System

Abstract

An object of a system according to an embodiment is to evaluate sports performance of a child in detail and provide individual feedback and improvement proposals.SOLUTION: A system includes a collection part, an analysis part, a generation part, and a generation part. The collection unit collects a video of a game or practice. The analysis unit analyzes the video collected by the collection unit and evaluates the performance of the child. The generation unit generates a feedback and an improvement proposal based on the evaluation result obtained by the analysis unit. The generation unit generates a virtual opponent based on the proposal generated by the generation 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 drawback of making it difficult to evaluate children's sports performance in detail and provide individual feedback and suggestions for improvement.

[0005] The system of the embodiment aims to provide a detailed evaluation of a child's sports performance and provide individual feedback and suggestions for improvement. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects video of matches and practices. The analysis unit analyzes the video collected by the collection unit and evaluates the child's performance. The generation unit generates feedback and suggestions for improvement based on the evaluation results obtained by the analysis unit. The generation unit generates a virtual opponent based on the suggestions generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a detailed assessment of a child's sports performance and provide individualized feedback and suggestions for improvement. [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 performance analysis system according to an embodiment of the present invention collects video footage of games and practice sessions, analyzes it using AI, evaluates a child's performance, generates specific feedback and suggestions for improvement, and generates a virtual opponent. The performance analysis system collects video footage of games and practice sessions, analyzes it using AI, and evaluates a child's performance. Based on the evaluation results, specific feedback and suggestions for improvement are generated. Furthermore, a virtual opponent tailored to the player's abilities is generated, providing an environment in which the child can practice independently. For example, a performance analysis system may collect video footage of games and practice sessions using cameras and sensors. For example, it may collect video footage of soccer games or basketball practice sessions. Next, AI analyzes the collected video footage and performs detailed analysis of the movements and performance in the footage to identify the child's strengths and areas for improvement. For example, it evaluates the accuracy of shots and the timing of passes. Next, it generates specific feedback and suggestions for improvement based on the analysis results. Based on the analysis results, the AI ​​suggests what kind of practice the child needs and what areas need improvement. For example, it provides specific advice on correcting shooting form. Next, it generates a virtual opponent tailored to the player's abilities. Based on a child's performance data, AI generates a virtual opponent of an appropriate level, providing an environment in which the child can practice independently. For example, a virtual defender is generated and the child practices breaking through that defender. This allows the performance analysis system to improve the child's performance. The performance analysis system then automatically analyzes the child's performance and provides specific feedback and suggestions for improvement. For example, this allows the child to overcome their weaknesses and improve their skills. Furthermore, practicing against a virtual opponent allows for practice in an environment closer to a real game. For example, practicing shooting against a virtual goalkeeper can be expected to improve performance in a real match.

[0029] A performance analysis system according to an embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects video of games and practices. Examples of video of games and practices include, but are not limited to, soccer game video and basketball practice video. The collection unit collects video using, for example, a camera or a sensor. The collection unit can also collect video in real time. For example, the collection unit collects video of games in real time and transmits it to the analysis unit. The analysis unit analyzes the video collected by the collection unit and evaluates the child's performance. The analysis unit uses, for example, AI to perform a detailed analysis of the movements and performance in the video. For example, the analysis unit evaluates shooting accuracy and pass timing. The analysis unit can also identify the child's strengths and areas for improvement. For example, the analysis unit identifies areas for improvement in the child's shooting form. The generation unit generates specific feedback and improvement suggestions based on the evaluation results obtained by the analysis unit. For example, the generation unit uses AI to suggest what kind of practice the child needs and what areas need improvement based on the analysis results. For example, the generation unit may provide specific advice for correcting a child's shooting form. The generation unit may also generate a virtual opponent of an appropriate level based on the child's performance data. For example, the generation unit may generate a virtual defender, and the child may practice breaking through the defender. This allows the performance analysis system according to the embodiment to improve the child's performance. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit may generate suggestions using an AI model that receives the evaluation results obtained by the analysis unit as input and outputs feedback and suggestions for improvement.

[0030] The collection unit can collect video of a game or practice using a camera or a sensor. The collection unit collects video of a game or practice using, for example, a camera or a sensor. For example, the collection unit collects video using a camera during a soccer game. The collection unit can also collect video using a sensor during basketball practice. For example, the collection unit tracks player movements using a sensor and collects the data as video. In this way, video of a game or practice can be effectively collected using a camera or a sensor. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input video data acquired by a camera or a sensor into a generation AI and have the generation AI analyze the video data.

[0031] The analysis unit can perform a detailed analysis of the movements or performances in the collected video to identify the child's strengths or areas for improvement. The analysis unit, for example, performs a detailed analysis of the movements or performances in the collected video. For example, the analysis unit evaluates the accuracy of shots and the timing of passes. The analysis unit can also identify the child's strengths and areas for improvement. For example, the analysis unit can identify areas for improvement in shooting form. The analysis unit can also identify the child's strengths and provide advice to utilize those strengths. For example, the analysis unit can identify the strengths of the child's dribbling technique and provide advice to utilize those techniques. In this way, the child's strengths and areas for improvement can be identified by analyzing the movements and performances in detail. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the collected video data into a generation AI and have the generation AI analyze the movements and performance.

[0032] The generation unit can suggest the necessary practice content and areas for improvement for the child based on the analysis results. For example, the generation unit can suggest what kind of practice the child needs and which areas need improvement based on the analysis results. For example, the generation unit can provide specific advice on correcting shooting form. The generation unit can also suggest an appropriate practice menu based on the child's performance data. For example, the generation unit can suggest practice menus to overcome the child's weaknesses, such as shooting practice and passing practice. The generation unit can also suggest practice menus to make use of the child's strengths. For example, the generation unit can suggest practice menus to improve dribbling skills. By making specific practice suggestions based on the analysis results, it is expected that the child's performance will improve. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis results into a generation AI and cause the generation AI to suggest practice content and areas for improvement.

[0033] The generation unit can generate a virtual opponent appropriate for the child's level based on the child's performance data. The generation unit, for example, generates a virtual opponent of an appropriate level based on the child's performance data. For example, the generation unit generates a virtual defender, and the child practices breaking through the defender. The generation unit can also generate a virtual goalkeeper, and the child can practice shooting against the goalkeeper. For example, the generation unit generates a goalkeeper of an appropriate level based on the child's shooting success rate. The generation unit can also customize the behavior patterns of the virtual opponent. For example, the generation unit realistically reproduces the movements of defenders, allowing the child to practice in an environment similar to a real game. By generating a virtual opponent based on the child's performance data, a practice environment tailored to each individual player can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the child's performance data into the generation AI and cause the generation AI to generate a virtual opponent.

[0034] The collection unit can automatically select the type of video to collect according to the type of game or the content of practice. The collection unit automatically selects the type of video to collect according to, for example, the type of game or the content of practice. For example, in a soccer game, the collection unit collects goal scenes and passing combination scenes. The collection unit can also collect video of shooting practice and defense practice in basketball practice. For example, in track and field practice, the collection unit collects video of starting dashes and form. In this way, by selecting video according to the type of game or the content of practice, necessary video can be effectively collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the type of video to collect according to the type of game or practice to a generation AI, and cause the generation AI to select the video.

[0035] The collection unit can automatically adjust the camera angle or position when collecting video to obtain optimal video. For example, the collection unit automatically adjusts the camera angle or position when collecting video to obtain optimal video. For example, the collection unit adjusts the camera angle to capture the scene in front of the goal during a soccer game. The collection unit can also adjust the camera position to capture the shooting form during basketball practice. For example, the collection unit adjusts the camera angle to capture the moment of a starting sprint during track and field practice. In this way, optimal video can be collected by automatically adjusting the camera angle and position. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the adjustments to the camera angle and position to the generation AI and cause the generation AI to obtain optimal video.

[0036] The collection unit can evaluate the image quality of the collected video in real time and make adjustments to improve the image quality. For example, the collection unit can evaluate the image quality of the collected video in real time and make adjustments to improve the image quality as needed. For example, if the image is not bright enough, the collection unit can adjust the brightness in real time. The collection unit can also perform image stabilization in real time if the image is blurred. For example, if the resolution of the video is low, the collection unit can improve the resolution in real time. This allows the image quality to be evaluated in real time and adjusted as needed, thereby collecting high-quality video. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input image quality data of the collected video to a generation AI and have the generation AI evaluate and adjust the image quality.

[0037] The collection unit can select the optimal collection method by taking into account weather and lighting conditions when collecting video. For example, the collection unit selects the optimal collection method by taking into account weather and lighting conditions when collecting video. For example, the collection unit uses a waterproof camera to collect video when it is raining. The collection unit can also adjust camera settings by taking lighting into account during nighttime practice. For example, the collection unit adjusts the camera exposure according to the brightness of the lighting during indoor practice. In this way, appropriate video can be collected by selecting a collection method by taking weather and lighting conditions into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on weather and lighting conditions into the generation AI and cause the generation AI to select the optimal collection method.

[0038] The collection unit can acquire multi-angle images by coordinating multiple cameras and sensors when collecting video. For example, the collection unit can acquire multi-angle images by coordinating multiple cameras and sensors when collecting video. For example, the collection unit can collect video by coordinating cameras in front of the goal and in the midfield during a soccer game. The collection unit can also use multiple cameras to simultaneously capture a player's shooting form and defensive movements during basketball practice. For example, the collection unit can use multiple cameras to simultaneously capture a player's starting sprint and the moment of a goal during track and field practice. In this way, multi-angle images can be collected by coordinating multiple cameras and sensors. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data from multiple cameras and sensors into a generation AI and cause the generation AI to acquire multi-angle images.

[0039] The collection unit can customize the collection method by reflecting the user's past feedback when collecting video. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting video. For example, the collection unit prioritizes the collection method for videos that the user has previously rated highly. The collection unit can also adjust the collection method by reflecting improvements previously pointed out by the user. For example, the collection unit automatically selects optimal camera settings based on the user's past feedback. This allows the collection method to be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the user's past feedback data into a generation AI and cause the generation AI to customize the collection method.

[0040] During analysis, the analysis unit can subdivide movements in the video and evaluate the accuracy of each movement. During analysis, the analysis unit, for example, subdivides movements in the video and evaluates the accuracy of each movement. For example, the analysis unit subdivides a shooting movement and evaluates the foot position and the trajectory of the ball. The analysis unit can also subdivide a passing movement and evaluate the hand position and the ball speed. For example, the analysis unit subdivides a dribbling movement and evaluates the hand movement and ball control. In this way, by subdividing and evaluating movements, the accuracy of each movement can be evaluated in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input movement data in the video to a generation AI and have the generation AI perform movement subdivision and accuracy evaluation.

[0041] The analysis unit can evaluate the child's growth level by comparing the data with the child's past performance data during analysis. For example, the analysis unit evaluates the child's growth level by comparing the data with the child's past performance data during analysis. For example, the analysis unit evaluates the child's growth level by comparing the child's past shooting accuracy with the child's current shooting accuracy. The analysis unit can also evaluate the child's growth level by comparing the child's past passing success rate with the child's current passing success rate. For example, the analysis unit evaluates the child's growth level by comparing the child's past dribbling success rate with the child's current dribbling success rate. In this way, the child's growth level can be evaluated by comparing the child's past performance data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the past performance data and the current performance data into the generation AI and cause the generation AI to evaluate the child's growth level.

[0042] The analysis unit can switch analysis algorithms depending on different sports or practice content during analysis. The analysis unit, for example, switches analysis algorithms depending on different sports or practice content during analysis. For example, the analysis unit uses analysis algorithms for shooting and passing in a soccer game. The analysis unit can also use analysis algorithms for shooting and defense in basketball practice. For example, the analysis unit uses analysis algorithms for starting dashes and form in track and field practice. In this way, by switching the analysis algorithm depending on the sport or practice content, appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input an analysis algorithm depending on the sport or practice content to the generation AI and cause the generation AI to switch the algorithm.

[0043] During analysis, the analysis unit can also analyze audio data in the video to evaluate the coach's instructions and the child's reactions. During analysis, the analysis unit can, for example, also analyze audio data in the video to evaluate the coach's instructions and the child's reactions. For example, the analysis unit can analyze the content of the coach's instructions and evaluate the relevance to the child's reactions. The analysis unit can also analyze the tone and content of the child's voice to evaluate their emotions and level of concentration. For example, the analysis unit can analyze the content of the conversation between the coach and the child and evaluate the child's understanding of the instructions. In this way, the coach's instructions and the child's reactions can be evaluated by analyzing the audio data. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, AI, for example. For example, the analysis unit can input the audio data in the video to a generation AI and have the generation AI analyze the audio data.

[0044] The analysis unit can perform a relative evaluation by comparing the performance data of other players during analysis. The analysis unit, for example, performs a relative evaluation by comparing the performance data of other players during analysis. For example, the analysis unit compares the shooting accuracy with that of other players on the same team to perform an evaluation. The analysis unit can also perform an evaluation by comparing the pass success rate of other players. For example, the analysis unit compares the dribbling success rate of other players to perform an evaluation. In this way, a relative evaluation can be performed by comparing with the data of other players. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the performance data of other players into the generation AI and cause the generation AI to perform a relative evaluation.

[0045] The analysis unit can correct the analysis results during analysis by taking into account environmental factors in the video. For example, the analysis unit corrects the analysis results during analysis by taking into account environmental factors in the video (e.g., wind speed and temperature). For example, when the wind speed is high, the analysis unit corrects the trajectory of the shot in the evaluation. Furthermore, when the temperature is high, the analysis unit can also take into account the player's physical exhaustion in the evaluation. For example, when it is raining, the analysis unit takes into account the slipperiness of the ball in the evaluation. In this way, by taking environmental factors into account, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on environmental factors into the generation AI and cause the generation AI to correct the analysis results.

[0046] The generation unit can adjust the level of detail of the feedback according to the child's level of understanding when generating the feedback. For example, the generation unit adjusts the level of detail of the feedback according to the child's level of understanding when generating the feedback. For example, the generation unit provides concise feedback that is easy for the child to understand. The generation unit can also provide detailed feedback if the child requires more detailed information. For example, the generation unit customizes the content of the feedback according to the child's level of understanding. In this way, appropriate feedback can be provided by adjusting the level of detail of the feedback according to the child's level of understanding. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the child's level of understanding data into the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0047] The generation unit can make optimal suggestions by referring to the child's past feedback history when generating feedback. For example, the generation unit can make optimal suggestions by referring to the child's past feedback history when generating feedback. For example, the generation unit adjusts current feedback based on feedback the child has received in the past. The generation unit can also analyze the child's past feedback history to make optimal suggestions. For example, the generation unit prioritizes and provides feedback content that the child has previously given high ratings to. This makes it possible to make optimal suggestions by referring to the past feedback history. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the child's past feedback history data into the generation AI and cause the generation AI to generate optimal suggestions.

[0048] The generation unit can customize the content of the feedback according to different sports or practice details when generating the feedback. For example, the generation unit customizes the content of the feedback according to different sports or practice details when generating the feedback. For example, the generation unit provides feedback on shooting and passing in soccer practice. The generation unit can also provide feedback on shooting and defense in basketball practice. For example, the generation unit provides feedback on starting sprints and form in track and field practice. In this way, appropriate feedback can be provided by customizing the feedback according to the sport or practice details. Some or all of the above-described processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input feedback content according to the sport or practice details into the generation AI and cause the generation AI to customize the feedback.

[0049] The generation unit can select the form of feedback according to the child's learning style when generating feedback. For example, the generation unit selects the form of feedback according to the child's learning style when generating feedback. For example, if the child has a visual learning style, the generation unit provides visual feedback. Also, if the child has a text-based learning style, the generation unit can provide text feedback. For example, if the child has an auditory learning style, the generation unit provides audio feedback. In this way, by selecting the form of feedback according to the child's learning style, appropriate feedback can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the child's learning style data into the generation AI and cause the generation AI to select the form of feedback.

[0050] The generation unit can adjust the content of the feedback based on the child's goal setting when generating the feedback. For example, the generation unit adjusts the content of the feedback based on the child's goal setting when generating the feedback. For example, the generation unit provides specific feedback based on the short-term goals set by the child. The generation unit can also provide gradual feedback based on the long-term goals set by the child. For example, the generation unit adjusts the content of the feedback according to the child's level of goal achievement. In this way, appropriate feedback can be provided by adjusting the content of the feedback based on the child's goal setting. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the child's goal setting data into the generation AI and cause the generation AI to adjust the content of the feedback.

[0051] The generation unit can make specific improvement suggestions by referring to success stories of other players when generating feedback. The generation unit, for example, makes specific improvement suggestions by referring to success stories of other players when generating feedback. For example, the generation unit makes specific improvement suggestions by referring to the shooting forms of other players who have been successful. The generation unit can also make specific improvement suggestions by referring to the timing of passes made by other players who have been successful. For example, the generation unit makes specific improvement suggestions by referring to dribbling techniques of other players who have been successful. In this way, specific improvement suggestions can be made by referring to success stories of other players. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input success story data of other players into the generation AI and cause the generation AI to generate specific improvement suggestions.

[0052] The generation unit can generate an optimal opponent based on the child's past performance data when generating a virtual opponent. For example, the generation unit generates an optimal opponent based on the child's past performance data when generating a virtual opponent. For example, the generation unit generates a goalkeeper of an appropriate level based on the child's past shooting success rate. The generation unit can also generate a defender of an appropriate level based on the child's past passing success rate. For example, the generation unit generates a defender of an appropriate level based on the child's past dribbling success rate. In this way, an appropriate practice environment can be provided by generating an optimal opponent based on past performance data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the child's past performance data into the generation AI and cause the generation AI to generate an optimal opponent.

[0053] The generation unit can customize the characteristics of the opponent according to different sports or practice content when generating a virtual opponent. For example, the generation unit customizes the characteristics of the opponent according to different sports or practice content when generating a virtual opponent. For example, in soccer practice, the generation unit generates a goalkeeper who blocks shots. The generation unit can also generate a defender who blocks shots in basketball practice. For example, in track and field practice, the generation unit generates an opponent who competes for a starting sprint. In this way, by customizing the characteristics of the opponent according to the sport or practice content, an appropriate practice environment can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the characteristics of the opponent according to the sport or practice content into the generation AI and cause the generation AI to customize the characteristics.

[0054] The generation unit can gradually adjust the level of the opponent according to the child's level of growth when generating a virtual opponent. For example, the generation unit gradually adjusts the level of the opponent according to the child's level of growth when generating a virtual opponent. For example, the generation unit may increase the goalkeeper's level if the child's shooting success rate improves. The generation unit may also increase the defender's level if the child's passing success rate improves. For example, the generation unit may increase the defender's level if the child's dribbling success rate improves. In this way, an appropriate practice environment can be provided by adjusting the opponent's level according to the child's level of growth. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the child's growth data into the generation AI and cause the generation AI to adjust the opponent's level.

[0055] The generation unit can simultaneously generate multiple opponents when generating virtual opponents, thereby practicing team play. For example, the generation unit simultaneously generates multiple opponents when generating virtual opponents, thereby practicing team play. For example, in soccer practice, the generation unit simultaneously generates multiple defenders and goalkeepers. Furthermore, in basketball practice, the generation unit can simultaneously generate multiple defenders and shot blockers. For example, in track and field practice, the generation unit simultaneously generates multiple runners, thereby practicing competition. In this way, by generating multiple opponents, team play can be practiced. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the generation of multiple opponents into the generation AI, and cause the generation AI to execute team play practice.

[0056] The generation unit can provide a variety of practice sessions by generating opponents with different tactics and strategies when generating virtual opponents. For example, the generation unit can provide a variety of practice sessions by generating opponents with different tactics and strategies when generating virtual opponents. For example, the generation unit can generate opponents with offensive tactics for soccer practice. The generation unit can also generate opponents with defensive-focused tactics for basketball practice. For example, the generation unit can generate opponents with different pace distributions for track and field practice. This enables a variety of practice sessions by generating opponents with different tactics and strategies. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the generation of opponents with different tactics and strategies into the generation AI and cause the generation AI to perform a variety of practice sessions.

[0057] The generation unit can customize the opponent's characteristics by reflecting the child's feedback when generating a virtual opponent. The generation unit, for example, customizes the opponent's characteristics by reflecting the child's feedback when generating a virtual opponent. For example, the generation unit adjusts the opponent's characteristics by reflecting points pointed out by the child in the feedback. The generation unit can also customize the opponent's behavior pattern based on the child's feedback. For example, the generation unit adjusts the opponent's level by referring to the child's feedback. In this way, the opponent's characteristics can be optimized by reflecting the child's feedback. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the child's feedback data into the generation AI and cause the generation AI to customize the opponent's characteristics.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The analysis unit can propose new training menus that combine techniques from different sports based on the child's performance data. For example, it can propose a training menu that combines soccer dribbling techniques with basketball defensive techniques. The analysis unit can also propose new training methods that combine movements from different sports based on the child's performance data. For example, it can propose a training method that combines the starting sprint of track and field with the shooting movement of soccer. In this way, by combining techniques and movements from different sports, it is possible to improve the child's overall athletic ability.

[0060] When collecting video footage, the collection unit can simultaneously collect biometric data such as the child's heart rate and body temperature, which can be used for performance analysis. For example, the collection unit can collect heart rate data during a match to help evaluate performance. The collection unit can also collect body temperature data during practice to use for fatigue evaluation. For example, the collection unit can suggest appropriate rest times based on the child's biometric data. In this way, using biometric data makes it possible to more accurately evaluate performance and suggest appropriate training.

[0061] The generation unit can create and regularly update individual training plans based on the child's performance data. For example, the generation unit creates weekly training plans based on the child's shooting success rate and passing success rate. The generation unit can also adjust the training plans according to the child's level of growth. For example, if the child's dribbling technique improves, the generation unit can add a new training menu. In this way, by providing an individual training plan, the generation unit can support the child in improving their performance.

[0062] The collection unit can analyze the child's movements in real time while collecting the video and provide immediate feedback. For example, the collection unit can analyze a shooting motion in real time and immediately display corrections to form. The collection unit can also analyze the timing of a pass in real time and provide immediate feedback on areas for improvement. For example, the collection unit can analyze a dribbling motion in real time and provide immediate advice on hand position and ball control. This provides real-time feedback to quickly support the child's improvement in technique.

[0063] The generation unit can propose new training menus that combine techniques from different sports based on the child's performance data. For example, it can propose a training menu that combines soccer dribbling techniques with basketball defensive techniques. The generation unit can also propose new training methods that combine movements from different sports based on the child's performance data. For example, it can propose a training method that combines the starting sprint of track and field with the shooting movement of soccer. In this way, by combining techniques and movements from different sports, the child's overall athletic ability can be improved.

[0064] When collecting video footage, the collection unit can simultaneously collect biometric data such as the child's heart rate and body temperature, which can be used for performance analysis. For example, the collection unit can collect heart rate data during a match to help evaluate performance. The collection unit can also collect body temperature data during practice to use for fatigue evaluation. For example, the collection unit can suggest appropriate rest times based on the child's biometric data. In this way, using biometric data makes it possible to more accurately evaluate performance and suggest appropriate training.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects video of a game or practice. Video of a game or practice includes, but is not limited to, for example, video of a soccer game or video of a basketball practice. The collection unit collects the video using, for example, a camera or a sensor. The collection unit can also collect the video in real time. For example, the collection unit collects video of a game in real time and transmits it to the analysis unit. Step 2: The analysis unit analyzes the video collected by the collection unit and evaluates the child's performance. The analysis unit, for example, uses AI to perform a detailed analysis of the movements and performances in the video. For example, the analysis unit evaluates the accuracy of shots and the timing of passes. The analysis unit can also identify the child's strengths and areas for improvement. For example, the analysis unit can identify areas for improvement in the child's shooting form. Step 3: The generator generates specific feedback and suggestions for improvement based on the evaluation results obtained by the analyzer. For example, the generator uses AI to suggest what kind of practice the child needs and what areas they should improve based on the analysis results. For example, the generator might provide specific advice on correcting shooting form. Step 4: The generator generates a virtual opponent of an appropriate level based on the child's performance data. For example, the generator generates a virtual defender and the child practices breaking through the defender.

[0067] (Example 2) A performance analysis system according to an embodiment of the present invention collects video footage of games and practice sessions, analyzes it using AI, evaluates a child's performance, generates specific feedback and suggestions for improvement, and generates a virtual opponent. The performance analysis system collects video footage of games and practice sessions, analyzes it using AI, and evaluates a child's performance. Based on the evaluation results, specific feedback and suggestions for improvement are generated. Furthermore, a virtual opponent tailored to the player's abilities is generated, providing an environment in which the child can practice independently. For example, a performance analysis system may collect video footage of games and practice sessions using cameras and sensors. For example, it may collect video footage of soccer games or basketball practice sessions. Next, AI analyzes the collected video footage and performs detailed analysis of the movements and performance in the footage to identify the child's strengths and areas for improvement. For example, it evaluates the accuracy of shots and the timing of passes. Next, it generates specific feedback and suggestions for improvement based on the analysis results. Based on the analysis results, the AI ​​suggests what kind of practice the child needs and what areas need improvement. For example, it provides specific advice on correcting shooting form. Next, it generates a virtual opponent tailored to the player's abilities. Based on a child's performance data, AI generates a virtual opponent of an appropriate level, providing an environment in which the child can practice independently. For example, a virtual defender is generated and the child practices breaking through that defender. This allows the performance analysis system to improve the child's performance. The performance analysis system then automatically analyzes the child's performance and provides specific feedback and suggestions for improvement. For example, this allows the child to overcome their weaknesses and improve their skills. Furthermore, practicing against a virtual opponent allows for practice in an environment closer to a real game. For example, practicing shooting against a virtual goalkeeper can be expected to improve performance in a real match.

[0068] A performance analysis system according to an embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects video of games and practices. Examples of video of games and practices include, but are not limited to, soccer game video and basketball practice video. The collection unit collects video using, for example, a camera or a sensor. The collection unit can also collect video in real time. For example, the collection unit collects video of games in real time and transmits it to the analysis unit. The analysis unit analyzes the video collected by the collection unit and evaluates the child's performance. The analysis unit uses, for example, AI to perform a detailed analysis of the movements and performance in the video. For example, the analysis unit evaluates shooting accuracy and pass timing. The analysis unit can also identify the child's strengths and areas for improvement. For example, the analysis unit identifies areas for improvement in the child's shooting form. The generation unit generates specific feedback and improvement suggestions based on the evaluation results obtained by the analysis unit. For example, the generation unit uses AI to suggest what kind of practice the child needs and what areas need improvement based on the analysis results. For example, the generation unit may provide specific advice for correcting a child's shooting form. The generation unit may also generate a virtual opponent of an appropriate level based on the child's performance data. For example, the generation unit may generate a virtual defender, and the child may practice breaking through the defender. This allows the performance analysis system according to the embodiment to improve the child's performance. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit may generate suggestions using an AI model that receives the evaluation results obtained by the analysis unit as input and outputs feedback and suggestions for improvement.

[0069] The collection unit can collect video of a game or practice using a camera or a sensor. The collection unit collects video of a game or practice using, for example, a camera or a sensor. For example, the collection unit collects video using a camera during a soccer game. The collection unit can also collect video using a sensor during basketball practice. For example, the collection unit tracks player movements using a sensor and collects the data as video. In this way, video of a game or practice can be effectively collected using a camera or a sensor. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input video data acquired by a camera or a sensor into a generation AI and have the generation AI analyze the video data.

[0070] The analysis unit can perform a detailed analysis of the movements or performances in the collected video to identify the child's strengths or areas for improvement. The analysis unit, for example, performs a detailed analysis of the movements or performances in the collected video. For example, the analysis unit evaluates the accuracy of shots and the timing of passes. The analysis unit can also identify the child's strengths and areas for improvement. For example, the analysis unit can identify areas for improvement in shooting form. The analysis unit can also identify the child's strengths and provide advice to utilize those strengths. For example, the analysis unit can identify the strengths of the child's dribbling technique and provide advice to utilize those techniques. In this way, the child's strengths and areas for improvement can be identified by analyzing the movements and performances in detail. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the collected video data into a generation AI and have the generation AI analyze the movements and performance.

[0071] The generation unit can suggest the necessary practice content and areas for improvement for the child based on the analysis results. For example, the generation unit can suggest what kind of practice the child needs and which areas need improvement based on the analysis results. For example, the generation unit can provide specific advice on correcting shooting form. The generation unit can also suggest an appropriate practice menu based on the child's performance data. For example, the generation unit can suggest practice menus to overcome the child's weaknesses, such as shooting practice and passing practice. The generation unit can also suggest practice menus to make use of the child's strengths. For example, the generation unit can suggest practice menus to improve dribbling skills. By making specific practice suggestions based on the analysis results, it is expected that the child's performance will improve. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis results into a generation AI and cause the generation AI to suggest practice content and areas for improvement.

[0072] The generation unit can generate a virtual opponent appropriate for the child's level based on the child's performance data. The generation unit, for example, generates a virtual opponent of an appropriate level based on the child's performance data. For example, the generation unit generates a virtual defender, and the child practices breaking through the defender. The generation unit can also generate a virtual goalkeeper, and the child can practice shooting against the goalkeeper. For example, the generation unit generates a goalkeeper of an appropriate level based on the child's shooting success rate. The generation unit can also customize the behavior patterns of the virtual opponent. For example, the generation unit realistically reproduces the movements of defenders, allowing the child to practice in an environment similar to a real game. By generating a virtual opponent based on the child's performance data, a practice environment tailored to each individual player can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the child's performance data into the generation AI and cause the generation AI to generate a virtual opponent.

[0073] The collection unit can estimate the user's emotions and adjust the timing of video collection based on the emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of video collection based on the emotions. For example, if the user is excited, the collection unit prioritizes collecting highlight scenes of a game. Furthermore, if the user is relaxed, the collection unit can also collect the overall flow of practice. For example, if the user is concentrating, the collection unit collects scenes of specific skill practice. By adjusting the timing of video collection based on the user's emotions, more appropriate video can be collected. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of video collection.

[0074] The collection unit can automatically select the type of video to collect according to the type of game or the content of practice. The collection unit automatically selects the type of video to collect according to, for example, the type of game or the content of practice. For example, in a soccer game, the collection unit collects goal scenes and passing combination scenes. The collection unit can also collect video of shooting practice and defense practice in basketball practice. For example, in track and field practice, the collection unit collects video of starting dashes and form. In this way, by selecting video according to the type of game or the content of practice, necessary video can be effectively collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the type of video to collect according to the type of game or practice to a generation AI, and cause the generation AI to select the video.

[0075] The collection unit can automatically adjust the camera angle or position when collecting video to obtain optimal video. For example, the collection unit automatically adjusts the camera angle or position when collecting video to obtain optimal video. For example, the collection unit adjusts the camera angle to capture the scene in front of the goal during a soccer game. The collection unit can also adjust the camera position to capture the shooting form during basketball practice. For example, the collection unit adjusts the camera angle to capture the moment of a starting sprint during track and field practice. In this way, optimal video can be collected by automatically adjusting the camera angle and position. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the adjustments to the camera angle and position to the generation AI and cause the generation AI to obtain optimal video.

[0076] The collection unit can evaluate the image quality of the collected video in real time and make adjustments to improve the image quality. For example, the collection unit can evaluate the image quality of the collected video in real time and make adjustments to improve the image quality as needed. For example, if the image is not bright enough, the collection unit can adjust the brightness in real time. The collection unit can also perform image stabilization in real time if the image is blurred. For example, if the resolution of the video is low, the collection unit can improve the resolution in real time. This allows the image quality to be evaluated in real time and adjusted as needed, thereby collecting high-quality video. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input image quality data of the collected video to a generation AI and have the generation AI evaluate and adjust the image quality.

[0077] The collection unit can estimate the user's emotions and determine the priority of videos to be collected based on the emotions. The collection unit, for example, estimates the user's emotions and determines the priority of videos to be collected based on the emotions. For example, if the user is excited, the collection unit prioritizes collecting highlight scenes of a game. Furthermore, if the user is relaxed, the collection unit can also collect the overall flow of practice. For example, if the user is concentrating, the collection unit collects scenes of specific technical practice. Thus, by determining the priority of videos based on the user's emotions, important videos can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of videos.

[0078] The collection unit can select the optimal collection method by taking into account weather and lighting conditions when collecting video. For example, the collection unit selects the optimal collection method by taking into account weather and lighting conditions when collecting video. For example, the collection unit uses a waterproof camera to collect video when it is raining. The collection unit can also adjust camera settings by taking lighting into account during nighttime practice. For example, the collection unit adjusts the camera exposure according to the brightness of the lighting during indoor practice. In this way, appropriate video can be collected by selecting a collection method by taking weather and lighting conditions into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on weather and lighting conditions into the generation AI and cause the generation AI to select the optimal collection method.

[0079] The collection unit can acquire multi-angle images by coordinating multiple cameras and sensors when collecting video. For example, the collection unit can acquire multi-angle images by coordinating multiple cameras and sensors when collecting video. For example, the collection unit can collect video by coordinating cameras in front of the goal and in the midfield during a soccer game. The collection unit can also use multiple cameras to simultaneously capture a player's shooting form and defensive movements during basketball practice. For example, the collection unit can use multiple cameras to simultaneously capture a player's starting sprint and the moment of a goal during track and field practice. In this way, multi-angle images can be collected by coordinating multiple cameras and sensors. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data from multiple cameras and sensors into a generation AI and cause the generation AI to acquire multi-angle images.

[0080] The collection unit can customize the collection method by reflecting the user's past feedback when collecting video. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting video. For example, the collection unit prioritizes the collection method for videos that the user has previously rated highly. The collection unit can also adjust the collection method by reflecting improvements previously pointed out by the user. For example, the collection unit automatically selects optimal camera settings based on the user's past feedback. This allows the collection method to be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the user's past feedback data into a generation AI and cause the generation AI to customize the collection method.

[0081] The analysis unit can estimate the user's emotion and adjust the level of analysis detail based on the estimated user's emotion. For example, the analysis unit estimates the user's emotion and adjusts the level of analysis detail based on the emotion. For example, the analysis unit provides detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. For example, the analysis unit provides visually easy-to-understand analysis results when the user is excited. This allows for appropriate analysis results to be provided by adjusting the level of analysis detail based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the level of analysis detail.

[0082] During analysis, the analysis unit can subdivide movements in the video and evaluate the accuracy of each movement. During analysis, the analysis unit, for example, subdivides movements in the video and evaluates the accuracy of each movement. For example, the analysis unit subdivides a shooting movement and evaluates the foot position and the trajectory of the ball. The analysis unit can also subdivide a passing movement and evaluate the hand position and the ball speed. For example, the analysis unit subdivides a dribbling movement and evaluates the hand movement and ball control. In this way, by subdividing and evaluating movements, the accuracy of each movement can be evaluated in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input movement data in the video to a generation AI and have the generation AI perform movement subdivision and accuracy evaluation.

[0083] The analysis unit can evaluate the child's growth level by comparing the data with the child's past performance data during analysis. For example, the analysis unit evaluates the child's growth level by comparing the data with the child's past performance data during analysis. For example, the analysis unit evaluates the child's growth level by comparing the child's past shooting accuracy with the child's current shooting accuracy. The analysis unit can also evaluate the child's growth level by comparing the child's past passing success rate with the child's current passing success rate. For example, the analysis unit evaluates the child's growth level by comparing the child's past dribbling success rate with the child's current dribbling success rate. In this way, the child's growth level can be evaluated by comparing the child's past performance data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the past performance data and the current performance data into the generation AI and cause the generation AI to evaluate the child's growth level.

[0084] The analysis unit can switch analysis algorithms depending on different sports or practice content during analysis. The analysis unit, for example, switches analysis algorithms depending on different sports or practice content during analysis. For example, the analysis unit uses analysis algorithms for shooting and passing in a soccer game. The analysis unit can also use analysis algorithms for shooting and defense in basketball practice. For example, the analysis unit uses analysis algorithms for starting dashes and form in track and field practice. In this way, by switching the analysis algorithm depending on the sport or practice content, appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input an analysis algorithm depending on the sport or practice content to the generation AI and cause the generation AI to switch the algorithm.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that focuses on the main points. This allows for an appropriate display method to be provided by adjusting the display method of the analysis results based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0086] During analysis, the analysis unit can also analyze audio data in the video to evaluate the coach's instructions and the child's reactions. During analysis, the analysis unit can, for example, also analyze audio data in the video to evaluate the coach's instructions and the child's reactions. For example, the analysis unit can analyze the content of the coach's instructions and evaluate the relevance to the child's reactions. The analysis unit can also analyze the tone and content of the child's voice to evaluate their emotions and level of concentration. For example, the analysis unit can analyze the content of the conversation between the coach and the child and evaluate the child's understanding of the instructions. In this way, the coach's instructions and the child's reactions can be evaluated by analyzing the audio data. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, AI, for example. For example, the analysis unit can input the audio data in the video to a generation AI and have the generation AI analyze the audio data.

[0087] The analysis unit can perform a relative evaluation by comparing the performance data of other players during analysis. The analysis unit, for example, performs a relative evaluation by comparing the performance data of other players during analysis. For example, the analysis unit compares the shooting accuracy with that of other players on the same team to perform an evaluation. The analysis unit can also perform an evaluation by comparing the pass success rate of other players. For example, the analysis unit compares the dribbling success rate of other players to perform an evaluation. In this way, a relative evaluation can be performed by comparing with the data of other players. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the performance data of other players into the generation AI and cause the generation AI to perform a relative evaluation.

[0088] The analysis unit can correct the analysis results during analysis by taking into account environmental factors in the video. For example, the analysis unit corrects the analysis results during analysis by taking into account environmental factors in the video (e.g., wind speed and temperature). For example, when the wind speed is high, the analysis unit corrects the trajectory of the shot in the evaluation. Furthermore, when the temperature is high, the analysis unit can also take into account the player's physical exhaustion in the evaluation. For example, when it is raining, the analysis unit takes into account the slipperiness of the ball in the evaluation. In this way, by taking environmental factors into account, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on environmental factors into the generation AI and cause the generation AI to correct the analysis results.

[0089] The generation unit can estimate the user's emotion and adjust the feedback expression method based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the feedback expression method based on the emotion. For example, the generation unit provides detailed feedback when the user is relaxed. The generation unit can also provide concise feedback that focuses on the main points when the user is in a hurry. For example, the generation unit provides visually easy-to-understand feedback when the user is excited. This allows appropriate feedback to be provided by adjusting the feedback expression method based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using an AI, for example, or without an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the feedback expression method.

[0090] The generation unit can adjust the level of detail of the feedback according to the child's level of understanding when generating the feedback. For example, the generation unit adjusts the level of detail of the feedback according to the child's level of understanding when generating the feedback. For example, the generation unit provides concise feedback that is easy for the child to understand. The generation unit can also provide detailed feedback if the child requires more detailed information. For example, the generation unit customizes the content of the feedback according to the child's level of understanding. In this way, appropriate feedback can be provided by adjusting the level of detail of the feedback according to the child's level of understanding. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the child's level of understanding data into the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0091] The generation unit can make optimal suggestions by referring to the child's past feedback history when generating feedback. For example, the generation unit can make optimal suggestions by referring to the child's past feedback history when generating feedback. For example, the generation unit adjusts current feedback based on feedback the child has received in the past. The generation unit can also analyze the child's past feedback history to make optimal suggestions. For example, the generation unit prioritizes and provides feedback content that the child has previously given high ratings to. This makes it possible to make optimal suggestions by referring to the past feedback history. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the child's past feedback history data into the generation AI and cause the generation AI to generate optimal suggestions.

[0092] The generation unit can customize the content of the feedback according to different sports or practice details when generating the feedback. For example, the generation unit customizes the content of the feedback according to different sports or practice details when generating the feedback. For example, the generation unit provides feedback on shooting and passing in soccer practice. The generation unit can also provide feedback on shooting and defense in basketball practice. For example, the generation unit provides feedback on starting sprints and form in track and field practice. In this way, appropriate feedback can be provided by customizing the feedback according to the sport or practice details. Some or all of the above-described processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input feedback content according to the sport or practice details into the generation AI and cause the generation AI to customize the feedback.

[0093] The generation unit can estimate the user's emotions and determine the priority of feedback based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and determines the priority of feedback based on the emotions. For example, if the user is excited, the generation unit can prioritize providing important feedback. The generation unit can also prioritize providing detailed feedback if the user is relaxed. For example, if the user is in a hurry, the generation unit can prioritize providing feedback that focuses on the main points. In this way, by determining the priority of feedback based on the user's emotions, important feedback can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit may be performed using an AI, for example, or without an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of feedback.

[0094] The generation unit can select the form of feedback according to the child's learning style when generating feedback. For example, the generation unit selects the form of feedback according to the child's learning style when generating feedback. For example, if the child has a visual learning style, the generation unit provides visual feedback. Also, if the child has a text-based learning style, the generation unit can provide text feedback. For example, if the child has an auditory learning style, the generation unit provides audio feedback. In this way, by selecting the form of feedback according to the child's learning style, appropriate feedback can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the child's learning style data into the generation AI and cause the generation AI to select the form of feedback.

[0095] The generation unit can adjust the content of the feedback based on the child's goal setting when generating the feedback. For example, the generation unit adjusts the content of the feedback based on the child's goal setting when generating the feedback. For example, the generation unit provides specific feedback based on the short-term goals set by the child. The generation unit can also provide gradual feedback based on the long-term goals set by the child. For example, the generation unit adjusts the content of the feedback according to the child's level of goal achievement. In this way, appropriate feedback can be provided by adjusting the content of the feedback based on the child's goal setting. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the child's goal setting data into the generation AI and cause the generation AI to adjust the content of the feedback.

[0096] The generation unit can make specific improvement suggestions by referring to success stories of other players when generating feedback. The generation unit, for example, makes specific improvement suggestions by referring to success stories of other players when generating feedback. For example, the generation unit makes specific improvement suggestions by referring to the shooting forms of other players who have been successful. The generation unit can also make specific improvement suggestions by referring to the timing of passes made by other players who have been successful. For example, the generation unit makes specific improvement suggestions by referring to dribbling techniques of other players who have been successful. In this way, specific improvement suggestions can be made by referring to success stories of other players. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input success story data of other players into the generation AI and cause the generation AI to generate specific improvement suggestions.

[0097] The generation unit can estimate the user's emotions and adjust the difficulty of the virtual opponent based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the difficulty of the virtual opponent based on the emotions. For example, if the user is relaxed, the generation unit generates a virtual opponent with a moderate level of difficulty. The generation unit can also generate a virtual opponent with a high level of difficulty if the user is excited. For example, if the user is nervous, the generation unit generates a virtual opponent with a low level of difficulty. This allows for adjusting the difficulty of the virtual opponent based on the user's emotions, thereby providing an appropriate practice environment. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using an AI, for example, or without an AI. For example, the generation unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the difficulty of the virtual opponent.

[0098] The generation unit can generate an optimal opponent based on the child's past performance data when generating a virtual opponent. For example, the generation unit generates an optimal opponent based on the child's past performance data when generating a virtual opponent. For example, the generation unit generates a goalkeeper of an appropriate level based on the child's past shooting success rate. The generation unit can also generate a defender of an appropriate level based on the child's past passing success rate. For example, the generation unit generates a defender of an appropriate level based on the child's past dribbling success rate. In this way, an appropriate practice environment can be provided by generating an optimal opponent based on past performance data. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the child's past performance data into the generation AI and cause the generation AI to generate an optimal opponent.

[0099] The generation unit can customize the characteristics of the opponent according to different sports or practice content when generating a virtual opponent. For example, the generation unit customizes the characteristics of the opponent according to different sports or practice content when generating a virtual opponent. For example, in soccer practice, the generation unit generates a goalkeeper who blocks shots. The generation unit can also generate a defender who blocks shots in basketball practice. For example, in track and field practice, the generation unit generates an opponent who competes for a starting sprint. In this way, by customizing the characteristics of the opponent according to the sport or practice content, an appropriate practice environment can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the characteristics of the opponent according to the sport or practice content into the generation AI and cause the generation AI to customize the characteristics.

[0100] The generation unit can gradually adjust the level of the opponent according to the child's level of growth when generating a virtual opponent. For example, the generation unit gradually adjusts the level of the opponent according to the child's level of growth when generating a virtual opponent. For example, the generation unit may increase the goalkeeper's level if the child's shooting success rate improves. The generation unit may also increase the defender's level if the child's passing success rate improves. For example, the generation unit may increase the defender's level if the child's dribbling success rate improves. In this way, an appropriate practice environment can be provided by adjusting the opponent's level according to the child's level of growth. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the child's growth data into the generation AI and cause the generation AI to adjust the opponent's level.

[0101] The generation unit can estimate the user's emotions and adjust the behavior pattern of the virtual opponent based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the behavior pattern of the virtual opponent based on the emotions. For example, if the user is relaxed, the generation unit generates a virtual opponent with a moderate behavior pattern. Furthermore, if the user is excited, the generation unit can generate a virtual opponent with an aggressive behavior pattern. For example, if the user is nervous, the generation unit generates a virtual opponent with a defensive behavior pattern. This allows for an appropriate practice environment by adjusting the behavior pattern of the virtual opponent based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the behavior pattern of the virtual opponent.

[0102] The generation unit can simultaneously generate multiple opponents when generating virtual opponents, thereby practicing team play. For example, the generation unit simultaneously generates multiple opponents when generating virtual opponents, thereby practicing team play. For example, in soccer practice, the generation unit simultaneously generates multiple defenders and goalkeepers. Furthermore, in basketball practice, the generation unit can simultaneously generate multiple defenders and shot blockers. For example, in track and field practice, the generation unit simultaneously generates multiple runners, thereby practicing competition. In this way, by generating multiple opponents, team play can be practiced. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the generation of multiple opponents into the generation AI, and cause the generation AI to execute team play practice.

[0103] The generation unit can provide a variety of practice sessions by generating opponents with different tactics and strategies when generating virtual opponents. For example, the generation unit can provide a variety of practice sessions by generating opponents with different tactics and strategies when generating virtual opponents. For example, the generation unit can generate opponents with offensive tactics for soccer practice. The generation unit can also generate opponents with defensive-focused tactics for basketball practice. For example, the generation unit can generate opponents with different pace distributions for track and field practice. This enables a variety of practice sessions by generating opponents with different tactics and strategies. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the generation of opponents with different tactics and strategies into the generation AI and cause the generation AI to perform a variety of practice sessions.

[0104] The generation unit can customize the opponent's characteristics by reflecting the child's feedback when generating a virtual opponent. The generation unit, for example, customizes the opponent's characteristics by reflecting the child's feedback when generating a virtual opponent. For example, the generation unit adjusts the opponent's characteristics by reflecting points pointed out by the child in the feedback. The generation unit can also customize the opponent's behavior pattern based on the child's feedback. For example, the generation unit adjusts the opponent's level by referring to the child's feedback. In this way, the opponent's characteristics can be optimized by reflecting the child's feedback. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the child's feedback data into the generation AI and cause the generation AI to customize the opponent's characteristics. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and generation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect video of a game or practice using the camera 42 or sensor of the smart device 14. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12, and collect video of a game in real time and transmit it to the analysis unit. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12, and analyze the collected video using AI to evaluate the child's performance. For example, the generation unit can be realized by the control unit 46A of the smart device 14, and generate specific feedback and suggestions for improvement based on the analysis results. For example, the generation unit can also be realized by the specific processing unit 290 of the data processing device 12, and generate a virtual opponent based on the child's performance data. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and generation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect video of a game or practice using the camera 42 or sensor of the smart glasses 214. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12, and collect video of a game in real time and transmit it to the analysis unit. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12, and analyze the collected video using AI to evaluate the child's performance. For example, the generation unit can be realized by the control unit 46A of the smart glasses 214, and generate specific feedback and suggestions for improvement based on the analysis results. For example, the generation unit can also be realized by the specific processing unit 290 of the data processing device 12, and generate a virtual opponent based on the child's performance data. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, and generation unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect video of a game or practice using the camera 42 or sensor of the headset-type terminal 314. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12, and collect video of a game in real time and transmit it to the analysis unit. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12, and analyze the collected video using AI to evaluate the child's performance. For example, the generation unit can be realized by the control unit 46A of the headset-type terminal 314, and generate specific feedback and suggestions for improvement based on the analysis results. For example, the generation unit can also be realized by the specific processing unit 290 of the data processing device 12, and generate a virtual opponent based on the child's performance data. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, and generation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect video of a game or practice using the camera 42 or sensors of the robot 414. For example, the collection unit can also be realized by the specific processing unit 290 of the data processing device 12, and collect video of the game in real time and transmit it to the analysis unit. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12, and analyze the collected video using AI to evaluate the child's performance. For example, the generation unit can be realized by the control unit 46A of the robot 414, and generate specific feedback and suggestions for improvement based on the analysis results. For example, the generation unit can also be realized by the specific processing unit 290 of the data processing device 12, and generate a virtual opponent based on the child's performance data.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The analysis unit can propose new training menus that combine techniques from different sports based on the child's performance data. For example, it can propose a training menu that combines soccer dribbling techniques with basketball defensive techniques. The analysis unit can also propose new training methods that combine movements from different sports based on the child's performance data. For example, it can propose a training method that combines the starting sprint of track and field with the shooting movement of soccer. In this way, by combining techniques and movements from different sports, it is possible to improve the child's overall athletic ability.

[0107] When collecting video footage, the collection unit can simultaneously collect biometric data such as the child's heart rate and body temperature, which can be used for performance analysis. For example, the collection unit can collect heart rate data during a match to help evaluate performance. The collection unit can also collect body temperature data during practice to use for fatigue evaluation. For example, the collection unit can suggest appropriate rest times based on the child's biometric data. In this way, using biometric data makes it possible to more accurately evaluate performance and suggest appropriate training.

[0108] The analysis unit can estimate the child's emotions and adjust the content of the analysis result feedback based on the estimated emotions. For example, if the child is depressed, the analysis unit can provide feedback including an encouraging message. Also, if the child is excited, the analysis unit can provide challenging feedback to encourage the child to move on to the next step. For example, if the child is relaxed, the analysis unit can provide specific points for improvement along with detailed analysis results. In this way, providing feedback according to the child's emotions makes it easier to maintain motivation.

[0109] The generation unit can create and regularly update individual training plans based on the child's performance data. For example, the generation unit creates weekly training plans based on the child's shooting success rate and passing success rate. The generation unit can also adjust the training plans according to the child's level of growth. For example, if the child's dribbling technique improves, the generation unit can add a new training menu. In this way, by providing an individual training plan, the generation unit can support the child in improving their performance.

[0110] The generation unit can estimate the child's emotions and adjust the behavior pattern of the virtual opponent based on the estimated emotions. For example, if the child is relaxed, the generation unit generates a virtual opponent with a moderate level of difficulty. The generation unit can also generate a virtual opponent with an aggressive behavior pattern if the child is excited. For example, if the child is nervous, the generation unit generates a virtual opponent with a defensive behavior pattern. In this way, an appropriate practice environment can be provided by adjusting the behavior pattern of the virtual opponent based on the child's emotions.

[0111] The collection unit can analyze the child's movements in real time while collecting the video and provide immediate feedback. For example, the collection unit can analyze a shooting motion in real time and immediately display corrections to form. The collection unit can also analyze the timing of a pass in real time and provide immediate feedback on areas for improvement. For example, the collection unit can analyze a dribbling motion in real time and provide immediate advice on hand position and ball control. This provides real-time feedback to quickly support the child's improvement in technique.

[0112] The analysis unit can estimate the child's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the child is nervous, the analysis unit provides a simple, highly visible display method. The analysis unit can also provide a display method that includes detailed information if the child is relaxed. For example, if the child is in a hurry, the analysis unit provides a display method that focuses on the main points. In this way, an appropriate display method can be provided by adjusting the display method of the analysis results based on the child's emotions.

[0113] The generation unit can propose new training menus that combine techniques from different sports based on the child's performance data. For example, it can propose a training menu that combines soccer dribbling techniques with basketball defensive techniques. The generation unit can also propose new training methods that combine movements from different sports based on the child's performance data. For example, it can propose a training method that combines the starting sprint of track and field with the shooting movement of soccer. In this way, by combining techniques and movements from different sports, the child's overall athletic ability can be improved.

[0114] When collecting video footage, the collection unit can simultaneously collect biometric data such as the child's heart rate and body temperature, which can be used for performance analysis. For example, the collection unit can collect heart rate data during a match to help evaluate performance. The collection unit can also collect body temperature data during practice to use for fatigue evaluation. For example, the collection unit can suggest appropriate rest times based on the child's biometric data. In this way, using biometric data makes it possible to more accurately evaluate performance and suggest appropriate training.

[0115] The generation unit can estimate the child's emotions and adjust the way in which feedback is expressed based on the estimated emotions. For example, the generation unit can provide detailed feedback when the child is relaxed. The generation unit can also provide concise feedback that gets to the point when the child is in a hurry. For example, the generation unit can provide visually easy-to-understand feedback when the child is excited. In this way, appropriate feedback can be provided by adjusting the way in which feedback is expressed based on the child's emotions.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The collection unit collects video of a game or practice. Video of a game or practice includes, but is not limited to, for example, video of a soccer game or video of a basketball practice. The collection unit collects the video using, for example, a camera or a sensor. The collection unit can also collect the video in real time. For example, the collection unit collects video of a game in real time and transmits it to the analysis unit. Step 2: The analysis unit analyzes the video collected by the collection unit and evaluates the child's performance. The analysis unit, for example, uses AI to perform a detailed analysis of the movements and performances in the video. For example, the analysis unit evaluates the accuracy of shots and the timing of passes. The analysis unit can also identify the child's strengths and areas for improvement. For example, the analysis unit can identify areas for improvement in the child's shooting form. Step 3: The generator generates specific feedback and suggestions for improvement based on the evaluation results obtained by the analyzer. For example, the generator uses AI to suggest what kind of practice the child needs and what areas they should improve based on the analysis results. For example, the generator might provide specific advice on correcting shooting form. Step 4: The generator generates a virtual opponent of an appropriate level based on the child's performance data. For example, the generator generates a virtual defender and the child practices breaking through the defender.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects footage of matches and practices, an analysis unit that analyzes the video collected by the collection unit and evaluates the performance of the child; a generation unit that generates feedback and improvement suggestions based on the evaluation results obtained by the analysis unit; a generation unit that generates a virtual opponent based on the proposal generated by the generation unit; Equipped with A system characterized by:

2. The collecting unit Use cameras or sensors to collect footage of games or practices 2. The system of claim 1.

3. The analysis unit Detailed analysis of the actions or performances in the collected footage to identify the child's strengths or areas for improvement 2. The system of claim 1.

4. The generation unit Based on the analysis results, we suggest the necessary practice content and areas for improvement for each child.

2. The system of claim 1.

5. The generation unit Generate a virtual opponent based on the child's performance data, according to their level 2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and adjust the timing of video collection based on those emotions 2. The system of claim 1.

7. The collecting unit The type of footage to be collected is automatically selected based on the type of match or practice.

2. The system of claim 1.

8. The collecting unit When collecting footage, automatically adjust the camera angle or position to get the best possible footage 2. The system of claim 1.

9. The collecting unit Evaluate the quality of collected video in real time and make adjustments to improve the quality.

2. The system of claim 1.

10. The collecting unit Estimate the user's emotions and prioritize the videos to be collected based on those emotions.

2. The system of claim 1.

Images