system

Abstract

The system according to the embodiment aims to provide appropriate advice to a user to efficiently improve his / her exercise or sports technique. [Solution] A system according to an embodiment includes a posting unit, an uploading unit, an analysis unit, and an advice unit. The posting unit posts professional advice videos. The uploading unit allows users to upload their own videos. The analysis unit analyzes the videos uploaded by the uploading unit. The advice unit provides advice based on the results of the analysis by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult for users to obtain appropriate advice for efficiently improving their exercise or sports skills.

[0005] The system according to the embodiment aims to provide appropriate advice to a user to efficiently improve his / her exercise or sports technique. [Means for solving the problem]

[0006] The system according to the embodiment includes a posting unit, an uploading unit, an analysis unit, and an advice unit. The posting unit posts professional advice videos. The uploading unit allows users to upload their own videos. The analysis unit analyzes the videos uploaded by the uploading unit. The advice unit provides advice based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate advice to the user to efficiently improve his / her exercise or sports technique. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An exercise support system according to an embodiment of the present invention provides a platform where exercise and sports professionals post advice videos, allowing users to watch and learn from those videos. The exercise support system includes a posting unit where professionals post advice videos, an uploading unit where users upload their own exercise and sports videos, an analysis unit (including AI processing) that analyzes the uploaded videos, and an advice unit (including AI processing) that provides advice based on the analysis results. For example, videos provided by professionals with specialized knowledge, such as basketball shooting form and correct yoga poses, are included. Users can upload videos of their exercise and sports performance to the platform. For example, videos of various exercises and sports, such as running form and golf swing, are eligible. Uploaded videos are automatically analyzed by AI. The AI ​​analyzes the movements in the videos to identify areas where the user is doing well and areas where they are not. For example, analyzing running form evaluates the foot's landing position and arm swing and identifies areas for improvement. The system also includes a function for providing advice based on the analysis results. The AI ​​provides specific advice to users based on the analysis results. For example, in golf swing analysis, advice is provided regarding swing trajectory and body movement. This advice provides users with specific guidelines for improving their performance. In this way, the present invention provides a platform where users can watch professional advice videos and receive AI analysis and advice on their performance. This allows users to efficiently improve their exercise or sports skills. In this way, the exercise support system allows users to improve their exercise or sports skills while receiving professional advice.

[0029] An exercise support system according to an embodiment includes a posting unit, an uploading unit, an analysis unit, and an advice unit. The posting unit posts professional advice videos. Professional advice videos include, but are not limited to, fields such as sports, business, and education. The posting unit can post videos provided by professionals with specialized knowledge, such as basketball shooting form and correct yoga poses. The posting unit can also have a function for the professionals to filter the posted content based on their field of expertise. The uploading unit allows users to upload their own exercise or sports videos. User videos may include, but are not limited to, resolution, length, and content. The uploading unit can upload videos of various exercises and sports, such as running form and golf swing. The uploading unit can also have a function for estimating a user's emotions and adjusting the timing of video uploads based on the estimated emotions. The analysis unit analyzes the videos uploaded by the uploading unit. Analysis may include, but is not limited to, motion analysis, audio analysis, and facial expression analysis. For example, in analyzing a running form, the analysis unit evaluates the landing position of the feet and the swing of the arms, and points out areas for improvement. The analysis unit may also have a function of estimating the user's emotions and adjusting the accuracy of the analysis based on the estimated emotions. The advice unit provides advice based on the results of the analysis by the analysis unit. The advice may include, but is not limited to, text, audio, video, and the like. For example, in analyzing a golf swing, the advice unit provides advice regarding the swing trajectory and body movement. The advice unit may also have a function of estimating the user's emotions and adjusting the way the advice is presented based on the estimated emotions. In this way, the exercise support system according to the embodiment allows the user to improve their exercise or sports technique while receiving advice from a professional.

[0030] The device may include a posture estimation unit that uses a motion analysis algorithm. The posture estimation unit estimates posture using, for example, a motion analysis algorithm. Examples of the motion analysis algorithm include, but are not limited to, machine learning algorithms and deep learning algorithms. The posture estimation unit can analyze the user's posture in real time using, for example, a machine learning algorithm. The posture estimation unit can also estimate the user's posture with high accuracy using a deep learning algorithm. Furthermore, the posture estimation unit can analyze the user's posture in three dimensions using a motion analysis algorithm. For example, the posture estimation unit receives video data captured by a camera as input and estimates posture using a motion analysis algorithm. This allows posture to be estimated using the motion analysis algorithm. Some or all of the above-described processing in the posture estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the posture estimation unit may input video data captured by a camera to a generation AI and cause the generation AI to estimate posture.

[0031] The platform may include a certification unit that manages a professional certification system. The certification unit, for example, manages the professional certification system. The certification system may include, but is not limited to, evaluation criteria and a certification process. For example, the certification unit may set evaluation criteria for professionals and certify them based on those criteria. The certification unit may also manage the professional certification process to ensure the transparency of the certification. Furthermore, the certification unit may publish the results of professional certification so that users can obtain reliable information. For example, the certification unit may certify professionals based on their achievements and evaluations and publish the results on the platform. This allows the professional certification system to be managed. Some or all of the above-described processing in the certification unit may be performed using AI, for example, or may be performed without using AI. For example, the certification unit may input the professional's achievement data into a generation AI and have the generation AI set the certification criteria.

[0032] The video system may include a privacy protection unit that protects the privacy of a user's video. The privacy protection unit protects the privacy of the user's video, for example. Privacy protection includes, but is not limited to, data encryption, access control, and the like. For example, the privacy protection unit can encrypt the user's video data to prevent third parties from accessing it. The privacy protection unit can also control access to the user's video data to ensure that only authorized users can access it. Furthermore, the privacy protection unit can anonymize the user's video data to prevent personal information from being leaked. For example, the privacy protection unit protects the user's privacy by encrypting the video data, controlling access, and anonymizing it. This allows the privacy of the user's video to be protected. Some or all of the above-described processing in the privacy protection unit may be performed using, or without, AI. For example, the privacy protection unit may input video data to a generation AI and have the generation AI encrypt and anonymize the data.

[0033] When posting, the posting unit can analyze the Professional's past posting history and select the optimal posting method. The posting unit, for example, analyzes the Professional's past posting history and selects the optimal posting method. The past posting history includes, for example, but is not limited to, the frequency of posts, content, and reactions. The posting unit, for example, can analyze the style of posts that the Professional has previously received high ratings for and recommend a similar style. The posting unit can also suggest optimal tags by referring to tags and keywords used by the Professional in the past. Furthermore, the posting unit can analyze the Professional's past posting frequency and suggest the optimal posting timing. For example, the posting unit analyzes the Professional's past posting data and selects the optimal posting method. This makes it possible to select the optimal posting method by analyzing the Professional's past posting history. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can input the Professional's past posting data into a generation AI and cause the generation AI to select the optimal posting method.

[0034] The posting unit can filter the posted content based on the professional's field of expertise when posting. The posting unit, for example, filters the posted content based on the professional's field of expertise. Fields of expertise include, but are not limited to, medicine, education, and sports. For example, if the professional is a basketball expert, the posting unit can prioritize displaying posted content related to basketball. Furthermore, if the professional is a yoga expert, the posting unit can prioritize displaying posted content related to yoga. Furthermore, if the professional is a running expert, the posting unit can prioritize displaying posted content related to running. For example, the posting unit analyzes the professional's field of expertise data and filters the posted content. This makes it possible to filter the posted content based on the professional's field of expertise. Some or all of the above-described processing by the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can input the professional's field of expertise data to a generation AI and cause the generation AI to filter the posted content.

[0035] When posting, the posting unit can prioritize displaying highly relevant posts by taking into consideration the geographical location information of the professional. The posting unit, for example, prioritizes displaying highly relevant posts by taking into consideration the geographical location information of the professional. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the professional is in a specific area, the posting unit can prioritize displaying posts related to that area. Furthermore, if the professional is traveling, the posting unit can prioritize displaying posts related to the travel destination. Furthermore, if the professional is at home, the posting unit can prioritize displaying posts related to the area around the professional's home. For example, the posting unit analyzes the geographical location data of the professional and prioritizes displaying highly relevant posts. This makes it possible to prioritize displaying highly relevant posts by taking into consideration the geographical location information of the professional. Some or all of the above-described processing by the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can input the geographical location data of the professional to a generation AI and cause the generation AI to select highly relevant posts.

[0036] The posting unit can analyze the social media activity of the professional at the time of posting and recommend related posts. The posting unit, for example, analyzes the social media activity of the professional and recommends related posts. Social media activity includes, for example, but is not limited to, the content of the post, reactions, and the number of followers. The posting unit, for example, can analyze the content shared by the professional on social media and recommend related posts. The posting unit can also recommend related posts based on the content posted by accounts the professional follows on social media. The posting unit can also analyze the activities of groups in which the professional participates on social media and recommend related posts. For example, the posting unit can analyze the social media data of the professional and recommend related posts. This makes it possible to analyze the social media activity of the professional and recommend related posts. Some or all of the above-described processing by the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can input the social media data of the professional into a generation AI and cause the generation AI to recommend related posts.

[0037] The upload unit can analyze the user's past upload history and select the optimal upload method. The upload unit, for example, analyzes the user's past upload history and selects the optimal upload method. The past upload history includes, for example, upload frequency, content, reactions, etc., but is not limited to these examples. The upload unit, for example, can analyze upload methods that the user has previously rated highly and recommend similar methods. The upload unit can also suggest optimal tags based on tags and keywords used by the user in the past. Furthermore, the upload unit can analyze the user's past upload frequency and suggest the optimal upload timing. For example, the upload unit analyzes the user's past upload data and selects the optimal upload method. In this way, the user's past upload history can be analyzed to select the optimal upload method. Some or all of the above-described processing in the upload unit may be performed using, for example, AI, or may be performed without using AI. For example, the upload unit can input the user's past upload data into a generation AI and cause the generation AI to select the optimal upload method.

[0038] The uploading unit may filter videos based on the user's current exercise status and areas of interest when uploading the videos. For example, the uploading unit may filter videos based on the user's current exercise status and areas of interest when uploading the videos. Examples of exercise status include, but are not limited to, heart rate, step count, and exercise time. Examples of areas of interest include, but are not limited to, survey results and browsing history. For example, if the user is running, the uploading unit may prioritize uploading videos related to running. Furthermore, if the user is interested in yoga, the uploading unit may prioritize uploading videos related to yoga. Furthermore, if the user is performing strength training, the uploading unit may prioritize uploading videos related to strength training. For example, the uploading unit may analyze the user's exercise status data and filter videos based on the user's areas of interest. This allows videos to be filtered based on the user's current exercise status and areas of interest. Some or all of the above-described processing in the uploading unit may be performed using, for example, AI, or may be performed without using AI. For example, the upload unit can input the user's exercise status data into the generation AI and have the generation AI perform video filtering.

[0039] When uploading videos, the uploading unit can prioritize uploading highly relevant videos by taking into account the user's geographical location information. For example, when uploading videos, the uploading unit prioritizes uploading highly relevant videos by 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, when a user is in a specific area, the uploading unit can prioritize uploading videos related to that area. Furthermore, when a user is traveling, the uploading unit can prioritize uploading videos related to the user's travel destination. Furthermore, when a user is at home, the uploading unit can prioritize uploading videos related to the area around the user's home. For example, the uploading unit analyzes the user's geographical location data and prioritizes uploading highly relevant videos. This makes it possible to prioritize uploading highly relevant videos by taking into account the user's geographical location information. Some or all of the above-described processing by the uploading unit may be performed using, or without using, AI. For example, the uploading unit can input the user's geographical location data to a generation AI and cause the generation AI to select highly relevant videos.

[0040] The uploading unit may analyze the user's social media activity and upload related videos when uploading a video. The uploading unit may, for example, analyze the user's social media activity and upload related videos. Social media activity may include, but is not limited to, the content of posts, reactions, and the number of followers. The uploading unit may, for example, analyze the content shared by the user on social media and upload related videos. The uploading unit may also upload related videos based on the content of videos from accounts the user follows on social media. The uploading unit may also analyze the activities of groups the user participates in on social media and upload related videos. For example, the uploading unit may analyze the user's social media data and upload related videos. This allows the user's social media activity to be analyzed and related videos to be uploaded. Some or all of the above-described processing by the uploading unit may be performed using, or without, AI. For example, the uploading unit may input the user's social media data into a generation AI and cause the generation AI to select related videos.

[0041] The analysis unit can optimize the analysis algorithm by referring to the user's past exercise data during analysis. For example, the analysis unit can adjust the analysis algorithm by referring to the user's past exercise data during analysis. Past exercise data includes, but is not limited to, the type, frequency, and intensity of exercise. For example, the analysis unit can adjust the analysis algorithm based on the user's past exercise data. The analysis unit can also extract specific movement patterns from the user's past exercise data and incorporate them into the analysis. Furthermore, the analysis unit can analyze the user's past exercise data and select an optimal analysis method. For example, the analysis unit can analyze the user's past exercise data and select an optimal analysis algorithm. This allows the analysis algorithm to be optimized by referring to the user's past exercise data. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the user's past exercise data into a generation AI and cause the generation AI to optimize the analysis algorithm.

[0042] The analysis unit can apply different analysis methods depending on the user's exercise category during analysis. For example, the analysis unit can apply different analysis methods depending on the user's exercise category during analysis. Exercise categories include, but are not limited to, aerobic exercise, strength training, etc. For example, if the user is running, the analysis unit can apply an analysis method specialized for running. Furthermore, if the user is doing yoga, the analysis unit can apply an analysis method specialized for yoga. Furthermore, if the user is doing strength training, the analysis unit can apply an analysis method specialized for strength training. For example, the analysis unit analyzes the user's exercise category data and selects an optimal analysis method. This allows different analysis methods to be applied depending on the user's exercise category. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's exercise category data to a generation AI and cause the generation AI to select an optimal analysis method.

[0043] The analysis unit may perform the analysis while taking into account the geographical distribution of the user. For example, the analysis unit may perform the analysis while taking into account the geographical distribution of the user. Examples of geographical distribution include, but are not limited to, user data by region and demographic statistics. For example, if the user is in a specific region, the analysis unit may perform the analysis while taking into account the characteristics of that region. Furthermore, if the user is traveling, the analysis unit may perform the analysis while taking into account the characteristics of the travel destination. Furthermore, if the user is at home, the analysis unit may perform the analysis while taking into account the characteristics of the area around the user's home. For example, the analysis unit may analyze the geographical distribution data of the user and select an optimal analysis method. This allows the analysis to be performed while taking into account the geographical distribution of the user. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the geographical distribution data of the user into a generation AI and cause the generation AI to select an optimal analysis method.

[0044] The analysis unit can improve the accuracy of the analysis by referring to the user's related literature during the analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's related literature during the analysis. Related literature includes, but is not limited to, academic papers and technical reports. For example, the analysis unit can improve the accuracy of the analysis based on literature previously referenced by the user. Furthermore, if the user has referenced literature related to a specific exercise, the analysis unit can perform the analysis based on that literature. Furthermore, the analysis unit can incorporate data from the literature referenced by the user into the analysis to improve accuracy. For example, the analysis unit analyzes the user's related literature data and selects an optimal analysis method. This can improve the accuracy of the analysis by referring to the user's related literature. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's related literature data into a generation AI and cause the generation AI to optimize the analysis method.

[0045] The advice unit can adjust the level of detail of the advice based on the importance of the analysis result when providing advice. For example, the advice unit adjusts the level of detail of the advice based on the importance of the analysis result when providing advice. The importance includes, but is not limited to, impact and urgency. For example, the advice unit can provide detailed advice for important analysis results. The advice unit can also provide concise advice for less important analysis results. Furthermore, the advice unit can gradually adjust the level of detail of the advice depending on the importance of the analysis result. For example, the advice unit analyzes importance data of the analysis result and selects an optimal level of detail of the advice. This allows the level of detail of the advice to be adjusted based on the importance of the analysis result. Some or all of the above-described processing in the advice unit may be performed using, or without, AI. For example, the advice unit can input importance data of the analysis result to a generation AI and cause the generation AI to adjust the level of detail of the advice.

[0046] The advice unit can apply different advice algorithms depending on the exercise category when giving advice. For example, the advice unit can apply different advice algorithms depending on the exercise category when giving advice. Exercise categories include, but are not limited to, aerobic exercise, strength training, etc. For example, the advice unit can apply an algorithm specialized for running when giving advice about running. Furthermore, the advice unit can apply an algorithm specialized for yoga when giving advice about yoga. Furthermore, the advice unit can apply an algorithm specialized for strength training when giving advice about strength training. For example, the advice unit analyzes the user's exercise category data and selects an optimal advice algorithm. This makes it possible to apply different advice algorithms depending on the exercise category. Some or all of the above-described processing in the advice unit can be performed using, for example, AI, or can be performed without using AI. For example, the advice unit can input the user's exercise category data to a generation AI and cause the generation AI to select an optimal advice algorithm.

[0047] The advice unit can determine the priority of advice based on the submission time of the analysis results when providing advice. For example, the advice unit can determine the priority of advice based on the submission time of the analysis results when providing advice. The submission time includes, but is not limited to, for example, the submission date and time, the submission deadline, etc. For example, if the analysis result is the most recent, the advice unit can prioritize advice based on that result. Furthermore, if the analysis result is an older result, the advice unit can give next priority to advice based on that result. Furthermore, the advice unit can gradually adjust the priority of advice based on the submission time of the analysis results. For example, the advice unit analyzes data on the submission time of the analysis results and selects an optimal priority of advice. This allows the priority of advice to be determined based on the submission time of the analysis results. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input data on the submission time of the analysis results to the generation AI and cause the generation AI to adjust the priority of advice.

[0048] The advice unit can adjust the order of advice based on the relevance of the analysis results when providing advice. For example, the advice unit adjusts the order of advice based on the relevance of the analysis results when providing advice. Relevance includes, but is not limited to, for example, the degree of content consistency and related keywords. For example, if the analysis results have high relevance, the advice unit can provide advice based on those results first. Furthermore, if the analysis results have medium relevance, the advice unit can provide advice based on those results next. Furthermore, if the analysis results have low relevance, the advice unit can provide advice based on those results last. For example, the advice unit analyzes relevance data of the analysis results and selects an optimal order of advice. This makes it possible to adjust the order of advice based on the relevance of the analysis results. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the relevance data of the analysis results to a generation AI and cause the generation AI to adjust the order of advice.

[0049] The posture estimation unit can optimize the estimation algorithm by referring to the user's past posture data during posture estimation. For example, the posture estimation unit can adjust the estimation algorithm by referring to the user's past posture data during posture estimation. Past posture data includes, but is not limited to, the type, frequency, and accuracy of postures. For example, the posture estimation unit can adjust the estimation algorithm based on the user's past posture data. The posture estimation unit can also extract specific movement patterns from the user's past posture data and reflect them in the estimation. Furthermore, the posture estimation unit can analyze the user's past posture data and select an optimal estimation method. For example, the posture estimation unit can analyze the user's past posture data and select an optimal estimation algorithm. This allows the estimation algorithm to be optimized by referring to the user's past posture data. Some or all of the above-described processing in the posture estimation unit can be performed using, or without, AI. For example, the posture estimation unit can input the user's past posture data into a generation AI and cause the generation AI to optimize the estimation algorithm.

[0050] The posture estimation unit can perform posture estimation by taking into account the geographical distribution of the user. For example, the posture estimation unit can perform posture estimation by taking into account the geographical distribution of the user. Geographical distribution includes, but is not limited to, user data by region and demographic statistics. For example, if the user is in a specific region, the posture estimation unit can perform estimation by taking into account the characteristics of that region. Furthermore, if the user is traveling, the posture estimation unit can perform estimation by taking into account the characteristics of the travel destination. Furthermore, if the user is at home, the posture estimation unit can perform estimation by taking into account the characteristics of the area around the user's home. For example, the posture estimation unit analyzes the geographical distribution data of the user and selects an optimal estimation method. This allows estimation to be performed by taking into account the geographical distribution of the user. Some or all of the above-described processing in the posture estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the posture estimation unit can input the geographical distribution data of the user to a generation AI and cause the generation AI to select an optimal estimation method.

[0051] The certification unit can optimize the certification algorithm by referring to the Professional's past performance during certification. For example, the certification unit can adjust the certification algorithm by referring to the Professional's past performance during certification. Past performance includes, but is not limited to, achievements, evaluations, and awards. For example, the certification unit can adjust the certification algorithm based on the Professional's past achievements. The certification unit can also extract specific patterns from the Professional's past performance and reflect them in the certification. Furthermore, the certification unit can analyze the Professional's past performance and select an optimal certification method. For example, the certification unit can analyze the Professional's past performance data and select an optimal certification algorithm. This allows the certification algorithm to be optimized by referring to the Professional's past performance. Some or all of the above-described processing in the certification unit can be performed using, or without, AI. For example, the certification unit can input the Professional's past performance data into a generation AI and cause the generation AI to optimize the certification algorithm.

[0052] The certification unit may perform certification taking into account the geographic distribution of professionals. For example, the certification unit may perform certification taking into account the geographic distribution of professionals. Examples of geographic distribution include, but are not limited to, user data by region and demographic statistics. For example, if a professional is in a specific region, the certification unit may perform certification taking into account the characteristics of that region. Furthermore, if a professional is traveling, the certification unit may perform certification taking into account the characteristics of the travel destination. Furthermore, if a professional is at home, the certification unit may perform certification taking into account the characteristics of the area around the professional's home. For example, the certification unit may analyze the geographic distribution data of professionals and select an optimal certification method. This allows certification to be performed taking into account the geographic distribution of professionals. Some or all of the above-described processing in the certification unit may be performed using, or without, AI. For example, the certification unit may input the geographic distribution data of professionals into a generation AI and cause the generation AI to select an optimal certification method.

[0053] The privacy protection unit can optimize the protection algorithm by referring to the user's past privacy settings during privacy protection. For example, the privacy protection unit can optimize the protection algorithm by referring to the user's past privacy settings during privacy protection. Past privacy settings include, but are not limited to, the type of setting, frequency, and change history. For example, the privacy protection unit can adjust the protection algorithm based on the user's past privacy settings. The privacy protection unit can also extract specific patterns from the user's past privacy settings and reflect them in the protection. Furthermore, the privacy protection unit can analyze the user's past privacy settings and select an optimal protection method. For example, the privacy protection unit can analyze the user's past privacy setting data and select an optimal protection algorithm. This allows the protection algorithm to be optimized by referring to the user's past privacy settings. Some or all of the above-described processing in the privacy protection unit can be performed using, or without, AI. For example, the privacy protection unit can input the user's past privacy setting data into a generation AI and cause the generation AI to optimize the protection algorithm.

[0054] The privacy protection unit may perform privacy protection by taking into account the geographical distribution of the user. For example, the privacy protection unit may perform privacy protection by taking into account the geographical distribution of the user. Examples of geographical distribution include, but are not limited to, user data by region and demographic statistics. For example, if the user is in a specific region, the privacy protection unit may perform privacy protection by taking into account the characteristics of the region. Furthermore, if the user is traveling, the privacy protection unit may perform privacy protection by taking into account the characteristics of the travel destination. Furthermore, if the user is at home, the privacy protection unit may perform privacy protection by taking into account the characteristics of the area around the user's home. For example, the privacy protection unit may analyze the user's geographical distribution data and select an optimal protection method. This allows privacy protection by taking into account the user's geographical distribution. Some or all of the above-described processing in the privacy protection unit may be performed using, or without, AI. For example, the privacy protection unit may input the user's geographical distribution data into a generation AI and cause the generation AI to select an optimal protection method.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The exercise support system may further include a real-time feedback unit. The real-time feedback unit provides real-time feedback while the user is exercising. For example, the real-time feedback unit can provide a running user with immediate advice on where to land their feet or how to swing their arms. It can also provide a user performing yoga poses with real-time instructions on how to fine-tune their posture. It can also provide immediate feedback on the trajectory of a golf swing and body movement during a golf swing. This allows the user to immediately identify areas for improvement while exercising and efficiently improve their skills.

[0057] The exercise support system may further include a community section. The community section provides a platform for users to interact with each other and share information. For example, users can post their exercise videos to the community and receive feedback from other users. Discussion forums related to specific exercises or sports may also be set up, allowing users to share advice and experiences. Furthermore, professionals may hold regular webinars to provide direct advice to users. This allows users to improve their exercise skills while interacting with other users and professionals.

[0058] The exercise support system can further include a gamification unit. The gamification unit makes the user's exercise experience more fun by incorporating game elements. For example, the user can earn points every time they exercise and use those points to customize their avatar or items. A ranking system can also be introduced that allows users to compete with each other on their exercise results. Furthermore, users can earn badges and trophies by achieving specific exercise goals. This can increase the motivation for users to continue exercising while having fun.

[0059] The exercise support system may further include a nutrition advice unit. The nutrition advice unit provides appropriate nutrition advice based on the user's exercise data and goals. For example, if the user is doing strength training, advice can be provided regarding the amount and timing of protein intake. Also, if the user is training for a marathon, advice can be provided regarding how to replenish energy and when to replenish fluids. Furthermore, if the user is aiming to lose weight, calorie restriction and a balanced meal plan can be suggested. This allows the user to effectively achieve their goals in terms of both exercise and nutrition.

[0060] The exercise support system may further include a performance analysis unit. The performance analysis unit performs detailed analysis of the user's exercise data and provides information useful for improving performance. For example, it can analyze running data and visualize fluctuations in pace and heart rate. It can also analyze golf swing data and evaluate swing consistency and power fluctuations. It can also analyze yoga pose data and evaluate improvements in flexibility and balance. This allows the user to objectively evaluate their performance and identify areas for improvement.

[0061] The exercise support system may further include a virtual reality (VR) unit. The VR unit provides the user with the experience of exercising in a virtual space. For example, a user can run in a virtual natural environment. A user can also practice yoga poses in a virtual studio. A user can also practice a golf swing on a virtual golf course. This allows the user to enjoy exercise in a variety of situations without being bound by the real environment.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The posting unit posts professional advice videos. Professional advice videos can cover fields such as sports, business, and education. For example, videos provided by professionals with specialized knowledge, such as basketball shooting form or the correct way to perform yoga poses, can be posted. The posting unit can also provide a function that allows professionals to filter posts based on their area of ​​expertise. Step 2: The uploading unit allows the user to upload their exercise or sports video. The user's video includes resolution, length, content, etc. For example, videos of various exercises or sports, such as running form or golf swing, can be uploaded. The uploading unit can also have a function to estimate the user's emotions and adjust the timing of video uploads based on the estimated emotions. Step 3: The analysis unit analyzes the video uploaded by the upload unit. The analysis includes motion analysis, audio analysis, and facial expression analysis. For example, when analyzing a running form, the analysis unit evaluates the foot landing position and arm swing, and points out areas for improvement. The analysis unit can also estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. Step 4: The advice unit provides advice based on the results of the analysis by the analysis unit. The advice may include text, audio, video, etc. For example, in a golf swing analysis, advice is provided regarding the swing trajectory and body movement. The advice unit may also have a function to estimate the user's emotions and adjust the way the advice is presented based on the estimated emotions.

[0064] (Example 2) An exercise support system according to an embodiment of the present invention provides a platform where exercise and sports professionals post advice videos, allowing users to watch and learn from those videos. The exercise support system includes a posting unit where professionals post advice videos, an uploading unit where users upload their own exercise and sports videos, an analysis unit (including AI processing) that analyzes the uploaded videos, and an advice unit (including AI processing) that provides advice based on the analysis results. For example, videos provided by professionals with specialized knowledge, such as basketball shooting form and correct yoga poses, are included. Users can upload videos of their exercise and sports performance to the platform. For example, videos of various exercises and sports, such as running form and golf swing, are eligible. Uploaded videos are automatically analyzed by AI. The AI ​​analyzes the movements in the videos to identify areas where the user is doing well and areas where they are not. For example, analyzing running form evaluates the foot's landing position and arm swing and identifies areas for improvement. The system also includes a function for providing advice based on the analysis results. The AI ​​provides specific advice to users based on the analysis results. For example, in golf swing analysis, advice is provided regarding swing trajectory and body movement. This advice provides users with specific guidelines for improving their performance. In this way, the present invention provides a platform where users can watch professional advice videos and receive AI analysis and advice on their performance. This allows users to efficiently improve their exercise or sports skills. In this way, the exercise support system allows users to improve their exercise or sports skills while receiving professional advice.

[0065] An exercise support system according to an embodiment includes a posting unit, an uploading unit, an analysis unit, and an advice unit. The posting unit posts professional advice videos. Professional advice videos include, but are not limited to, fields such as sports, business, and education. The posting unit can post videos provided by professionals with specialized knowledge, such as basketball shooting form and correct yoga poses. The posting unit can also have a function for the professionals to filter the posted content based on their field of expertise. The uploading unit allows users to upload their own exercise or sports videos. User videos may include, but are not limited to, resolution, length, and content. The uploading unit can upload videos of various exercises and sports, such as running form and golf swing. The uploading unit can also have a function for estimating a user's emotions and adjusting the timing of video uploads based on the estimated emotions. The analysis unit analyzes the videos uploaded by the uploading unit. Analysis may include, but is not limited to, motion analysis, audio analysis, and facial expression analysis. For example, in analyzing a running form, the analysis unit evaluates the landing position of the feet and the swing of the arms, and points out areas for improvement. The analysis unit may also have a function of estimating the user's emotions and adjusting the accuracy of the analysis based on the estimated emotions. The advice unit provides advice based on the results of the analysis by the analysis unit. The advice may include, but is not limited to, text, audio, video, and the like. For example, in analyzing a golf swing, the advice unit provides advice regarding the swing trajectory and body movement. The advice unit may also have a function of estimating the user's emotions and adjusting the way the advice is presented based on the estimated emotions. In this way, the exercise support system according to the embodiment allows the user to improve their exercise or sports technique while receiving advice from a professional.

[0066] The device may include a posture estimation unit that uses a motion analysis algorithm. The posture estimation unit estimates posture using, for example, a motion analysis algorithm. Examples of the motion analysis algorithm include, but are not limited to, machine learning algorithms and deep learning algorithms. The posture estimation unit can analyze the user's posture in real time using, for example, a machine learning algorithm. The posture estimation unit can also estimate the user's posture with high accuracy using a deep learning algorithm. Furthermore, the posture estimation unit can analyze the user's posture in three dimensions using a motion analysis algorithm. For example, the posture estimation unit receives video data captured by a camera as input and estimates posture using a motion analysis algorithm. This allows posture to be estimated using the motion analysis algorithm. Some or all of the above-described processing in the posture estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the posture estimation unit may input video data captured by a camera to a generation AI and cause the generation AI to estimate posture.

[0067] The platform may include a certification unit that manages a professional certification system. The certification unit, for example, manages the professional certification system. The certification system may include, but is not limited to, evaluation criteria and a certification process. For example, the certification unit may set evaluation criteria for professionals and certify them based on those criteria. The certification unit may also manage the professional certification process to ensure the transparency of the certification. Furthermore, the certification unit may publish the results of professional certification so that users can obtain reliable information. For example, the certification unit may certify professionals based on their achievements and evaluations and publish the results on the platform. This allows the professional certification system to be managed. Some or all of the above-described processing in the certification unit may be performed using AI, for example, or may be performed without using AI. For example, the certification unit may input the professional's achievement data into a generation AI and have the generation AI set the certification criteria.

[0068] The video system may include a privacy protection unit that protects the privacy of a user's video. The privacy protection unit protects the privacy of the user's video, for example. Privacy protection includes, but is not limited to, data encryption, access control, and the like. For example, the privacy protection unit can encrypt the user's video data to prevent third parties from accessing it. The privacy protection unit can also control access to the user's video data to ensure that only authorized users can access it. Furthermore, the privacy protection unit can anonymize the user's video data to prevent personal information from being leaked. For example, the privacy protection unit protects the user's privacy by encrypting the video data, controlling access, and anonymizing it. This allows the privacy of the user's video to be protected. Some or all of the above-described processing in the privacy protection unit may be performed using, or without, AI. For example, the privacy protection unit may input video data to a generation AI and have the generation AI encrypt and anonymize the data.

[0069] The posting unit can estimate the professional's emotions and adjust the expression of the posted content based on the estimated emotions. The posting unit, for example, estimates the professional's emotions and adjusts the expression of the posted content based on the estimated emotions. Emotion estimation includes, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the professional is excited, the posting unit can emphasize energetic expressions. Furthermore, if the professional is relaxed, the posting unit can explain in a calm tone. Furthermore, if the professional is nervous, the posting unit can use concise and clear expressions. For example, the posting unit analyzes the professional's facial expression data to estimate the emotions and adjust the expression of the posted content based on the estimated emotions. This makes it possible to adjust the expression of the posted content according to the professional'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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the posting unit may be performed using, for example, AI, or without AI. For example, the posting department can input professional facial expression data into the generation AI and have the generation AI estimate the emotions.

[0070] When posting, the posting unit can analyze the Professional's past posting history and select the optimal posting method. The posting unit, for example, analyzes the Professional's past posting history and selects the optimal posting method. The past posting history includes, for example, but is not limited to, the frequency of posts, content, and reactions. The posting unit, for example, can analyze the style of posts that the Professional has previously received high ratings for and recommend a similar style. The posting unit can also suggest optimal tags by referring to tags and keywords used by the Professional in the past. Furthermore, the posting unit can analyze the Professional's past posting frequency and suggest the optimal posting timing. For example, the posting unit analyzes the Professional's past posting data and selects the optimal posting method. This makes it possible to select the optimal posting method by analyzing the Professional's past posting history. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can input the Professional's past posting data into a generation AI and cause the generation AI to select the optimal posting method.

[0071] The posting unit can filter the posted content based on the professional's field of expertise when posting. The posting unit, for example, filters the posted content based on the professional's field of expertise. Fields of expertise include, but are not limited to, medicine, education, and sports. For example, if the professional is a basketball expert, the posting unit can prioritize displaying posted content related to basketball. Furthermore, if the professional is a yoga expert, the posting unit can prioritize displaying posted content related to yoga. Furthermore, if the professional is a running expert, the posting unit can prioritize displaying posted content related to running. For example, the posting unit analyzes the professional's field of expertise data and filters the posted content. This makes it possible to filter the posted content based on the professional's field of expertise. Some or all of the above-described processing by the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can input the professional's field of expertise data to a generation AI and cause the generation AI to filter the posted content.

[0072] The posting unit can estimate the professional's emotions and prioritize posts based on the estimated emotions. The posting unit, for example, estimates the professional's emotions and prioritizes posts based on the estimated emotions. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, and text analysis. For example, if the professional is excited, the posting unit can prioritize the professional's posts. Furthermore, if the professional is relaxed, the posting unit can prioritize the professional's posts next. Furthermore, if the professional is nervous, the posting unit can display the professional's posts last. For example, the posting unit analyzes the professional's facial expression data, estimates the professional's emotions, and prioritizes posts based on the estimated emotions. This allows the prioritization of posts according to the professional'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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the posting unit may be performed using, for example, AI, or without AI. For example, the posting department can input professional facial expression data into the generation AI and have the generation AI estimate the emotions.

[0073] When posting, the posting unit can prioritize displaying highly relevant posts by taking into consideration the geographical location information of the professional. The posting unit, for example, prioritizes displaying highly relevant posts by taking into consideration the geographical location information of the professional. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the professional is in a specific area, the posting unit can prioritize displaying posts related to that area. Furthermore, if the professional is traveling, the posting unit can prioritize displaying posts related to the travel destination. Furthermore, if the professional is at home, the posting unit can prioritize displaying posts related to the area around the professional's home. For example, the posting unit analyzes the geographical location data of the professional and prioritizes displaying highly relevant posts. This makes it possible to prioritize displaying highly relevant posts by taking into consideration the geographical location information of the professional. Some or all of the above-described processing by the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can input the geographical location data of the professional to a generation AI and cause the generation AI to select highly relevant posts.

[0074] The posting unit can analyze the social media activity of the professional at the time of posting and recommend related posts. The posting unit, for example, analyzes the social media activity of the professional and recommends related posts. Social media activity includes, for example, but is not limited to, the content of the post, reactions, and the number of followers. The posting unit, for example, can analyze the content shared by the professional on social media and recommend related posts. The posting unit can also recommend related posts based on the content posted by accounts the professional follows on social media. The posting unit can also analyze the activities of groups in which the professional participates on social media and recommend related posts. For example, the posting unit can analyze the social media data of the professional and recommend related posts. This makes it possible to analyze the social media activity of the professional and recommend related posts. Some or all of the above-described processing by the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can input the social media data of the professional into a generation AI and cause the generation AI to recommend related posts.

[0075] The upload unit can estimate a user's emotions and adjust the timing of uploading videos based on the estimated emotions. The upload unit, for example, estimates a user's emotions and adjusts the timing of uploading videos based on the estimated emotions. Emotion estimation includes, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. For example, the upload unit can upload videos immediately if the user is relaxed. Furthermore, the upload unit can delay uploading slightly if the user is nervous. Furthermore, the upload unit can upload quickly if the user is excited. For example, the upload unit can analyze the user's facial expression data to estimate the user's emotions and adjust the timing of uploading videos based on the estimated emotions. This makes it possible to adjust the timing of uploading videos 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the upload unit may be performed using, for example, AI, or without AI. For example, the upload unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0076] The upload unit can analyze the user's past upload history and select the optimal upload method. The upload unit, for example, analyzes the user's past upload history and selects the optimal upload method. The past upload history includes, for example, upload frequency, content, reactions, etc., but is not limited to these examples. The upload unit, for example, can analyze upload methods that the user has previously rated highly and recommend similar methods. The upload unit can also suggest optimal tags based on tags and keywords used by the user in the past. Furthermore, the upload unit can analyze the user's past upload frequency and suggest the optimal upload timing. For example, the upload unit analyzes the user's past upload data and selects the optimal upload method. In this way, the user's past upload history can be analyzed to select the optimal upload method. Some or all of the above-described processing in the upload unit may be performed using, for example, AI, or may be performed without using AI. For example, the upload unit can input the user's past upload data into a generation AI and cause the generation AI to select the optimal upload method.

[0077] The uploading unit may filter videos based on the user's current exercise status and areas of interest when uploading the videos. For example, the uploading unit may filter videos based on the user's current exercise status and areas of interest when uploading the videos. Examples of exercise status include, but are not limited to, heart rate, step count, and exercise time. Examples of areas of interest include, but are not limited to, survey results and browsing history. For example, if the user is running, the uploading unit may prioritize uploading videos related to running. Furthermore, if the user is interested in yoga, the uploading unit may prioritize uploading videos related to yoga. Furthermore, if the user is performing strength training, the uploading unit may prioritize uploading videos related to strength training. For example, the uploading unit may analyze the user's exercise status data and filter videos based on the user's areas of interest. This allows videos to be filtered based on the user's current exercise status and areas of interest. Some or all of the above-described processing in the uploading unit may be performed using, for example, AI, or may be performed without using AI. For example, the upload unit can input the user's exercise status data into the generation AI and have the generation AI perform video filtering.

[0078] The upload unit can estimate the user's emotions and determine the priority of videos to be uploaded based on the estimated emotions. The upload unit can, for example, estimate the user's emotions and determine the priority of videos to be uploaded based on the estimated emotions. Emotion estimation includes, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is excited, the upload unit can prioritize uploading the video. Furthermore, if the user is relaxed, the upload unit can prioritize the video. Furthermore, if the user is nervous, the upload unit can upload the video last. For example, the upload unit can analyze the user's facial expression data, estimate the emotion, and determine the priority of videos based on the emotion. This makes it possible to determine the priority of videos to be uploaded 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the upload unit can be performed, for example, using AI or without AI. For example, the upload unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0079] When uploading videos, the uploading unit can prioritize uploading highly relevant videos by taking into account the user's geographical location information. For example, when uploading videos, the uploading unit prioritizes uploading highly relevant videos by 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, when a user is in a specific area, the uploading unit can prioritize uploading videos related to that area. Furthermore, when a user is traveling, the uploading unit can prioritize uploading videos related to the user's travel destination. Furthermore, when a user is at home, the uploading unit can prioritize uploading videos related to the area around the user's home. For example, the uploading unit analyzes the user's geographical location data and prioritizes uploading highly relevant videos. This makes it possible to prioritize uploading highly relevant videos by taking into account the user's geographical location information. Some or all of the above-described processing by the uploading unit may be performed using, or without using, AI. For example, the uploading unit can input the user's geographical location data to a generation AI and cause the generation AI to select highly relevant videos.

[0080] The uploading unit may analyze the user's social media activity and upload related videos when uploading a video. The uploading unit may, for example, analyze the user's social media activity and upload related videos. Social media activity may include, but is not limited to, the content of posts, reactions, and the number of followers. The uploading unit may, for example, analyze the content shared by the user on social media and upload related videos. The uploading unit may also upload related videos based on the content of videos from accounts the user follows on social media. The uploading unit may also analyze the activities of groups the user participates in on social media and upload related videos. For example, the uploading unit may analyze the user's social media data and upload related videos. This allows the user's social media activity to be analyzed and related videos to be uploaded. Some or all of the above-described processing by the uploading unit may be performed using, or without, AI. For example, the uploading unit may input the user's social media data into a generation AI and cause the generation AI to select related videos.

[0081] The analysis unit can estimate the user's emotion and adjust the accuracy of the analysis based on the estimated emotion. The analysis unit, for example, estimates the user's emotion and adjusts the accuracy of the analysis based on the estimated emotion. Emotion estimation includes, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a concise analysis when the user is nervous. Furthermore, the analysis unit can perform a quick analysis when the user is excited. For example, the analysis unit analyzes the user's facial expression data, estimates the emotion, and adjusts the accuracy of the analysis based on the emotion. This allows the accuracy of the analysis to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or without using an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0082] The analysis unit can optimize the analysis algorithm by referring to the user's past exercise data during analysis. For example, the analysis unit can adjust the analysis algorithm by referring to the user's past exercise data during analysis. Past exercise data includes, but is not limited to, the type, frequency, and intensity of exercise. For example, the analysis unit can adjust the analysis algorithm based on the user's past exercise data. The analysis unit can also extract specific movement patterns from the user's past exercise data and incorporate them into the analysis. Furthermore, the analysis unit can analyze the user's past exercise data and select an optimal analysis method. For example, the analysis unit can analyze the user's past exercise data and select an optimal analysis algorithm. This allows the analysis algorithm to be optimized by referring to the user's past exercise data. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the user's past exercise data into a generation AI and cause the generation AI to optimize the analysis algorithm.

[0083] The analysis unit can apply different analysis methods depending on the user's exercise category during analysis. For example, the analysis unit can apply different analysis methods depending on the user's exercise category during analysis. Exercise categories include, but are not limited to, aerobic exercise, strength training, etc. For example, if the user is running, the analysis unit can apply an analysis method specialized for running. Furthermore, if the user is doing yoga, the analysis unit can apply an analysis method specialized for yoga. Furthermore, if the user is doing strength training, the analysis unit can apply an analysis method specialized for strength training. For example, the analysis unit analyzes the user's exercise category data and selects an optimal analysis method. This allows different analysis methods to be applied depending on the user's exercise category. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's exercise category data to a generation AI and cause the generation AI to select an optimal analysis method.

[0084] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis results based on the estimated emotion. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, text analysis, etc. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, the analysis unit analyzes the user's facial expression data, estimates the emotion, and adjusts the display method of the analysis results based on the estimated emotion. This makes it possible to adjust the display method of the analysis results according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0085] The analysis unit may perform the analysis while taking into account the geographical distribution of the user. For example, the analysis unit may perform the analysis while taking into account the geographical distribution of the user. Examples of geographical distribution include, but are not limited to, user data by region and demographic statistics. For example, if the user is in a specific region, the analysis unit may perform the analysis while taking into account the characteristics of that region. Furthermore, if the user is traveling, the analysis unit may perform the analysis while taking into account the characteristics of the travel destination. Furthermore, if the user is at home, the analysis unit may perform the analysis while taking into account the characteristics of the area around the user's home. For example, the analysis unit may analyze the geographical distribution data of the user and select an optimal analysis method. This allows the analysis to be performed while taking into account the geographical distribution of the user. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the geographical distribution data of the user into a generation AI and cause the generation AI to select an optimal analysis method.

[0086] The analysis unit can improve the accuracy of the analysis by referring to the user's related literature during the analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's related literature during the analysis. Related literature includes, but is not limited to, academic papers and technical reports. For example, the analysis unit can improve the accuracy of the analysis based on literature previously referenced by the user. Furthermore, if the user has referenced literature related to a specific exercise, the analysis unit can perform the analysis based on that literature. Furthermore, the analysis unit can incorporate data from the literature referenced by the user into the analysis to improve accuracy. For example, the analysis unit analyzes the user's related literature data and selects an optimal analysis method. This can improve the accuracy of the analysis by referring to the user's related literature. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's related literature data into a generation AI and cause the generation AI to optimize the analysis method.

[0087] The advice unit can estimate the user's emotion and adjust the way the advice is expressed based on the estimated emotion. The advice unit, for example, estimates the user's emotion and adjusts the way the advice is expressed based on the estimated emotion. Emotion estimation includes, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is relaxed, the advice unit can provide advice in a gentle tone. Furthermore, if the user is nervous, the advice unit can provide concise and clear advice. Furthermore, if the user is excited, the advice unit can provide advice in an energetic manner. For example, the advice unit analyzes the user's facial expression data to estimate the emotion and adjust the way the advice is expressed based on the emotion. This makes it possible to adjust the way the advice is expressed depending on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0088] The advice unit can adjust the level of detail of the advice based on the importance of the analysis result when providing advice. For example, the advice unit adjusts the level of detail of the advice based on the importance of the analysis result when providing advice. The importance includes, but is not limited to, impact and urgency. For example, the advice unit can provide detailed advice for important analysis results. The advice unit can also provide concise advice for less important analysis results. Furthermore, the advice unit can gradually adjust the level of detail of the advice depending on the importance of the analysis result. For example, the advice unit analyzes importance data of the analysis result and selects an optimal level of detail of the advice. This allows the level of detail of the advice to be adjusted based on the importance of the analysis result. Some or all of the above-described processing in the advice unit may be performed using, or without, AI. For example, the advice unit can input importance data of the analysis result to a generation AI and cause the generation AI to adjust the level of detail of the advice.

[0089] The advice unit can apply different advice algorithms depending on the exercise category when giving advice. For example, the advice unit can apply different advice algorithms depending on the exercise category when giving advice. Exercise categories include, but are not limited to, aerobic exercise, strength training, etc. For example, the advice unit can apply an algorithm specialized for running when giving advice about running. Furthermore, the advice unit can apply an algorithm specialized for yoga when giving advice about yoga. Furthermore, the advice unit can apply an algorithm specialized for strength training when giving advice about strength training. For example, the advice unit analyzes the user's exercise category data and selects an optimal advice algorithm. This makes it possible to apply different advice algorithms depending on the exercise category. Some or all of the above-described processing in the advice unit can be performed using, for example, AI, or can be performed without using AI. For example, the advice unit can input the user's exercise category data to a generation AI and cause the generation AI to select an optimal advice algorithm.

[0090] The advice unit can estimate the user's emotion and adjust the length of the advice based on the estimated emotion. The advice unit, for example, estimates the user's emotion and adjusts the length of the advice based on the estimated emotion. Emotion estimation includes, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. For example, if the user is in a hurry, the advice unit can provide short, to-the-point advice. Furthermore, if the user is relaxed, the advice unit can provide longer advice with detailed explanations. Furthermore, if the user is excited, the advice unit can provide advice with visually stimulating effects. For example, the advice unit analyzes the user's facial expression data, estimates the emotion, and adjusts the length of the advice based on the emotion. This allows the length of the advice to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or without using an AI. For example, the advice unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0091] The advice unit can determine the priority of advice based on the submission time of the analysis results when providing advice. For example, the advice unit can determine the priority of advice based on the submission time of the analysis results when providing advice. The submission time includes, but is not limited to, for example, the submission date and time, the submission deadline, etc. For example, if the analysis result is the most recent, the advice unit can prioritize advice based on that result. Furthermore, if the analysis result is an older result, the advice unit can give next priority to advice based on that result. Furthermore, the advice unit can gradually adjust the priority of advice based on the submission time of the analysis results. For example, the advice unit analyzes data on the submission time of the analysis results and selects an optimal priority of advice. This allows the priority of advice to be determined based on the submission time of the analysis results. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input data on the submission time of the analysis results to the generation AI and cause the generation AI to adjust the priority of advice.

[0092] The advice unit can adjust the order of advice based on the relevance of the analysis results when providing advice. For example, the advice unit adjusts the order of advice based on the relevance of the analysis results when providing advice. Relevance includes, but is not limited to, for example, the degree of content consistency and related keywords. For example, if the analysis results have high relevance, the advice unit can provide advice based on those results first. Furthermore, if the analysis results have medium relevance, the advice unit can provide advice based on those results next. Furthermore, if the analysis results have low relevance, the advice unit can provide advice based on those results last. For example, the advice unit analyzes relevance data of the analysis results and selects an optimal order of advice. This makes it possible to adjust the order of advice based on the relevance of the analysis results. Some or all of the above-described processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the relevance data of the analysis results to a generation AI and cause the generation AI to adjust the order of advice.

[0093] The posture estimation unit can estimate the user's emotion and adjust the accuracy of the posture estimation based on the estimated emotion. The posture estimation unit, for example, estimates the user's emotion and adjusts the accuracy of the posture estimation based on the estimated emotion. Emotion estimation includes, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to these examples. The posture estimation unit can perform detailed posture estimation, for example, when the user is relaxed. The posture estimation unit can also perform simple posture estimation, for example, when the user is nervous. Furthermore, the posture estimation unit can perform quick posture estimation, for example, when the user is excited. For example, the posture estimation unit analyzes the user's facial expression data to estimate the emotion and adjust the accuracy of the posture estimation based on the emotion. This allows the accuracy of the posture estimation to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the posture estimation unit may be performed using, for example, an AI, or without using an AI. For example, the posture estimation unit can input the user's facial expression data into the generation AI and have the generation AI estimate emotions.

[0094] The posture estimation unit can optimize the estimation algorithm by referring to the user's past posture data during posture estimation. For example, the posture estimation unit can adjust the estimation algorithm by referring to the user's past posture data during posture estimation. Past posture data includes, but is not limited to, the type, frequency, and accuracy of postures. For example, the posture estimation unit can adjust the estimation algorithm based on the user's past posture data. The posture estimation unit can also extract specific movement patterns from the user's past posture data and reflect them in the estimation. Furthermore, the posture estimation unit can analyze the user's past posture data and select an optimal estimation method. For example, the posture estimation unit can analyze the user's past posture data and select an optimal estimation algorithm. This allows the estimation algorithm to be optimized by referring to the user's past posture data. Some or all of the above-described processing in the posture estimation unit can be performed using, or without, AI. For example, the posture estimation unit can input the user's past posture data into a generation AI and cause the generation AI to optimize the estimation algorithm.

[0095] The posture estimation unit can estimate the user's emotion and adjust the display method of the posture estimation result based on the estimated emotion. The posture estimation unit can, for example, estimate the user's emotion and adjust the display method of the posture estimation result based on the estimated emotion. Emotion estimation can include, but is not limited to, facial expression analysis, voice analysis, text analysis, etc. For example, if the user is nervous, the posture estimation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the posture estimation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the posture estimation unit can provide a display method that focuses on the main points. For example, the posture estimation unit can analyze the user's facial expression data, estimate the emotion, and adjust the display method of the posture estimation result based on the emotion. This makes it possible to adjust the display method of the posture estimation result according to the user's emotion. 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. Some or all of the above-described processing in the posture estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the posture estimation unit may input the user's facial expression data to the generation AI and have the generation AI estimate the emotion.

[0096] The posture estimation unit can perform posture estimation by taking into account the geographical distribution of the user. For example, the posture estimation unit can perform posture estimation by taking into account the geographical distribution of the user. Geographical distribution includes, but is not limited to, user data by region and demographic statistics. For example, if the user is in a specific region, the posture estimation unit can perform estimation by taking into account the characteristics of that region. Furthermore, if the user is traveling, the posture estimation unit can perform estimation by taking into account the characteristics of the travel destination. Furthermore, if the user is at home, the posture estimation unit can perform estimation by taking into account the characteristics of the area around the user's home. For example, the posture estimation unit analyzes the geographical distribution data of the user and selects an optimal estimation method. This allows estimation to be performed by taking into account the geographical distribution of the user. Some or all of the above-described processing in the posture estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the posture estimation unit can input the geographical distribution data of the user to a generation AI and cause the generation AI to select an optimal estimation method.

[0097] The certification unit can estimate the professional's emotions and adjust the certification criteria based on the estimated emotions. For example, the certification unit can estimate the professional's emotions and adjust the certification criteria based on the estimated emotions. Emotion estimation can include, but is not limited to, facial expression analysis, voice analysis, text analysis, etc. For example, the certification unit can apply detailed certification criteria when the professional is relaxed. Furthermore, the certification unit can apply simple certification criteria when the professional is nervous. Furthermore, the certification unit can apply quick certification criteria when the professional is excited. For example, the certification unit can analyze the professional's facial expression data to estimate the professional's emotions and adjust the certification criteria based on the estimated emotions. This allows the certification criteria to be adjusted according to the professional'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. Some or all of the above-mentioned processing in the certification unit can be performed using, for example, AI, or without AI. For example, the certification unit can input facial expression data of a professional into the generation AI and have the generation AI perform emotion estimation.

[0098] The certification unit can optimize the certification algorithm by referring to the Professional's past performance during certification. For example, the certification unit can adjust the certification algorithm by referring to the Professional's past performance during certification. Past performance includes, but is not limited to, achievements, evaluations, and awards. For example, the certification unit can adjust the certification algorithm based on the Professional's past achievements. The certification unit can also extract specific patterns from the Professional's past performance and reflect them in the certification. Furthermore, the certification unit can analyze the Professional's past performance and select an optimal certification method. For example, the certification unit can analyze the Professional's past performance data and select an optimal certification algorithm. This allows the certification algorithm to be optimized by referring to the Professional's past performance. Some or all of the above-described processing in the certification unit can be performed using, or without, AI. For example, the certification unit can input the Professional's past performance data into a generation AI and cause the generation AI to optimize the certification algorithm.

[0099] The certification unit can estimate the professional's emotions and adjust the display method of the certification result based on the estimated emotions. The certification unit, for example, estimates the professional's emotions and adjusts the display method of the certification result based on the estimated emotions. Emotion estimation includes, but is not limited to, facial expression analysis, voice analysis, text analysis, and the like. For example, if the professional is nervous, the certification unit can provide a simple, highly visible display method. Furthermore, if the professional is relaxed, the certification unit can provide a display method that includes detailed information. Furthermore, if the professional is in a hurry, the certification unit can provide a display method that focuses on the main points. For example, the certification unit can analyze the professional's facial expression data, estimate the emotions, and adjust the display method of the certification result based on the estimated emotions. This allows the display method of the certification result to be adjusted according to the professional'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. Some or all of the above-described processing in the certification unit may be performed using, for example, AI, or may be performed without using AI. For example, the certification unit may input facial expression data of a professional into the generation AI and have the generation AI estimate the emotion.

[0100] The certification unit may perform certification taking into account the geographic distribution of professionals. For example, the certification unit may perform certification taking into account the geographic distribution of professionals. Examples of geographic distribution include, but are not limited to, user data by region and demographic statistics. For example, if a professional is in a specific region, the certification unit may perform certification taking into account the characteristics of that region. Furthermore, if a professional is traveling, the certification unit may perform certification taking into account the characteristics of the travel destination. Furthermore, if a professional is at home, the certification unit may perform certification taking into account the characteristics of the area around the professional's home. For example, the certification unit may analyze the geographic distribution data of professionals and select an optimal certification method. This allows certification to be performed taking into account the geographic distribution of professionals. Some or all of the above-described processing in the certification unit may be performed using, or without, AI. For example, the certification unit may input the geographic distribution data of professionals into a generation AI and cause the generation AI to select an optimal certification method.

[0101] The privacy protection unit can estimate a user's emotion and adjust the level of privacy protection based on the estimated emotion. For example, the privacy protection unit can estimate a user's emotion and adjust the level of privacy protection based on the estimated emotion. Emotion estimation can include, but is not limited to, facial expression analysis, voice analysis, text analysis, etc. For example, the privacy protection unit can provide detailed privacy protection settings when the user is relaxed. Furthermore, the privacy protection unit can provide simple privacy protection settings when the user is nervous. Furthermore, the privacy protection unit can provide quick privacy protection settings when the user is excited. For example, the privacy protection unit can analyze the user's facial expression data to estimate the emotion and adjust the level of privacy protection based on the emotion. This allows the level of privacy protection to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the privacy protection unit can be performed using, for example, AI or without AI. For example, the privacy protection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0102] The privacy protection unit can optimize the protection algorithm by referring to the user's past privacy settings during privacy protection. For example, the privacy protection unit can optimize the protection algorithm by referring to the user's past privacy settings during privacy protection. Past privacy settings include, but are not limited to, the type of setting, frequency, and change history. For example, the privacy protection unit can adjust the protection algorithm based on the user's past privacy settings. The privacy protection unit can also extract specific patterns from the user's past privacy settings and reflect them in the protection. Furthermore, the privacy protection unit can analyze the user's past privacy settings and select an optimal protection method. For example, the privacy protection unit can analyze the user's past privacy setting data and select an optimal protection algorithm. This allows the protection algorithm to be optimized by referring to the user's past privacy settings. Some or all of the above-described processing in the privacy protection unit can be performed using, or without, AI. For example, the privacy protection unit can input the user's past privacy setting data into a generation AI and cause the generation AI to optimize the protection algorithm.

[0103] The privacy protection unit can estimate the user's emotions and determine the priority of privacy protection based on the estimated emotions. The privacy protection unit can, for example, estimate the user's emotions and determine the priority of privacy protection based on the estimated emotions. Emotion estimation can include, but is not limited to, facial expression analysis, voice analysis, text analysis, etc. For example, if the user is relaxed, the privacy protection unit can prioritize privacy protection for that user. Furthermore, if the user is nervous, the privacy protection unit can prioritize privacy protection for that user. Furthermore, if the user is excited, the privacy protection unit can prioritize privacy protection for that user last. For example, the privacy protection unit can analyze the user's facial expression data, estimate the emotion, and determine the priority of privacy protection based on the emotion. This allows the priority of privacy protection to be determined 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. Some or all of the above-described processing in the privacy protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the privacy protection unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0104] The privacy protection unit may perform privacy protection by taking into account the geographical distribution of the user. For example, the privacy protection unit may perform privacy protection by taking into account the geographical distribution of the user. Examples of geographical distribution include, but are not limited to, user data by region and demographic statistics. For example, if the user is in a specific region, the privacy protection unit may perform privacy protection by taking into account the characteristics of the region. Furthermore, if the user is traveling, the privacy protection unit may perform privacy protection by taking into account the characteristics of the travel destination. Furthermore, if the user is at home, the privacy protection unit may perform privacy protection by taking into account the characteristics of the area around the user's home. For example, the privacy protection unit may analyze the user's geographical distribution data and select an optimal protection method. This allows privacy protection by taking into account the user's geographical distribution. Some or all of the above-described processing in the privacy protection unit may be performed using, or without, AI. For example, the privacy protection unit may input the user's geographical distribution data into a generation AI and cause the generation AI to select an optimal protection method. === Hard Collateral 1-1 === Each of the multiple elements including the posting unit, uploading unit, analysis unit, and advice unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the posting unit is realized by the control unit 46A of the smart device 14 and provides a function for professionals to post advice videos. The uploading unit is realized by the control unit 46A of the smart device 14 and provides a function for users to upload videos of their own exercise or sports. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded videos. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the posting unit, uploading unit, analysis unit, and advice unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the posting unit is realized by the control unit 46A of the smart glasses 214 and provides a function for professionals to post advice videos. The uploading unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides a function for users to upload videos of their own exercise or sports. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded videos. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the posting unit, uploading unit, analysis unit, and advice unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the posting unit is realized by the control unit 46A of the headset type terminal 314 and provides a function for professionals to post advice videos. The uploading unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides a function for users to upload videos of their own exercise or sports. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded videos. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned posting unit, uploading unit, analysis unit, and advice unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the posting unit is realized by the control unit 46A of the robot 414 and provides a function for professionals to post advice videos. The uploading unit is realized, for example, by the control unit 46A of the robot 414 and provides a function for users to upload videos of their own exercise or sports. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the uploaded videos. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides advice based on the analysis results.

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

[0106] The exercise support system may further include a real-time feedback unit. The real-time feedback unit provides real-time feedback while the user is exercising. For example, the real-time feedback unit can provide a running user with immediate advice on where to land their feet or how to swing their arms. It can also provide a user performing yoga poses with real-time instructions on how to fine-tune their posture. It can also provide immediate feedback on the trajectory of a golf swing and body movement during a golf swing. This allows the user to immediately identify areas for improvement while exercising and efficiently improve their skills.

[0107] The exercise support system may further include a community section. The community section provides a platform for users to interact with each other and share information. For example, users can post their exercise videos to the community and receive feedback from other users. Discussion forums related to specific exercises or sports may also be set up, allowing users to share advice and experiences. Furthermore, professionals may hold regular webinars to provide direct advice to users. This allows users to improve their exercise skills while interacting with other users and professionals.

[0108] The exercise support system can further include a gamification unit. The gamification unit makes the user's exercise experience more fun by incorporating game elements. For example, the user can earn points every time they exercise and use those points to customize their avatar or items. A ranking system can also be introduced that allows users to compete with each other on their exercise results. Furthermore, users can earn badges and trophies by achieving specific exercise goals. This can increase the motivation for users to continue exercising while having fun.

[0109] The exercise support system may further include a nutrition advice unit. The nutrition advice unit provides appropriate nutrition advice based on the user's exercise data and goals. For example, if the user is doing strength training, advice can be provided regarding the amount and timing of protein intake. Also, if the user is training for a marathon, advice can be provided regarding how to replenish energy and when to replenish fluids. Furthermore, if the user is aiming to lose weight, calorie restriction and a balanced meal plan can be suggested. This allows the user to effectively achieve their goals in terms of both exercise and nutrition.

[0110] The exercise support system can further include a virtual coaching unit. The virtual coaching unit uses AI to provide the user with an individual training plan. For example, it can create a weekly training menu based on the user's exercise data and goals. It can also flexibly adjust the plan according to the user's training progress. It can also estimate the emotions the user feels during training and provide messages to increase motivation. This allows the user to implement a training plan that suits them and effectively achieve their goals.

[0111] The exercise support system may further include a recovery advice unit. The recovery advice unit supports the user's post-exercise recovery. For example, it may provide videos on post-exercise stretching and massage techniques. It may also estimate the user's emotions and provide music or meditation guides to help them relax. It may also provide advice on post-exercise nutrition to promote recovery. This allows the user to effectively recover after exercise and prepare for the next training session.

[0112] The exercise support system may further include a motivation management unit. The motivation management unit estimates the user's emotions and provides support to maintain motivation. For example, if the user loses motivation to exercise, it may provide encouraging messages or success stories. Also, when the user achieves a goal, it may provide congratulatory messages or rewards. It may also suggest challenges or events to encourage the user to continue exercising. This allows the user to maintain motivation and continue exercising.

[0113] The exercise support system may further include a performance analysis unit. The performance analysis unit performs detailed analysis of the user's exercise data and provides information useful for improving performance. For example, it can analyze running data and visualize fluctuations in pace and heart rate. It can also analyze golf swing data and evaluate swing consistency and power fluctuations. It can also analyze yoga pose data and evaluate improvements in flexibility and balance. This allows the user to objectively evaluate their performance and identify areas for improvement.

[0114] The exercise support system may further include a customizable notification unit. The notification unit provides notifications at appropriate times based on the user's schedule and goals. For example, the notification unit may notify the user of the start time of training or provide reminders. The notification unit may also estimate the user's emotions and send messages to increase motivation. Furthermore, the notification unit may provide notifications of specific events or challenges so that the user does not miss an opportunity to participate. This allows the user to train efficiently according to their schedule.

[0115] The exercise support system may further include a virtual reality (VR) unit. The VR unit provides the user with the experience of exercising in a virtual space. For example, a user can run in a virtual natural environment. A user can also practice yoga poses in a virtual studio. A user can also practice a golf swing on a virtual golf course. This allows the user to enjoy exercise in a variety of situations without being bound by the real environment.

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

[0117] Step 1: The posting unit posts professional advice videos. Professional advice videos can cover fields such as sports, business, and education. For example, videos provided by professionals with specialized knowledge, such as basketball shooting form or the correct way to perform yoga poses, can be posted. The posting unit can also provide a function that allows professionals to filter posts based on their area of ​​expertise. Step 2: The uploading unit allows the user to upload their exercise or sports video. The user's video includes resolution, length, content, etc. For example, videos of various exercises or sports, such as running form or golf swing, can be uploaded. The uploading unit can also have a function to estimate the user's emotions and adjust the timing of video uploads based on the estimated emotions. Step 3: The analysis unit analyzes the video uploaded by the upload unit. The analysis includes motion analysis, audio analysis, and facial expression analysis. For example, when analyzing a running form, the analysis unit evaluates the foot landing position and arm swing, and points out areas for improvement. The analysis unit can also estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. Step 4: The advice unit provides advice based on the results of the analysis by the analysis unit. The advice may include text, audio, video, etc. For example, in a golf swing analysis, advice is provided regarding the swing trajectory and body movement. The advice unit may also have a function to estimate the user's emotions and adjust the way the advice is presented based on the estimated emotions.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0120] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0123] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0155] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0162] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0169] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0171] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0172] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0173] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0174] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0175] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0177] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0178] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0179] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0180] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0181] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0182] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0183] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0184] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0185] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0186] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0187] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, 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.

[0188] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0189] [Explanation of symbols]

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

Claims

1. A posting section that posts professional advice videos, an upload section where users upload their videos; an analysis unit that analyzes the video uploaded by the upload unit; an advice unit that provides advice based on the results of the analysis by the analysis unit; Equipped with A system characterized by:

2. Equipped with a posture estimation unit that uses a motion analysis algorithm 2. The system of claim 1.

3. Establish a certification department to manage the professional certification system 2. The system of claim 1.

4. Equipped with a privacy protection section that protects the privacy of users' videos 2. The system of claim 1.

5. The posting unit: Estimate the emotions of professionals and adjust the way they post based on those emotions.

2. The system of claim 1.

6. The posting unit: When posting, analyze the professional's past posting history and select the appropriate posting method.

2. The system of claim 1.

7. The posting unit: Filter posts based on professional expertise when posting 2. The system of claim 1.

8. The posting unit: Estimate the sentiment of professionals and prioritize posts based on the estimated sentiment 2. The system of claim 1.

9. The posting unit: When posting, it takes into account a professional's geographic location to prioritize relevant posts.

2. The system of claim 1.

10. The posting unit: Analyzes a professional's social media activity and recommends relevant posts as they post 2. The system of claim 1.

11. The upload unit Estimate user emotions and adjust video upload timing based on the estimated emotions 2. The system of claim 1.

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