System

Abstract

An object of a system according to an embodiment is to provide specific guidance for effectively improving a user's play in a sport of individual competition.SOLUTION: A system includes a play style input part, a moving image upload part, a comparison analysis part, an explanation moving image generation part, and an action indication part. A play style input part inputs the desire of a play style and the image of a professional player. The video upload unit uploads a play-video of the user. The comparison analysis unit compares and analyzes the play-video with the accumulated data. An explanation moving image generation part explains improvement points by a moving image on the basis of a result obtained by the comparison analysis part. The action indication unit indicates an improved action or an unimproved action on the basis of the play-video.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of lacking specific instruction to effectively improve one's play in individual sports.

[0005] The system according to the embodiment aims to provide specific instruction for effectively improving one's own play in individual sports. [Means for solving the problem]

[0006] The system according to the embodiment comprises a playing style input unit, a video upload unit, a comparison and analysis unit, a commentary video generation unit, and a behavior suggestion unit. The playing style input unit inputs the desired playing style and the image of a professional player. The video upload unit uploads a video of the player's play. The comparison and analysis unit compares and analyzes the play video with accumulated data. The commentary video generation unit explains points for improvement in a video based on the results obtained by the comparison and analysis unit. The behavior suggestion unit points out behaviors that could be improved and behaviors that need to be improved based on the play video. [Effects of the Invention]

[0007] The system according to the embodiment can provide specific guidance for effectively improving one's play in individual sports. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The sports play improvement system according to an embodiment of the present invention analyzes a video of a user's play, explains points for improvement in the video based on the user's desired playing style and the image of a professional player, and suggests improvements to the user's behavior. This allows the sports play improvement system to efficiently improve the user's play and raise the user's level.

[0029] A sports play improvement system according to an embodiment includes a playing style input unit, a video upload unit, a comparison and analysis unit, a commentary video generation unit, and a behavior suggestion unit. The playing style input unit inputs the user's desired playing style and their image of a professional player. For example, the user may input, "I want an aggressive playing style in tennis, and I aim to play like professional player A." The video upload unit uploads videos of the user's play. For example, the user uploads a video of their tennis serve to the system. The comparison and analysis unit compares and analyzes the play video with accumulated data. For example, a generation AI compares the serve of professional player A with the user's serve and analyzes the differences. The commentary video generation unit provides video commentary on areas for improvement based on the results obtained by the comparison and analysis unit. For example, the commentary video may explain, "Your arm swing in your tennis serve is slower than that of professional player A. To improve this, you need to practice swinging your arm faster." The behavior suggestion unit points out behaviors that can be improved and behaviors that need to be improved based on the play video. For example, the system may point out, "Your arm swing for serving has become faster, but your foot position is still unstable. Let's improve this further." In this way, the sports play improvement system according to the embodiment can efficiently improve the user's play and raise the user's level.

[0030] The playing style input unit can analyze the user's past playing history and automatically suggest the optimal playing style. For example, the playing style input unit analyzes playing videos uploaded by the user in the past to extract playing style trends. For example, if there are many offensive playing styles, the playing style input unit suggests the image of an offensive professional player. In this way, the playing history of the user is analyzed and the optimal playing style is automatically suggested, thereby supporting the user in selecting a playing style.

[0031] The playing style input unit can input the user's physical characteristics and suggest the image of the most suitable professional player based on the input. For example, the playing style input unit can input the user's height and weight and suggest professional players with a similar physique based on the input. For example, for a tall user, professional players with a similar height can be suggested. In this way, the system can support the user in selecting a playing style by suggesting the most suitable image of a professional player based on the user's physical characteristics.

[0032] The playing style input unit can automatically generate a training menu based on the user's desired playing style. For example, when the user inputs their desired playing style, the playing style input unit automatically generates an optimal training menu based on that input. For example, if an attacking playing style is desired, the playing style input unit suggests a training menu that will enhance attacking power. In this way, the automatic generation of a training menu based on the user's desired playing style supports the user's training.

[0033] The playing style input unit can suggest equipment suitable for the user based on the image of a professional player. The playing style input unit suggests equipment that is best suited to the user based on, for example, the image of a professional player. For example, it suggests the racket used by professional player A. In this way, the playing style input unit supports the user's play by suggesting equipment that is suitable for the user based on the image of a professional player.

[0034] The video uploading unit can convert the movements into 3D models when analyzing the video, allowing for more detailed analysis. For example, the video uploading unit can convert a play video uploaded by a user into a 3D model and perform a detailed analysis of the movements. For example, the form of a tennis serve can be analyzed using a 3D model. By converting the movements into 3D models when analyzing the video, more detailed analysis becomes possible.

[0035] The video uploading unit analyzes each frame of the video and can detect subtle changes in movement. For example, the video uploading unit analyzes each frame of a video of a user playing and detects subtle changes in movement. For example, the video uploading unit analyzes changes in a tennis serve form frame by frame. In this way, by analyzing each frame of the video, subtle changes in movement can be detected.

[0036] The video uploading unit can feed back the video analysis results to the user's smartphone or wearable device in real time. The video uploading unit, for example, builds a system that feeds back the video analysis results to the user's smartphone in real time. For example, the analysis results of a tennis serve are displayed on the smartphone. This allows the user to immediately understand areas for improvement by feeding back the video analysis results in real time.

[0037] The video uploading unit can share the video analysis results with other users and obtain feedback within the community. The video uploading unit, for example, builds a system for sharing the video analysis results with other users and obtaining feedback within the community. For example, the analysis results of a tennis serve can be shared and advice can be obtained from other users. In this way, by sharing the video analysis results with other users and obtaining feedback within the community, the user's play can be improved.

[0038] The comparison and analysis unit can visualize the progress of a user's play in chronological order based on the accumulated data. The comparison and analysis unit, for example, compares a video of the user's play with the accumulated data and builds a system that visualizes the progress in chronological order. For example, the progress of a tennis serve can be displayed in a graph. This makes it easier for the user to understand their own growth by visualizing the progress of the play in chronological order based on the accumulated data.

[0039] The comparative analysis unit can perform comparative analysis not only with data from professional players but also with data from amateur players of the same level. For example, the comparative analysis unit constructs a system that compares and analyzes a user's playing video not only with data from professional players but also with data from amateur players of the same level. For example, a tennis serve can be compared with that of amateur players of the same level. This allows for comparative analysis not only with data from professional players but also with data from amateur players of the same level, making it possible to grasp areas for improvement in the user's play from multiple angles.

[0040] The comparison and analysis unit shares the results of the comparison and analysis with the user's coach or trainer, allowing the user to obtain professional advice. The comparison and analysis unit, for example, builds a system for sharing the results of the comparison and analysis with the user's coach or trainer and obtaining professional advice. For example, the analysis results of a tennis serve are shared with the coach. In this way, the results of the comparison and analysis can be shared with the coach or trainer, and professional advice can be obtained, helping the user improve their play.

[0041] The explanatory video generation unit can explain points for improvement at different levels depending on the user's level of understanding. The explanatory video generation unit, for example, builds a system that explains points for improvement at different levels depending on the user's level of understanding. For example, points for improvement in a tennis serve are explained separately for beginners and advanced players. This allows the user's understanding to be deepened by explaining points for improvement at different levels depending on the user's level of understanding.

[0042] The commentary video generation unit can explain the points to be improved together with specific practice methods that match the user's playing style. The commentary video generation unit, for example, builds a system that explains the points to be improved together with specific practice methods that match the user's playing style. For example, the commentary video generation unit explains the points to be improved in tennis serves in accordance with an aggressive playing style. This helps the user improve their play by explaining the points to be improved together with specific practice methods that match the user's playing style.

[0043] The explanatory video generation unit can make the explanatory video on the points for improvement downloadable to the user's smartphone or tablet. The explanatory video generation unit, for example, builds a system that makes it possible to download the explanatory video on the points for improvement to the user's smartphone. For example, a video on points for improvement in tennis serves is downloaded to the smartphone. By making the explanatory video on the points for improvement downloadable to the smartphone or tablet, the user can watch the explanatory video anytime, anywhere.

[0044] The commentary video generation unit can share the commentary video on points for improvement with other users and obtain feedback within the community. The commentary video generation unit, for example, builds a system for sharing the commentary video on points for improvement with other users and obtaining feedback within the community. For example, a commentary video on points for improvement in tennis serves can be shared and advice can be obtained from other users. In this way, by sharing the commentary video on points for improvement with other users and obtaining feedback within the community, the user's play can be improved.

[0045] The behavior indication unit can automatically record improved behaviors and unimproved behaviors in the user's training log. The behavior indication unit, for example, builds a system that automatically records improved behaviors and unimproved behaviors in the user's training log. For example, the behavior indication unit records areas for improvement and areas for unimproved behaviors in a tennis serve in the training log. In this way, by automatically recording improved behaviors and unimproved behaviors in the training log, it becomes easier to understand the user's training progress.

[0046] The behavior suggestion unit can provide improved behaviors and unimproved behaviors together with specific advice tailored to the user's playing style. The behavior suggestion unit, for example, builds a system that provides improved behaviors and unimproved behaviors together with specific advice tailored to the user's playing style. For example, advice on areas for improvement and areas for improvement in a tennis serve is provided in accordance with an aggressive playing style. In this way, the improved behaviors and unimproved behaviors are provided together with specific advice tailored to the playing style, thereby supporting the user in improving their play.

[0047] The behavior suggestion unit shares improved behaviors and unimproved behaviors with the user's coach or trainer, allowing the user to obtain professional advice. The behavior suggestion unit, for example, builds a system that shares improved behaviors and unimproved behaviors with the user's coach or trainer and allows the user to obtain professional advice. For example, the behavior suggestion unit shares areas for improvement and areas for unimproved behaviors in a tennis serve with the coach. In this way, the improved behaviors and unimproved behaviors can be shared with the coach or trainer, and professional advice can be obtained, thereby supporting the user in improving their play.

[0048] The behavior suggestion unit can share improved behaviors and unimproved behaviors with other users and obtain feedback within the community. The behavior suggestion unit, for example, builds a system for sharing improved behaviors and unimproved behaviors with other users and obtaining feedback within the community. For example, a user can share areas for improvement and areas for improvement in a tennis serve and obtain advice from other users. In this way, the user can be supported in improving their play by sharing improved behaviors and unimproved behaviors with other users and obtaining feedback within the community.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The playing style input unit inputs the user's desired playing style and their image of a professional player. For example, a user might enter, "I want an aggressive playing style in tennis, and I aim to play like professional player A." The video upload unit uploads the user's playing video. For example, the user uploads a video of their tennis serve to the system. The comparison and analysis unit compares and analyzes the playing video with accumulated data. For example, the generation AI compares the user's serve with that of professional player A and analyzes the differences. The explanatory video generation unit provides video explanations of areas for improvement based on the results obtained by the comparison and analysis unit. For example, it might explain, "Your arm swing in your tennis serve is slower than that of professional player A. To improve this, you need to practice swinging your arm faster." The behavior suggestion unit uses the playing video to point out behaviors that can be improved and behaviors that need to be improved. For example, it might point out, "Your arm swing in your serve has become faster, but your foot position is still unstable. You need to improve this further." As a result, the sports play improvement system according to the embodiment can efficiently improve the play of the user and raise the level.

[0051] The playing style input unit can analyze the user's past playing history and automatically suggest the optimal playing style. For example, it can analyze playing videos uploaded by the user in the past and extract playing style trends. For example, if there are many offensive playing styles, it can suggest the image of an offensive professional player. In this way, the system analyzes the user's past playing history and automatically suggests the optimal playing style, helping the user select a playing style.

[0052] The playing style input unit can input the user's physical characteristics and suggest the image of the most suitable professional player based on that. For example, the user's height and weight can be input and professional players with a similar physique can be suggested based on that. For example, for a tall user, professional players with the same height can be suggested. In this way, the system can support the user in selecting a playing style by suggesting the most suitable image of a professional player based on the user's physical characteristics.

[0053] The playing style input unit can automatically generate a training menu based on the user's desired playing style. For example, when the user inputs their desired playing style, the unit automatically generates an optimal training menu based on that. For example, if the user desires an offensive playing style, the unit suggests a training menu that will enhance their offensive power. This automatically generates a training menu based on the user's desired playing style, thereby supporting the user's training.

[0054] The playing style input unit can suggest equipment suitable for the user based on the image of a professional player. For example, it can suggest the most suitable equipment for the user based on the image of a professional player. For example, it can suggest the racket used by professional player A. This supports the user's play by suggesting equipment suitable for the user based on the image of a professional player.

[0055] The video uploading unit can convert movements into 3D models during video analysis, allowing for more detailed analysis. For example, a user can upload a video of a game and convert it into a 3D model to perform a more detailed analysis of the movements. For example, a tennis serve form can be analyzed using a 3D model. This allows for more detailed analysis by converting movements into 3D models during video analysis.

[0056] The video uploading unit analyzes each frame of the video and can detect subtle changes in movement. For example, it analyzes each frame of a video of a user playing to detect subtle changes in movement. For example, it analyzes changes in a tennis serve form frame by frame. In this way, by analyzing each frame of the video, it is possible to detect subtle changes in movement.

[0057] The video upload unit can provide real-time feedback of video analysis results to the user's smartphone or wearable device. For example, we will build a system that provides real-time feedback of video analysis results to the user's smartphone. For example, we will display the analysis results of a tennis serve on the smartphone. This allows the user to immediately understand areas for improvement by providing real-time feedback of video analysis results.

[0058] The video uploading unit can share the video analysis results with other users and receive feedback within the community. For example, a system can be constructed in which video analysis results are shared with other users and feedback is received within the community. For example, analysis results of a tennis serve can be shared and advice can be received from other users. This allows the video analysis results to be shared with other users and feedback can be received within the community, helping users improve their play.

[0059] The comparison and analysis unit can visualize the progress of a user's play over time based on the accumulated data. For example, a system can be constructed that compares a video of the user's play with the accumulated data and visualizes the progress over time. For example, the progress of a tennis serve can be displayed in a graph. This makes it easier for users to understand their own growth by visualizing the progress of their play over time based on the accumulated data.

[0060] The comparative analysis unit can perform comparative analysis not only with data from professional players, but also with data from amateur players of the same level. For example, we will build a system that can compare and analyze videos of a user's play not only with data from professional players, but also with data from amateur players of the same level. For example, a tennis serve can be compared with that of amateur players of the same level. This allows for comparative analysis not only with data from professional players, but also with data from amateur players of the same level, making it possible to understand areas for improvement in the user's play from multiple angles.

[0061] The comparison and analysis unit can analyze the user's playing video and provide real-time feedback on specific actions. For example, when a user serves in tennis, the comparison and analysis unit can provide real-time feedback on the form and timing of the serve. This allows the user to immediately identify areas for improvement and make corrections on the spot during practice.

[0062] The comparative analysis unit shares the comparative analysis results with the user's coach or trainer, allowing the user to obtain professional advice. For example, a system can be constructed in which the comparative analysis results are shared with the user's coach or trainer, allowing the user to obtain professional advice. For example, the analysis results of a tennis serve can be shared with a coach. In this way, the comparative analysis results can be shared with the coach or trainer, allowing the user to obtain professional advice and help improve their play.

[0063] The explanatory video generation unit can explain points for improvement at different levels depending on the user's level of understanding. For example, a system can be constructed that explains points for improvement at different levels depending on the user's level of understanding. For example, points for improvement in tennis serves can be explained separately for beginners and advanced players. This allows the user's understanding to be deepened by explaining points for improvement at different levels depending on the user's level of understanding.

[0064] The commentary video generation unit can explain the points to be improved along with specific practice methods that match the user's playing style. For example, a system can be constructed that explains the points to be improved along with specific practice methods that match the user's playing style. For example, improvement points for a tennis serve can be explained in accordance with an aggressive playing style. This helps the user improve their play by explaining the points to be improved along with specific practice methods that match the user's playing style.

[0065] The explanatory video generation unit can make the explanatory video of the improvement points available for download to the user's smartphone or tablet. For example, a system can be constructed that allows the explanatory video of the improvement points to be downloaded to the user's smartphone. For example, a video explaining the improvement points for a tennis serve can be downloaded to a smartphone. By making the explanatory video of the improvement points available for download to a smartphone or tablet, the user can watch the explanatory video anytime, anywhere.

[0066] The commentary video generation unit can share the commentary video on points for improvement with other users and obtain feedback within the community. For example, a system can be constructed in which the commentary video on points for improvement is shared with other users and feedback is obtained within the community. For example, a commentary video on points for improvement in tennis serves can be shared and advice from other users can be obtained. This allows the user to share the commentary video on points for improvement with other users and obtain feedback within the community, thereby supporting the user in improving their play.

[0067] The behavior suggestion unit can automatically record improved behaviors and unimproved behaviors in the user's training log. For example, a system can be constructed that automatically records improved behaviors and unimproved behaviors in the user's training log. For example, areas for improvement and areas for unimproved behaviors in a tennis serve can be recorded in the training log. By automatically recording improved behaviors and unimproved behaviors in the training log, the user's training progress can be easily grasped.

[0068] The behavior suggestion unit can provide improved behaviors and unimproved behaviors together with specific advice tailored to the user's playing style. For example, a system can be constructed that provides improved behaviors and unimproved behaviors together with specific advice tailored to the user's playing style. For example, advice on areas for improvement and areas for unimproved behavior in tennis serves can be provided in accordance with an aggressive playing style. This helps the user improve their play by providing improved behaviors and unimproved behaviors together with specific advice tailored to the playing style.

[0069] The behavior suggestion unit shares improved and unimproved behaviors with the user's coach or trainer, allowing the user to obtain professional advice. For example, a system can be constructed in which improved and unimproved behaviors are shared with the user's coach or trainer, allowing the user to obtain professional advice. For example, a system can be constructed in which areas for improvement and areas for unimproved behavior in tennis serves are shared with the coach. In this way, improved and unimproved behaviors can be shared with the coach or trainer, and professional advice can be obtained, helping the user improve their play.

[0070] The behavior suggestion unit shares improved and unimproved behaviors with other users and receives feedback within the community. For example, a system is constructed in which improved and unimproved behaviors are shared with other users and feedback is received within the community. For example, areas for improvement and areas for improvement in a tennis serve are shared and advice is received from other users. In this way, improved and unimproved behaviors can be shared with other users and feedback is received within the community, helping users improve their play.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The playing style input section inputs the user's desired playing style and their image of a professional player. For example, the user might input, "I want an aggressive playing style in tennis, and I aim to play like professional player A." Step 2: The video uploading unit uploads the user's playing video. For example, the user uploads a video of his or her tennis serve to the system. Step 3: The comparison and analysis unit compares and analyzes the gameplay video with the accumulated data. For example, the generation AI compares the serve of professional player A with the user's serve and analyzes which parts are different. Step 4: The explanatory video generator generates a video explaining points to improve based on the results obtained by the comparative analysis unit. For example, it might say, "Your arm swing in your tennis serve is slower than that of professional player A. To improve this, you need to practice swinging your arm faster." Step 5: The behavioral guidance team will use the video of the player's play to point out behaviors that could be improved and behaviors that need improvement. For example, they might say, "Your arm swing has gotten faster when you serve, but your foot position is still unstable. This is something you need to improve on."

[0073] (Example 2) The sports play improvement system according to an embodiment of the present invention analyzes a video of a user's play, explains points for improvement in the video based on the user's desired playing style and the image of a professional player, and suggests improvements to the user's behavior. This allows the sports play improvement system to efficiently improve the user's play and raise the user's level.

[0074] A sports play improvement system according to an embodiment includes a playing style input unit, a video upload unit, a comparison and analysis unit, a commentary video generation unit, and a behavior suggestion unit. The playing style input unit inputs the user's desired playing style and their image of a professional player. For example, the user may input, "I want an aggressive playing style in tennis, and I aim to play like professional player A." The video upload unit uploads videos of the user's play. For example, the user uploads a video of their tennis serve to the system. The comparison and analysis unit compares and analyzes the play video with accumulated data. For example, a generation AI compares the serve of professional player A with the user's serve and analyzes the differences. The commentary video generation unit provides video commentary on areas for improvement based on the results obtained by the comparison and analysis unit. For example, the commentary video may explain, "Your arm swing in your tennis serve is slower than that of professional player A. To improve this, you need to practice swinging your arm faster." The behavior suggestion unit points out behaviors that can be improved and behaviors that need to be improved based on the play video. For example, the system may point out, "Your arm swing for serving has become faster, but your foot position is still unstable. Let's improve this further." In this way, the sports play improvement system according to the embodiment can efficiently improve the user's play and raise the user's level.

[0075] The playing style input unit can analyze the user's past playing history and automatically suggest the optimal playing style. For example, the playing style input unit analyzes playing videos uploaded by the user in the past to extract playing style trends. For example, if there are many offensive playing styles, the playing style input unit suggests the image of an offensive professional player. In this way, the playing history of the user is analyzed and the optimal playing style is automatically suggested, thereby supporting the user in selecting a playing style.

[0076] The playing style input unit can input the user's physical characteristics and suggest the image of the most suitable professional player based on the input. For example, the playing style input unit can input the user's height and weight and suggest professional players with a similar physique based on the input. For example, for a tall user, professional players with a similar height can be suggested. In this way, the system can support the user in selecting a playing style by suggesting the most suitable image of a professional player based on the user's physical characteristics.

[0077] The playing style input unit can use the emotion estimation function to estimate the playing style that most motivates the user and suggest that style. For example, the playing style input unit analyzes the facial expressions and voice of the user when inputting the playing style and calculates an emotion score. For example, the playing style that is most likely to involve smiling or excited voices can be preferentially suggested. In this way, the emotion estimation function can be used to suggest the playing style that most motivates the user, thereby assisting the user in selecting a playing style.

[0078] The playing style input unit can automatically generate a training menu based on the user's desired playing style. For example, when the user inputs their desired playing style, the playing style input unit automatically generates an optimal training menu based on that input. For example, if an attacking playing style is desired, the playing style input unit suggests a training menu that will enhance attacking power. In this way, the automatic generation of a training menu based on the user's desired playing style supports the user's training.

[0079] The playing style input unit can suggest equipment suitable for the user based on the image of a professional player. The playing style input unit suggests equipment that is best suited to the user based on, for example, the image of a professional player. For example, it suggests the racket used by professional player A. In this way, the playing style input unit supports the user's play by suggesting equipment that is suitable for the user based on the image of a professional player.

[0080] The playing style input unit can use the emotion estimation function to analyze the emotions of the user when inputting a playing style in real time and provide positive feedback. For example, the playing style input unit can analyze the facial expressions and voice of the user when inputting a playing style in real time and provide positive feedback. For example, if there are a lot of smiles, it can display "Great choice!" In this way, the emotion estimation function can be used to analyze the emotions of the user when inputting a playing style in real time and provide positive feedback, thereby improving the user's motivation.

[0081] The video uploading unit can convert the movements into 3D models when analyzing the video, allowing for more detailed analysis. For example, the video uploading unit can convert a play video uploaded by a user into a 3D model and perform a detailed analysis of the movements. For example, the form of a tennis serve can be analyzed using a 3D model. By converting the movements into 3D models when analyzing the video, more detailed analysis becomes possible.

[0082] The video uploading unit analyzes each frame of the video and can detect subtle changes in movement. For example, the video uploading unit analyzes each frame of a video of a user playing and detects subtle changes in movement. For example, the video uploading unit analyzes changes in a tennis serve form frame by frame. In this way, by analyzing each frame of the video, subtle changes in movement can be detected.

[0083] The video uploading unit can use the emotion estimation function to analyze the user's emotions while playing and provide technical advice based on changes in emotions. The video uploading unit, for example, analyzes a video of the user's play and estimates the emotions during play. For example, it analyzes facial expressions and voices during a tennis serve and calculates an emotion score. This allows the emotion estimation function to analyze the emotions during play and provide technical advice based on changes in emotions, thereby supporting the user's play.

[0084] The video uploading unit can feed back the video analysis results to the user's smartphone or wearable device in real time. The video uploading unit, for example, builds a system that feeds back the video analysis results to the user's smartphone in real time. For example, the analysis results of a tennis serve are displayed on the smartphone. This allows the user to immediately understand areas for improvement by feeding back the video analysis results in real time.

[0085] The video uploading unit can share the video analysis results with other users and obtain feedback within the community. The video uploading unit, for example, builds a system for sharing the video analysis results with other users and obtaining feedback within the community. For example, the analysis results of a tennis serve can be shared and advice can be obtained from other users. In this way, by sharing the video analysis results with other users and obtaining feedback within the community, the user's play can be improved.

[0086] The video uploading unit can use the emotion estimation function to analyze the user's emotions when uploading a gameplay video and motivate the user to upload. The video uploading unit, for example, analyzes the user's emotions when uploading a gameplay video and provides positive feedback. For example, it analyzes the user's facial expressions and voice at the time of uploading and calculates an emotion score. In this way, the emotion estimation function can be used to analyze the user's emotions when uploading a gameplay video and motivate the user to upload, thereby supporting the user's continuous improvement in gameplay.

[0087] The comparison and analysis unit can visualize the progress of a user's play in chronological order based on the accumulated data. The comparison and analysis unit, for example, compares a video of the user's play with the accumulated data and builds a system that visualizes the progress in chronological order. For example, the progress of a tennis serve can be displayed in a graph. This makes it easier for the user to understand their own growth by visualizing the progress of the play in chronological order based on the accumulated data.

[0088] The comparative analysis unit can perform comparative analysis not only with data from professional players but also with data from amateur players of the same level. For example, the comparative analysis unit constructs a system that compares and analyzes a user's playing video not only with data from professional players but also with data from amateur players of the same level. For example, a tennis serve can be compared with that of amateur players of the same level. This allows for comparative analysis not only with data from professional players but also with data from amateur players of the same level, making it possible to grasp areas for improvement in the user's play from multiple angles.

[0089] The comparison analysis unit can use the emotion estimation function to select a comparison object that will evoke the most positive emotion from the user. For example, the comparison analysis unit uses the emotion estimation function to build a system that selects a comparison object that will evoke the most positive emotion from the user. For example, a comparison object for a tennis serve is selected based on an emotion score. In this way, the emotion estimation function can be used to select a comparison object that will evoke the most positive emotion from the user, thereby improving the user's motivation.

[0090] The comparison and analysis unit shares the results of the comparison and analysis with the user's coach or trainer, allowing the user to obtain professional advice. The comparison and analysis unit, for example, builds a system for sharing the results of the comparison and analysis with the user's coach or trainer and obtaining professional advice. For example, the analysis results of a tennis serve are shared with the coach. In this way, the results of the comparison and analysis can be shared with the coach or trainer, and professional advice can be obtained, helping the user improve their play.

[0091] The comparison and analysis unit can use the emotion estimation function to collect the user's emotional reactions to the comparison and analysis results and improve the feedback. The comparison and analysis unit, for example, uses the emotion estimation function to build a system that collects the user's emotional reactions to the comparison and analysis results. For example, the comparison and analysis unit collects emotional reactions to the analysis results of a tennis serve. In this way, the emotion estimation function is used to collect the emotional reactions to the comparison and analysis results and improve the feedback, thereby supporting the user in improving their play.

[0092] The explanatory video generation unit can explain points for improvement at different levels depending on the user's level of understanding. The explanatory video generation unit, for example, builds a system that explains points for improvement at different levels depending on the user's level of understanding. For example, points for improvement in a tennis serve are explained separately for beginners and advanced players. This allows the user's understanding to be deepened by explaining points for improvement at different levels depending on the user's level of understanding.

[0093] The commentary video generation unit can explain the points to be improved together with specific practice methods that match the user's playing style. The commentary video generation unit, for example, builds a system that explains the points to be improved together with specific practice methods that match the user's playing style. For example, the commentary video generation unit explains the points to be improved in tennis serves in accordance with an aggressive playing style. This helps the user improve their play by explaining the points to be improved together with specific practice methods that match the user's playing style.

[0094] The commentary video generation unit can use the emotion estimation function to select the commentary method that is easiest for the user to understand. The commentary video generation unit, for example, uses the emotion estimation function to build a system that selects the commentary method that is easiest for the user to understand. For example, the commentary video generation unit selects points to improve in a tennis serve based on an emotion score. In this way, the commentary method that is easiest for the user to understand is selected using the emotion estimation function, thereby deepening the user's understanding.

[0095] The explanatory video generation unit can make the explanatory video on the points for improvement downloadable to the user's smartphone or tablet. The explanatory video generation unit, for example, builds a system that makes it possible to download the explanatory video on the points for improvement to the user's smartphone. For example, a video on points for improvement in tennis serves is downloaded to the smartphone. By making the explanatory video on the points for improvement downloadable to the smartphone or tablet, the user can watch the explanatory video anytime, anywhere.

[0096] The commentary video generation unit can share the commentary video on points for improvement with other users and obtain feedback within the community. The commentary video generation unit, for example, builds a system for sharing the commentary video on points for improvement with other users and obtaining feedback within the community. For example, a commentary video on points for improvement in tennis serves can be shared and advice can be obtained from other users. In this way, by sharing the commentary video on points for improvement with other users and obtaining feedback within the community, the user's play can be improved.

[0097] The commentary video generation unit can use the emotion estimation function to analyze the user's emotions when watching the commentary video and improve the viewing experience. The commentary video generation unit, for example, uses the emotion estimation function to analyze the user's emotions when watching the commentary video and builds a system that improves the viewing experience. For example, the commentary video generation unit analyzes the emotion score when watching a commentary video on tennis serves. In this way, the emotion estimation function is used to analyze the emotions when watching the commentary video and improve the viewing experience, thereby deepening the user's understanding.

[0098] The behavior indication unit can automatically record improved behaviors and unimproved behaviors in the user's training log. The behavior indication unit, for example, builds a system that automatically records improved behaviors and unimproved behaviors in the user's training log. For example, the behavior indication unit records areas for improvement and areas for unimproved behaviors in a tennis serve in the training log. In this way, by automatically recording improved behaviors and unimproved behaviors in the training log, it becomes easier to understand the user's training progress.

[0099] The behavior suggestion unit can provide improved behaviors and unimproved behaviors together with specific advice tailored to the user's playing style. The behavior suggestion unit, for example, builds a system that provides improved behaviors and unimproved behaviors together with specific advice tailored to the user's playing style. For example, advice on areas for improvement and areas for improvement in a tennis serve is provided in accordance with an aggressive playing style. In this way, the improved behaviors and unimproved behaviors are provided together with specific advice tailored to the playing style, thereby supporting the user in improving their play.

[0100] The behavior suggestion unit can use the emotion estimation function to suggest an improvement method that will most motivate the user. For example, the behavior suggestion unit uses the emotion estimation function to build a system that suggests an improvement method that will most motivate the user. For example, the behavior suggestion unit suggests an improvement method for a tennis serve based on an emotion score. In this way, the emotion estimation function is used to suggest an improvement method that will most motivate the user, thereby supporting the user in improving their play.

[0101] The behavior suggestion unit shares improved behaviors and unimproved behaviors with the user's coach or trainer, allowing the user to obtain professional advice. The behavior suggestion unit, for example, builds a system that shares improved behaviors and unimproved behaviors with the user's coach or trainer and allows the user to obtain professional advice. For example, the behavior suggestion unit shares areas for improvement and areas for unimproved behaviors in a tennis serve with the coach. In this way, the improved behaviors and unimproved behaviors can be shared with the coach or trainer, and professional advice can be obtained, thereby supporting the user in improving their play.

[0102] The behavior suggestion unit can share improved behaviors and unimproved behaviors with other users and obtain feedback within the community. The behavior suggestion unit, for example, builds a system for sharing improved behaviors and unimproved behaviors with other users and obtaining feedback within the community. For example, a user can share areas for improvement and areas for improvement in a tennis serve and obtain advice from other users. In this way, the user can be supported in improving their play by sharing improved behaviors and unimproved behaviors with other users and obtaining feedback within the community.

[0103] The behavior suggestion unit can use the emotion estimation function to collect the user's emotional reactions to improved behavior and unimproved behavior and improve the feedback. The behavior suggestion unit, for example, uses the emotion estimation function to build a system that collects the user's emotional reactions to improved behavior and unimproved behavior. For example, the behavior suggestion unit collects emotional reactions to areas for improvement and areas for improvement in a tennis serve. In this way, the emotion estimation function is used to collect the emotional reactions to improved behavior and unimproved behavior and improve the feedback, thereby supporting the user in improving their play.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The playing style input unit inputs the user's desired playing style and their image of a professional player. For example, a user might enter, "I want an aggressive playing style in tennis, and I aim to play like professional player A." The video upload unit uploads the user's playing video. For example, the user uploads a video of their tennis serve to the system. The comparison and analysis unit compares and analyzes the playing video with accumulated data. For example, the generation AI compares the user's serve with that of professional player A and analyzes the differences. The explanatory video generation unit provides video explanations of areas for improvement based on the results obtained by the comparison and analysis unit. For example, it might explain, "Your arm swing in your tennis serve is slower than that of professional player A. To improve this, you need to practice swinging your arm faster." The behavior suggestion unit uses the playing video to point out behaviors that can be improved and behaviors that need to be improved. For example, it might point out, "Your arm swing in your serve has become faster, but your foot position is still unstable. You need to improve this further." As a result, the sports play improvement system according to the embodiment can efficiently improve the play of the user and raise the level.

[0106] The playing style input unit can analyze the user's past playing history and automatically suggest the optimal playing style. For example, it can analyze playing videos uploaded by the user in the past and extract playing style trends. For example, if there are many offensive playing styles, it can suggest the image of an offensive professional player. In this way, the system analyzes the user's past playing history and automatically suggests the optimal playing style, helping the user select a playing style.

[0107] The playing style input unit can input the user's physical characteristics and suggest the image of the most suitable professional player based on that. For example, the user's height and weight can be input and professional players with a similar physique can be suggested based on that. For example, for a tall user, professional players with the same height can be suggested. In this way, the system can support the user in selecting a playing style by suggesting the most suitable image of a professional player based on the user's physical characteristics.

[0108] The playing style input unit can use the emotion estimation function to estimate the playing style that most motivates the user and suggest that style. For example, it can analyze the facial expressions and voice of the user when inputting the playing style and calculate an emotion score. For example, it can preferentially suggest playing styles that involve a lot of smiling and excited voices. In this way, the emotion estimation function can suggest the playing style that most motivates the user, thereby supporting the user in selecting a playing style.

[0109] The playing style input unit can automatically generate a training menu based on the user's desired playing style. For example, when the user inputs their desired playing style, the unit automatically generates an optimal training menu based on that. For example, if the user desires an offensive playing style, the unit suggests a training menu that will enhance their offensive power. This automatically generates a training menu based on the user's desired playing style, thereby supporting the user's training.

[0110] The playing style input unit can suggest equipment suitable for the user based on the image of a professional player. For example, it can suggest the most suitable equipment for the user based on the image of a professional player. For example, it can suggest the racket used by professional player A. This supports the user's play by suggesting equipment suitable for the user based on the image of a professional player.

[0111] The play style input unit can use the emotion estimation function to analyze the emotions of the user when inputting a play style in real time and provide positive feedback. For example, the unit can analyze the facial expressions and voice of the user when inputting a play style in real time and provide positive feedback. For example, if there are a lot of smiles, it can display "Great choice!" In this way, the emotion estimation function can be used to analyze the emotions of the user when inputting a play style in real time and provide positive feedback, thereby improving the user's motivation.

[0112] The video uploading unit can convert movements into 3D models during video analysis, allowing for more detailed analysis. For example, a user can upload a video of a game and convert it into a 3D model to perform a more detailed analysis of the movements. For example, a tennis serve form can be analyzed using a 3D model. This allows for more detailed analysis by converting movements into 3D models during video analysis.

[0113] The video uploading unit analyzes each frame of the video and can detect subtle changes in movement. For example, it analyzes each frame of a video of a user playing to detect subtle changes in movement. For example, it analyzes changes in a tennis serve form frame by frame. In this way, by analyzing each frame of the video, it is possible to detect subtle changes in movement.

[0114] The video uploading unit can use the emotion estimation function to analyze the user's emotions while playing and provide technical advice based on changes in emotions. For example, the video uploading unit analyzes a video of the user's play and estimates the emotions during play. For example, the video uploading unit analyzes facial expressions and voices during a tennis serve and calculates an emotion score. This allows the emotion estimation function to analyze the user's emotions during play and provide technical advice based on changes in emotions, thereby supporting the user's play.

[0115] The video upload unit can provide real-time feedback of video analysis results to the user's smartphone or wearable device. For example, we will build a system that provides real-time feedback of video analysis results to the user's smartphone. For example, we will display the analysis results of a tennis serve on the smartphone. This allows the user to immediately understand areas for improvement by providing real-time feedback of video analysis results.

[0116] The video uploading unit can share the video analysis results with other users and receive feedback within the community. For example, a system can be constructed in which video analysis results are shared with other users and feedback is received within the community. For example, analysis results of a tennis serve can be shared and advice can be received from other users. This allows the video analysis results to be shared with other users and feedback can be received within the community, helping users improve their play.

[0117] The video uploading unit can use the emotion estimation function to analyze the user's emotions when uploading a gameplay video and motivate the user to upload. For example, it can analyze the user's emotions when uploading a gameplay video and provide positive feedback. For example, it can analyze the user's facial expressions and voice when uploading and calculate an emotion score. In this way, the emotion estimation function can be used to analyze the user's emotions when uploading a gameplay video and motivate the user to upload, thereby supporting the user's continuous improvement in gameplay.

[0118] The comparison and analysis unit can visualize the progress of a user's play over time based on the accumulated data. For example, a system can be constructed that compares a video of the user's play with the accumulated data and visualizes the progress over time. For example, the progress of a tennis serve can be displayed in a graph. This makes it easier for users to understand their own growth by visualizing the progress of their play over time based on the accumulated data.

[0119] The comparative analysis unit can perform comparative analysis not only with data from professional players, but also with data from amateur players of the same level. For example, we will build a system that can compare and analyze videos of a user's play not only with data from professional players, but also with data from amateur players of the same level. For example, a tennis serve can be compared with that of amateur players of the same level. This allows for comparative analysis not only with data from professional players, but also with data from amateur players of the same level, making it possible to understand areas for improvement in the user's play from multiple angles.

[0120] The comparison analysis unit can use the emotion estimation function to select a comparison object that will evoke the most positive emotion in the user. For example, a system can be constructed that uses the emotion estimation function to select a comparison object that will evoke the most positive emotion in the user. For example, a comparison object for a tennis serve can be selected based on an emotion score. In this way, the emotion estimation function can be used to select a comparison object that will evoke the most positive emotion in the user, thereby improving the user's motivation.

[0121] The comparison and analysis unit can analyze the user's playing video and provide real-time feedback on specific actions. For example, when a user serves in tennis, the comparison and analysis unit can provide real-time feedback on the form and timing of the serve. This allows the user to immediately identify areas for improvement and make corrections on the spot during practice.

[0122] The comparative analysis unit shares the comparative analysis results with the user's coach or trainer, allowing the user to obtain professional advice. For example, a system can be constructed in which the comparative analysis results are shared with the user's coach or trainer, allowing the user to obtain professional advice. For example, the analysis results of a tennis serve can be shared with a coach. In this way, the comparative analysis results can be shared with the coach or trainer, allowing the user to obtain professional advice and help improve their play.

[0123] The comparative analysis unit can use the emotion estimation function to collect the user's emotional reactions to the comparative analysis results and improve the feedback. For example, a system can be constructed that uses the emotion estimation function to collect the user's emotional reactions to the comparative analysis results. For example, emotional reactions to the analysis results of a tennis serve can be collected. In this way, the emotion estimation function can be used to collect the emotional reactions to the comparative analysis results and improve the feedback, thereby supporting the user in improving their play.

[0124] The explanatory video generation unit can explain points for improvement at different levels depending on the user's level of understanding. For example, a system can be constructed that explains points for improvement at different levels depending on the user's level of understanding. For example, points for improvement in tennis serves can be explained separately for beginners and advanced players. This allows the user's understanding to be deepened by explaining points for improvement at different levels depending on the user's level of understanding.

[0125] The commentary video generation unit can explain the points to be improved along with specific practice methods that match the user's playing style. For example, a system can be constructed that explains the points to be improved along with specific practice methods that match the user's playing style. For example, improvement points for a tennis serve can be explained in accordance with an aggressive playing style. This helps the user improve their play by explaining the points to be improved along with specific practice methods that match the user's playing style.

[0126] The commentary video generation unit can use the emotion estimation function to select the commentary method that is easiest for the user to understand. For example, a system can be constructed that uses the emotion estimation function to select the commentary method that is easiest for the user to understand. For example, improvement points for a tennis serve can be selected based on an emotion score. In this way, the emotion estimation function can be used to select the commentary method that is easiest for the user to understand, thereby deepening the user's understanding.

[0127] The explanatory video generation unit can make the explanatory video of the improvement points available for download to the user's smartphone or tablet. For example, a system can be constructed that allows the explanatory video of the improvement points to be downloaded to the user's smartphone. For example, a video explaining the improvement points for a tennis serve can be downloaded to a smartphone. By making the explanatory video of the improvement points available for download to a smartphone or tablet, the user can watch the explanatory video anytime, anywhere.

[0128] The commentary video generation unit can share the commentary video on points for improvement with other users and obtain feedback within the community. For example, a system can be constructed in which the commentary video on points for improvement is shared with other users and feedback is obtained within the community. For example, a commentary video on points for improvement in tennis serves can be shared and advice from other users can be obtained. This allows the user to share the commentary video on points for improvement with other users and obtain feedback within the community, thereby supporting the user in improving their play.

[0129] The commentary video generation unit can use the emotion estimation function to analyze the user's emotions when watching the commentary video and improve the viewing experience. For example, a system can be constructed that uses the emotion estimation function to analyze the user's emotions when watching the commentary video and improve the viewing experience. For example, the emotion score can be analyzed when watching a commentary video on tennis serves. In this way, the emotion estimation function can be used to analyze the user's emotions when watching the commentary video and improve the viewing experience, thereby deepening the user's understanding.

[0130] The behavior suggestion unit can automatically record improved behaviors and unimproved behaviors in the user's training log. For example, a system can be constructed that automatically records improved behaviors and unimproved behaviors in the user's training log. For example, areas for improvement and areas for unimproved behaviors in a tennis serve can be recorded in the training log. By automatically recording improved behaviors and unimproved behaviors in the training log, the user's training progress can be easily grasped.

[0131] The behavior suggestion unit can provide improved behaviors and unimproved behaviors together with specific advice tailored to the user's playing style. For example, a system can be constructed that provides improved behaviors and unimproved behaviors together with specific advice tailored to the user's playing style. For example, advice on areas for improvement and areas for unimproved behavior in tennis serves can be provided in accordance with an aggressive playing style. This helps the user improve their play by providing improved behaviors and unimproved behaviors together with specific advice tailored to the playing style.

[0132] The behavior suggestion unit can use the emotion estimation function to suggest an improvement method that will most motivate the user. For example, a system can be constructed that uses the emotion estimation function to suggest an improvement method that will most motivate the user. For example, a method for improving a tennis serve can be suggested based on an emotion score. In this way, the emotion estimation function can be used to suggest an improvement method that will most motivate the user, thereby supporting the user in improving their play.

[0133] The behavior suggestion unit shares improved and unimproved behaviors with the user's coach or trainer, allowing the user to obtain professional advice. For example, a system can be constructed in which improved and unimproved behaviors are shared with the user's coach or trainer, allowing the user to obtain professional advice. For example, a system can be constructed in which areas for improvement and areas for unimproved behavior in tennis serves are shared with the coach. In this way, improved and unimproved behaviors can be shared with the coach or trainer, and professional advice can be obtained, helping the user improve their play.

[0134] The behavior suggestion unit shares improved and unimproved behaviors with other users and receives feedback within the community. For example, a system is constructed in which improved and unimproved behaviors are shared with other users and feedback is received within the community. For example, areas for improvement and areas for improvement in a tennis serve are shared and advice is received from other users. In this way, improved and unimproved behaviors can be shared with other users and feedback is received within the community, helping users improve their play.

[0135] The behavior suggestion unit can use the emotion estimation function to collect the user's emotional reactions to improved and unimproved behaviors and improve the feedback. For example, a system can be constructed that uses the emotion estimation function to collect the user's emotional reactions to improved and unimproved behaviors. For example, emotional reactions to areas for improvement and areas for improvement in a tennis serve can be collected. In this way, the emotion estimation function can be used to collect the emotional reactions to improved and unimproved behaviors and improve the feedback, thereby supporting the user in improving their play.

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

[0137] Step 1: The playing style input section inputs the user's desired playing style and their image of a professional player. For example, the user might input, "I want an aggressive playing style in tennis, and I aim to play like professional player A." Step 2: The video uploading unit uploads the user's playing video. For example, the user uploads a video of his or her tennis serve to the system. Step 3: The comparison and analysis unit compares and analyzes the gameplay video with the accumulated data. For example, the generation AI compares the serve of professional player A with the user's serve and analyzes which parts are different. Step 4: The explanatory video generator generates a video explaining points to improve based on the results obtained by the comparative analysis unit. For example, it might say, "Your arm swing in your tennis serve is slower than that of professional player A. To improve this, you need to practice swinging your arm faster." Step 5: The behavioral guidance team will use the video of the player's play to point out behaviors that could be improved and behaviors that need improvement. For example, they might say, "Your arm swing has gotten faster when you serve, but your foot position is still unstable. This is something you need to improve on."

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0151] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] 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.

[0156] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] 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.

[0165] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0166] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 7, the 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0182] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0183] 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.

[0184] 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.

[0185] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0204] 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. [Explanation of symbols]

[0205] 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 play style input section where you can input your desired play style and your image of a professional player. A video upload section where you can upload videos of your gameplay, a comparison and analysis unit that compares and analyzes the play video with accumulated data; an explanatory video generation unit that generates a video explaining points to be improved based on the results obtained by the comparison analysis unit; and a behavior suggestion unit that suggests behaviors that can be improved and behaviors that remain to be improved based on the play video. A system characterized by:

2. The video upload unit During video analysis, movements are modeled in 3D for more detailed analysis 2. The system of claim 1.

3. The comparison and analysis unit Based on the accumulated data, the progress of the user's play is visualized in chronological order.

2. The system of claim 1.

4. The explanation video generation unit Explain the above improvement points at different levels depending on the user's level of understanding 2. The system of claim 1.

5. The behavior indication unit Automatically record corrective and non-corrective actions in a user's training log 2. The system of claim 1.

6. The playing style input unit Using emotion estimation, we estimate the playing style that motivates the user and suggest that style.

2. The system of claim 1.

7. The video upload unit Using emotion estimation function, analyze the user's emotions while playing and provide technical advice based on the changes in those emotions.

2. The system of claim 1.

8. The comparison and analysis unit Using emotion estimation, we select the comparison target that elicits the most positive emotions from the user.

2. The system of claim 1.

Images