System

Abstract

An object of a system according to an embodiment is to enable a user to easily receive sports guidance suitable for the user.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a provision unit. The receiving unit receives a moving image from a user. The analysis unit analyzes the moving image received by the reception unit and analyzes the motion of the user by using a specific technical method using a motion capture technique. The generation unit generates an instruction content on the basis of the problem or the improvement specified by the analysis unit. The providing unit provides the user with the instruction content generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, sports instruction requires expensive lesson fees and makes it difficult to receive instruction that is tailored to your needs.

[0005] The system according to the embodiment aims to enable users to easily receive sports instruction suited to them. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit receives a video from a user. The analyzing unit analyzes the video received by the receiving unit and analyzes the user's movements using specific technical techniques, such as motion capture technology. The generating unit generates instruction content based on the problems and areas for improvement identified by the analyzing unit. The providing unit provides the instruction content generated by the generating unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily receive sports instruction suited to them. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A sports trainer system according to an embodiment of the present invention analyzes videos sent by a user and provides appropriate instructional content. This system operates when a user films their sports movements with a smartphone and sends the video to a service. A generation AI analyzes the transmitted video and performs a detailed analysis of the user's movements using motion capture technology. The generation AI identifies problems and areas for improvement in the user's movements and generates instructional content appropriate for the user. For example, if a sports movement is not going well, the generation AI identifies the cause and suggests specific ways to improve. Furthermore, the generation AI creates an individualized training plan based on the user's concerns and goals. This training plan is updated in real time based on the user's progress. This allows the user to receive personalized instruction and improve their sports skills. This allows the sports trainer system to provide appropriate instruction to improve the user's sports skills. For example, simply by a user filming their own movements and sending the video, the generation AI performs a detailed analysis and suggests specific ways to improve. Furthermore, an individualized training plan can be created based on the user's concerns and goals and updated in real time based on the user's progress. This allows the user to receive personalized instruction and improve their sports skills.

[0029] A sports trainer system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a video from a user. The video transmitted by the user may include, but is not limited to, a resolution, a frame rate, and a file format. The reception unit may receive, for example, a video shot with a smartphone. The reception unit may also receive the user's concerns and goals. The analysis unit uses motion capture technology to analyze the video received by the reception unit and analyze the user's movements in detail. Examples of motion capture technology include, but are not limited to, optical, inertial, and marker motion capture. The analysis unit may use, for example, optical motion capture technology to analyze the user's movements with high accuracy. The analysis unit may also use inertial motion capture technology to analyze the user's movements in real time. The analysis unit may also use marker motion capture technology to analyze the user's movements in detail. The generation unit uses a generation AI to generate instruction content based on the problems and areas for improvement identified by the analysis unit. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the generation unit may use the generation AI to identify problems in the user's movements and propose specific improvement methods. The generation unit may also use the generation AI to create an individual training plan based on the user's concerns and goals. Furthermore, the generation unit may also use the generation AI to update the training plan in real time according to the user's progress. The provision unit provides the instruction content generated by the generation unit to the user. The provision unit may provide the instruction content in a format such as text, video, or audio. The provision unit may provide the instruction content in, for example, text format. The provision unit may also provide the instruction content in a video format. Furthermore, the provision unit may also provide the instruction content in an audio format. As a result, the sports trainer system according to the embodiment may provide appropriate instruction to improve the user's sports technique.

[0030] The sports trainer system includes a reception unit that receives the user's specific concerns and goals. The reception unit receives the user's concerns and goals. The concerns and goals include, but are not limited to, technical concerns and mental goals, for example. The reception unit allows the user to input, for example, technical concerns. The reception unit also allows the user to input mental goals. Furthermore, the reception unit can provide an interface for the user to input the user's specific concerns and goals. This makes it possible to provide more appropriate instruction content based on the user's concerns and goals.

[0031] The sports trainer system includes a monitoring unit that monitors the specific progress of the user. The monitoring unit monitors the user's progress. The progress includes, but is not limited to, for example, achievement level, practice time, and feedback. The monitoring unit can monitor, for example, the user's achievement level. The monitoring unit can also monitor the user's practice time. Furthermore, the monitoring unit can monitor the user's feedback. In this way, by monitoring the user's progress, it is possible to appropriately adjust the training plan.

[0032] The sports trainer system includes an update unit that instantly updates the training plan. The update unit updates the training plan in real time. Immediate updating includes, but is not limited to, real time, regular intervals, and the like. The update unit can update the training plan in real time, for example, according to the user's progress. The update unit can also update the training plan at regular intervals. Furthermore, the update unit can update the training plan based on user feedback. This allows the training plan to be updated in real time according to the user's progress.

[0033] The analysis unit can use motion capture technology to analyze the user's movements using specific technical techniques. The analysis unit uses motion capture technology to analyze the user's movements in detail. Motion capture technology includes, but is not limited to, optical, inertial, and marker types. The analysis unit can analyze the user's movements with high precision using, for example, optical motion capture technology. The analysis unit can also analyze the user's movements in real time using inertial motion capture technology. Furthermore, the analysis unit can also analyze the user's movements in detail using marker motion capture technology. As a result, the use of motion capture technology allows the user's movements to be analyzed in detail.

[0034] The generation unit can create an individual training plan based on the user's specific concerns and goals. The generation unit uses a generation AI to create an individual training plan based on the user's concerns and goals. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. The generation unit can use, for example, the generation AI to create an individual training plan based on the user's concerns and goals. The generation unit can also use the generation AI to update the training plan in real time according to the user's progress. This makes it possible to create an individual training plan based on the user's concerns and goals.

[0035] The reception unit can analyze the user's past video submission history and select the optimal reception method. The reception unit analyzes the user's past video submission history and selects the optimal reception method. Reception methods include, but are not limited to, online forms, emails, apps, etc. For example, the reception unit analyzes the frequency of videos the user has submitted in the past and suggests the optimal timing for submission. The reception unit can also analyze the content of videos the user has submitted in the past and provide related advice. Furthermore, the reception unit can suggest the optimal submission method (audio, text, image, etc.) based on the user's past submission history. In this way, the optimal reception method can be selected by analyzing the user's past video submission history.

[0036] The reception unit may use a specific filtering method based on the user's current athletic level or area of ​​interest when receiving the video. The reception unit may perform filtering based on the user's current athletic level or area of ​​interest when receiving the video. Examples of filtering methods include, but are not limited to, keyword matching and machine learning algorithms. The reception unit may provide appropriate feedback based on the user's athletic level, for example. The reception unit may also preferentially receive related training videos based on the user's area of ​​interest. Furthermore, the reception unit may propose an optimal training plan based on the user's athletic level or area of ​​interest. This allows appropriate feedback to be provided based on the user's athletic level or area of ​​interest.

[0037] The reception unit can select the optimal reception means depending on the user's input method (voice, text, image, etc.) when receiving a video. The reception unit selects the optimal reception means depending on the user's input method when receiving a video. Reception means include, but are not limited to, voice input, text input, image upload, etc. For example, when the user uses voice input, the reception unit can use voice recognition technology to receive the video. Furthermore, when the user uses text input, the reception unit can also use text analysis technology to receive the video. Furthermore, when the user uses image input, the reception unit can also use image recognition technology to receive the video. This makes it possible to select the optimal reception means depending on the user's input method.

[0038] When receiving videos, the reception unit can prioritize receiving highly relevant videos based on the user's geographical location information. When receiving videos, the reception unit prioritizes receiving highly relevant videos taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the reception unit can prioritize receiving training videos related to that area. Furthermore, if the user is traveling, the reception unit can also prioritize receiving training videos related to the user's travel destination. Furthermore, if the user is participating in a specific sporting event, the reception unit can prioritize receiving training videos related to that event. In this way, highly relevant videos can be prioritized by taking into account the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity using a specific method when receiving a video and receive related videos. The reception unit can analyze the user's social media activity when receiving a video and receive related videos. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, the number of followers, etc. The reception unit, for example, preferentially receives sports videos shared by the user on social media. The reception unit can also analyze the content of the user's posts on social media and receive related training videos. Furthermore, the reception unit can receive related videos by referring to the activities of the user's friends on social media. In this way, related videos can be received by analyzing the user's social media activity.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback in a specific manner when receiving a video. The reception unit customizes the reception method by reflecting the user's past feedback when receiving a video. Feedback includes, but is not limited to, for example, survey results, comments, and ratings. The reception unit can, for example, suggest an optimal reception method based on the user's past feedback. The reception unit can also preferentially select a specific reception method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and improve the reception method. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0041] The analysis unit can adjust the level of detail of the analysis based on the specific importance of the action during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the action during analysis. The importance of the action includes, for example, athletic level, technical difficulty, etc., but is not limited to these examples. For example, the analysis unit performs a detailed analysis for an important action. The analysis unit can also perform a simplified analysis for an action with low importance. Furthermore, the analysis unit can gradually adjust the level of detail of the analysis according to the importance of the action. In this way, by adjusting the level of detail of the analysis based on the importance of the action, more appropriate analysis results can be provided.

[0042] The analysis unit can apply different analysis algorithms depending on the specific category of the movement during analysis. The analysis unit applies different analysis algorithms depending on the category of the movement during analysis. Movement categories include, for example, jumping, throwing, running, etc., but are not limited to these examples. For example, the analysis unit applies an analysis algorithm dedicated to running to a running movement. Furthermore, the analysis unit can also apply an analysis algorithm dedicated to swinging to a swinging movement. Furthermore, the analysis unit can also apply an analysis algorithm dedicated to jumping to a jumping movement. In this way, by applying different analysis algorithms depending on the category of the movement, more appropriate analysis results can be provided.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results using a specific method. During analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, database reference, history analysis, etc. For example, the analysis unit corrects the current analysis results based on the user's past analysis results. The analysis unit can also extract specific behavior patterns from the user's past analysis results to improve the accuracy of the analysis. Furthermore, the analysis unit can analyze the user's past analysis results and optimize the analysis algorithm. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the specific submission time of the motion. During analysis, the analysis unit determines the priority of analysis based on the submission time of the motion. The submission time includes, for example, but is not limited to, the submission date and submission time. For example, the analysis unit prioritizes analysis of the most recently submitted motion. The analysis unit can also analyze older submitted motions later. Furthermore, the analysis unit can gradually adjust the priority of analysis depending on the submission time. In this way, by determining the priority of analysis based on the submission time of the motion, more appropriate analysis results can be provided.

[0045] The analysis unit can adjust the order of analysis based on the specific relevance of the actions during analysis. The analysis unit adjusts the order of analysis based on the relevance of the actions during analysis. The relevance of the actions includes, but is not limited to, technical relevance, temporal relevance, and the like. For example, the analysis unit prioritizes analysis of highly relevant actions. The analysis unit can also postpone analysis of less relevant actions. Furthermore, the analysis unit can gradually adjust the order of analysis according to the relevance of the actions. In this way, by adjusting the order of analysis based on the relevance of the actions, more appropriate analysis results can be provided.

[0046] The analysis unit can adjust the use of analytical terminology during analysis according to the user's specific level of expertise. The analysis unit can adjust the use of analytical terminology during analysis according to the user's level of expertise. Expertise levels include, but are not limited to, beginner, intermediate, and advanced levels. For example, the analysis unit can explain the analysis results in simple terms to a user with little expertise. The analysis unit can also explain the analysis results using detailed terminology to a user with extensive expertise. Furthermore, the analysis unit can gradually adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide more appropriate analysis results by adjusting the use of analytical terminology according to the user's level of expertise.

[0047] The generation unit can adjust the level of detail of the instruction content based on the specific importance of the movement when generating the instruction content. The generation unit uses the generation AI to adjust the level of detail of the instruction content based on the importance of the movement when generating the instruction content. The importance of the movement includes, but is not limited to, for example, the competitive level and technical difficulty. For example, the generation unit can provide detailed instruction content for important movements. The generation unit can also provide simplified instruction content for movements with low importance. Furthermore, the generation unit can gradually adjust the level of detail of the instruction content according to the importance of the movement. In this way, by adjusting the level of detail of the instruction content based on the importance of the movement, more appropriate instruction content can be provided.

[0048] The generation unit can apply different teaching algorithms depending on the specific category of the movement when generating the teaching content. The generation unit uses the generation AI to apply different teaching algorithms depending on the category of the movement when generating the teaching content. Movement categories include, but are not limited to, for example, jumping, throwing, and running. For example, the generation unit applies a running-specific teaching algorithm to a running movement. Furthermore, the generation unit can also apply a swing-specific teaching algorithm to a swing movement. Furthermore, the generation unit can also apply a jump-specific teaching algorithm to a jump movement. In this way, by applying different teaching algorithms depending on the category of the movement, more appropriate teaching content can be provided.

[0049] When generating instruction content, the generation unit can improve the accuracy of the instruction content by referring to the user's past instruction results in a specific manner. When generating instruction content, the generation unit uses the generation AI to improve the accuracy of the instruction content by referring to the user's past instruction results. Past instruction results include, but are not limited to, database reference and history analysis, for example. The generation unit corrects the current instruction content based on the user's past instruction results, for example. The generation unit can also extract specific movement patterns from the user's past instruction results to improve the accuracy of the instruction content. Furthermore, the generation unit can analyze the user's past instruction results and optimize the instruction algorithm. In this way, the accuracy of the instruction content can be improved by referring to the user's past instruction results.

[0050] The generation unit, when generating the instruction content, can determine the priority of the instruction content based on the specific submission time of the action. The generation unit, using the generation AI, determines the priority of the instruction content based on the submission time of the action when generating the instruction content. The submission time includes, for example, but is not limited to, the submission date and the submission time. For example, the generation unit gives priority to the most recently submitted action. The generation unit can also postpone the instruction of an action that was submitted earlier. Furthermore, the generation unit can gradually adjust the priority of the instruction content according to the submission time. In this way, by determining the priority of the instruction content based on the submission time of the action, more appropriate instruction content can be provided.

[0051] The generation unit can adjust the order of the instruction contents based on the specific relevance of the actions when generating the instruction contents. The generation unit uses the generation AI to adjust the order of the instruction contents based on the relevance of the actions when generating the instruction contents. The relevance of the actions includes, but is not limited to, for example, technical relevance and temporal relevance. For example, the generation unit prioritizes instruction of highly relevant actions. The generation unit can also postpone instruction of less relevant actions. Furthermore, the generation unit can gradually adjust the order of the instruction contents according to the relevance of the actions. In this way, by adjusting the order of the instruction contents based on the relevance of the actions, more appropriate instruction contents can be provided.

[0052] The generation unit can adjust the use of technical terms in the instruction content according to the user's specific level of expertise when generating the instruction content. The generation unit uses the generation AI to adjust the use of technical terms in the instruction content according to the user's level of expertise when generating the instruction content. Expertise levels include, but are not limited to, beginner, intermediate, and advanced, for example. The generation unit, for example, explains the instruction content in simple terms to a user with little expertise. The generation unit can also explain the instruction content using detailed technical terms to a user with extensive expertise. Furthermore, the generation unit can gradually adjust the way the instruction content is expressed according to the user's level of expertise. This makes it possible to provide more appropriate instruction content by adjusting the use of technical terms in the instruction content according to the user's level of expertise.

[0053] The providing unit can optimize the method of providing instruction content by referring to the user's past feedback in a specific manner when providing instruction content. The providing unit optimizes the method of providing instruction content by referring to the user's past feedback when providing instruction content. Feedback includes, but is not limited to, for example, survey results, comments, and ratings. The providing unit can, for example, suggest an optimal method of providing instruction content based on feedback provided by the user in the past. The providing unit can also preferentially select a specific method of providing instruction content based on the user's past feedback. Furthermore, the providing unit can analyze the user's past feedback and improve the method of providing instruction content. This makes it possible to optimize the method of providing instruction content by referring to the user's past feedback.

[0054] The providing unit can customize specific content to be provided based on the user's current sports level when providing instruction content. The providing unit customizes the content to be provided based on the user's current sports level when providing instruction content. Sports levels include, but are not limited to, beginner, intermediate, and advanced, for example. The providing unit provides appropriate feedback according to the user's sports level, for example. The providing unit can also provide related training videos based on the user's sports level. Furthermore, the providing unit can suggest an optimal training plan based on the user's sports level. This makes it possible to provide appropriate feedback based on the user's sports level.

[0055] The providing unit can improve the provision method by reflecting the user's feedback in a specific manner when providing the instruction content. The providing unit improves the provision method by reflecting the user's feedback when providing the instruction content. Feedback includes, for example, survey results, comments, evaluations, etc., but is not limited to these examples. The providing unit provides feedback on the instruction content provided by the user, for example. The providing unit can also improve the provision method based on the user's feedback. Furthermore, the providing unit can analyze the user's feedback and optimize the provision method. In this way, the provision method can be improved by reflecting the user's feedback.

[0056] The providing unit can select the optimal providing method based on the user's geographical location information when providing the instruction content. The providing unit selects the optimal providing method by taking the user's geographical location information into consideration when providing the instruction content. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the providing unit can provide training videos related to that area. Furthermore, if the user is traveling, the providing unit can also provide training videos related to the user's travel destination. Furthermore, if the user is participating in a specific sporting event, the providing unit can also provide training videos related to that event. In this way, the optimal providing method can be selected by taking the user's geographical location information into consideration.

[0057] The providing unit, when providing coaching content, can analyze the user's social media activity in a specific manner and customize the content to be provided. The providing unit, when providing coaching content, analyzes the user's social media activity and customizes the content to be provided. Social media activity includes, for example, but is not limited to, posted content, the number of likes, the number of followers, etc. The providing unit, for example, provides related training videos based on sports videos shared by the user on social media. The providing unit can also analyze the content posted by the user on social media and provide related training videos. Furthermore, the providing unit can provide related training videos by referring to the activity of the user's friends on social media. In this way, the content to be provided can be customized by analyzing the user's social media activity.

[0058] The providing unit can customize the delivery method by reflecting the user's past feedback in a specific manner when providing the instruction content. The providing unit customizes the delivery method by reflecting the user's past feedback when providing the instruction content. Feedback includes, for example, survey results, comments, and ratings, but is not limited to these examples. The providing unit can, for example, suggest an optimal delivery method based on the user's past feedback. The providing unit can also preferentially select a specific delivery method from the user's past feedback. Furthermore, the providing unit can analyze the user's past feedback and improve the delivery method. In this way, the delivery method can be customized by reflecting the user's past feedback.

[0059] The monitoring unit can improve the accuracy of monitoring based on the user's past progress data during monitoring. The monitoring unit can improve the accuracy of monitoring by referring to the user's past progress data during monitoring. The progress data includes, but is not limited to, examples of practice time, achievement level, and feedback. The monitoring unit can correct the current monitoring result based on the user's past progress data, for example. The monitoring unit can also extract specific movement patterns from the user's past progress data to improve the accuracy of monitoring. Furthermore, the monitoring unit can analyze the user's past progress data and optimize the monitoring algorithm. In this way, the accuracy of monitoring can be improved by referring to the user's past progress data.

[0060] The monitoring unit can customize specific monitoring content based on the user's current sports level during monitoring. The monitoring unit customizes monitoring content based on the user's current sports level during monitoring. Sports levels include, but are not limited to, beginner, intermediate, and advanced, for example. The monitoring unit provides appropriate monitoring content according to the user's sports level, for example. The monitoring unit can also provide related training videos based on the user's sports level. Furthermore, the monitoring unit can suggest an optimal training plan based on the user's sports level. This makes it possible to provide appropriate monitoring content based on the user's sports level.

[0061] The monitoring unit can optimize monitoring content based on the user's geographical location information during monitoring. The monitoring unit optimizes monitoring content taking into account the user's geographical location information during monitoring. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the monitoring unit can provide monitoring content related to that area. Furthermore, if the user is traveling, the monitoring unit can provide monitoring content related to the user's travel destination. Furthermore, if the user is participating in a specific sporting event, the monitoring unit can provide monitoring content related to the event. In this way, the monitoring content can be optimized by taking into account the user's geographical location information.

[0062] During monitoring, the monitoring unit can analyze the user's social media activity in a specific manner to customize the monitoring content. During monitoring, the monitoring unit analyzes the user's social media activity to customize the monitoring content. Social media activity includes, for example, but is not limited to, posted content, the number of likes, the number of followers, etc. The monitoring unit can provide related monitoring content, for example, based on sports videos shared by the user on social media. The monitoring unit can also analyze the user's social media posts to provide related monitoring content. Furthermore, the monitoring unit can provide related monitoring content by referring to the activities of the user's friends on social media. In this way, the monitoring content can be customized by analyzing the user's social media activity.

[0063] When updating a training plan, the update unit can optimize the update content based on the user's past progress data. When updating a training plan, the update unit optimizes the update content by referring to the user's past progress data. Progress data includes, but is not limited to, examples of practice time, achievement level, and feedback. The update unit, for example, corrects the current training plan based on the user's past progress data. The update unit can also extract specific movement patterns from the user's past progress data to improve the accuracy of the training plan. Furthermore, the update unit can analyze the user's past progress data and optimize the training plan. In this way, the update content of the training plan can be optimized by referring to the user's past progress data.

[0064] The update unit can customize specific update content based on the user's current sports level when updating the training plan. The update unit customizes update content based on the user's current sports level when updating the training plan. Sports levels include, but are not limited to, beginner, intermediate, and advanced, for example. The update unit provides an appropriate training plan according to the user's sports level, for example. The update unit can also provide related training videos based on the user's sports level. Furthermore, the update unit can suggest an optimal training plan based on the user's sports level. This makes it possible to provide an appropriate training plan based on the user's sports level.

[0065] The update unit can optimize the update content based on the user's geographical location information when updating a training plan. The update unit optimizes the update content by taking into account the user's geographical location information when updating a training plan. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the update unit can provide a training plan related to that area. Furthermore, if the user is traveling, the update unit can provide a training plan related to the travel destination. Furthermore, if the user is participating in a specific sporting event, the update unit can provide a training plan related to the event. In this way, the update content of the training plan can be optimized by taking into account the user's geographical location information.

[0066] When updating a training plan, the update unit can analyze the user's social media activity in a specific manner to customize the update content. When updating a training plan, the update unit analyzes the user's social media activity to customize the update content. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the update unit can provide a related training plan based on sports videos shared by the user on social media. The update unit can also analyze the content of the user's posts on social media to provide a related training plan. Furthermore, the update unit can provide a related training plan based on the activities of the user's friends on social media. In this way, the update content of the training plan can be customized by analyzing the user's social media activity.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The reception unit can analyze the user's past training data and propose an optimal training plan. For example, the reception unit can adjust the current training plan based on the frequency and intensity of the user's past training. The reception unit can also evaluate whether a particular training method was effective based on the user's past training data and prioritize the proposal of effective training methods. Furthermore, the reception unit can monitor the progress of the training plan in real time based on the user's past training data and update the plan as necessary. This makes it possible to provide a more effective training plan by utilizing the user's past training data.

[0069] When analyzing a user's movements, the analysis unit can provide analysis results that take into account the speed and rhythm of the movements. For example, if the user's movements are too fast, the analysis unit slows down the speed and performs a detailed analysis. Also, if the user's movements are not in sync with the rhythm, the analysis unit can identify the discrepancy in rhythm and suggest ways to improve it. Furthermore, the analysis unit can adjust a training plan based on the speed and rhythm of the user's movements and recommend training at an appropriate speed and rhythm. This makes it possible to provide analysis results that take into account the speed and rhythm of the user's movements.

[0070] The reception unit can analyze the user's past training data and propose an optimal training plan. For example, the reception unit can adjust the current training plan based on the frequency and intensity of the user's past training. The reception unit can also evaluate whether a particular training method was effective based on the user's past training data and prioritize the proposal of effective training methods. Furthermore, the reception unit can monitor the progress of the training plan in real time based on the user's past training data and update the plan as necessary. This makes it possible to provide a more effective training plan by utilizing the user's past training data.

[0071] When analyzing a user's movements, the analysis unit can provide analysis results that take into account the speed and rhythm of the movements. For example, if the user's movements are too fast, the analysis unit slows down the speed and performs a detailed analysis. Also, if the user's movements are not in sync with the rhythm, the analysis unit can identify the discrepancy in rhythm and suggest ways to improve it. Furthermore, the analysis unit can adjust a training plan based on the speed and rhythm of the user's movements and recommend training at an appropriate speed and rhythm. This makes it possible to provide analysis results that take into account the speed and rhythm of the user's movements.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The reception unit receives a video from a user. The video sent by the user includes, but is not limited to, the resolution, frame rate, file format, etc. The reception unit can receive videos taken with a smartphone, and can also receive the user's concerns and goals. Step 2: The analysis unit uses motion capture technology to analyze the video received by the reception unit and perform a detailed analysis of the user's movements. Motion capture technologies include, but are not limited to, optical, inertial, and marker motion capture. The analysis unit can perform high-precision analysis using optical motion capture technology, real-time analysis using inertial motion capture technology, and detailed analysis using marker motion capture technology. Step 3: The generation unit uses a generation AI to generate instructional content based on the problems and areas for improvement identified by the analysis unit. The generation AI may include, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit uses the generation AI to identify problems in the user's movements, suggest specific ways to improve them, create an individual training plan based on the user's concerns and goals, and update the training plan in real time according to the user's progress. Step 4: The providing unit provides the instruction content generated by the generating unit to the user. The providing unit can provide the instruction content in a format such as text, video, or audio. For example, the instruction content is provided in a text format, video format, or audio format.

[0074] (Example 2) A sports trainer system according to an embodiment of the present invention analyzes videos sent by a user and provides appropriate instructional content. This system operates when a user films their sports movements with a smartphone and sends the video to a service. A generation AI analyzes the transmitted video and performs a detailed analysis of the user's movements using motion capture technology. The generation AI identifies problems and areas for improvement in the user's movements and generates instructional content appropriate for the user. For example, if a sports movement is not going well, the generation AI identifies the cause and suggests specific ways to improve. Furthermore, the generation AI creates an individualized training plan based on the user's concerns and goals. This training plan is updated in real time based on the user's progress. This allows the user to receive personalized instruction and improve their sports skills. This allows the sports trainer system to provide appropriate instruction to improve the user's sports skills. For example, simply by a user filming their own movements and sending the video, the generation AI performs a detailed analysis and suggests specific ways to improve. Furthermore, an individualized training plan can be created based on the user's concerns and goals and updated in real time based on the user's progress. This allows the user to receive personalized instruction and improve their sports skills.

[0075] A sports trainer system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a video from a user. The video transmitted by the user may include, but is not limited to, a resolution, a frame rate, and a file format. The reception unit may receive, for example, a video shot with a smartphone. The reception unit may also receive the user's concerns and goals. The analysis unit uses motion capture technology to analyze the video received by the reception unit and analyze the user's movements in detail. Examples of motion capture technology include, but are not limited to, optical, inertial, and marker motion capture. The analysis unit may use, for example, optical motion capture technology to analyze the user's movements with high accuracy. The analysis unit may also use inertial motion capture technology to analyze the user's movements in real time. The analysis unit may also use marker motion capture technology to analyze the user's movements in detail. The generation unit uses a generation AI to generate instruction content based on the problems and areas for improvement identified by the analysis unit. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. For example, the generation unit may use the generation AI to identify problems in the user's movements and propose specific improvement methods. The generation unit may also use the generation AI to create an individual training plan based on the user's concerns and goals. Furthermore, the generation unit may also use the generation AI to update the training plan in real time according to the user's progress. The provision unit provides the instruction content generated by the generation unit to the user. The provision unit may provide the instruction content in a format such as text, video, or audio. The provision unit may provide the instruction content in, for example, text format. The provision unit may also provide the instruction content in a video format. Furthermore, the provision unit may also provide the instruction content in an audio format. As a result, the sports trainer system according to the embodiment may provide appropriate instruction to improve the user's sports technique.

[0076] The sports trainer system includes a reception unit that receives the user's specific concerns and goals. The reception unit receives the user's concerns and goals. The concerns and goals include, but are not limited to, technical concerns and mental goals, for example. The reception unit allows the user to input, for example, technical concerns. The reception unit also allows the user to input mental goals. Furthermore, the reception unit can provide an interface for the user to input the user's specific concerns and goals. This makes it possible to provide more appropriate instruction content based on the user's concerns and goals.

[0077] The sports trainer system includes a monitoring unit that monitors the specific progress of the user. The monitoring unit monitors the user's progress. The progress includes, but is not limited to, for example, achievement level, practice time, and feedback. The monitoring unit can monitor, for example, the user's achievement level. The monitoring unit can also monitor the user's practice time. Furthermore, the monitoring unit can monitor the user's feedback. In this way, by monitoring the user's progress, it is possible to appropriately adjust the training plan.

[0078] The sports trainer system includes an update unit that instantly updates the training plan. The update unit updates the training plan in real time. Immediate updating includes, but is not limited to, real time, regular intervals, and the like. The update unit can update the training plan in real time, for example, according to the user's progress. The update unit can also update the training plan at regular intervals. Furthermore, the update unit can update the training plan based on user feedback. This allows the training plan to be updated in real time according to the user's progress.

[0079] The analysis unit can use motion capture technology to analyze the user's movements using specific technical techniques. The analysis unit uses motion capture technology to analyze the user's movements in detail. Motion capture technology includes, but is not limited to, optical, inertial, and marker types. The analysis unit can analyze the user's movements with high precision using, for example, optical motion capture technology. The analysis unit can also analyze the user's movements in real time using inertial motion capture technology. Furthermore, the analysis unit can also analyze the user's movements in detail using marker motion capture technology. As a result, the use of motion capture technology allows the user's movements to be analyzed in detail.

[0080] The generation unit can create an individual training plan based on the user's specific concerns and goals. The generation unit uses a generation AI to create an individual training plan based on the user's concerns and goals. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. The generation unit can use, for example, the generation AI to create an individual training plan based on the user's concerns and goals. The generation unit can also use the generation AI to update the training plan in real time according to the user's progress. This makes it possible to create an individual training plan based on the user's concerns and goals.

[0081] The reception unit can estimate the user's emotions and adjust the timing of video reception based on the estimated user emotions. The reception unit can estimate the user's emotions and adjust the timing of video reception based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if a user is feeling stressed, the reception unit can receive videos at a time when the user is able to relax. Furthermore, if a user is excited, the reception unit can immediately receive videos and provide prompt feedback. Furthermore, if a user is tired, the reception unit can adjust the reception of videos after a rest. In this way, by adjusting the timing of video reception according to the user's emotions, videos can be received at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0082] The reception unit can analyze the user's past video submission history and select the optimal reception method. The reception unit analyzes the user's past video submission history and selects the optimal reception method. Reception methods include, but are not limited to, online forms, emails, apps, etc. For example, the reception unit analyzes the frequency of videos the user has submitted in the past and suggests the optimal timing for submission. The reception unit can also analyze the content of videos the user has submitted in the past and provide related advice. Furthermore, the reception unit can suggest the optimal submission method (audio, text, image, etc.) based on the user's past submission history. In this way, the optimal reception method can be selected by analyzing the user's past video submission history.

[0083] The reception unit may use a specific filtering method based on the user's current athletic level or area of ​​interest when receiving the video. The reception unit may perform filtering based on the user's current athletic level or area of ​​interest when receiving the video. Examples of filtering methods include, but are not limited to, keyword matching and machine learning algorithms. The reception unit may provide appropriate feedback based on the user's athletic level, for example. The reception unit may also preferentially receive related training videos based on the user's area of ​​interest. Furthermore, the reception unit may propose an optimal training plan based on the user's athletic level or area of ​​interest. This allows appropriate feedback to be provided based on the user's athletic level or area of ​​interest.

[0084] The reception unit can select the optimal reception means depending on the user's input method (voice, text, image, etc.) when receiving a video. The reception unit selects the optimal reception means depending on the user's input method when receiving a video. Reception means include, but are not limited to, voice input, text input, image upload, etc. For example, when the user uses voice input, the reception unit can use voice recognition technology to receive the video. Furthermore, when the user uses text input, the reception unit can also use text analysis technology to receive the video. Furthermore, when the user uses image input, the reception unit can also use image recognition technology to receive the video. This makes it possible to select the optimal reception means depending on the user's input method.

[0085] The reception unit can estimate the user's emotions and determine the priority of the videos to be received based on the estimated user emotions. The reception unit can estimate the user's emotions and determine the priority of the videos to be received based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and self-reporting. For example, when the user is feeling stressed, the reception unit can prioritize receiving videos and provide quick feedback. Furthermore, when the user is relaxed, the reception unit can also prioritize receiving videos as an emergency response when the user is excited. This allows for more appropriate feedback by determining the priority of videos according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] When receiving videos, the reception unit can prioritize receiving highly relevant videos based on the user's geographical location information. When receiving videos, the reception unit prioritizes receiving highly relevant videos taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the reception unit can prioritize receiving training videos related to that area. Furthermore, if the user is traveling, the reception unit can also prioritize receiving training videos related to the user's travel destination. Furthermore, if the user is participating in a specific sporting event, the reception unit can prioritize receiving training videos related to that event. In this way, highly relevant videos can be prioritized by taking into account the user's geographical location information.

[0087] The reception unit can analyze the user's social media activity using a specific method when receiving a video and receive related videos. The reception unit can analyze the user's social media activity when receiving a video and receive related videos. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, the number of followers, etc. The reception unit, for example, preferentially receives sports videos shared by the user on social media. The reception unit can also analyze the content of the user's posts on social media and receive related training videos. Furthermore, the reception unit can receive related videos by referring to the activities of the user's friends on social media. In this way, related videos can be received by analyzing the user's social media activity.

[0088] The reception unit can customize the reception method by reflecting the user's past feedback in a specific manner when receiving a video. The reception unit customizes the reception method by reflecting the user's past feedback when receiving a video. Feedback includes, but is not limited to, for example, survey results, comments, and ratings. The reception unit can, for example, suggest an optimal reception method based on the user's past feedback. The reception unit can also preferentially select a specific reception method based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and improve the reception method. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0089] The analysis unit can estimate the user's emotions using a specific method and adjust the analysis presentation method based on the estimated user's emotions. The analysis unit can estimate the user's emotions and adjust the analysis presentation method based on the estimated user's emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. This allows for adjusting the analysis presentation method according to the user's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] The analysis unit can adjust the level of detail of the analysis based on the specific importance of the action during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the action during analysis. The importance of the action includes, for example, athletic level, technical difficulty, etc., but is not limited to these examples. For example, the analysis unit performs a detailed analysis for an important action. The analysis unit can also perform a simplified analysis for an action with low importance. Furthermore, the analysis unit can gradually adjust the level of detail of the analysis according to the importance of the action. In this way, by adjusting the level of detail of the analysis based on the importance of the action, more appropriate analysis results can be provided.

[0091] The analysis unit can apply different analysis algorithms depending on the specific category of the movement during analysis. The analysis unit applies different analysis algorithms depending on the category of the movement during analysis. Movement categories include, for example, jumping, throwing, running, etc., but are not limited to these examples. For example, the analysis unit applies an analysis algorithm dedicated to running to a running movement. Furthermore, the analysis unit can also apply an analysis algorithm dedicated to swinging to a swinging movement. Furthermore, the analysis unit can also apply an analysis algorithm dedicated to jumping to a jumping movement. In this way, by applying different analysis algorithms depending on the category of the movement, more appropriate analysis results can be provided.

[0092] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results using a specific method. During analysis, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. Past analysis results include, but are not limited to, database reference, history analysis, etc. For example, the analysis unit corrects the current analysis results based on the user's past analysis results. The analysis unit can also extract specific behavior patterns from the user's past analysis results to improve the accuracy of the analysis. Furthermore, the analysis unit can analyze the user's past analysis results and optimize the analysis algorithm. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0093] The analysis unit can estimate the user's emotion using a specific method and adjust the length of the analysis based on the estimated user emotion. The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user emotion. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. This allows for adjusting the length of the analysis according to the user's emotion to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0094] During analysis, the analysis unit can determine the priority of analysis based on the specific submission time of the motion. During analysis, the analysis unit determines the priority of analysis based on the submission time of the motion. The submission time includes, for example, but is not limited to, the submission date and submission time. For example, the analysis unit prioritizes analysis of the most recently submitted motion. The analysis unit can also analyze older submitted motions later. Furthermore, the analysis unit can gradually adjust the priority of analysis depending on the submission time. In this way, by determining the priority of analysis based on the submission time of the motion, more appropriate analysis results can be provided.

[0095] The analysis unit can adjust the order of analysis based on the specific relevance of the actions during analysis. The analysis unit adjusts the order of analysis based on the relevance of the actions during analysis. The relevance of the actions includes, but is not limited to, technical relevance, temporal relevance, and the like. For example, the analysis unit prioritizes analysis of highly relevant actions. The analysis unit can also postpone analysis of less relevant actions. Furthermore, the analysis unit can gradually adjust the order of analysis according to the relevance of the actions. In this way, by adjusting the order of analysis based on the relevance of the actions, more appropriate analysis results can be provided.

[0096] The analysis unit can adjust the use of analytical terminology during analysis according to the user's specific level of expertise. The analysis unit can adjust the use of analytical terminology during analysis according to the user's level of expertise. Expertise levels include, but are not limited to, beginner, intermediate, and advanced levels. For example, the analysis unit can explain the analysis results in simple terms to a user with little expertise. The analysis unit can also explain the analysis results using detailed terminology to a user with extensive expertise. Furthermore, the analysis unit can gradually adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to provide more appropriate analysis results by adjusting the use of analytical terminology according to the user's level of expertise.

[0097] The generation unit can estimate the user's emotions using a specific method and adjust the way in which the instruction content is presented based on the estimated user's emotions. The generation unit can estimate the user's emotions using a generation AI and adjust the way in which the instruction content is presented based on the estimated user's emotions. Emotion estimation can include, but is not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is nervous, the generation unit can provide simple, highly visible instruction content. Furthermore, if the user is relaxed, the generation unit can provide detailed instruction content. Furthermore, if the user is excited, the generation unit can provide instruction content with a visually stimulating effect. This allows for more appropriate instruction content to be provided by adjusting the way in which the instruction content is presented 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 can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] The generation unit can adjust the level of detail of the instruction content based on the specific importance of the movement when generating the instruction content. The generation unit uses the generation AI to adjust the level of detail of the instruction content based on the importance of the movement when generating the instruction content. The importance of the movement includes, but is not limited to, for example, the competitive level and technical difficulty. For example, the generation unit can provide detailed instruction content for important movements. The generation unit can also provide simplified instruction content for movements with low importance. Furthermore, the generation unit can gradually adjust the level of detail of the instruction content according to the importance of the movement. In this way, by adjusting the level of detail of the instruction content based on the importance of the movement, more appropriate instruction content can be provided.

[0099] The generation unit can apply different teaching algorithms depending on the specific category of the movement when generating the teaching content. The generation unit uses the generation AI to apply different teaching algorithms depending on the category of the movement when generating the teaching content. Movement categories include, but are not limited to, for example, jumping, throwing, and running. For example, the generation unit applies a running-specific teaching algorithm to a running movement. Furthermore, the generation unit can also apply a swing-specific teaching algorithm to a swing movement. Furthermore, the generation unit can also apply a jump-specific teaching algorithm to a jump movement. In this way, by applying different teaching algorithms depending on the category of the movement, more appropriate teaching content can be provided.

[0100] When generating instruction content, the generation unit can improve the accuracy of the instruction content by referring to the user's past instruction results in a specific manner. When generating instruction content, the generation unit uses the generation AI to improve the accuracy of the instruction content by referring to the user's past instruction results. Past instruction results include, but are not limited to, database reference and history analysis, for example. The generation unit corrects the current instruction content based on the user's past instruction results, for example. The generation unit can also extract specific movement patterns from the user's past instruction results to improve the accuracy of the instruction content. Furthermore, the generation unit can analyze the user's past instruction results and optimize the instruction algorithm. In this way, the accuracy of the instruction content can be improved by referring to the user's past instruction results.

[0101] The generation unit can estimate the user's emotion using a specific method and adjust the length of the instruction content based on the estimated user emotion. The generation unit can estimate the user's emotion using a generation AI and adjust the length of the instruction content based on the estimated user emotion. Emotion estimation can include, but is not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is in a hurry, the generation unit can provide short and concise instruction content. Furthermore, if the user is relaxed, the generation unit can provide detailed instruction content. Furthermore, if the user is excited, the generation unit can provide instruction content with visually stimulating effects. This allows for more appropriate instruction content to be provided by adjusting the length of the instruction content according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] The generation unit, when generating the instruction content, can determine the priority of the instruction content based on the specific submission time of the action. The generation unit, using the generation AI, determines the priority of the instruction content based on the submission time of the action when generating the instruction content. The submission time includes, for example, but is not limited to, the submission date and the submission time. For example, the generation unit gives priority to the most recently submitted action. The generation unit can also postpone the instruction of an action that was submitted earlier. Furthermore, the generation unit can gradually adjust the priority of the instruction content according to the submission time. In this way, by determining the priority of the instruction content based on the submission time of the action, more appropriate instruction content can be provided.

[0103] The generation unit can adjust the order of the instruction contents based on the specific relevance of the actions when generating the instruction contents. The generation unit uses the generation AI to adjust the order of the instruction contents based on the relevance of the actions when generating the instruction contents. The relevance of the actions includes, but is not limited to, for example, technical relevance and temporal relevance. For example, the generation unit prioritizes instruction of highly relevant actions. The generation unit can also postpone instruction of less relevant actions. Furthermore, the generation unit can gradually adjust the order of the instruction contents according to the relevance of the actions. In this way, by adjusting the order of the instruction contents based on the relevance of the actions, more appropriate instruction contents can be provided.

[0104] The generation unit can adjust the use of technical terms in the instruction content according to the user's specific level of expertise when generating the instruction content. The generation unit uses the generation AI to adjust the use of technical terms in the instruction content according to the user's level of expertise when generating the instruction content. Expertise levels include, but are not limited to, beginner, intermediate, and advanced, for example. The generation unit, for example, explains the instruction content in simple terms to a user with little expertise. The generation unit can also explain the instruction content using detailed technical terms to a user with extensive expertise. Furthermore, the generation unit can gradually adjust the way the instruction content is expressed according to the user's level of expertise. This makes it possible to provide more appropriate instruction content by adjusting the use of technical terms in the instruction content according to the user's level of expertise.

[0105] The providing unit can estimate the user's emotions using a specific method and adjust the method of providing instruction content based on the estimated user's emotions. The providing unit can estimate the user's emotions and adjust the method of providing instruction content based on the estimated user's emotions. Examples of emotion estimation include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is nervous, the providing unit can provide a simple and highly visible method of providing instruction content. Furthermore, if the user is relaxed, the providing unit can provide a method of providing instruction content that includes detailed information. Furthermore, if the user is excited, the providing unit can provide a method of providing instruction content that adds a visually stimulating effect. This allows for more appropriate instruction content to be provided by adjusting the method of providing instruction content according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0106] The providing unit can optimize the method of providing instruction content by referring to the user's past feedback in a specific manner when providing instruction content. The providing unit optimizes the method of providing instruction content by referring to the user's past feedback when providing instruction content. Feedback includes, but is not limited to, for example, survey results, comments, and ratings. The providing unit can, for example, suggest an optimal method of providing instruction content based on feedback provided by the user in the past. The providing unit can also preferentially select a specific method of providing instruction content based on the user's past feedback. Furthermore, the providing unit can analyze the user's past feedback and improve the method of providing instruction content. This makes it possible to optimize the method of providing instruction content by referring to the user's past feedback.

[0107] The providing unit can customize specific content to be provided based on the user's current sports level when providing instruction content. The providing unit customizes the content to be provided based on the user's current sports level when providing instruction content. Sports levels include, but are not limited to, beginner, intermediate, and advanced, for example. The providing unit provides appropriate feedback according to the user's sports level, for example. The providing unit can also provide related training videos based on the user's sports level. Furthermore, the providing unit can suggest an optimal training plan based on the user's sports level. This makes it possible to provide appropriate feedback based on the user's sports level.

[0108] The providing unit can improve the provision method by reflecting the user's feedback in a specific manner when providing the instruction content. The providing unit improves the provision method by reflecting the user's feedback when providing the instruction content. Feedback includes, for example, survey results, comments, evaluations, etc., but is not limited to these examples. The providing unit provides feedback on the instruction content provided by the user, for example. The providing unit can also improve the provision method based on the user's feedback. Furthermore, the providing unit can analyze the user's feedback and optimize the provision method. In this way, the provision method can be improved by reflecting the user's feedback.

[0109] The providing unit can estimate the user's emotions using a specific method and determine the order in which to provide instructional content based on the estimated user's emotions. The providing unit can estimate the user's emotions and determine the order in which to provide instructional content based on the estimated user's emotions. Examples of emotion estimation include, but are not limited to, facial expression recognition, voice analysis, and self-reporting. For example, when the user is nervous, the providing unit can prioritize providing important instructional content. Furthermore, when the user is relaxed, the providing unit can also prioritize providing detailed instructional content in an orderly manner. Furthermore, when the user is excited, the providing unit can prioritize providing visually stimulating instructional content. In this way, by determining the order in which to provide instructional content according to the user's emotions, more appropriate instructional content can be provided. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0110] The providing unit can select the optimal providing method based on the user's geographical location information when providing the instruction content. The providing unit selects the optimal providing method by taking the user's geographical location information into consideration when providing the instruction content. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the providing unit can provide training videos related to that area. Furthermore, if the user is traveling, the providing unit can also provide training videos related to the user's travel destination. Furthermore, if the user is participating in a specific sporting event, the providing unit can also provide training videos related to that event. In this way, the optimal providing method can be selected by taking the user's geographical location information into consideration.

[0111] The providing unit, when providing coaching content, can analyze the user's social media activity in a specific manner and customize the content to be provided. The providing unit, when providing coaching content, analyzes the user's social media activity and customizes the content to be provided. Social media activity includes, for example, but is not limited to, posted content, the number of likes, the number of followers, etc. The providing unit, for example, provides related training videos based on sports videos shared by the user on social media. The providing unit can also analyze the content posted by the user on social media and provide related training videos. Furthermore, the providing unit can provide related training videos by referring to the activity of the user's friends on social media. In this way, the content to be provided can be customized by analyzing the user's social media activity.

[0112] The providing unit can customize the delivery method by reflecting the user's past feedback in a specific manner when providing the instruction content. The providing unit customizes the delivery method by reflecting the user's past feedback when providing the instruction content. Feedback includes, for example, survey results, comments, and ratings, but is not limited to these examples. The providing unit can, for example, suggest an optimal delivery method based on the user's past feedback. The providing unit can also preferentially select a specific delivery method from the user's past feedback. Furthermore, the providing unit can analyze the user's past feedback and improve the delivery method. In this way, the delivery method can be customized by reflecting the user's past feedback.

[0113] The monitoring unit can estimate the user's emotions using a specific method and adjust the monitoring method based on the estimated user emotions. The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is nervous, the monitoring unit can provide a simple and highly visible monitoring method. Furthermore, if the user is relaxed, the monitoring unit can provide a detailed monitoring method. Furthermore, if the user is excited, the monitoring unit can provide a monitoring method with a visually stimulating effect. This allows for more appropriate monitoring by adjusting the monitoring method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0114] The monitoring unit can improve the accuracy of monitoring based on the user's past progress data during monitoring. The monitoring unit can improve the accuracy of monitoring by referring to the user's past progress data during monitoring. The progress data includes, but is not limited to, examples of practice time, achievement level, and feedback. The monitoring unit can correct the current monitoring result based on the user's past progress data, for example. The monitoring unit can also extract specific movement patterns from the user's past progress data to improve the accuracy of monitoring. Furthermore, the monitoring unit can analyze the user's past progress data and optimize the monitoring algorithm. In this way, the accuracy of monitoring can be improved by referring to the user's past progress data.

[0115] The monitoring unit can customize specific monitoring content based on the user's current sports level during monitoring. The monitoring unit customizes monitoring content based on the user's current sports level during monitoring. Sports levels include, but are not limited to, beginner, intermediate, and advanced, for example. The monitoring unit provides appropriate monitoring content according to the user's sports level, for example. The monitoring unit can also provide related training videos based on the user's sports level. Furthermore, the monitoring unit can suggest an optimal training plan based on the user's sports level. This makes it possible to provide appropriate monitoring content based on the user's sports level.

[0116] The monitoring unit can estimate the user's emotions using a specific method and adjust the monitoring frequency based on the estimated user emotions. The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is nervous, the monitoring unit can monitor frequently to provide a sense of security. Also, if the user is relaxed, the monitoring unit can monitor at a normal frequency. Furthermore, if the user is excited, the monitoring unit can monitor frequently as an emergency response. This allows for more appropriate monitoring by adjusting the monitoring frequency according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0117] The monitoring unit can optimize monitoring content based on the user's geographical location information during monitoring. The monitoring unit optimizes monitoring content taking into account the user's geographical location information during monitoring. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the monitoring unit can provide monitoring content related to that area. Furthermore, if the user is traveling, the monitoring unit can provide monitoring content related to the user's travel destination. Furthermore, if the user is participating in a specific sporting event, the monitoring unit can provide monitoring content related to the event. In this way, the monitoring content can be optimized by taking into account the user's geographical location information.

[0118] During monitoring, the monitoring unit can analyze the user's social media activity in a specific manner to customize the monitoring content. During monitoring, the monitoring unit analyzes the user's social media activity to customize the monitoring content. Social media activity includes, for example, but is not limited to, posted content, the number of likes, the number of followers, etc. The monitoring unit can provide related monitoring content, for example, based on sports videos shared by the user on social media. The monitoring unit can also analyze the user's social media posts to provide related monitoring content. Furthermore, the monitoring unit can provide related monitoring content by referring to the activities of the user's friends on social media. In this way, the monitoring content can be customized by analyzing the user's social media activity.

[0119] The update unit can estimate the user's emotions using a specific method and adjust the training plan update frequency based on the estimated user emotions. The update unit can estimate the user's emotions and adjust the training plan update frequency based on the estimated user emotions. Emotion estimation can include, but is not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is nervous, the update unit can frequently update the training plan to provide a sense of security. If the user is relaxed, the update unit can also update the training plan at a normal frequency. Furthermore, if the user is excited, the update unit can frequently update the training plan as an emergency response. This allows for adjusting the training plan update frequency according to the user's emotions, thereby providing a more appropriate training plan. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0120] When updating a training plan, the update unit can optimize the update content based on the user's past progress data. When updating a training plan, the update unit optimizes the update content by referring to the user's past progress data. Progress data includes, but is not limited to, examples of practice time, achievement level, and feedback. The update unit, for example, corrects the current training plan based on the user's past progress data. The update unit can also extract specific movement patterns from the user's past progress data to improve the accuracy of the training plan. Furthermore, the update unit can analyze the user's past progress data and optimize the training plan. In this way, the update content of the training plan can be optimized by referring to the user's past progress data.

[0121] The update unit can customize specific update content based on the user's current sports level when updating the training plan. The update unit customizes update content based on the user's current sports level when updating the training plan. Sports levels include, but are not limited to, beginner, intermediate, and advanced, for example. The update unit provides an appropriate training plan according to the user's sports level, for example. The update unit can also provide related training videos based on the user's sports level. Furthermore, the update unit can suggest an optimal training plan based on the user's sports level. This makes it possible to provide an appropriate training plan based on the user's sports level.

[0122] The update unit can estimate the user's emotions using a specific method and determine the training plan update order based on the estimated user emotions. The update unit estimates the user's emotions and determines the training plan update order based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and self-reporting. For example, if the user is nervous, the update unit can prioritize updating important training plans. Furthermore, if the user is relaxed, the update unit can also prioritize updating detailed training plans. Furthermore, if the user is excited, the update unit can prioritize updating visually stimulating training plans. This allows for the provision of more appropriate training plans by determining the training plan update order based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0123] The update unit can optimize the update content based on the user's geographical location information when updating a training plan. The update unit optimizes the update content by taking into account the user's geographical location information when updating a training plan. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the update unit can provide a training plan related to that area. Furthermore, if the user is traveling, the update unit can provide a training plan related to the travel destination. Furthermore, if the user is participating in a specific sporting event, the update unit can provide a training plan related to the event. In this way, the update content of the training plan can be optimized by taking into account the user's geographical location information.

[0124] When updating a training plan, the update unit can analyze the user's social media activity in a specific manner to customize the update content. When updating a training plan, the update unit analyzes the user's social media activity to customize the update content. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. For example, the update unit can provide a related training plan based on sports videos shared by the user on social media. The update unit can also analyze the content of the user's posts on social media to provide a related training plan. Furthermore, the update unit can provide a related training plan based on the activities of the user's friends on social media. In this way, the update content of the training plan can be customized by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives a video sent from a user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the video using motion capture technology. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates instruction content using a generation AI. For example, the provision unit is realized by the output device 40 of the smart device 14 and provides the generated instruction content to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a video transmitted from a user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the video using motion capture technology. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates instruction content using a generation AI. For example, the provision unit is realized by the speaker 240 of the smart glasses 214 and provides the generated instruction content to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives a video sent from a user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the video using motion capture technology. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates instruction content using a generation AI. For example, the provision unit is realized by the display 343 of the headset-type terminal 314 and provides the generated instruction content to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives a video sent from a user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the video using motion capture technology. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates instruction content using a generation AI. For example, the provision unit is realized by the speaker 240 of the robot 414 and provides the generated instruction content to the user.

[0125] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0126] The reception unit can analyze the user's past training data and propose an optimal training plan. For example, the reception unit can adjust the current training plan based on the frequency and intensity of the user's past training. The reception unit can also evaluate whether a particular training method was effective based on the user's past training data and prioritize the proposal of effective training methods. Furthermore, the reception unit can monitor the progress of the training plan in real time based on the user's past training data and update the plan as necessary. This makes it possible to provide a more effective training plan by utilizing the user's past training data.

[0127] When analyzing a user's movements, the analysis unit can provide analysis results that take into account the speed and rhythm of the movements. For example, if the user's movements are too fast, the analysis unit slows down the speed and performs a detailed analysis. Also, if the user's movements are not in sync with the rhythm, the analysis unit can identify the discrepancy in rhythm and suggest ways to improve it. Furthermore, the analysis unit can adjust a training plan based on the speed and rhythm of the user's movements and recommend training at an appropriate speed and rhythm. This makes it possible to provide analysis results that take into account the speed and rhythm of the user's movements.

[0128] The generation unit can estimate the user's emotions and adjust the difficulty of the training plan based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can lower the difficulty of the training plan and change it to a more relaxing content. Also, if the user is highly motivated, the generation unit can increase the difficulty of the training plan and change it to a more challenging content. Furthermore, if the user is tired, the generation unit can lighten the content of the training plan and change it to a content that encourages rest. In this way, the difficulty of the training plan can be adjusted according to the user's emotions.

[0129] The providing unit can estimate the user's emotion and adjust the form of feedback based on the estimated user's emotion. For example, if the user is nervous, the providing unit can provide simple, highly visible feedback. If the user is relaxed, the providing unit can provide detailed feedback. Furthermore, if the user is excited, the providing unit can provide feedback with visually stimulating effects. In this way, by adjusting the form of feedback according to the user's emotion, more appropriate feedback can be provided.

[0130] The monitoring unit can estimate the user's emotions and adjust the frequency of monitoring based on the estimated user emotions. For example, if the user is nervous, monitoring can be performed frequently to provide a sense of security. If the user is relaxed, monitoring can be performed at a normal frequency. Furthermore, if the user is excited, monitoring can be performed frequently as an emergency response. In this way, by adjusting the monitoring frequency according to the user's emotions, more appropriate monitoring can be provided.

[0131] The update unit can estimate the user's emotions and adjust the training plan update frequency based on the estimated user emotions. For example, if the user is nervous, the training plan can be updated frequently to provide a sense of security. If the user is relaxed, the training plan can be updated at a normal frequency. Furthermore, if the user is excited, the training plan can be updated frequently as an emergency response. In this way, by adjusting the training plan update frequency according to the user's emotions, a more appropriate training plan can be provided.

[0132] The reception unit can analyze the user's past training data and propose an optimal training plan. For example, the reception unit can adjust the current training plan based on the frequency and intensity of the user's past training. The reception unit can also evaluate whether a particular training method was effective based on the user's past training data and prioritize the proposal of effective training methods. Furthermore, the reception unit can monitor the progress of the training plan in real time based on the user's past training data and update the plan as necessary. This makes it possible to provide a more effective training plan by utilizing the user's past training data.

[0133] When analyzing a user's movements, the analysis unit can provide analysis results that take into account the speed and rhythm of the movements. For example, if the user's movements are too fast, the analysis unit slows down the speed and performs a detailed analysis. Also, if the user's movements are not in sync with the rhythm, the analysis unit can identify the discrepancy in rhythm and suggest ways to improve it. Furthermore, the analysis unit can adjust a training plan based on the speed and rhythm of the user's movements and recommend training at an appropriate speed and rhythm. This makes it possible to provide analysis results that take into account the speed and rhythm of the user's movements.

[0134] The generation unit can estimate the user's emotions and adjust the difficulty of the training plan based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can lower the difficulty of the training plan and change it to a more relaxing content. Also, if the user is highly motivated, the generation unit can increase the difficulty of the training plan and change it to a more challenging content. Furthermore, if the user is tired, the generation unit can lighten the content of the training plan and change it to a content that encourages rest. In this way, the difficulty of the training plan can be adjusted according to the user's emotions.

[0135] The providing unit can estimate the user's emotion and adjust the form of feedback based on the estimated user's emotion. For example, if the user is nervous, the providing unit can provide simple, highly visible feedback. If the user is relaxed, the providing unit can provide detailed feedback. Furthermore, if the user is excited, the providing unit can provide feedback with visually stimulating effects. In this way, by adjusting the form of feedback according to the user's emotion, more appropriate feedback can be provided.

[0136] The processing flow of the second embodiment will be briefly explained below.

[0137] Step 1: The reception unit receives a video from a user. The video sent by the user includes, but is not limited to, the resolution, frame rate, file format, etc. The reception unit can receive videos taken with a smartphone, and can also receive the user's concerns and goals. Step 2: The analysis unit uses motion capture technology to analyze the video received by the reception unit and perform a detailed analysis of the user's movements. Motion capture technologies include, but are not limited to, optical, inertial, and marker motion capture. The analysis unit can perform high-precision analysis using optical motion capture technology, real-time analysis using inertial motion capture technology, and detailed analysis using marker motion capture technology. Step 3: The generation unit uses a generation AI to generate instructional content based on the problems and areas for improvement identified by the analysis unit. The generation AI may include, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit uses the generation AI to identify problems in the user's movements, suggest specific ways to improve them, create an individual training plan based on the user's concerns and goals, and update the training plan in real time according to the user's progress. Step 4: The providing unit provides the instruction content generated by the generating unit to the user. The providing unit can provide the instruction content in a format such as text, video, or audio. For example, the instruction content is provided in a text format, video format, or audio format.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0143] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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).

[0195] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0196] 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."

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0208] 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.

[0209] [Explanation of symbols]

[0210] 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 reception unit that receives videos from users; an analysis unit that analyzes the video received by the reception unit and analyzes the user's movements using a specific technical method using motion capture technology; a generation unit that generates guidance content based on the problems and improvements identified by the analysis unit; a providing unit that provides the instruction content generated by the generating unit to the user. A system characterized by:

2. Equipped with a reception section that accepts specific concerns and goals of users 2. The system of claim 1.

3. Equipped with a monitoring unit that monitors the specific progress of the user 2. The system of claim 1.

4. Equipped with an update section that instantly updates training plans 2. The system of claim 1.

5. The analysis unit Using motion capture technology, we analyze user movements using specific technical methods.

2. The system of claim 1.

6. The generation unit Create personalized training plans based on the user's specific concerns and goals 2. The system of claim 1.

7. The reception unit Estimate user emotions using a specific method and adjust the timing of video reception based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past video submission history and select a specific reception method 2. The system of claim 1.

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