generation AI provides optimal instructions based on the athlete's past instruction history. The generation AI can also identify areas for improvement in movements based on the athlete's past instruction history. The generation AI can also analyze changes in movements based on the athlete's past instruction history. In this way, optimal instructions can be provided by referring to the past instruction history. Some or all of the above-mentioned processing in the instruction unit is performed using the generation AI. For example, the instruction unit inputs the athlete's past instruction history into the generation AI, which then performs the analysis.

[0061] The instruction unit, when giving instructions, compares the athlete's movements with those of other athletes and provides instructions. The instruction unit, when giving instructions, uses the generation AI to compare the athlete's movements with those of other athletes and provides instructions. The generation AI compares the athlete's movements with those of other athletes and provides instructions. For example, the generation AI compares the athlete's movements with those of other athletes to identify areas for improvement in the movements. The generation AI can also compare the athlete's movements with those of other athletes and analyze commonalities between the movements. This makes it easier to identify areas for improvement in the movements by comparing them with those of other athletes. Some or all of the above-mentioned processing in the instruction unit is performed using the generation AI. For example, the instruction unit inputs the athlete's movement data into the generation AI, which then compares and analyzes the movements of other athletes.

[0062] The instruction unit has a function to explain, by voice, how to correct the athlete's movement when instructed. The instruction unit has a function to explain, by voice, how to correct the athlete's movement when instructed, using the generation AI. The generation AI analyzes how to correct the athlete's movement and explains it by voice. For example, the generation AI explains by voice how to correct the athlete's movement and provides specific correction steps. The generation AI can also explain by voice how to correct the athlete's movement and support movement improvement. The generation AI can also explain by voice how to correct the athlete's movement and confirm changes in movement. This makes it easier for the athlete to understand how to correct their movement by providing explanations by voice. Some or all of the above-mentioned processing in the instruction unit is performed using the generation AI. For example, the instruction unit inputs the athlete's movement data into the generation AI, and the generation AI provides explanations by voice.

[0063] The real-time analysis unit provides analysis results by taking into account the continuity of the athlete's movements during real-time analysis. The real-time analysis unit uses a generation AI to provide analysis results by taking into account the continuity of the athlete's movements during real-time analysis. The generation AI analyzes the continuity of the athlete's movements and provides real-time analysis results from the start to the end of the movement. For example, the generation AI takes into account the continuity of the athlete's movements and provides real-time analysis results from the start to the end of the movement. The generation AI can also take into account the continuity of the athlete's movements and provide real-time analysis results for the intermediate part of the movement. The generation AI can also take into account the continuity of the athlete's movements and identify change points in the movement to provide real-time analysis results. In this way, by taking into account the continuity of the movement, more accurate real-time analysis results can be provided. Some or all of the above-mentioned processing in the real-time analysis unit is performed using the generation AI. For example, the real-time analysis unit inputs the athlete's movement data into the generation AI, which analyzes the continuity of the movement and provides real-time analysis results.

[0064] The real-time analysis unit applies a highly accurate algorithm to detect subtle changes in the athlete's movements during real-time analysis. The real-time analysis unit uses a generation AI to apply a highly accurate algorithm to detect subtle changes in the athlete's movements during real-time analysis. The generation AI analyzes subtle changes in the athlete's movements and identifies areas for improvement in the movements. For example, the generation AI applies a highly accurate algorithm to detect subtle changes in the athlete's movements. The generation AI can also detect subtle changes in the athlete's movements and reflect them in the real-time analysis results. The generation AI can also detect subtle changes in the athlete's movements and identify areas for improvement in the movements. In this way, by applying a highly accurate algorithm, subtle changes can be detected and reflected in the real-time analysis results. Some or all of the above-mentioned processing in the real-time analysis unit is performed using the generation AI. For example, the real-time analysis unit inputs the athlete's movement data into the generation AI, and the generation AI applies a highly accurate algorithm to perform analysis.

[0065] The real-time analysis unit has a function to simultaneously analyze an athlete's movements from different perspectives during real-time analysis. The real-time analysis unit has a function to simultaneously analyze an athlete's movements from different perspectives during real-time analysis using a generative AI. The generative AI analyzes an athlete's movements from different perspectives simultaneously and provides real-time analysis results. For example, the generative AI analyzes an athlete's movements from different perspectives simultaneously to identify areas for improvement in the movements. The generative AI can also analyze an athlete's movements from different perspectives simultaneously to confirm changes in the movements. This makes it easier to identify areas for improvement in the movements by analyzing them simultaneously from different perspectives. Some or all of the above-mentioned processing in the real-time analysis unit is performed using the generative AI. For example, the real-time analysis unit inputs the athlete's movement data into the generative AI, which then performs analysis from different perspectives simultaneously.

[0066] The real-time analysis unit improves the accuracy of analysis by referring to the athlete's past training data during real-time analysis. The real-time analysis unit uses the generation AI to improve the accuracy of analysis by referring to the athlete's past training data during real-time analysis. The generation AI analyzes the athlete's past training data to improve the accuracy of real-time analysis. For example, the generation AI improves the accuracy of real-time analysis based on the athlete's past training data. The generation AI can also identify areas for improvement in movement based on the athlete's past training data. The generation AI can also analyze changes in movement based on the athlete's past training data. In this way, by referring to the past training data, the accuracy of real-time analysis can be improved. Some or all of the above-mentioned processing in the real-time analysis unit is performed using the generation AI. For example, the real-time analysis unit inputs the athlete's past training data into the generation AI, which then performs the analysis.

[0067] The real-time analysis unit analyzes the athlete's movements by comparing them with movements of different sports during real-time analysis. The real-time analysis unit uses a generation AI to analyze the athlete's movements by comparing them with movements of different sports during real-time analysis. The generation AI compares the athlete's movements with movements of different sports and provides real-time analysis results. For example, the generation AI compares the athlete's movements with movements of different sports to identify areas for improvement in the movements. The generation AI can also compare the athlete's movements with movements of different sports to analyze commonalities between the movements. This makes it easier to identify areas for improvement in the movements by comparing them with movements of different sports. Some or all of the above-mentioned processing in the real-time analysis unit is performed using the generation AI. For example, the real-time analysis unit inputs the athlete's movement data into the generation AI, which then analyzes them by comparing them with movements of different sports.

[0068] The real-time analysis unit analyzes the athlete's movements in synchronization with the audio data during real-time analysis. The real-time analysis unit uses the generation AI to analyze the athlete's movements in synchronization with the audio data during real-time analysis. The generation AI synchronizes the athlete's movements with the audio data and provides real-time analysis results. For example, the generation AI synchronizes the athlete's movements with the audio data to identify areas for improvement in the movements. The generation AI can also synchronize the athlete's movements with the audio data to analyze changes in the movements. This makes it easier to identify areas for improvement in the movements by synchronizing with the audio data. Some or all of the above-mentioned processing in the real-time analysis unit is performed using the generation AI. For example, the real-time analysis unit inputs the athlete's movement data and audio data into the generation AI, and the generation AI performs the analysis in synchronization.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] When filming an athlete's movements, the camera unit can monitor the athlete's heart rate and adjust the timing of filming according to heart rate fluctuations. For example, if an athlete's heart rate suddenly rises, the camera unit will start filming to capture that moment. If the heart rate remains stable, the camera unit will continue filming for a longer period of time, allowing for detailed movement analysis. Furthermore, if the heart rate drops, the camera unit can pause filming to allow the athlete time to rest. This allows for optimal filming according to the athlete's physiological state.

[0071] When analyzing an athlete's movements, the analysis unit can estimate the athlete's muscle fatigue level and adjust the analysis results based on the fatigue level. For example, if the athlete's muscles are fatigued, the analysis unit can provide advice to reduce the movement. If the muscle fatigue level is low, the analysis unit can recommend higher-intensity training. Furthermore, if the muscle fatigue level is moderate, the analysis unit can suggest a training plan that incorporates appropriate rest. This enables optimal training based on the athlete's muscle condition.

[0072] When analyzing an athlete's movements, the real-time analysis unit can monitor the athlete's breathing patterns and adjust the analysis results based on their breathing patterns. For example, if the athlete's breathing is shallow and rapid, the real-time analysis unit can suggest breathing techniques to help them relax. If their breathing is deep and steady, the real-time analysis unit can recommend high-intensity training. Furthermore, if their breathing is irregular, the real-time analysis unit can suggest training to regulate their breathing. This allows for optimal training tailored to the athlete's breathing condition.

[0073] When filming an athlete's movements, the camera unit can automatically adjust the frame rate according to the speed of the athlete's movements. For example, if the athlete is moving quickly, the camera unit will film at a high frame rate, allowing for detailed movement analysis. On the other hand, if the athlete is moving slowly, the camera unit will film at a low frame rate, reducing the amount of data. Furthermore, if the athlete's movements change, the camera unit can adjust the frame rate in real time to provide optimal footage. This allows for optimal filming according to the athlete's movement speed.

[0074] When filming an athlete's movements, the camera unit can track the athlete's location information in real time and film from the optimal angle. For example, as the athlete moves, the camera unit automatically tracks them to maintain the optimal angle. The camera can also automatically adjust the camera's zoom based on the athlete's location information. Furthermore, the camera can adjust the camera's position to film from the optimal angle when the athlete performs a specific movement. This makes it possible to film from the optimal angle by tracking the athlete's location information.

[0075] The camera unit can also be equipped with an automatic exposure adjustment function to adapt to different lighting conditions when filming athletes' movements. For example, when filming outdoors, the exposure can be automatically adjusted according to changes in sunlight. When filming indoors, the exposure can also be automatically adjusted according to the brightness of the lighting. Furthermore, when filming in the evening or at night, the exposure can be automatically adjusted to adapt to low-light environments. This allows the camera to adapt to different lighting conditions and always capture images with the optimal exposure.

[0076] When filming an athlete's movements, the camera unit can also select optimal filming settings by referencing the athlete's past performance data. For example, the optimal camera angle can be selected based on the athlete's past performance data. The optimal frame rate can also be set based on the athlete's past performance data. Furthermore, the optimal exposure setting can also be selected based on the athlete's past performance data. In this way, optimal filming settings can be selected by referring to past performance data.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The camera unit films the athletes' play. The camera unit uses high-resolution cameras to capture the athletes' movements in detail and multiple cameras to film the athletes' movements from different angles. The camera unit also has the ability to acquire movement speed and position information in real time, allowing it to film the athletes' movements at high speed and play them back in slow motion. It can also track the athletes' position information in real time and film them from the optimal angle. Step 2: The analysis unit uses generative AI to analyze the footage captured by the camera unit. The analysis unit compares the athlete's movements with past data and ideal form to identify areas for improvement. Specifically, it analyzes key points of movement such as running form and swing angle. The analysis unit can also analyze the athlete's movements in real time and provide immediate feedback. Step 3: The feedback unit provides feedback based on the analysis results obtained by the analysis unit. The feedback unit provides specific instructions to the athlete on how to correct their form, and the generation AI automatically generates specific instructions showing the athlete how to correct their movement. For example, it provides specific advice such as bending the knee angle a little more or swinging the arms more widely. The feedback unit can also provide feedback in real time as the athlete trains.

[0079] (Example 2) A form evaluation system according to an embodiment of the present invention analyzes an athlete's play in real time and provides accurate form evaluation. The form evaluation system films the athlete's play, and a generation AI analyzes the footage to perform a detailed analysis of the athlete's movements. The generation AI compares the athlete's play with past data and ideal form to identify areas for improvement. For example, it analyzes specific movement points, such as running form and swing angle. Based on the analysis results, the generation AI automatically generates an appropriate form for the athlete. This generated form includes specific instructions on how the athlete should correct their movements. For example, specific advice is provided, such as bending the knee angle a little more or swinging the arms more widely. Furthermore, the form evaluation system provides real-time feedback to the athlete as they train. Each time the athlete performs a movement, the generation AI analyzes the movement and provides instant feedback. This allows the athlete to immediately correct their form and conduct effective training. The form evaluation system is applicable to athletes of all levels, from professional to amateur. The accurate form evaluation and real-time feedback provided by the generation AI enable athletes to continuously improve their form and enhance their performance. This allows the form evaluation system to analyze an athlete's play in real time and provide an accurate form evaluation. For example, as an athlete plays, a video is captured on camera. The generative AI then analyzes the video and performs a detailed analysis of the athlete's movements. When analyzing the athlete's movements, the generative AI compares them with past data and ideal form to identify areas that need improvement. For example, it analyzes specific movement points such as running form and swing angle. Based on the analysis results, the generative AI automatically generates an appropriate form for the athlete. This generated form includes specific instructions showing the athlete how to correct their movements.For example, specific advice is provided, such as bending the knee angle a little more or swinging the arms wider. In addition, the form evaluation system provides real-time feedback to athletes as they train. Every time an athlete performs a movement, the generative AI analyzes that movement and provides instant feedback. This allows athletes to correct their form on the spot and train more effectively. The form evaluation system is available to athletes of all levels, from professional athletes to amateurs. The accurate form evaluation and real-time feedback provided by the generative AI allows athletes to continuously improve their form and enhance their performance.

[0080] A form evaluation system according to an embodiment includes a camera unit, an analysis unit, and a feedback unit. The camera unit captures an athlete's play. For example, the camera unit uses a high-resolution camera to capture the athlete's movements in detail. The camera unit can also capture the athlete's movements from different angles using multiple cameras. The camera unit also has the ability to acquire movement speed and position information in real time. For example, the camera unit can capture the athlete's movements at high speed and play them back in slow motion. The camera unit can also track the athlete's position information in real time and capture the athlete's movements from the optimal angle. The analysis unit uses a generation AI to analyze the footage captured by the camera unit. For example, the analysis unit compares the athlete's movements with past data or ideal form. The generation AI analyzes the athlete's movements in detail and identifies areas that need improvement. For example, the generation AI analyzes specific movement points, such as running form and swing angle. The analysis unit can also analyze the athlete's movements in real time and provide immediate feedback. For example, the generation AI analyzes the athlete's movements in real time and provides immediate feedback. The feedback unit provides feedback based on the analysis results obtained by the analysis unit. For example, the feedback unit provides the athlete with specific instructions for correcting their form. The generation AI automatically generates specific instructions showing how the athlete should correct their movements. For example, the generation AI provides specific advice such as bending the knee angle a little more or swinging their arms more widely. The feedback unit can also provide real-time feedback to the athlete while they are training. For example, the generation AI analyzes the athlete's movements every time they perform a movement and returns instant feedback. This allows the form evaluation system according to the embodiment to analyze the athlete's play in real time and provide accurate form evaluation. This allows the athlete to correct their form on the spot and conduct effective training.

[0081] The analysis unit includes a comparison unit that analyzes the athlete's movements by comparing them with past data or an ideal form. The comparison unit uses a generation AI to analyze the athlete's movements by comparing them with past data or an ideal form. Past data includes, for example, past match data and training data. Ideal form includes, for example, the form of professional players and forms recommended by coaches. The comparison unit uses the generation AI to analyze the athlete's movements in detail and identify areas for improvement. For example, the generation AI analyzes specific movement points, such as running form and swing angle. The comparison unit can also analyze the athlete's movements in real time and provide immediate feedback. For example, the generation AI analyzes the athlete's movements in real time and provides immediate feedback. This allows areas for improvement in the athlete's movements to be identified by comparing them with past data and an ideal form. Some or all of the above-described processing in the comparison unit is performed by the generation AI. For example, the comparison unit inputs the athlete's movement data into the generation AI, which then performs analysis by comparing it with past data and an ideal form.

[0082] The feedback unit includes an instruction unit that provides the athlete with specific instructions for correcting their form. The instruction unit uses a generation AI to provide the athlete with specific instructions for correcting their form. The generation AI automatically generates specific instructions showing how the athlete should correct their movements. For example, the generation AI provides specific advice such as bending their knees a little more or swinging their arms more widely. The instruction unit can also provide real-time feedback to the athlete while they are training. For example, the generation AI analyzes the athlete's movements every time they perform a movement and returns instant feedback. This allows the athlete to correct their form on the spot and perform effective training. Some or all of the above-mentioned processing in the instruction unit is performed using the generation AI. For example, the instruction unit inputs the athlete's movement data into the generation AI, which then automatically generates specific instructions for correcting their form.

[0083] The analysis unit includes a real-time analysis unit that analyzes an athlete's movements in real time and provides rapid feedback. The real-time analysis unit uses a generation AI to analyze an athlete's movements in real time and provide rapid feedback. The generation AI analyzes an athlete's movements in real time and provides immediate feedback. For example, the generation AI analyzes an athlete's movements in real time and provides immediate feedback. The real-time analysis unit can also provide real-time feedback when an athlete is training. For example, the generation AI analyzes an athlete's movements every time the athlete performs a movement and provides immediate feedback. This allows the athlete to correct their form on the spot and perform effective training. Some or all of the above-mentioned processing in the real-time analysis unit is performed using the generation AI. For example, the real-time analysis unit inputs the athlete's movement data into the generation AI, which then performs analysis in real time and provides feedback.

[0084] The camera unit analyzes the athlete's emotions and adjusts the timing of filming based on the analyzed emotions. The camera unit uses a generation AI to analyze the athlete's emotions and adjusts the timing of filming based on the analyzed emotions. The generation AI analyzes the athlete's facial expressions and movements to estimate their emotions. For example, if the athlete is nervous, the generation AI delays filming until they relax. If the athlete is concentrating, the generation AI can also start filming without missing that moment. If the athlete is tired, the generation AI can also resume filming after a break. This allows the optimal moment to be captured by adjusting the timing of filming according to the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the camera unit is performed using the generation AI. For example, the camera unit inputs the athlete's facial expression data into the generation AI, which analyzes the emotions and adjusts the timing of filming.

[0085] The camera unit automatically adjusts the frame rate according to the athlete's movement speed during filming. The camera unit uses the generation AI to automatically adjust the frame rate according to the athlete's movement speed during filming. The generation AI analyzes the athlete's movement speed and sets the optimal frame rate. For example, the generation AI sets the frame rate high if the athlete is moving fast. The generation AI can also set the frame rate low if the athlete is moving slowly. The generation AI can also adjust the frame rate in real time if the athlete's movement changes. This allows optimal footage to be captured by adjusting the frame rate according to the athlete's movement speed. Some or all of the above-mentioned processing in the camera unit is performed using the generation AI. For example, the camera unit inputs the athlete's movement speed data into the generation AI, which then automatically adjusts the frame rate.

[0086] The camera unit tracks the athlete's location information in real time during filming and shoots from the optimal angle. The camera unit uses the generation AI to track the athlete's location information in real time during filming and shoots from the optimal angle. The generation AI analyzes the athlete's location information and sets the optimal angle. For example, the generation AI automatically tracks the camera as the athlete moves to maintain the optimal angle. The generation AI can also automatically adjust the camera zoom based on the athlete's location information. The generation AI can also adjust the camera position to shoot from the optimal angle when the athlete performs a specific movement. In this way, tracking the athlete's location information makes it possible to shoot from the optimal angle. Some or all of the above-mentioned processing in the camera unit is performed using the generation AI. For example, the camera unit inputs the athlete's location information data into the generation AI, which then sets the optimal angle.

[0087] The camera unit is equipped with an automatic exposure adjustment function to adapt to different lighting conditions when shooting. The camera unit is equipped with an automatic exposure adjustment function to adapt to different lighting conditions when shooting using a generation AI. The generation AI analyzes lighting conditions and sets the optimal exposure. For example, when shooting outdoors, the generation AI automatically adjusts the exposure according to changes in sunlight. When shooting indoors, the generation AI can also automatically adjust the exposure according to the brightness of the lighting. The generation AI can also automatically adjust the exposure to adapt to low-light environments when shooting in the evening or at night. This allows for adapting to different lighting conditions and always enabling shooting with the optimal exposure. Some or all of the above-mentioned processing in the camera unit is performed using the generation AI. For example, the camera unit inputs lighting condition data into the generation AI, which then automatically adjusts the exposure.

[0088] The camera unit estimates the athlete's emotions and prioritizes the footage to be captured based on the estimated emotions. The camera unit uses a generation AI to estimate the athlete's emotions and prioritizes the footage to be captured based on the estimated emotions. The generation AI analyzes the athlete's facial expressions and movements to estimate emotions. For example, if the athlete is excited, the generation AI prioritizes capturing footage of that moment. Also, if the athlete is relaxed, the generation AI can prioritize capturing footage of the athlete in a relaxed state. Also, if the athlete is concentrating, the generation AI can prioritize capturing footage of the athlete in a concentrated state. In this way, by prioritizing footage based on the athlete's emotions, important moments can be prioritized for capture. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the camera unit is performed using the generation AI. For example, the camera unit inputs the athlete's facial expression data into the generation AI, which then analyzes the emotions and determines the priority of the footage.

[0089] The camera unit selects optimal shooting settings by referring to the athlete's past performance data when shooting. The camera unit uses the generation AI to select optimal shooting settings by referring to the athlete's past performance data when shooting. The generation AI analyzes the athlete's past performance data and selects optimal shooting settings. For example, the generation AI selects the optimal camera angle based on the athlete's past performance data. The generation AI can also set the optimal frame rate based on the athlete's past performance data. The generation AI can also select optimal exposure settings based on the athlete's past performance data. In this way, optimal shooting settings can be selected by referring to past performance data. Some or all of the above-mentioned processing in the camera unit is performed using the generation AI. For example, the camera unit inputs the athlete's past performance data into the generation AI, which selects optimal shooting settings.

[0090] The camera unit is equipped with a zoom function for emphasizing specific parts of the athlete's movements when filming. The camera unit is equipped with a zoom function for emphasizing specific parts of the athlete's movements when filming, using a generation AI. The generation AI analyzes the athlete's movements and sets the zoom to emphasize specific parts. For example, the generation AI zooms in on the movement of the athlete's legs to emphasize the athlete's running form. The generation AI can also zoom in on the movement of the athlete's arms to emphasize the angle of the athlete's swing. The generation AI can also zoom in on the movement of the athlete's entire body to emphasize the height of the athlete's jump. This enables detailed analysis by emphasizing specific parts of the athlete's movements. Some or all of the above-mentioned processing in the camera unit is performed using the generation AI. For example, the camera unit inputs the athlete's movement data into the generation AI, and the generation AI sets the zoom to emphasize specific parts.

[0091] The camera unit has a function to capture the athlete's movements as a 3D model when filming. The camera unit has a function to capture the athlete's movements as a 3D model when filming using a generative AI. The generative AI analyzes the athlete's movements and captures them as a 3D model. For example, the generative AI captures the athlete's movements as a 3D model and performs detailed analysis. The generative AI can also capture the athlete's movements as a 3D model and analyze them from different perspectives. The generative AI can also capture the athlete's movements as a 3D model and detect subtle changes in the movements. This enables detailed analysis by capturing the athlete's movements as a 3D model. Some or all of the above-mentioned processing in the camera unit is performed using the generative AI. For example, the camera unit inputs the athlete's movement data into the generative AI, which then captures it as a 3D model.

[0092] The analysis unit estimates the athlete's emotions and adjusts the level of analysis detail based on the estimated athlete's emotions. The analysis unit uses a generation AI to estimate the athlete's emotions and adjusts the level of analysis detail based on the estimated athlete's emotions. The generation AI analyzes the athlete's facial expressions and movements to estimate emotions. For example, the generation AI can provide detailed analysis results when the athlete is relaxed. The generation AI can also provide concise analysis results when the athlete is nervous. The generation AI can also provide analysis results that focus on specific movements when the athlete is concentrating. This allows optimal analysis results to be provided by adjusting the level of analysis detail based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the athlete's facial expression data into the generation AI, which analyzes the emotions and adjusts the level of analysis detail.

[0093] The analysis unit also uses electromyogram data to analyze the athlete's muscle movements during analysis. The analysis unit also uses generation AI to also use electromyogram data to analyze the athlete's muscle movements during analysis. The generation AI analyzes the athlete's electromyogram data and analyzes the muscle movements in detail. For example, the generation AI analyzes the muscle movements in detail based on the athlete's electromyogram data. The generation AI can also analyze the muscle fatigue level based on the athlete's electromyogram data. The generation AI can also analyze the muscle movement patterns based on the athlete's electromyogram data. In this way, by using electromyogram data in combination, muscle movements can be analyzed in detail. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the athlete's electromyogram data into the generation AI, and the generation AI analyzes the muscle movements.

[0094] The analysis unit provides analysis results by taking into account the continuity of the athlete's movements during analysis. The analysis unit uses a generation AI to provide analysis results by taking into account the continuity of the athlete's movements during analysis. The generation AI analyzes the continuity of the athlete's movements and provides analysis results from the start to the end of the movement. For example, the generation AI takes into account the continuity of the athlete's movements and provides analysis results from the start to the end of the movement. The generation AI can also take into account the continuity of the athlete's movements and provide analysis results for the intermediate part of the movement. The generation AI can also take into account the continuity of the athlete's movements and identify points of change in the movement and provide analysis results. In this way, by taking into account the continuity of the movement, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the athlete's movement data into the generation AI, which analyzes the continuity of the movement and provides analysis results.

[0095] The analysis unit applies a highly accurate algorithm to detect subtle changes in the athlete's movements during analysis. The analysis unit uses a generation AI to apply a highly accurate algorithm to detect subtle changes in the athlete's movements during analysis. The generation AI analyzes subtle changes in the athlete's movements and identifies areas for improvement in the movements. For example, the generation AI applies a highly accurate algorithm to detect subtle changes in the athlete's movements. The generation AI can also detect subtle changes in the athlete's movements and reflect them in the analysis results. The generation AI can also detect subtle changes in the athlete's movements and identify areas for improvement in the movements. In this way, by applying a highly accurate algorithm, subtle changes can be detected and reflected in the analysis results. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the athlete's movement data into the generation AI, and the generation AI performs analysis by applying a highly accurate algorithm.

[0096] The analysis unit estimates the athlete's emotions and adjusts the display method of the analysis results based on the estimated emotions. The analysis unit uses a generation AI to estimate the athlete's emotions and adjusts the display method of the analysis results based on the estimated emotions. The generation AI analyzes the athlete's facial expressions and movements to estimate emotions. For example, if the athlete is relaxed, the generation AI can display detailed analysis results. If the athlete is nervous, the generation AI can also display concise analysis results. If the athlete is concentrating, the generation AI can also display analysis results that focus on specific movements. This allows optimal analysis results to be provided by adjusting the display method based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the athlete's facial expression data into the generation AI, which analyzes the emotions and adjusts the display method.

[0097] The analysis unit improves the accuracy of the analysis by referring to the athlete's past training data during analysis. The analysis unit uses the generation AI to improve the accuracy of the analysis by referring to the athlete's past training data during analysis. The generation AI analyzes the athlete's past training data to improve the accuracy of the analysis. For example, the generation AI improves the accuracy of the analysis based on the athlete's past training data. The generation AI can also identify areas for improvement in movement based on the athlete's past training data. The generation AI can also analyze changes in movement based on the athlete's past training data. In this way, by referring to the past training data, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the athlete's past training data into the generation AI, which then performs the analysis.

[0098] The analysis unit analyzes the athlete's movements by comparing them with movements of different sports during analysis. The analysis unit uses the generation AI to analyze the athlete's movements by comparing them with movements of different sports during analysis. The generation AI compares the athlete's movements with movements of different sports and provides analysis results. For example, the generation AI compares the athlete's movements with movements of different sports to identify areas for improvement in the movements. The generation AI can also compare the athlete's movements with movements of different sports and analyze commonalities between the movements. This makes it possible to identify areas for improvement in the movements by comparing them with movements of different sports. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the athlete's movement data into the generation AI, which then performs analysis by comparing the athlete's movement data with movements of different sports.

[0099] The analysis unit analyzes the athlete's movements in synchronization with the audio data during analysis. The analysis unit uses the generation AI to analyze the athlete's movements in synchronization with the audio data during analysis. The generation AI synchronizes the athlete's movements with the audio data and provides the analysis results. For example, the generation AI synchronizes the athlete's movements with the audio data to identify areas for improvement in the movements. The generation AI can also synchronize the athlete's movements with the audio data to analyze changes in the movements. This makes it easier to identify areas for improvement in the movements by synchronizing with the audio data. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the athlete's movement data and audio data into the generation AI, and the generation AI performs the analysis in synchronization.

[0100] The feedback unit estimates the athlete's emotions and adjusts the way the feedback is presented based on the estimated emotions. The feedback unit uses a generation AI to estimate the athlete's emotions and adjusts the way the feedback is presented based on the estimated emotions. The generation AI analyzes the athlete's facial expressions and movements to estimate emotions. For example, if the athlete is relaxed, the generation AI can provide detailed feedback. If the athlete is nervous, the generation AI can also provide brief feedback. If the athlete is focused, the generation AI can also provide feedback that focuses on specific movements. This allows for more effective feedback by adjusting the way the feedback is presented based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the feedback unit is performed using the generation AI. For example, the feedback unit inputs the athlete's facial expression data into the generation AI, which analyzes the emotions and adjusts the way the feedback is presented.

[0101] The feedback unit generates an animation to visually show areas for improvement in the athlete's movement when feedback is provided. The feedback unit uses a generation AI to generate an animation to visually show areas for improvement in the athlete's movement when feedback is provided. The generation AI analyzes the athlete's movement and generates an animation that visually shows areas for improvement. For example, the generation AI generates an animation to visually show areas for improvement in the athlete's movement. The generation AI can also visually show areas for improvement in the athlete's movement and provide specific methods for correction. The generation AI can also visually show areas for improvement in the athlete's movement and confirm changes in movement. In this way, by generating a visual animation, the athlete can intuitively understand areas for improvement in their movement. Some or all of the above-mentioned processing in the feedback unit is performed using the generation AI. For example, the feedback unit inputs the athlete's movement data into the generation AI, which then generates an animation.

[0102] The feedback unit provides a step-by-step guide on how to correct the athlete's movement when receiving feedback. The feedback unit uses a generation AI to provide a step-by-step guide on how to correct the athlete's movement when receiving feedback. The generation AI analyzes the athlete's movement and provides a step-by-step guide on how to correct it. For example, the generation AI provides a step-by-step guide on how to correct the athlete's movement. The generation AI can also show the athlete how to correct their movement step-by-step and provide specific correction procedures. The generation AI can also show the athlete how to correct their movement step-by-step and support the improvement of their movement. In this way, by providing a step-by-step guide, the athlete can easily understand how to correct their movement. Some or all of the above-mentioned processing in the feedback unit is performed using the generation AI. For example, the feedback unit inputs the athlete's movement data into the generation AI, which then provides a step-by-step guide.

[0103] The feedback unit has a function for comparing the results of corrections to the athlete's movements in real time when feedback is provided. The feedback unit has a function for comparing the results of corrections to the athlete's movements in real time when feedback is provided using the generation AI. The generation AI analyzes the results of corrections to the athlete's movements and compares them in real time. For example, the generation AI compares the results of corrections to the athlete's movements in real time and provides feedback. The generation AI can also compare the results of corrections to the athlete's movements in real time to identify areas for improvement in the movements. The generation AI can also compare the results of corrections to the athlete's movements in real time to confirm changes in the movements. This allows the athlete to immediately confirm areas for improvement in their movements by comparing the results of corrections in real time. Some or all of the above-mentioned processing in the feedback unit is performed using the generation AI. For example, the feedback unit inputs the athlete's movement data into the generation AI, which then performs a comparison in real time.

[0104] The feedback unit estimates the athlete's emotions and prioritizes feedback based on the estimated emotions. The feedback unit uses a generation AI to estimate the athlete's emotions and prioritizes feedback based on the estimated emotions. The generation AI analyzes the athlete's facial expressions and movements to estimate emotions. For example, if the athlete is nervous, the generation AI can prioritize providing the most important feedback. Also, if the athlete is relaxed, the generation AI can prioritize providing detailed feedback. Also, if the athlete is concentrating, the generation AI can prioritize providing feedback regarding specific movements. In this way, by prioritizing feedback based on the athlete's emotions, the most important feedback can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the feedback unit is performed using the generation AI. For example, the feedback unit inputs the athlete's facial expression data into the generation AI, which analyzes the emotions and prioritizes the feedback.

[0105] The feedback unit provides optimal feedback by referring to the athlete's past feedback history when providing feedback. The feedback unit uses the generation AI to provide optimal feedback by referring to the athlete's past feedback history when providing feedback. The generation AI analyzes the athlete's past feedback history and provides optimal feedback. For example, the generation AI provides optimal feedback based on the athlete's past feedback history. The generation AI can also identify areas for improvement in movements based on the athlete's past feedback history. The generation AI can also analyze changes in movements based on the athlete's past feedback history. This makes it possible to provide optimal feedback by referring to the past feedback history. Some or all of the above-mentioned processing in the feedback unit is performed using the generation AI. For example, the feedback unit inputs the athlete's past feedback history into the generation AI, which then performs the analysis.

[0106] The feedback unit compares the athlete's movements with the movements of other athletes at the time of feedback and provides feedback. The feedback unit uses the generation AI to compare the athlete's movements with the movements of other athletes at the time of feedback and provides feedback. The generation AI compares the athlete's movements with the movements of other athletes and provides feedback. For example, the generation AI compares the athlete's movements with the movements of other athletes to identify areas for improvement in the movements. The generation AI can also compare the athlete's movements with the movements of other athletes and analyze commonalities in the movements. This makes it easier to identify areas for improvement in the movements by comparing them with the movements of other athletes. Some or all of the above-mentioned processing in the feedback unit is performed using the generation AI. For example, the feedback unit inputs the athlete's movement data into the generation AI, which then compares and analyzes the movements of other athletes.

[0107] The feedback unit has a function of explaining, by voice, how to correct the athlete's movement when providing feedback. The feedback unit has a function of using the generation AI to explain, by voice, how to correct the athlete's movement when providing feedback. The generation AI analyzes how to correct the athlete's movement and explains it by voice. For example, the generation AI explains, by voice, how to correct the athlete's movement and provides specific correction steps. The generation AI can also explain, by voice, how to correct the athlete's movement and support the improvement of the movement. The generation AI can also explain, by voice, how to correct the athlete's movement and confirm changes in the movement. This makes it easier for the athlete to understand how to correct their movement by providing explanations by voice. Some or all of the above-mentioned processing in the feedback unit is performed using the generation AI. For example, the feedback unit inputs the athlete's movement data into the generation AI, and the generation AI provides explanations by voice.

[0108] The comparison unit estimates the athlete's emotions and adjusts the comparison criteria based on the estimated athlete's emotions. The comparison unit uses a generation AI to estimate the athlete's emotions and adjusts the comparison criteria based on the estimated athlete's emotions. The generation AI analyzes the athlete's facial expressions and movements to estimate emotions. For example, if the athlete is relaxed, the generation AI can provide detailed comparison criteria. If the athlete is nervous, the generation AI can also provide concise comparison criteria. If the athlete is concentrating, the generation AI can also provide comparison criteria that focus on specific movements. This allows for optimal comparison results by adjusting the comparison criteria based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the comparison unit is performed using the generation AI. For example, the comparison unit inputs the athlete's facial expression data into the generation AI, which analyzes the emotions and adjusts the comparison criteria.

[0109] The comparison unit compares and analyzes the athlete's movements at different time periods during the comparison. The comparison unit uses the generation AI to compare and analyze the athlete's movements at different time periods during the comparison. The generation AI compares the athlete's movements at different time periods and provides analysis results. For example, the generation AI compares the athlete's movements in the morning and evening and provides analysis results. The generation AI can also compare the athlete's movements before and after training and provide analysis results. The generation AI can also compare the athlete's movements in different seasons and provide analysis results. This allows for detailed analysis of changes in movements by comparing them at different time periods. Some or all of the above-mentioned processing in the comparison unit is performed using the generation AI. For example, the comparison unit inputs the athlete's movement data into the generation AI, which then compares and analyzes them at different time periods.

[0110] The comparison unit compares and analyzes the athlete's movements under different environmental conditions during the comparison. The comparison unit uses the generation AI to compare and analyze the athlete's movements under different environmental conditions during the comparison. The generation AI compares the athlete's movements under different environmental conditions and provides analysis results. For example, the generation AI compares the athlete's movements indoors and outdoors and provides analysis results. The generation AI can also compare the athlete's movements under different weather conditions and provide analysis results. The generation AI can also compare the athlete's movements under different temperature conditions and provide analysis results. This makes it easier to identify areas for improvement in movements by comparing under different environmental conditions. Some or all of the above-mentioned processing in the comparison unit is performed using the generation AI. For example, the comparison unit inputs the athlete's movement data into the generation AI, which then compares and analyzes under different environmental conditions.

[0111] The comparison unit compares and analyzes the athlete's movements with different training methods during the comparison. The comparison unit uses the generation AI to compare and analyze the athlete's movements with different training methods during the comparison. The generation AI compares the athlete's movements with different training methods and provides analysis results. For example, the generation AI compares the athlete's movements with different training methods to identify areas for improvement in the movements. The generation AI can also compare the athlete's movements with different training methods to analyze commonalities in the movements. This makes it easier to identify areas for improvement in the movements by comparing with different training methods. Some or all of the above-mentioned processing in the comparison unit is performed using the generation AI. For example, the comparison unit inputs the athlete's movement data into the generation AI, which then compares and analyzes the movements with different training methods.

[0112] The comparison unit estimates the athlete's emotion and adjusts the display method of the comparison results based on the estimated athlete's emotion. The comparison unit uses a generation AI to estimate the athlete's emotion and adjusts the display method of the comparison results based on the estimated athlete's emotion. The generation AI analyzes the athlete's facial expressions and movements to estimate the emotion. For example, if the athlete is relaxed, the generation AI can display detailed comparison results. If the athlete is nervous, the generation AI can also display concise comparison results. If the athlete is concentrating, the generation AI can also display comparison results that focus on specific movements. This allows for optimal comparison results to be provided by adjusting the display method based on the athlete's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the comparison unit is performed using the generation AI. For example, the comparison unit inputs the athlete's facial expression data into the generation AI, which analyzes the emotion and adjusts the display method.

[0113] The comparison unit compares and analyzes the athlete's movements with movements of different sports during the comparison. The comparison unit uses the generation AI to compare and analyze the athlete's movements with movements of different sports during the comparison. The generation AI compares the athlete's movements with movements of different sports and provides analysis results. For example, the generation AI compares the athlete's movements with movements of different sports to identify areas for improvement in the movements. The generation AI can also compare the athlete's movements with movements of different sports to analyze commonalities between the movements. This makes it easier to identify areas for improvement in the movements by comparing them with movements of different sports. Some or all of the above-mentioned processing in the comparison unit is performed using the generation AI. For example, the comparison unit inputs the athlete's movement data into the generation AI, which then compares and analyzes the movements with movements of different sports.

[0114] The comparison unit compares and analyzes the athlete's movements with athletes of different age groups during the comparison. The comparison unit uses the generation AI to compare and analyze the athlete's movements with athletes of different age groups during the comparison. The generation AI compares the athlete's movements with athletes of different age groups and provides analysis results. For example, the generation AI compares the athlete's movements with athletes of different age groups to identify areas for improvement in the movements. The generation AI can also compare the athlete's movements with athletes of different age groups to analyze commonalities in the movements. This makes it easier to identify areas for improvement in the movements by comparing with athletes of different age groups. Some or all of the above-mentioned processing in the comparison unit is performed using the generation AI. For example, the comparison unit inputs the athlete's movement data into the generation AI, and the generation AI performs analysis by comparing with athletes of different age groups.

[0115] The comparison unit compares and analyzes the athlete's movements with athletes of a different gender during the comparison. The comparison unit uses the generation AI to compare and analyze the athlete's movements with athletes of a different gender during the comparison. The generation AI compares the athlete's movements with athletes of a different gender and provides analysis results. For example, the generation AI compares the athlete's movements with athletes of a different gender to identify areas for improvement in the movements. The generation AI can also compare the athlete's movements with athletes of a different gender to analyze commonalities in the movements. This makes it easier to identify areas for improvement in the movements by comparing with athletes of a different gender. Some or all of the above-mentioned processing in the comparison unit is performed using the generation AI. For example, the comparison unit inputs the athlete's movement data into the generation AI, which then performs analysis by comparing with athletes of a different gender.

[0116] The instruction unit estimates the athlete's emotions and adjusts the way instructions are expressed based on the estimated emotions. The instruction unit uses a generation AI to estimate the athlete's emotions and adjusts the way instructions are expressed based on the estimated emotions. The generation AI analyzes the athlete's facial expressions and movements to estimate emotions. For example, if the athlete is relaxed, the generation AI can provide detailed instructions. If the athlete is nervous, the generation AI can also provide concise instructions. If the athlete is concentrating, the generation AI can also provide instructions that focus on specific movements. This allows for more effective instructions to be provided by adjusting the way instructions are expressed based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the instruction unit is performed using the generation AI. For example, the instruction unit inputs the athlete's facial expression data into the generation AI, which analyzes the emotions and adjusts the way instructions are expressed.

[0117] The instruction unit generates an animation to visually show how to correct the athlete's movement when instructed. The instruction unit uses a generation AI to generate an animation to visually show how to correct the athlete's movement when instructed. The generation AI analyzes the athlete's movement and generates an animation that visually shows how to correct the movement. For example, the generation AI generates an animation to visually show how to correct the athlete's movement. The generation AI can also visually show how to correct the athlete's movement and provide specific correction steps. The generation AI can also visually show how to correct the athlete's movement and confirm changes in the movement. In this way, by generating a visual animation, the athlete can intuitively understand how to correct the movement. Some or all of the above-mentioned processing in the instruction unit is performed using the generation AI. For example, the instruction unit inputs the athlete's movement data into the generation AI, and the generation AI generates an animation.

[0118] The instruction unit provides a step-by-step guide on how to correct the athlete's movement when instructed. The instruction unit uses a generating AI to provide a step-by-step guide on how to correct the athlete's movement when instructed. The generating AI analyzes the athlete's movement and provides a step-by-step guide on how to correct it. For example, the generating AI provides a step-by-step guide on how to correct the athlete's movement. The generating AI can also show step-by-step how to correct the athlete's movement and provide specific correction procedures. The generating AI can also show step-by-step how to correct the athlete's movement and support the improvement of the movement. In this way, by providing a step-by-step guide, the athlete can easily understand how to correct their movement. Some or all of the above-mentioned processing in the instruction unit is performed using the generating AI. For example, the instruction unit inputs the athlete's movement data into the generating AI, which then provides a step-by-step guide.

[0119] The instruction unit has a function for comparing the results of corrections to the athlete's movements in real time when instructions are given. The instruction unit has a function for using the generation AI to compare the results of corrections to the athlete's movements in real time when instructions are given. The generation AI analyzes the results of corrections to the athlete's movements and compares them in real time. For example, the generation AI compares the results of corrections to the athlete's movements in real time and provides instructions. The generation AI can also compare the results of corrections to the athlete's movements in real time to identify areas for improvement in movements. The generation AI can also compare the results of corrections to the athlete's movements in real time to confirm changes in movements. In this way, by comparing the results of corrections in real time, the athlete can immediately confirm areas for improvement in their movements. Some or all of the above-mentioned processing in the instruction unit is performed using the generation AI. For example, the instruction unit inputs the athlete's movement data into the generation AI, which then performs a comparison in real time.

[0120] The instruction unit estimates the athlete's emotions and determines the priority of instructions based on the estimated emotions. The instruction unit uses a generation AI to estimate the athlete's emotions and determines the priority of instructions based on the estimated emotions. The generation AI analyzes the athlete's facial expressions and movements to estimate emotions. For example, if the athlete is nervous, the generation AI can prioritize providing the most important instructions. Also, if the athlete is relaxed, the generation AI can prioritize providing detailed instructions. Also, if the athlete is concentrating, the generation AI can prioritize providing instructions related to specific movements. In this way, by determining the priority of instructions based on the athlete's emotions, the most important instructions can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the instruction unit is performed using the generation AI. For example, the instruction unit inputs the athlete's facial expression data into the generation AI, which analyzes the emotions and determines the priority of instructions.

[0121] The instruction unit provides optimal instructions by referring to the athlete's past instruction history when giving instructions. The instruction unit uses the generation AI to provide optimal instructions by referring to the athlete's past instruction history when giving instructions. The generation AI analyzes the athlete's past instruction history and provides optimal instructions. For example, the generation AI provides optimal instructions based on the athlete's past instruction history. The generation AI can also identify areas for improvement in movements based on the athlete's past instruction history. The generation AI can also analyze changes in movements based on the athlete's past instruction history. In this way, optimal instructions can be provided by referring to the past instruction history. Some or all of the above-mentioned processing in the instruction unit is performed using the generation AI. For example, the instruction unit inputs the athlete's past instruction history into the generation AI, which then performs the analysis.

[0122] The instruction unit, when giving instructions, compares the athlete's movements with those of other athletes and provides instructions. The instruction unit, when giving instructions, uses the generation AI to compare the athlete's movements with those of other athletes and provides instructions. The generation AI compares the athlete's movements with those of other athletes and provides instructions. For example, the generation AI compares the athlete's movements with those of other athletes to identify areas for improvement in the movements. The generation AI can also compare the athlete's movements with those of other athletes and analyze commonalities between the movements. This makes it easier to identify areas for improvement in the movements by comparing them with those of other athletes. Some or all of the above-mentioned processing in the instruction unit is performed using the generation AI. For example, the instruction unit inputs the athlete's movement data into the generation AI, which then compares and analyzes the movements of other athletes.

[0123] The instruction unit has a function to explain, by voice, how to correct the athlete's movement when instructed. The instruction unit has a function to explain, by voice, how to correct the athlete's movement when instructed, using the generation AI. The generation AI analyzes how to correct the athlete's movement and explains it by voice. For example, the generation AI explains by voice how to correct the athlete's movement and provides specific correction steps. The generation AI can also explain by voice how to correct the athlete's movement and support movement improvement. The generation AI can also explain by voice how to correct the athlete's movement and confirm changes in movement. This makes it easier for the athlete to understand how to correct their movement by providing explanations by voice. Some or all of the above-mentioned processing in the instruction unit is performed using the generation AI. For example, the instruction unit inputs the athlete's movement data into the generation AI, and the generation AI provides explanations by voice.

[0124] The real-time analysis unit estimates the athlete's emotions and adjusts the level of detail of the real-time analysis based on the estimated athlete's emotions. The real-time analysis unit estimates the athlete's emotions using a generation AI and adjusts the level of detail of the real-time analysis based on the estimated athlete's emotions. The generation AI analyzes the athlete's facial expressions and movements to estimate emotions. For example, if the athlete is relaxed, the generation AI can provide detailed real-time analysis results. If the athlete is nervous, the generation AI can also provide concise real-time analysis results. If the athlete is concentrating, the generation AI can also provide real-time analysis results that focus on specific movements. This allows for optimal analysis results to be provided by adjusting the level of detail of the real-time analysis based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the real-time analysis unit is performed using the generation AI. For example, the real-time analysis unit inputs the athlete's facial expression data into the generation AI, which then analyzes the emotions and adjusts the level of detail of the real-time analysis.

[0125] The real-time analysis unit provides analysis results by taking into account the continuity of the athlete's movements during real-time analysis. The real-time analysis unit uses a generation AI to provide analysis results by taking into account the continuity of the athlete's movements during real-time analysis. The generation AI analyzes the continuity of the athlete's movements and provides real-time analysis results from the start to the end of the movement. For example, the generation AI takes into account the continuity of the athlete's movements and provides real-time analysis results from the start to the end of the movement. The generation AI can also take into account the continuity of the athlete's movements and provide real-time analysis results for the intermediate part of the movement. The generation AI can also take into account the continuity of the athlete's movements and identify change points in the movement to provide real-time analysis results. In this way, by taking into account the continuity of the movement, more accurate real-time analysis results can be provided. Some or all of the above-mentioned processing in the real-time analysis unit is performed using the generation AI. For example, the real-time analysis unit inputs the athlete's movement data into the generation AI, which analyzes the continuity of the movement and provides real-time analysis results.

[0126] The real-time analysis unit applies a highly accurate algorithm to detect subtle changes in the athlete's movements during real-time analysis. The real-time analysis unit uses a generation AI to apply a highly accurate algorithm to detect subtle changes in the athlete's movements during real-time analysis. The generation AI analyzes subtle changes in the athlete's movements and identifies areas for improvement in the movements. For example, the generation AI applies a highly accurate algorithm to detect subtle changes in the athlete's movements. The generation AI can also detect subtle changes in the athlete's movements and reflect them in the real-time analysis results. The generation AI can also detect subtle changes in the athlete's movements and identify areas for improvement in the movements. In this way, by applying a highly accurate algorithm, subtle changes can be detected and reflected in the real-time analysis results. Some or all of the above-mentioned processing in the real-time analysis unit is performed using the generation AI. For example, the real-time analysis unit inputs the athlete's movement data into the generation AI, and the generation AI applies a highly accurate algorithm to perform analysis.

[0127] The real-time analysis unit has a function to simultaneously analyze an athlete's movements from different perspectives during real-time analysis. The real-time analysis unit has a function to simultaneously analyze an athlete's movements from different perspectives during real-time analysis using a generative AI. The generative AI analyzes an athlete's movements from different perspectives simultaneously and provides real-time analysis results. For example, the generative AI analyzes an athlete's movements from different perspectives simultaneously to identify areas for improvement in the movements. The generative AI can also analyze an athlete's movements from different perspectives simultaneously to confirm changes in the movements. This makes it easier to identify areas for improvement in the movements by analyzing them simultaneously from different perspectives. Some or all of the above-mentioned processing in the real-time analysis unit is performed using the generative AI. For example, the real-time analysis unit inputs the athlete's movement data into the generative AI, which then performs analysis from different perspectives simultaneously.

[0128] The real-time analysis unit estimates the athlete's emotions and adjusts the display method of the real-time analysis results based on the estimated athlete's emotions. The real-time analysis unit estimates the athlete's emotions using a generation AI and adjusts the display method of the real-time analysis results based on the estimated athlete's emotions. The generation AI analyzes the athlete's facial expressions and movements to estimate emotions. For example, if the athlete is relaxed, the generation AI can display detailed real-time analysis results. If the athlete is nervous, the generation AI can also display concise real-time analysis results. If the athlete is concentrating, the generation AI can also display real-time analysis results that focus on specific movements. This allows optimal real-time analysis results to be provided by adjusting the display method based on the athlete's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the real-time analysis unit is performed using the generation AI. For example, the real-time analysis unit inputs the athlete's facial expression data into the generation AI, which then analyzes the emotions and adjusts the way the real-time analysis results are displayed.

[0129] The real-time analysis unit improves the accuracy of analysis by referring to the athlete's past training data during real-time analysis. The real-time analysis unit uses the generation AI to improve the accuracy of analysis by referring to the athlete's past training data during real-time analysis. The generation AI analyzes the athlete's past training data to improve the accuracy of real-time analysis. For example, the generation AI improves the accuracy of real-time analysis based on the athlete's past training data. The generation AI can also identify areas for improvement in movement based on the athlete's past training data. The generation AI can also analyze changes in movement based on the athlete's past training data. In this way, by referring to the past training data, the accuracy of real-time analysis can be improved. Some or all of the above-mentioned processing in the real-time analysis unit is performed using the generation AI. For example, the real-time analysis unit inputs the athlete's past training data into the generation AI, which then performs the analysis.

[0130] The real-time analysis unit analyzes the athlete's movements by comparing them with movements of different sports during real-time analysis. The real-time analysis unit uses a generation AI to analyze the athlete's movements by comparing them with movements of different sports during real-time analysis. The generation AI compares the athlete's movements with movements of different sports and provides real-time analysis results. For example, the generation AI compares the athlete's movements with movements of different sports to identify areas for improvement in the movements. The generation AI can also compare the athlete's movements with movements of different sports to analyze commonalities between the movements. This makes it easier to identify areas for improvement in the movements by comparing them with movements of different sports. Some or all of the above-mentioned processing in the real-time analysis unit is performed using the generation AI. For example, the real-time analysis unit inputs the athlete's movement data into the generation AI, which then analyzes them by comparing them with movements of different sports.

[0131] The real-time analysis unit analyzes the athlete's movements in synchronization with the audio data during real-time analysis. The real-time analysis unit uses the generation AI to analyze the athlete's movements in synchronization with the audio data during real-time analysis. The generation AI synchronizes the athlete's movements with the audio data and provides real-time analysis results. For example, the generation AI synchronizes the athlete's movements with the audio data to identify areas for improvement in the movements. The generation AI can also synchronize the athlete's movements with the audio data to analyze changes in the movements. This makes it easier to identify areas for improvement in the movements by synchronizing with the audio data. Some or all of the above-mentioned processing in the real-time analysis unit is performed using the generation AI. For example, the real-time analysis unit inputs the athlete's movement data and audio data into the generation AI, and the generation AI performs the analysis in synchronization. === Hard Collateral 1-1 === Each of the multiple elements including the camera unit, analysis unit, and feedback 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 camera unit uses the camera 42 of the smart device 14 to capture footage of the athlete's play. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the captured footage using a generative AI. The feedback unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides feedback to the athlete based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned camera unit, analysis unit, and feedback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the camera unit uses the camera 42 of the smart glasses 214 to capture images of the athlete's play. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the captured images using a generative AI. The feedback unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides feedback to the athlete based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the camera unit, analysis unit, and feedback 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 camera unit uses the camera 42 of the headset-type device 314 to capture footage of the athlete's play. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the captured footage using a generative AI. The feedback unit is realized by the control unit 46A of the headset-type device 314 or the specific processing unit 290 of the data processing device 12 and provides feedback to the athlete based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the camera unit, analysis unit, and feedback unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the camera unit uses the camera 42 of the robot 414 to film the athlete's play. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the filmed video using a generative AI. The feedback unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and provides feedback to the athlete based on the analysis results.

[0132] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0133] When filming an athlete's movements, the camera unit can monitor the athlete's heart rate and adjust the timing of filming according to heart rate fluctuations. For example, if an athlete's heart rate suddenly rises, the camera unit will start filming to capture that moment. If the heart rate remains stable, the camera unit will continue filming for a longer period of time, allowing for detailed movement analysis. Furthermore, if the heart rate drops, the camera unit can pause filming to allow the athlete time to rest. This allows for optimal filming according to the athlete's physiological state.

[0134] When analyzing an athlete's movements, the analysis unit can estimate the athlete's muscle fatigue level and adjust the analysis results based on the fatigue level. For example, if the athlete's muscles are fatigued, the analysis unit can provide advice to reduce the movement. If the muscle fatigue level is low, the analysis unit can recommend higher-intensity training. Furthermore, if the muscle fatigue level is moderate, the analysis unit can suggest a training plan that incorporates appropriate rest. This enables optimal training based on the athlete's muscle condition.

[0135] When correcting an athlete's movements, the feedback unit can also estimate the athlete's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the athlete is feeling anxious, the feedback unit can provide feedback that includes words of encouragement. If the athlete is feeling confident, the feedback unit can emphasize specific technical advice. Furthermore, if the athlete is focused, the feedback unit can provide detailed analysis results and suggest areas for further improvement. This enables effective feedback that is tailored to the athlete's emotions.

[0136] When analyzing an athlete's movements, the real-time analysis unit can monitor the athlete's breathing patterns and adjust the analysis results based on their breathing patterns. For example, if the athlete's breathing is shallow and rapid, the real-time analysis unit can suggest breathing techniques to help them relax. If their breathing is deep and steady, the real-time analysis unit can recommend high-intensity training. Furthermore, if their breathing is irregular, the real-time analysis unit can suggest training to regulate their breathing. This allows for optimal training tailored to the athlete's breathing condition.

[0137] The camera department can also analyze an athlete's emotions and adjust the shooting angle based on the analyzed emotions. For example, if an athlete is nervous, the camera department will shoot from a long distance to relieve the athlete's pressure. If the athlete is relaxed, the camera department will shoot from a close distance to enable detailed movement analysis. Furthermore, if the athlete is concentrating, the camera department can shoot from an angle that focuses on specific movements. This allows for optimal shooting according to the athlete's emotions.

[0138] When filming an athlete's movements, the camera unit can automatically adjust the frame rate according to the speed of the athlete's movements. For example, if the athlete is moving quickly, the camera unit will film at a high frame rate, allowing for detailed movement analysis. On the other hand, if the athlete is moving slowly, the camera unit will film at a low frame rate, reducing the amount of data. Furthermore, if the athlete's movements change, the camera unit can adjust the frame rate in real time to provide optimal footage. This allows for optimal filming according to the athlete's movement speed.

[0139] When filming an athlete's movements, the camera unit can track the athlete's location information in real time and film from the optimal angle. For example, as the athlete moves, the camera unit automatically tracks them to maintain the optimal angle. The camera can also automatically adjust the camera's zoom based on the athlete's location information. Furthermore, the camera can adjust the camera's position to film from the optimal angle when the athlete performs a specific movement. This makes it possible to film from the optimal angle by tracking the athlete's location information.

[0140] The camera unit can also be equipped with an automatic exposure adjustment function to adapt to different lighting conditions when filming athletes' movements. For example, when filming outdoors, the exposure can be automatically adjusted according to changes in sunlight. When filming indoors, the exposure can also be automatically adjusted according to the brightness of the lighting. Furthermore, when filming in the evening or at night, the exposure can be automatically adjusted to adapt to low-light environments. This allows the camera to adapt to different lighting conditions and always capture images with the optimal exposure.

[0141] The camera unit can also estimate the athlete's emotions and prioritize the footage to be shot based on the estimated emotions of the athlete. For example, if the athlete is excited, that moment can be shot first. If the athlete is relaxed, footage of the athlete in a relaxed state can be shot first. Furthermore, if the athlete is concentrating, the moment when the athlete is concentrating can be shot first. In this way, by prioritizing footage based on the athlete's emotions, important moments can be shot first.

[0142] When filming an athlete's movements, the camera unit can also select optimal filming settings by referencing the athlete's past performance data. For example, the optimal camera angle can be selected based on the athlete's past performance data. The optimal frame rate can also be set based on the athlete's past performance data. Furthermore, the optimal exposure setting can also be selected based on the athlete's past performance data. In this way, optimal filming settings can be selected by referring to past performance data.

[0143] The processing flow of the second embodiment will be briefly explained below.

[0144] Step 1: The camera unit films the athletes' play. The camera unit uses high-resolution cameras to capture the athletes' movements in detail and multiple cameras to film the athletes' movements from different angles. The camera unit also has the ability to acquire movement speed and position information in real time, allowing it to film the athletes' movements at high speed and play them back in slow motion. It can also track the athletes' position information in real time and film them from the optimal angle. Step 2: The analysis unit uses generative AI to analyze the footage captured by the camera unit. The analysis unit compares the athlete's movements with past data and ideal form to identify areas for improvement. Specifically, it analyzes key points of movement such as running form and swing angle. The analysis unit can also analyze the athlete's movements in real time and provide immediate feedback. Step 3: The feedback unit provides feedback based on the analysis results obtained by the analysis unit. The feedback unit provides specific instructions to the athlete on how to correct their form, and the generation AI automatically generates specific instructions showing the athlete how to correct their movement. For example, it provides specific advice such as bending the knee angle a little more or swinging the arms more widely. The feedback unit can also provide feedback in real time as the athlete trains.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0149] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0163] 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.

[0164] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0165] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0179] 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.

[0180] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0181] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0196] 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.

[0197] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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).

[0202] 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.

[0203] 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."

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0215] 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.

[0216] [Explanation of symbols]

[0217] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The camera team takes photos of the athletes playing, an analysis unit that analyzes the video captured by the camera unit; a feedback unit that provides feedback based on the analysis result obtained by the analysis unit; Equipped with A system characterized by:

2. The analysis unit Includes a comparison section that analyzes athletes' movements by comparing them with past data or ideal form 2. The system of claim 1.

3. The feedback unit Equipped with an instruction section that provides specific instructions to athletes on how to correct their form 2. The system of claim 1.

4. The analysis unit Equipped with a real-time analysis unit that analyzes athletes' movements in real time and provides rapid feedback 2. The system of claim 1.

5. The camera unit Analyze the athlete's emotions and adjust the timing of the shoot based on the analyzed emotions of the athlete.

2. The system of claim 1.

6. The camera unit Automatically adjusts the frame rate according to the athlete's movement speed during shooting 2. The system of claim 1.

7. The camera unit Tracking athletes' location in real time during filming to capture the best angle 2. The system of claim 1.

8. The camera unit Equipped with automatic exposure adjustment function to adapt to different lighting conditions when shooting 2. The system of claim 1.

9. The camera unit Estimate the emotions of athletes and prioritize the footage to be shot based on the estimated emotions of athletes 2. The system of claim 1.

Images