system

Abstract

The system according to the embodiment aims to intuitively understand the difference between one's own form and the ideal form and improve it. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a display unit, and an advice unit. The reception unit inputs a video of the user's ideal form. The reception unit inputs a video of the user's own form. The analysis unit analyzes the video input by the reception unit and extracts features of the form. The display unit displays and explains the differences between the ideal form and the user's own form based on the features extracted by the analysis unit. The advice unit presents areas for improvement to the user based on the differences displayed by the display 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] With conventional technology, it was difficult to intuitively understand the difference between one's own form and the ideal form and to improve it.

[0005] The system according to the embodiment aims to intuitively understand the difference between one's own form and the ideal form and improve it. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a display unit, and an advice unit. The reception unit inputs a video of the user's ideal form. The reception unit inputs a video of the user's own form. The analysis unit analyzes the video input by the reception unit and extracts features of the form. The display unit displays and explains the differences between the ideal form and the user's own form based on the features extracted by the analysis unit. The advice unit presents areas for improvement to the user based on the differences displayed by the display unit. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to intuitively understand the difference between the ideal form and their own form and improve it. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) A sports form analysis system according to an embodiment of the present invention compares a user's ideal form (e.g., the form of a professional athlete) with their actual form and explains the differences. The system allows the user to input a video of their ideal form, and then the user inputs a video of their own form. The system analyzes these videos and explains the differences between the ideal form and the user's own form, using slow motion. First, the user inputs a video of their ideal form. For example, the user inputs a video of a professional golf player's swing, a soccer player's shot, or a surfing riding video. This video is input into the system. Next, the user inputs a video of their own form. For example, the user may film a video of their golf swing, soccer shot, or surfing riding and input it into the system. The system analyzes the videos of the ideal form and the user's own form. AI is used for the analysis, extracting form characteristics for each frame. For example, in the case of a golf swing, characteristics such as the angle of the club, body rotation, and arm movement are extracted. Next, the system explains the differences between the ideal form and the user's own form, using slow motion. For example, if the ideal form requires a consistent club angle, but the user's own form deviates, the system will display and explain the difference in slow motion. This allows the user to intuitively understand which aspects of their own form differ from the ideal. This system allows the user to receive specific advice on how to improve their own form. For example, they can identify areas for improvement in their golf swing, corrections to their soccer shot, or adjustments to their surfing riding. This can lead to improved sports performance. This allows the sports form analysis system to explain the differences between the user's ideal form and their own form in slow motion and suggest specific areas for improvement, which can lead to improved sports performance.

[0029] A sports form analysis system according to an embodiment includes a reception unit, an analysis unit, a display unit, and an advice unit. The reception unit inputs a video of a user's ideal form. For example, the reception unit can input a video of a professional golf player's swing, a soccer player's shot, or a surfing riding video. The reception unit also inputs a video of the user's own form. For example, the user can film a video of their golf swing, soccer shot, or surfing riding and input it into the system. The analysis unit analyzes the video input by the reception unit and extracts form features. AI is used for the analysis, and form features are extracted for each frame. For example, in the case of a golf swing, features such as the angle of the club, body rotation, and arm movement are extracted. The analysis unit can extract features from each frame of the video using, for example, deep learning technology. The analysis unit can also extract form features using a machine learning algorithm. The display unit displays and explains the difference between the user's ideal form and their own form in slow motion based on the features extracted by the analysis unit. For example, if the ideal form has a constant club angle, but the angle is off in the user's own form, the difference can be displayed in slow motion and explained. The display unit can, for example, adjust the playback speed magnification or frame rate to display the image in slow motion. The advice unit presents specific improvements to the user based on the differences displayed by the display unit. For example, the advice unit can present improvements to a golf swing, corrections to a soccer shot, or adjustments to surfing riding. The advice unit can present, for example, methods for correcting movements or practicing. As a result, the sports form analysis system according to the embodiment can explain the differences between the user's ideal form and their own form in slow motion and present specific improvements, thereby helping to improve sports performance.

[0030] The analysis unit can extract form features for each frame using AI. The analysis unit can extract features from each frame of video using, for example, deep learning technology. For example, a deep learning model can extract features such as the club angle, body rotation, and arm movement in a golf swing. The analysis unit can also extract form features using a machine learning algorithm. For example, a support vector machine (SVM) can extract features such as foot movement and ball trajectory in a soccer shot. The analysis unit can also extract form features using image processing technology. For example, an edge detection algorithm can extract features such as the board angle and wave height in surfing. This improves the accuracy of form feature extraction by using AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input each frame of video into a generation AI, which then extracts features.

[0031] The display unit can display the difference between the ideal form and the user's own form in slow motion. The display unit can perform the slow motion display by, for example, adjusting the playback speed magnification or frame rate. For example, the slow motion display can be performed by setting the playback speed to half the normal speed. The display unit can also perform the slow motion display by changing the frame rate from 30 fps to 15 fps. Furthermore, in addition to the slow motion display, the display unit can also highlight the difference. For example, the difference between the ideal form and the user's own form can be highlighted in color so that the user can intuitively understand it. In this way, by displaying the difference in slow motion, the user can intuitively understand the difference in form. Some or all of the above-described processing in the display unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the display unit can input video data for the slow motion display into the generation AI, and the generation AI can perform the slow motion display.

[0032] The advice unit can present specific improvements to the user based on the differences. The advice unit can present, for example, methods for correcting movements or practice methods. For example, as an improvement to a golf swing, the advice unit can present a practice method for maintaining a constant club angle. The advice unit can also present drills for improving foot movement as a correction to a soccer shot. Furthermore, as an adjustment to surfing riding, the advice unit can also present a practice method for adjusting the angle of the board. This makes it easier for the user to improve their form by presenting specific improvements. Some or all of the above-described processing in the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input difference data into a generation AI, which then presents specific improvements.

[0033] When inputting a video of ideal form, the reception unit can analyze the user's past selection history and recommend the optimal video. For example, the reception unit can recommend the latest video of a professional athlete based on a video of the same athlete selected by the user in the past. The reception unit can also recommend related form videos based on the type of sport selected by the user in the past. Furthermore, the reception unit can recommend similar videos based on videos that the user has previously rated highly. This allows the user to efficiently find their ideal form by recommending the optimal video based on their past selection history. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past selection history data into the generation AI, which can then recommend the optimal video.

[0034] When inputting a video of ideal form, the reception unit can filter the video based on the user's sports experience and level. For example, the reception unit can preferentially display videos of basic form to a beginner user. The reception unit can also display videos including technical explanations to an intermediate user. Furthermore, the reception unit can display videos including advanced techniques of professional athletes to an advanced user. This allows for the selection of a more appropriate form video by providing videos according to the user's sports experience and level. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's sports experience and level data into the generation AI, which then performs filtering.

[0035] When inputting a video of ideal form, the reception unit can prioritize inputting highly relevant videos taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize displaying videos of form of sports popular in that region. Furthermore, if the user is in a specific country, the reception unit can prioritize displaying videos of form of professional athletes from that country. Furthermore, if the user is in a specific city, the reception unit can prioritize displaying videos of form of sport events held in that city. In this way, by taking geographical location information into consideration, highly relevant videos can be provided to the user. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI, which can then recommend highly relevant videos.

[0036] When the user inputs a video of their ideal form, the reception unit can analyze the user's social media activity and recommend related videos. For example, the reception unit can prioritize displaying videos of professional athletes that the user follows on social media. The reception unit can also recommend related form videos based on sports videos that the user has "liked" on social media. Furthermore, the reception unit can recommend related form videos based on videos of sporting events that the user has shared on social media. This allows the user to be provided with videos that interest them by recommending related videos based on social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into a generation AI, which can then recommend related videos.

[0037] When a user inputs a video for their own form, the reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit can prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also suggest the optimal input method based on the format (resolution, frame rate, etc.) of videos the user has previously input. Furthermore, the reception unit can predict and suggest the input method to be used at a specific time period based on the user's past input history. This allows the user to efficiently input videos by selecting the optimal input method based on the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past input history data into the generation AI, which then suggests the optimal input method.

[0038] When inputting a video of the user's form, the reception unit can perform filtering based on the user's current sports activity or area of ​​interest. The reception unit can, for example, preferentially display related form videos based on the type of sport the user is currently playing. The reception unit can also preferentially display form videos of sports in which the user is interested. Furthermore, the reception unit can also preferentially display form videos related to sporting events in which the user is participating. This allows for the selection of a more appropriate form video by providing videos according to the user's current sports activity or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's sports activity and area of ​​interest data into the generation AI, which then performs filtering.

[0039] When inputting a video of the user's form, the reception unit can prioritize inputting highly relevant videos taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize displaying videos of the form of sports popular in that region. Furthermore, if the user is in a specific country, the reception unit can prioritize displaying videos of the form of professional athletes from that country. Furthermore, if the user is in a specific city, the reception unit can prioritize displaying videos of the form of sport events held in that city. In this way, by taking geographical location information into account, highly relevant videos can be provided to the user. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI, which can then recommend highly relevant videos.

[0040] When a user inputs a video of their form, the reception unit can analyze the user's social media activity and recommend related videos. For example, the reception unit can prioritize displaying videos of professional athletes that the user follows on social media. The reception unit can also recommend related form videos based on sports videos that the user has "liked" on social media. Furthermore, the reception unit can recommend related form videos based on videos of sporting events that the user has shared on social media. This allows the user to be provided with videos that interest them by recommending related videos based on social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into a generation AI, which can then recommend related videos.

[0041] During analysis, the analysis unit can extract features by applying different analysis algorithms to each frame of the video. For example, the analysis unit can apply a basic motion analysis algorithm to the beginning of the video. The analysis unit can also apply a detailed motion analysis algorithm to the middle of the video. Furthermore, the analysis unit can apply a comprehensive motion analysis algorithm to the end of the video. This improves the accuracy of feature extraction by applying different analysis algorithms to each frame. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input each frame of the video into a generation AI, which then extracts features.

[0042] During analysis, the analysis unit can adjust analysis parameters based on the user's sports experience and level. For example, the analysis unit can apply basic analysis parameters to a beginner user. The analysis unit can also apply detailed analysis parameters to an intermediate user. Furthermore, the analysis unit can also apply advanced analysis parameters to an advanced user. This improves the accuracy of the analysis by applying analysis parameters according to the user's sports experience and level. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's sports experience and level data into the generation AI, which can then adjust the analysis parameters.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the shooting environment and conditions of the video. The analysis unit can adjust the accuracy of the analysis by taking into account, for example, the brightness of the location where the video was shot. The analysis unit can also adjust the accuracy of the analysis by taking into account the shooting angle of the video. Furthermore, the analysis unit can also adjust the accuracy of the analysis by taking into account the performance of the video shooting device. In this way, the accuracy of the analysis is improved by taking into account the shooting environment and conditions. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input video shooting environment data into the generation AI, which can then adjust the accuracy of the analysis.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to data of other users. For example, the analysis unit can improve the accuracy of the analysis by referring to data of other users playing the same sport. The analysis unit can also improve the accuracy of the analysis by referring to data of other users playing the same form. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to data of other users at the same level. In this way, the accuracy of the analysis is improved by referring to the data of other users. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data of other users into the generation AI, which can improve the accuracy of the analysis.

[0045] When displaying the differences, the display unit can adjust the level of detail of the commentary based on the user's sports experience and level. For example, the display unit can provide a slow-motion display including basic commentary for a beginner user. The display unit can also provide a slow-motion display including detailed technical commentary for an intermediate user. Furthermore, the display unit can also provide a slow-motion display including advanced technique commentary for an advanced user. This enables more appropriate commentary by providing commentary according to the user's sports experience and level. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's sports experience and level data into the generation AI, which can then adjust the level of detail of the commentary.

[0046] When displaying the differences, the display unit can apply a different display algorithm to each frame of the video. For example, the display unit can perform a slow-motion display including a basic explanation of the movements at the beginning of the video. The display unit can also perform a slow-motion display including a detailed technical explanation at the middle of the video. Furthermore, the display unit can also perform a slow-motion display including a comprehensive explanation of the movements at the end of the video. By applying a different display algorithm to each frame, the accuracy of the difference display is improved. Some or all of the above-mentioned processing in the display unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the display unit can input each frame of the video to a generation AI, which can then apply the display algorithm.

[0047] When displaying the differences, the display unit can prioritize displaying highly relevant differences by taking into account the user's geographical location information. For example, if the user is in a specific region, the display unit can prioritize displaying differences for sports popular in that region. Furthermore, if the user is in a specific country, the display unit can prioritize displaying differences for professional athletes from that country. Furthermore, if the user is in a specific city, the display unit can prioritize displaying differences for sporting events held in that city. In this way, highly relevant differences can be provided to the user by taking geographical location information into account. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's geographical location information data into the generation AI, which can then display highly relevant differences.

[0048] When displaying the difference, the display unit can improve the accuracy of the commentary by referring to data of other users. For example, the display unit can improve the accuracy of the commentary by referring to data of other users playing the same sport. The display unit can also improve the accuracy of the commentary by referring to data of other users playing the same form. Furthermore, the display unit can improve the accuracy of the commentary by referring to data of other users at the same level. In this way, the accuracy of the commentary is improved by referring to the data of other users. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input data of other users into the generation AI, which can then improve the accuracy of the commentary.

[0049] When presenting points for improvement, the advice unit can adjust the level of detail of the advice based on the user's sports experience and level. For example, the advice unit can present basic points for improvement to a beginner user. The advice unit can also present detailed points for technical improvement to an intermediate user. Furthermore, the advice unit can also present advanced technique points for an advanced user. This makes it possible to present more appropriate points for improvement by providing advice according to the user's sports experience and level. Some or all of the above-described processing in the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input the user's sports experience and level data into the generation AI, which can then adjust the level of detail of the advice.

[0050] When suggesting improvements, the advice unit can apply a different advice algorithm to each frame of the video. For example, the advice unit can suggest basic movement improvements at the beginning of the video. The advice unit can also suggest detailed technical improvements in the middle of the video. Furthermore, the advice unit can also suggest comprehensive movement improvements at the end of the video. In this way, by applying a different advice algorithm to each frame, the accuracy of suggesting improvements is improved. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input each frame of the video to a generation AI, which then applies the advice algorithm.

[0051] When suggesting improvements, the advice unit can prioritize relevant advice by taking into account the user's geographical location information. For example, if the user is in a specific region, the advice unit can prioritize suggesting improvements for sports that are popular in that region. Furthermore, if the user is in a specific country, the advice unit can prioritize suggesting improvements for professional athletes from that country. Furthermore, if the user is in a specific city, the advice unit can prioritize suggesting improvements for sporting events held in that city. In this way, by taking geographical location information into account, highly relevant advice can be provided to the user. Some or all of the above-described processing in the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input the user's geographical location information data into the generation AI, which can then present highly relevant advice.

[0052] When suggesting areas for improvement, the advice unit can improve the accuracy of the advice by referring to data of other users. For example, the advice unit can improve the accuracy of the advice by referring to data of other users playing the same sport. The advice unit can also improve the accuracy of the advice by referring to data of other users playing the same form. Furthermore, the advice unit can improve the accuracy of the advice by referring to data of other users at the same level. In this way, the accuracy of the advice is improved by referring to the data of other users. Some or all of the above-described processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice unit can input data of other users into the generation AI, which can improve the accuracy of the advice.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] When a user inputs a video of their ideal form, the reception unit can analyze the user's past selection history and recommend the optimal video. For example, based on a video of a professional athlete previously selected by the user, the reception unit can recommend the latest video of the same athlete. The reception unit can also recommend related form videos based on the type of sport previously selected by the user. Furthermore, the reception unit can recommend similar videos based on videos that the user has previously rated highly. This allows the user to efficiently find their ideal form by recommending the optimal video based on their past selection history. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past selection history data into the generation AI, which can then recommend the optimal video.

[0055] During analysis, the analysis unit can extract features by applying different analysis algorithms to each frame of the video. For example, a basic motion analysis algorithm can be applied to the beginning of the video. The analysis unit can also apply a detailed motion analysis algorithm to the middle of the video. Furthermore, the analysis unit can apply a comprehensive motion analysis algorithm to the end of the video. By applying different analysis algorithms to each frame, the accuracy of feature extraction is improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input each frame of the video into a generation AI, which then extracts features.

[0056] When displaying the differences, the display unit can adjust the level of detail of the commentary based on the user's sports experience and level. For example, the display unit can provide a slow-motion display including basic commentary for a beginner user. The display unit can also provide a slow-motion display including detailed technical commentary for an intermediate user. The display unit can also provide a slow-motion display including advanced technique commentary for an advanced user. This allows for more appropriate commentary by providing commentary according to the user's sports experience and level. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's sports experience and level data into the generation AI, which can then adjust the level of detail of the commentary.

[0057] When presenting points for improvement, the advice unit can adjust the level of detail of the advice based on the user's sports experience and level. For example, the advice unit can present basic points for improvement to a beginner user. The advice unit can also present detailed points for technical improvement to an intermediate user. Furthermore, the advice unit can also present advanced technique points for an advanced user. This allows more appropriate points for improvement to be presented by providing advice according to the user's sports experience and level. Some or all of the above-described processing in the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input the user's sports experience and level data into the generation AI, which can then adjust the level of detail of the advice.

[0058] When inputting videos of ideal form, the reception unit can prioritize inputting highly relevant videos taking into account the user's geographical location information. For example, if the user is in a specific region, it can prioritize displaying videos of form of sports popular in that region. Furthermore, if the user is in a specific country, the reception unit can prioritize displaying videos of form of professional athletes from that country. Furthermore, if the user is in a specific city, the reception unit can prioritize displaying videos of form of sport events held in that city. In this way, by taking geographical location information into account, highly relevant videos can be provided to the user. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI, which can then recommend highly relevant videos.

[0059] During analysis, the analysis unit can improve the accuracy of the analysis by referring to data from other users. For example, the analysis unit can improve the accuracy of the analysis by referring to data from other users playing the same sport. The analysis unit can also improve the accuracy of the analysis by referring to data from other users playing the same form. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to data from other users at the same level. In this way, the accuracy of the analysis is improved by referring to the data from other users. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data from other users into the generation AI, which can improve the accuracy of the analysis.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit inputs a video of the user's ideal form. For example, a video of a professional golf player's swing, a soccer player's shot, or a surfing riding video can be input. The reception unit also inputs a video of the user's own form. For example, the user can take a video of their own golf swing, soccer shot, or surfing riding and input it into the system. Step 2: The analysis unit analyzes the video input by the reception unit and extracts form features. AI is used for the analysis, and form features are extracted for each frame. For example, in the case of a golf swing, features such as the angle of the club, body rotation, and arm movement are extracted. The analysis unit can extract features from each frame of the video using, for example, deep learning technology. The analysis unit can also extract form features using machine learning algorithms. Step 3: The display unit displays and explains the difference between the ideal form and the user's own form in slow motion based on the features extracted by the analysis unit. For example, if the ideal form has a constant club angle, but the user's own form has a different angle, the display unit can display and explain the difference in slow motion. The display unit can adjust the playback speed ratio or frame rate to display the slow motion, for example. Step 4: The advice unit presents specific improvements to the user based on the differences displayed by the display unit. For example, the advice unit can present improvements to a golf swing, corrections to a soccer shot, adjustments to surfing riding, etc. The advice unit can present, for example, methods for correcting movements or practice methods.

[0062] (Example 2) A sports form analysis system according to an embodiment of the present invention compares a user's ideal form (e.g., the form of a professional athlete) with their actual form and explains the differences. The system allows the user to input a video of their ideal form, and then the user inputs a video of their own form. The system analyzes these videos and explains the differences between the ideal form and the user's own form, using slow motion. First, the user inputs a video of their ideal form. For example, the user inputs a video of a professional golf player's swing, a soccer player's shot, or a surfing riding video. This video is input into the system. Next, the user inputs a video of their own form. For example, the user may film a video of their golf swing, soccer shot, or surfing riding and input it into the system. The system analyzes the videos of the ideal form and the user's own form. AI is used for the analysis, extracting form characteristics for each frame. For example, in the case of a golf swing, characteristics such as the angle of the club, body rotation, and arm movement are extracted. Next, the system explains the differences between the ideal form and the user's own form, using slow motion. For example, if the ideal form requires a consistent club angle, but the user's own form deviates, the system will display and explain the difference in slow motion. This allows the user to intuitively understand which aspects of their own form differ from the ideal. This system allows the user to receive specific advice on how to improve their own form. For example, they can identify areas for improvement in their golf swing, corrections to their soccer shot, or adjustments to their surfing riding. This can lead to improved sports performance. This allows the sports form analysis system to explain the differences between the user's ideal form and their own form in slow motion and suggest specific areas for improvement, which can lead to improved sports performance.

[0063] A sports form analysis system according to an embodiment includes a reception unit, an analysis unit, a display unit, and an advice unit. The reception unit inputs a video of a user's ideal form. For example, the reception unit can input a video of a professional golf player's swing, a soccer player's shot, or a surfing riding video. The reception unit also inputs a video of the user's own form. For example, the user can film a video of their golf swing, soccer shot, or surfing riding and input it into the system. The analysis unit analyzes the video input by the reception unit and extracts form features. AI is used for the analysis, and form features are extracted for each frame. For example, in the case of a golf swing, features such as the angle of the club, body rotation, and arm movement are extracted. The analysis unit can extract features from each frame of the video using, for example, deep learning technology. The analysis unit can also extract form features using a machine learning algorithm. The display unit displays and explains the difference between the user's ideal form and their own form in slow motion based on the features extracted by the analysis unit. For example, if the ideal form has a constant club angle, but the angle is off in the user's own form, the difference can be displayed in slow motion and explained. The display unit can, for example, adjust the playback speed magnification or frame rate to display the image in slow motion. The advice unit presents specific improvements to the user based on the differences displayed by the display unit. For example, the advice unit can present improvements to a golf swing, corrections to a soccer shot, or adjustments to surfing riding. The advice unit can present, for example, methods for correcting movements or practicing. As a result, the sports form analysis system according to the embodiment can explain the differences between the user's ideal form and their own form in slow motion and present specific improvements, thereby helping to improve sports performance.

[0064] The analysis unit can extract form features for each frame using AI. The analysis unit can extract features from each frame of video using, for example, deep learning technology. For example, a deep learning model can extract features such as the club angle, body rotation, and arm movement in a golf swing. The analysis unit can also extract form features using a machine learning algorithm. For example, a support vector machine (SVM) can extract features such as foot movement and ball trajectory in a soccer shot. The analysis unit can also extract form features using image processing technology. For example, an edge detection algorithm can extract features such as the board angle and wave height in surfing. This improves the accuracy of form feature extraction by using AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input each frame of video into a generation AI, which then extracts features.

[0065] The display unit can display the difference between the ideal form and the user's own form in slow motion. The display unit can perform the slow motion display by, for example, adjusting the playback speed magnification or frame rate. For example, the slow motion display can be performed by setting the playback speed to half the normal speed. The display unit can also perform the slow motion display by changing the frame rate from 30 fps to 15 fps. Furthermore, in addition to the slow motion display, the display unit can also highlight the difference. For example, the difference between the ideal form and the user's own form can be highlighted in color so that the user can intuitively understand it. In this way, by displaying the difference in slow motion, the user can intuitively understand the difference in form. Some or all of the above-described processing in the display unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the display unit can input video data for the slow motion display into the generation AI, and the generation AI can perform the slow motion display.

[0066] The advice unit can present specific improvements to the user based on the differences. The advice unit can present, for example, methods for correcting movements or practice methods. For example, as an improvement to a golf swing, the advice unit can present a practice method for maintaining a constant club angle. The advice unit can also present drills for improving foot movement as a correction to a soccer shot. Furthermore, as an adjustment to surfing riding, the advice unit can also present a practice method for adjusting the angle of the board. This makes it easier for the user to improve their form by presenting specific improvements. Some or all of the above-described processing in the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input difference data into a generation AI, which then presents specific improvements.

[0067] The sports form analysis system further includes a reception unit that estimates the user's emotions and adjusts the timing of video input based on the estimated user emotions. For example, if the user is nervous, the reception unit can delay the input timing to allow the user to relax. Furthermore, if the user is excited, the reception unit can also advance the input timing to allow the user to input immediately. Furthermore, if the user is tired, the reception unit can also prompt the user to take a break before inputting. This allows the video to be input at a more appropriate timing by adjusting the input timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0068] When inputting a video of ideal form, the reception unit can analyze the user's past selection history and recommend the optimal video. For example, the reception unit can recommend the latest video of a professional athlete based on a video of the same athlete selected by the user in the past. The reception unit can also recommend related form videos based on the type of sport selected by the user in the past. Furthermore, the reception unit can recommend similar videos based on videos that the user has previously rated highly. This allows the user to efficiently find their ideal form by recommending the optimal video based on their past selection history. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past selection history data into the generation AI, which can then recommend the optimal video.

[0069] When inputting a video of ideal form, the reception unit can filter the video based on the user's sports experience and level. For example, the reception unit can preferentially display videos of basic form to a beginner user. The reception unit can also display videos including technical explanations to an intermediate user. Furthermore, the reception unit can display videos including advanced techniques of professional athletes to an advanced user. This allows for the selection of a more appropriate form video by providing videos according to the user's sports experience and level. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's sports experience and level data into the generation AI, which then performs filtering.

[0070] The reception unit can estimate the user's emotions and prioritize the ideal form videos to be input based on the estimated user emotions. For example, if the user is relaxed, the reception unit can prioritize displaying videos containing detailed commentary. Furthermore, if the user is in a hurry, the reception unit can prioritize displaying videos that quickly capture the main points. Furthermore, if the user is excited, the reception unit can prioritize displaying visually stimulating videos. This allows for more appropriate videos to be displayed by prioritizing videos according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or without the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0071] When inputting a video of ideal form, the reception unit can prioritize inputting highly relevant videos taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize displaying videos of form of sports popular in that region. Furthermore, if the user is in a specific country, the reception unit can prioritize displaying videos of form of professional athletes from that country. Furthermore, if the user is in a specific city, the reception unit can prioritize displaying videos of form of sport events held in that city. In this way, by taking geographical location information into consideration, highly relevant videos can be provided to the user. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI, which can then recommend highly relevant videos.

[0072] When the user inputs a video of their ideal form, the reception unit can analyze the user's social media activity and recommend related videos. For example, the reception unit can prioritize displaying videos of professional athletes that the user follows on social media. The reception unit can also recommend related form videos based on sports videos that the user has "liked" on social media. Furthermore, the reception unit can recommend related form videos based on videos of sporting events that the user has shared on social media. This allows the user to be provided with videos that interest them by recommending related videos based on social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into a generation AI, which can then recommend related videos.

[0073] The reception unit can estimate the user's emotions and adjust the timing of inputting the video of the user's form based on the estimated user emotions. For example, if the user is nervous, the reception unit can delay the input timing so that the user can relax. Furthermore, if the user is excited, the reception unit can also advance the input timing so that the user can input immediately. Furthermore, if the user is tired, the reception unit can prompt the user to take a break before inputting. By adjusting the input timing according to the user's emotions, the video can be input at a more appropriate timing. 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-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0074] When a user inputs a video for their own form, the reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit can prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also suggest the optimal input method based on the format (resolution, frame rate, etc.) of videos the user has previously input. Furthermore, the reception unit can predict and suggest the input method to be used at a specific time period based on the user's past input history. This allows the user to efficiently input videos by selecting the optimal input method based on the past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's past input history data into the generation AI, which then suggests the optimal input method.

[0075] When inputting a video of the user's form, the reception unit can perform filtering based on the user's current sports activity or area of ​​interest. The reception unit can, for example, preferentially display related form videos based on the type of sport the user is currently playing. The reception unit can also preferentially display form videos of sports in which the user is interested. Furthermore, the reception unit can also preferentially display form videos related to sporting events in which the user is participating. This allows for the selection of a more appropriate form video by providing videos according to the user's current sports activity or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's sports activity and area of ​​interest data into the generation AI, which then performs filtering.

[0076] The reception unit can estimate the user's emotions and prioritize the videos of the user's input form based on the estimated user emotions. For example, if the user is relaxed, the reception unit can prioritize displaying videos containing detailed commentary. Furthermore, if the user is in a hurry, the reception unit can prioritize displaying videos that quickly capture the main points. Furthermore, if the user is excited, the reception unit can prioritize displaying visually stimulating videos. This allows for more appropriate videos to be displayed by prioritizing videos according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or without the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0077] When inputting a video of the user's form, the reception unit can prioritize inputting highly relevant videos taking into account the user's geographical location information. For example, if the user is in a specific region, the reception unit can prioritize displaying videos of the form of sports popular in that region. Furthermore, if the user is in a specific country, the reception unit can prioritize displaying videos of the form of professional athletes from that country. Furthermore, if the user is in a specific city, the reception unit can prioritize displaying videos of the form of sport events held in that city. In this way, by taking geographical location information into account, highly relevant videos can be provided to the user. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI, which can then recommend highly relevant videos.

[0078] When a user inputs a video of their form, the reception unit can analyze the user's social media activity and recommend related videos. For example, the reception unit can prioritize displaying videos of professional athletes that the user follows on social media. The reception unit can also recommend related form videos based on sports videos that the user has "liked" on social media. Furthermore, the reception unit can recommend related form videos based on videos of sporting events that the user has shared on social media. This allows the user to be provided with videos that interest them by recommending related videos based on social media activity. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into a generation AI, which can then recommend related videos.

[0079] The analysis unit can estimate the user's emotions and adjust analysis parameters based on the estimated user emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a quick analysis when the user is in a hurry. Furthermore, the analysis unit can provide a visually stimulating analysis result when the user is excited. This allows for adjusting the accuracy of the analysis according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0080] During analysis, the analysis unit can extract features by applying different analysis algorithms to each frame of the video. For example, the analysis unit can apply a basic motion analysis algorithm to the beginning of the video. The analysis unit can also apply a detailed motion analysis algorithm to the middle of the video. Furthermore, the analysis unit can apply a comprehensive motion analysis algorithm to the end of the video. This improves the accuracy of feature extraction by applying different analysis algorithms to each frame. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input each frame of the video into a generation AI, which then extracts features.

[0081] During analysis, the analysis unit can adjust analysis parameters based on the user's sports experience and level. For example, the analysis unit can apply basic analysis parameters to a beginner user. The analysis unit can also apply detailed analysis parameters to an intermediate user. Furthermore, the analysis unit can also apply advanced analysis parameters to an advanced user. This improves the accuracy of the analysis by applying analysis parameters according to the user's sports experience and level. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's sports experience and level data into the generation AI, which can then adjust the analysis parameters.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, when the user is relaxed, the analysis unit can display detailed analysis results. When the user is in a hurry, the analysis unit can also display analysis results that focus on the main points. Furthermore, when the user is excited, the analysis unit can provide visually stimulating analysis results. This allows for adjusting the display method of the analysis results according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the shooting environment and conditions of the video. The analysis unit can adjust the accuracy of the analysis by taking into account, for example, the brightness of the location where the video was shot. The analysis unit can also adjust the accuracy of the analysis by taking into account the shooting angle of the video. Furthermore, the analysis unit can also adjust the accuracy of the analysis by taking into account the performance of the video shooting device. In this way, the accuracy of the analysis is improved by taking into account the shooting environment and conditions. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input video shooting environment data into the generation AI, which can then adjust the accuracy of the analysis.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis by referring to data of other users. For example, the analysis unit can improve the accuracy of the analysis by referring to data of other users playing the same sport. The analysis unit can also improve the accuracy of the analysis by referring to data of other users playing the same form. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to data of other users at the same level. In this way, the accuracy of the analysis is improved by referring to the data of other users. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data of other users into the generation AI, which can improve the accuracy of the analysis.

[0085] The display unit can estimate the user's emotions and adjust the display method of the differences based on the estimated user emotions. For example, if the user is relaxed, the display unit can display a slow-motion image including detailed commentary. If the user is in a hurry, the display unit can display a concise slow-motion image that focuses on the main points. If the user is excited, the display unit can display a slow-motion image with visually stimulating effects. This allows for more appropriate display by adjusting the display method of the differences according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit can be performed using, for example, the generation AI, or without the generation AI. For example, the display unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0086] When displaying the differences, the display unit can adjust the level of detail of the commentary based on the user's sports experience and level. For example, the display unit can provide a slow-motion display including basic commentary for a beginner user. The display unit can also provide a slow-motion display including detailed technical commentary for an intermediate user. Furthermore, the display unit can also provide a slow-motion display including advanced technique commentary for an advanced user. This enables more appropriate commentary by providing commentary according to the user's sports experience and level. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's sports experience and level data into the generation AI, which can then adjust the level of detail of the commentary.

[0087] When displaying the differences, the display unit can apply a different display algorithm to each frame of the video. For example, the display unit can perform a slow-motion display including a basic explanation of the movements at the beginning of the video. The display unit can also perform a slow-motion display including a detailed technical explanation at the middle of the video. Furthermore, the display unit can also perform a slow-motion display including a comprehensive explanation of the movements at the end of the video. By applying a different display algorithm to each frame, the accuracy of the difference display is improved. Some or all of the above-mentioned processing in the display unit can be performed using, for example, a generation AI, or can be performed without using the generation AI. For example, the display unit can input each frame of the video to a generation AI, which can then apply the display algorithm.

[0088] The display unit can estimate the user's emotions and adjust the display order of the differences based on the estimated user's emotions. For example, if the user is relaxed, the display unit can display the differences in an order that includes detailed explanations. Furthermore, if the user is in a hurry, the display unit can display the differences in an order that emphasizes the main points. Furthermore, if the user is excited, the display unit can display the differences in a visually stimulating order. This allows for more appropriate display by adjusting the display order of the differences according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the display unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the display unit can input the user's facial expression data into the generation AI, which then estimates the emotion.

[0089] When displaying the differences, the display unit can prioritize displaying highly relevant differences by taking into account the user's geographical location information. For example, if the user is in a specific region, the display unit can prioritize displaying differences for sports popular in that region. Furthermore, if the user is in a specific country, the display unit can prioritize displaying differences for professional athletes from that country. Furthermore, if the user is in a specific city, the display unit can prioritize displaying differences for sporting events held in that city. In this way, highly relevant differences can be provided to the user by taking geographical location information into account. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's geographical location information data into the generation AI, which can then display highly relevant differences.

[0090] When displaying the difference, the display unit can improve the accuracy of the commentary by referring to data of other users. For example, the display unit can improve the accuracy of the commentary by referring to data of other users playing the same sport. The display unit can also improve the accuracy of the commentary by referring to data of other users playing the same form. Furthermore, the display unit can improve the accuracy of the commentary by referring to data of other users at the same level. In this way, the accuracy of the commentary is improved by referring to the data of other users. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input data of other users into the generation AI, which can then improve the accuracy of the commentary.

[0091] The advice unit can estimate the user's emotions and adjust the method of presenting improvements based on the estimated user emotions. For example, if the user is relaxed, the advice unit can present detailed improvements. Furthermore, if the user is in a hurry, the advice unit can present improvements that focus on the key points. Furthermore, if the user is excited, the advice unit can present visually stimulating improvements. This allows for more appropriate advice by adjusting the method of presenting improvements according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the advice unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the user's facial expression data into the generation AI, which then estimates the emotion.

[0092] When presenting points for improvement, the advice unit can adjust the level of detail of the advice based on the user's sports experience and level. For example, the advice unit can present basic points for improvement to a beginner user. The advice unit can also present detailed points for technical improvement to an intermediate user. Furthermore, the advice unit can also present advanced technique points for an advanced user. This makes it possible to present more appropriate points for improvement by providing advice according to the user's sports experience and level. Some or all of the above-described processing in the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input the user's sports experience and level data into the generation AI, which can then adjust the level of detail of the advice.

[0093] When suggesting improvements, the advice unit can apply a different advice algorithm to each frame of the video. For example, the advice unit can suggest basic movement improvements at the beginning of the video. The advice unit can also suggest detailed technical improvements in the middle of the video. Furthermore, the advice unit can also suggest comprehensive movement improvements at the end of the video. In this way, by applying a different advice algorithm to each frame, the accuracy of suggesting improvements is improved. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input each frame of the video to a generation AI, which then applies the advice algorithm.

[0094] The advice unit can estimate the user's emotions and prioritize improvements based on the estimated user emotions. For example, if the user is relaxed, the advice unit can prioritize detailed improvements. Furthermore, if the user is in a hurry, the advice unit can prioritize improvements that focus on the key points. Furthermore, if the user is excited, the advice unit can prioritize improvements that are visually stimulating. This enables more appropriate advice by prioritizing improvements based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the advice unit can be performed using, for example, the generation AI. For example, the advice unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0095] When suggesting improvements, the advice unit can prioritize relevant advice by taking into account the user's geographical location information. For example, if the user is in a specific region, the advice unit can prioritize suggesting improvements for sports that are popular in that region. Furthermore, if the user is in a specific country, the advice unit can prioritize suggesting improvements for professional athletes from that country. Furthermore, if the user is in a specific city, the advice unit can prioritize suggesting improvements for sporting events held in that city. In this way, by taking geographical location information into account, highly relevant advice can be provided to the user. Some or all of the above-described processing in the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input the user's geographical location information data into the generation AI, which can then present highly relevant advice.

[0096] When suggesting areas for improvement, the advice unit can improve the accuracy of the advice by referring to data of other users. For example, the advice unit can improve the accuracy of the advice by referring to data of other users playing the same sport. The advice unit can also improve the accuracy of the advice by referring to data of other users playing the same form. Furthermore, the advice unit can improve the accuracy of the advice by referring to data of other users at the same level. In this way, the accuracy of the advice is improved by referring to the data of other users. Some or all of the above-described processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice unit can input data of other users into the generation AI, which can improve the accuracy of the advice. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, display unit, and advice unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, and the user inputs a video of their ideal form or their own form. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and extracts form characteristics from each frame of the video using AI. The display unit is implemented by the display 40A of the smart device 14, and displays and explains the difference between the ideal form and the user's own form in slow motion. The advice unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and presents the user with specific areas for improvement. Furthermore, the reception unit, which has an emotion estimation function, acquires the user's facial expression data using, for example, the camera 42 of the smart device 14 and estimates the user's emotion using generation AI. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, display 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 reception unit is realized by the control unit 46A of the smart glasses 214, and the user inputs a video of their ideal form or a video of their own form. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and extracts form characteristics from each frame of the video using AI. The display unit is realized by the display of the smart glasses 214, and displays and explains the difference between the ideal form and the user's own form in slow motion. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and presents the user with specific areas for improvement. Furthermore, the reception unit, which has an emotion estimation function, acquires the user's facial expression data using, for example, the camera 42 of the smart glasses 214 and estimates the user's emotion using generation AI. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, display 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 reception unit is realized by the control unit 46A of the headset-type terminal 314, and the user inputs a video of their ideal form or a video of their own form. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and extracts form characteristics from each frame of the video using AI. The display unit is realized by the display 343 of the headset-type terminal 314, and displays and explains the difference between the ideal form and the user's own form in slow motion. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and presents the user with specific areas for improvement. Furthermore, the reception unit, which has an emotion estimation function, acquires the user's facial expression data using, for example, the camera 42 of the headset-type terminal 314, and estimates the user's emotion using generation AI. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, display unit, and advice unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and the user inputs a video of their ideal form or a video of their own form. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and extracts form characteristics from each frame of the video using AI. The display unit is realized by the display of the robot 414, and displays and explains the difference between the ideal form and the user's own form in slow motion. The advice unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and presents the user with specific areas for improvement. Furthermore, the reception unit, which has an emotion estimation function, acquires the user's facial expression data using, for example, the camera 42 of the robot 414 and estimates the user's emotion using generation AI.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] When a user inputs a video of their ideal form, the reception unit can analyze the user's past selection history and recommend the optimal video. For example, based on a video of a professional athlete previously selected by the user, the reception unit can recommend the latest video of the same athlete. The reception unit can also recommend related form videos based on the type of sport previously selected by the user. Furthermore, the reception unit can recommend similar videos based on videos that the user has previously rated highly. This allows the user to efficiently find their ideal form by recommending the optimal video based on their past selection history. Some or all of the above-described processing by the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past selection history data into the generation AI, which can then recommend the optimal video.

[0099] During analysis, the analysis unit can extract features by applying different analysis algorithms to each frame of the video. For example, a basic motion analysis algorithm can be applied to the beginning of the video. The analysis unit can also apply a detailed motion analysis algorithm to the middle of the video. Furthermore, the analysis unit can apply a comprehensive motion analysis algorithm to the end of the video. By applying different analysis algorithms to each frame, the accuracy of feature extraction is improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input each frame of the video into a generation AI, which then extracts features.

[0100] When displaying the differences, the display unit can adjust the level of detail of the commentary based on the user's sports experience and level. For example, the display unit can provide a slow-motion display including basic commentary for a beginner user. The display unit can also provide a slow-motion display including detailed technical commentary for an intermediate user. The display unit can also provide a slow-motion display including advanced technique commentary for an advanced user. This allows for more appropriate commentary by providing commentary according to the user's sports experience and level. Some or all of the above-described processing in the display unit may be performed using, or without, a generation AI. For example, the display unit can input the user's sports experience and level data into the generation AI, which can then adjust the level of detail of the commentary.

[0101] When presenting points for improvement, the advice unit can adjust the level of detail of the advice based on the user's sports experience and level. For example, the advice unit can present basic points for improvement to a beginner user. The advice unit can also present detailed points for technical improvement to an intermediate user. Furthermore, the advice unit can also present advanced technique points for an advanced user. This allows more appropriate points for improvement to be presented by providing advice according to the user's sports experience and level. Some or all of the above-described processing in the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input the user's sports experience and level data into the generation AI, which can then adjust the level of detail of the advice.

[0102] The reception unit can estimate the user's emotions and adjust the timing of video input based on the estimated user emotions. For example, if the user is nervous, the reception unit can delay the input timing so that the user can relax. Furthermore, if the user is excited, the reception unit can advance the input timing so that the user can input immediately. Furthermore, if the user is tired, the reception unit can prompt the user to take a break before inputting. By adjusting the input timing according to the user's emotions, the video can be input at a more appropriate timing. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0103] The analysis unit can estimate the user's emotions and adjust analysis parameters based on the estimated user emotions. For example, if the user is relaxed, a detailed analysis can be performed. Furthermore, if the user is in a hurry, the analysis unit can also perform a quick analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows for adjusting the accuracy of the analysis according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0104] The display unit can estimate the user's emotions and adjust the display method of the differences based on the estimated user's emotions. For example, if the user is relaxed, the display unit can display a slow-motion image with detailed commentary. If the user is in a hurry, the display unit can display a concise slow-motion image with a focus on the main points. If the user is excited, the display unit can display a slow-motion image with visually stimulating effects. This allows for more appropriate display by adjusting the display method of the differences according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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 display unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the display unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0105] The advice unit can estimate the user's emotions and adjust the method of presenting improvement suggestions based on the estimated user emotions. For example, if the user is relaxed, the advice unit can present detailed improvement suggestions. If the user is in a hurry, the advice unit can also present key improvement suggestions. If the user is excited, the advice unit can also present visually stimulating improvement suggestions. This allows for more appropriate advice by adjusting the method of presenting improvement suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the advice unit can input the user's facial expression data into the generation AI, which can then estimate the emotion.

[0106] When inputting videos of ideal form, the reception unit can prioritize inputting highly relevant videos taking into account the user's geographical location information. For example, if the user is in a specific region, it can prioritize displaying videos of form of sports popular in that region. Furthermore, if the user is in a specific country, the reception unit can prioritize displaying videos of form of professional athletes from that country. Furthermore, if the user is in a specific city, the reception unit can prioritize displaying videos of form of sport events held in that city. In this way, by taking geographical location information into account, highly relevant videos can be provided to the user. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI, which can then recommend highly relevant videos.

[0107] During analysis, the analysis unit can improve the accuracy of the analysis by referring to data from other users. For example, the analysis unit can improve the accuracy of the analysis by referring to data from other users playing the same sport. The analysis unit can also improve the accuracy of the analysis by referring to data from other users playing the same form. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to data from other users at the same level. In this way, the accuracy of the analysis is improved by referring to the data from other users. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data from other users into the generation AI, which can improve the accuracy of the analysis.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The reception unit inputs a video of the user's ideal form. For example, a video of a professional golf player's swing, a soccer player's shot, or a surfing riding video can be input. The reception unit also inputs a video of the user's own form. For example, the user can take a video of their own golf swing, soccer shot, or surfing riding and input it into the system. Step 2: The analysis unit analyzes the video input by the reception unit and extracts form features. AI is used for the analysis, and form features are extracted for each frame. For example, in the case of a golf swing, features such as the angle of the club, body rotation, and arm movement are extracted. The analysis unit can extract features from each frame of the video using, for example, deep learning technology. The analysis unit can also extract form features using machine learning algorithms. Step 3: The display unit displays and explains the difference between the ideal form and the user's own form in slow motion based on the features extracted by the analysis unit. For example, if the ideal form has a constant club angle, but the user's own form has a different angle, the display unit can display and explain the difference in slow motion. The display unit can adjust the playback speed ratio or frame rate to display the slow motion, for example. Step 4: The advice unit presents specific improvements to the user based on the differences displayed by the display unit. For example, the advice unit can present improvements to a golf swing, corrections to a soccer shot, adjustments to surfing riding, etc. The advice unit can present, for example, methods for correcting movements or practice methods.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0180] 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.

[0181] [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit for inputting a video of the user's ideal form; A reception section where users input their own form videos; an analysis unit that analyzes the video input by the reception unit and extracts form features; a display unit that displays and explains the difference between the ideal form and the user's own form based on the characteristics extracted by the analysis unit; and an advice unit that suggests improvements to the user based on the differences displayed by the display unit. A system characterized by:

2. The analysis unit Using AI to extract form features for each frame 2. The system of claim 1.

3. The display unit Display the difference between your ideal form and your own form in slow motion 2. The system of claim 1.

4. The advice unit Based on the difference, provide specific improvements to the user 2. The system of claim 1.

5. The reception unit Estimate the user's emotions and adjust the timing of video input based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit When entering the video of the ideal form, the system analyzes the user's past selection history and recommends appropriate videos.

2. The system of claim 1.

7. The reception unit When inputting videos of ideal form, filtering is performed based on the user's sports experience and level.

2. The system of claim 1.

8. The reception unit Estimate user emotions and prioritize videos based on the estimated user emotions 2. The system of claim 1.

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