system

Abstract

The system according to the embodiment aims to analyze the form of a player in real time and provide immediate advice. [Solution] A system according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires footage of the player's form using a camera. The analysis unit analyzes the footage acquired by the acquisition unit. The generation unit generates advice based on the results of the analysis by the analysis unit. The provision unit provides the advice generated by the generation unit to the player.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of making it difficult to analyze a player's form in real time and provide immediate advice.

[0005] The system according to the embodiment aims to analyze the form of a player in real time and provide immediate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires the player's form using a camera. The analysis unit analyzes the video acquired by the acquisition unit. The generation unit generates advice based on the results of the analysis by the analysis unit. The provision unit provides the advice generated by the generation unit to the player. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the player's form in real time and provide immediate advice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A form analysis system according to an embodiment of the present invention analyzes an athlete's running form in real time and provides immediate advice. This form analysis system captures the athlete's running form with a camera and captures the video. Next, the captured video is analyzed using AI to identify areas for improvement. Based on the analysis results, specific advice is generated for the athlete. Finally, the generated advice is provided to the athlete in real time via AR (e.g., AR glasses, AR goggles) or audio (e.g., earphones). For example, when an athlete is running, a camera captures the athlete's movements, and AI analyzes their running form to identify areas for improvement. Next, visual advice is displayed through AR glasses or audio advice is provided through earphones. This system allows the athlete to correct their running form in real time and improve their performance. For example, a camera captures the athlete's running form and captures the video. It is desirable for the camera to have a high resolution so that it can capture the athlete's movements in detail. For example, when an athlete is running, the camera can capture the athlete's entire body movements. Next, the captured video is analyzed using AI. The AI ​​analyzes the athlete's movements in the video and identifies areas for improvement. For example, the system analyzes an athlete's running form, such as foot position and arm swing, to identify areas for improvement. Specific advice is generated for the athlete based on the analysis results. For example, advice may be generated such as moving the feet a little further forward to improve running form. Finally, the generated advice is provided to the athlete in real time via AR (AR glasses, AR goggles, etc.) or audio (earphones, etc.). For example, visual advice may be displayed through AR glasses, or audio advice may be provided through earphones. This allows the athlete to correct their form in real time and improve their performance. This allows the form analysis system to analyze the athlete's form in real time and provide immediate advice.

[0029] A form analysis system according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires video using a camera that captures the player's form. The acquisition unit can capture the player's entire body movements using, for example, a high-resolution camera. For example, the acquisition unit can capture the player's entire body movements in detail using a camera while the player is running. The acquisition unit can also estimate the player's emotions and adjust the timing of video acquisition based on the estimated player's emotions. For example, if the player is nervous, the acquisition of video can be delayed until the player is relaxed. The analysis unit analyzes the video acquired by the acquisition unit. The analysis unit can analyze the player's movements in the video using, for example, AI, to identify areas for improvement in the player's form. For example, the analysis unit can analyze the player's foot position and arm swing in the player's running form. The analysis unit can also estimate the player's emotions and adjust the accuracy of the analysis based on the estimated player's emotions. For example, if the player is nervous, the analysis accuracy can be improved to provide more detailed feedback. The generation unit generates advice based on the results of the analysis by the analysis unit. The generation unit generates specific advice using, for example, AI. For example, the generation unit can generate specific advice such as "put your feet forward" as an area for improving your running form. The generation unit can also estimate the athlete's emotions and adjust the way the advice is expressed based on the estimated emotions of the athlete. For example, if the athlete is nervous, the generation unit can provide the advice in gentler terms. The provision unit provides the advice generated by the generation unit to the athlete. The provision unit can provide visual advice through, for example, AR glasses. The provision unit can also provide audio advice through earphones. For example, if the athlete is training outdoors, the provision unit can provide audio advice. This allows the form analysis system according to the embodiment to analyze the athlete's form in real time and provide immediate advice.

[0030] The providing unit can provide visual advice through the AR glasses. The providing unit provides visual advice through, for example, the AR glasses. For example, the providing unit can display advice directly in the athlete's field of view. The providing unit can also provide visual advice using animations and graphics. For example, the providing unit can show an area for improvement in the athlete's running form through animation. This allows the athlete to correct their form in real time by providing visual advice.

[0031] The providing unit can provide advice by voice through earphones. The providing unit can provide advice by voice through earphones, for example. For example, the providing unit can deliver advice directly to the player's ear. The providing unit can also customize the type and content of the voice. For example, the providing unit can adjust the tone and speed of the voice according to the player's preferences. This allows the player to correct their form in real time by providing advice by voice.

[0032] The analysis unit can analyze the position of the feet or the way the arms swing in the athlete's running form. The analysis unit, for example, analyzes the position of the feet in the athlete's running form. For example, the analysis unit performs the analysis based on the measurement points of the foot positions and the measurement accuracy. The analysis unit can also analyze the way the arms swing in the athlete's running form. For example, the analysis unit performs the analysis based on the angle and speed of the arm swing. This enables a detailed analysis of the running form.

[0033] The generation unit can generate specific advice, such as to improve the running form by positioning the feet forward. For example, the generation unit generates specific advice, such as to improve the running form by positioning the feet forward. For example, the generation unit analyzes the athlete's running form and determines that the form can be improved by positioning the feet forward. The generation unit can also generate specific advice for other areas for improvement. For example, the generation unit generates advice to improve the way the arms swing. By providing specific areas for improvement, the athlete's form can be effectively improved.

[0034] The acquisition unit can capture the player's entire body movements using a high-resolution camera. The acquisition unit, for example, captures the player's entire body movements using a high-resolution camera. For example, the acquisition unit can capture the player's entire body movements in detail using the camera while the player is running. The acquisition unit can also adjust the camera resolution and shooting conditions. For example, the acquisition unit can set the camera resolution to be high and capture the player's movements in more detail. As a result, detailed images can be acquired by using a high-resolution camera.

[0035] The acquisition unit can analyze a player's past form data and select the optimal camera angle. The acquisition unit, for example, analyzes a player's past form data and selects the optimal camera angle. For example, the acquisition unit identifies a player's weak points from the past data and selects an angle that emphasizes those points. The acquisition unit can also select an angle that captures a player's strong movements from the past data. Furthermore, the acquisition unit can select an angle that captures the player's overall balance based on the past data. In this way, effective footage can be obtained by selecting the optimal camera angle based on the past data.

[0036] The acquisition unit can dynamically adjust the frame rate according to the movement speed of the player when acquiring the video. For example, the acquisition unit dynamically adjusts the frame rate according to the movement speed of the player when acquiring the video. For example, the acquisition unit sets the frame rate high when the player is moving fast. The acquisition unit can also set the frame rate low when the player is moving slowly. Furthermore, the acquisition unit can adjust the frame rate in real time when the movement of the player changes. In this way, appropriate video can be acquired by adjusting the frame rate according to the movement speed.

[0037] When acquiring video, the acquisition unit can prioritize acquiring highly relevant video by taking into account the geographical location information of the player. For example, when acquiring video, the acquisition unit prioritizes acquiring highly relevant video by taking into account the geographical location information of the player. For example, when a player is in a specific training area, the acquisition unit prioritizes acquiring video of that area. Furthermore, when a player is at a game venue, the acquisition unit can also prioritize acquiring video of the venue. Furthermore, when a player is traveling, the acquisition unit can also prioritize acquiring video of the player's destination. In this way, highly relevant video can be prioritized by taking into account the geographical location information.

[0038] The acquisition unit can analyze the social media activity of the player when acquiring the video and acquire related video. For example, the acquisition unit analyzes the social media activity of the player when acquiring the video and acquires related video. For example, if the player posts a specific training session on social media, the acquisition unit can prioritize acquiring video of that training session. Also, if the player posts highlights of a game, the acquisition unit can prioritize acquiring video of that game. Furthermore, if the player is participating in a specific event, the acquisition unit can prioritize acquiring video of that event. In this way, by analyzing social media activity, related video can be prioritized.

[0039] The analysis unit can optimize the analysis algorithm by referring to the player's past performance data during analysis. For example, the analysis unit can optimize the analysis algorithm by referring to the player's past performance data during analysis. For example, the analysis unit can identify the player's weak points from the past data and focus the analysis on those areas. The analysis unit can also reflect the player's strong moves from the past data in the analysis algorithm. Furthermore, the analysis unit can perform analysis that takes into account the overall balance based on the past data. In this way, the analysis algorithm can be optimized based on the past data, thereby improving the accuracy of the analysis.

[0040] The analysis unit can perform the analysis while taking into account the player's body shape and muscle movement. For example, the analysis unit performs the analysis while taking into account the player's body shape and muscle movement. For example, the analysis unit performs an analysis according to the player's body shape and proposes an optimal form. The analysis unit can also analyze the player's muscle movement and propose efficient movements. Furthermore, the analysis unit can comprehensively analyze the player's body shape and muscle movement and propose a balanced form. This allows for more appropriate analysis by taking into account the player's body shape and muscle movement.

[0041] The analysis unit can perform the analysis taking into account the geographical distribution of players. For example, when an athlete is training in a specific region, the analysis unit performs the analysis taking into account the characteristics of that region. In addition, when an athlete is training in different regions, the analysis unit can also perform the analysis taking into account the characteristics of each region. Furthermore, when an athlete is traveling, the analysis unit can also perform the analysis taking into account the characteristics of the region to which the athlete is traveling. In this way, by taking geographical distribution into account, more appropriate analysis is possible.

[0042] The analysis unit can improve the accuracy of the analysis by referring to literature related to the player during analysis. The analysis unit can improve the accuracy of the analysis by referring to literature related to the player during analysis, for example. For example, the analysis unit performs the analysis by referring to the latest research papers related to the player's training. The analysis unit can also perform the analysis by referring to past research papers related to the player's performance. Furthermore, the analysis unit can perform the analysis by referring to literature related to the player's body type and muscle movement. In this way, the accuracy of the analysis is improved by referring to related literature.

[0043] When generating advice, the generation unit can refer to the player's past training data to generate optimal advice. When generating advice, the generation unit, for example, refers to the player's past training data to generate optimal advice. For example, the generation unit can identify the player's weak points from the past data and generate advice to improve those areas. The generation unit can also generate advice to strengthen the player's strong movements from the past data. Furthermore, the generation unit can generate advice that takes into account the overall balance based on the past data. This enables effective training by generating optimal advice based on the past data.

[0044] When generating advice, the generation unit can customize the advice by taking into account the player's current physical condition and fatigue level. For example, when generating advice, the generation unit customizes the advice by taking into account the player's current physical condition and fatigue level. For example, if the player is tired, the generation unit generates advice encouraging the player to rest. Furthermore, if the player is in poor physical condition, the generation unit can also generate advice suggesting lighter training. Furthermore, if the player is in good physical condition, the generation unit can also generate advice suggesting harder training. This allows for more effective training by providing advice according to the player's physical condition and fatigue level.

[0045] The generation unit can generate optimal advice by taking into account the geographical location information of the player when generating advice. For example, the generation unit generates optimal advice by taking into account the geographical location information of the player when generating advice. For example, if the player is in a specific training area, the generation unit generates advice appropriate for that area. Also, if the player is at a game venue, the generation unit can generate advice appropriate for that venue. Furthermore, if the player is traveling, the generation unit can generate advice appropriate for the player's destination. In this way, by taking into account the geographical location information, more appropriate advice can be provided.

[0046] The generation unit can analyze the social media activity of the player and adjust the content of the advice when generating the advice. For example, the generation unit analyzes the social media activity of the player and adjusts the content of the advice when generating the advice. For example, if the player posts a specific training on social media, the generation unit can generate advice related to that training. Also, if the player posts highlights of a game, the generation unit can generate advice related to that game. Furthermore, if the player is participating in a specific event, the generation unit can generate advice related to that event. In this way, by analyzing social media activity, more relevant advice can be provided.

[0047] The providing unit can select the optimal method of providing advice by referring to the player's past feedback when providing advice. For example, the providing unit selects the optimal method of providing advice by referring to the player's past feedback when providing advice. For example, the providing unit prioritizes a method of providing advice that the player has preferred in the past. The providing unit can also avoid a method of providing advice that the player has avoided in the past. Furthermore, the providing unit can select the optimal method of providing advice based on the player's past feedback. In this way, more effective advice can be provided by selecting the optimal method of providing advice based on past feedback.

[0048] The providing unit can customize the means of providing advice according to the player's current environment and situation when providing advice. For example, the providing unit customizes the means of providing advice according to the player's current environment and situation when providing advice. For example, if the player is training outdoors, the providing unit can provide advice by voice. Also, if the player is training indoors, the providing unit can provide visual advice using AR glasses. Furthermore, if the player is on the move, the providing unit can provide advice via a smartphone. In this way, by customizing the means of providing advice according to the environment and situation, more appropriate advice can be provided.

[0049] The providing unit can select the optimal providing method by taking into consideration the device information of the player when providing advice. For example, the providing unit selects the optimal providing method by taking into consideration the device information of the player when providing advice. For example, if the player is using a smartphone, the providing unit can provide a display method that matches the screen size. Also, if the player is using a tablet, the providing unit can provide a display method that is optimized for a large screen. Furthermore, if the player is using a smartwatch, the providing unit can provide a display method that is simple and highly visible. In this way, more appropriate advice can be provided by taking into consideration the device information.

[0050] The providing unit can improve the accuracy of advice provision by referring to the player's related data when providing advice. The providing unit, for example, improves the accuracy of advice provision by referring to the player's related data when providing advice. For example, the providing unit provides optimal advice by referring to the player's past training data. The providing unit can also provide appropriate advice by referring to the player's current physical condition data. Furthermore, the providing unit can also provide effective advice by referring to the player's performance data. In this way, the accuracy of advice provision is improved by referring to the related data.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The providing unit can provide visual advice through the AR glasses. For example, the providing unit can display advice directly in the athlete's field of vision. The providing unit can also provide visual advice using animations and graphics. For example, the providing unit can use animations to show areas for improvement in the athlete's running form. Furthermore, the providing unit can visually show changes in the athlete's form in real time. This allows the athlete to correct their form in real time by providing visual advice.

[0053] The providing unit can provide advice by voice through earphones. For example, the providing unit can deliver advice directly to the player's ear. The providing unit can also customize the type and content of the audio. For example, the providing unit can adjust the tone and speed of the audio according to the player's preferences. Furthermore, the providing unit can change the content of the audio advice according to the player's training status. This allows the player to correct their form in real time by providing advice by voice.

[0054] The analysis unit can analyze the position of the feet or the way the arms swing in the athlete's running form. For example, the analysis unit analyzes the position of the feet in the athlete's running form. For example, the analysis unit performs an analysis based on the measurement points of the foot position and the measurement accuracy. The analysis unit can also analyze the way the arms swing in the athlete's running form. For example, the analysis unit performs an analysis based on the angle and speed of the arm swing. Furthermore, the analysis unit can analyze changes in the athlete's form in real time and provide immediate feedback. This enables detailed analysis of the running form.

[0055] The generation unit can generate specific advice, such as to improve the running form by positioning the feet forward. For example, the generation unit can analyze the athlete's running form and determine that the form can be improved by positioning the feet forward. The generation unit can also generate specific advice for other areas of improvement. For example, the generation unit can generate advice to improve the way the arms swing. Furthermore, the generation unit can customize the content of the advice depending on the athlete's training situation. In this way, the athlete's form can be effectively improved by providing specific areas of improvement.

[0056] The acquisition unit can capture the entire body movements of an athlete using a high-resolution camera. For example, the acquisition unit can capture the entire body movements of an athlete in detail using the camera while the athlete is running. The acquisition unit can also adjust the camera resolution and shooting conditions. For example, the acquisition unit can set the camera resolution to a high level to capture the athlete's movements in more detail. Furthermore, the acquisition unit can adjust the camera frame rate according to the athlete's movement speed. As a result, detailed images can be acquired by using a high-resolution camera.

[0057] The acquisition unit can analyze a player's past form data and select the optimal camera angle. For example, the acquisition unit can identify a player's weak points from past data and select an angle that emphasizes those areas. The acquisition unit can also select an angle from past data that captures a player's strong movements. Furthermore, the acquisition unit can select an angle that captures the player's overall balance based on past data. In this way, effective footage can be obtained by selecting the optimal camera angle based on past data.

[0058] The acquisition unit can dynamically adjust the frame rate according to the movement speed of the player when acquiring the video. For example, the acquisition unit sets the frame rate high when the player is moving fast. The acquisition unit can also set the frame rate low when the player is moving slowly. Furthermore, the acquisition unit can adjust the frame rate in real time when the player's movement changes. This allows appropriate video to be acquired by adjusting the frame rate according to the movement speed.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The acquisition unit acquires video footage using a camera that captures the player's form. The acquisition unit uses a high-resolution camera to capture the player's entire body movements in detail. The acquisition unit can also estimate the player's emotions and adjust the timing of video acquisition based on the estimated emotions. For example, if the player is nervous, it can delay video acquisition until the player is relaxed. Step 2: The analysis unit analyzes the video captured by the acquisition unit. The analysis unit uses AI to analyze the athlete's movements in the video and identify areas for improvement in form. For example, the analysis unit can analyze the athlete's foot position and arm swing in their running form. The analysis unit can also estimate the athlete's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the athlete is nervous, the analysis accuracy can be improved to provide more detailed feedback. Step 3: The generator generates advice based on the results of the analysis by the analyzer. The generator uses AI to generate specific advice. For example, it can generate specific advice such as "put your feet forward" to improve your running form. The generator can also estimate the athlete's emotions and adjust the way the advice is presented based on the estimated emotions. For example, if the athlete is nervous, it can provide advice in gentler words. Step 4: The providing unit provides the advice generated by the generating unit to the athlete. The providing unit can provide visual advice through AR glasses. It can also provide advice via audio through earphones. For example, if the athlete is training outdoors, advice can be provided via audio.

[0061] (Example 2) A form analysis system according to an embodiment of the present invention analyzes an athlete's running form in real time and provides immediate advice. This form analysis system captures the athlete's running form with a camera and captures the video. Next, the captured video is analyzed using AI to identify areas for improvement. Based on the analysis results, specific advice is generated for the athlete. Finally, the generated advice is provided to the athlete in real time via AR (e.g., AR glasses, AR goggles) or audio (e.g., earphones). For example, when an athlete is running, a camera captures the athlete's movements, and AI analyzes their running form to identify areas for improvement. Next, visual advice is displayed through AR glasses or audio advice is provided through earphones. This system allows the athlete to correct their running form in real time and improve their performance. For example, a camera captures the athlete's running form and captures the video. It is desirable for the camera to have a high resolution so that it can capture the athlete's movements in detail. For example, when an athlete is running, the camera can capture the athlete's entire body movements. Next, the captured video is analyzed using AI. The AI ​​analyzes the athlete's movements in the video and identifies areas for improvement. For example, the system analyzes an athlete's running form, such as foot position and arm swing, to identify areas for improvement. Specific advice is generated for the athlete based on the analysis results. For example, advice may be generated such as moving the feet a little further forward to improve running form. Finally, the generated advice is provided to the athlete in real time via AR (AR glasses, AR goggles, etc.) or audio (earphones, etc.). For example, visual advice may be displayed through AR glasses, or audio advice may be provided through earphones. This allows the athlete to correct their form in real time and improve their performance. This allows the form analysis system to analyze the athlete's form in real time and provide immediate advice.

[0062] A form analysis system according to an embodiment includes an acquisition unit, an analysis unit, a generation unit, and a provision unit. The acquisition unit acquires video using a camera that captures the player's form. The acquisition unit can capture the player's entire body movements using, for example, a high-resolution camera. For example, the acquisition unit can capture the player's entire body movements in detail using a camera while the player is running. The acquisition unit can also estimate the player's emotions and adjust the timing of video acquisition based on the estimated player's emotions. For example, if the player is nervous, the acquisition of video can be delayed until the player is relaxed. The analysis unit analyzes the video acquired by the acquisition unit. The analysis unit can analyze the player's movements in the video using, for example, AI, to identify areas for improvement in the player's form. For example, the analysis unit can analyze the player's foot position and arm swing in the player's running form. The analysis unit can also estimate the player's emotions and adjust the accuracy of the analysis based on the estimated player's emotions. For example, if the player is nervous, the analysis accuracy can be improved to provide more detailed feedback. The generation unit generates advice based on the results of the analysis by the analysis unit. The generation unit generates specific advice using, for example, AI. For example, the generation unit can generate specific advice such as "put your feet forward" as an area for improving your running form. The generation unit can also estimate the athlete's emotions and adjust the way the advice is expressed based on the estimated emotions of the athlete. For example, if the athlete is nervous, the generation unit can provide the advice in gentler terms. The provision unit provides the advice generated by the generation unit to the athlete. The provision unit can provide visual advice through, for example, AR glasses. The provision unit can also provide audio advice through earphones. For example, if the athlete is training outdoors, the provision unit can provide audio advice. This allows the form analysis system according to the embodiment to analyze the athlete's form in real time and provide immediate advice.

[0063] The providing unit can provide visual advice through the AR glasses. The providing unit provides visual advice through, for example, the AR glasses. For example, the providing unit can display advice directly in the athlete's field of view. The providing unit can also provide visual advice using animations and graphics. For example, the providing unit can show an area for improvement in the athlete's running form through animation. This allows the athlete to correct their form in real time by providing visual advice.

[0064] The providing unit can provide advice by voice through earphones. The providing unit can provide advice by voice through earphones, for example. For example, the providing unit can deliver advice directly to the player's ear. The providing unit can also customize the type and content of the voice. For example, the providing unit can adjust the tone and speed of the voice according to the player's preferences. This allows the player to correct their form in real time by providing advice by voice.

[0065] The analysis unit can analyze the position of the feet or the way the arms swing in the athlete's running form. The analysis unit, for example, analyzes the position of the feet in the athlete's running form. For example, the analysis unit performs the analysis based on the measurement points of the foot positions and the measurement accuracy. The analysis unit can also analyze the way the arms swing in the athlete's running form. For example, the analysis unit performs the analysis based on the angle and speed of the arm swing. This enables a detailed analysis of the running form.

[0066] The generation unit can generate specific advice, such as to improve the running form by positioning the feet forward. For example, the generation unit generates specific advice, such as to improve the running form by positioning the feet forward. For example, the generation unit analyzes the athlete's running form and determines that the form can be improved by positioning the feet forward. The generation unit can also generate specific advice for other areas for improvement. For example, the generation unit generates advice to improve the way the arms swing. By providing specific areas for improvement, the athlete's form can be effectively improved.

[0067] The acquisition unit can capture the player's entire body movements using a high-resolution camera. The acquisition unit, for example, captures the player's entire body movements using a high-resolution camera. For example, the acquisition unit can capture the player's entire body movements in detail using the camera while the player is running. The acquisition unit can also adjust the camera resolution and shooting conditions. For example, the acquisition unit can set the camera resolution to be high and capture the player's movements in more detail. As a result, detailed images can be acquired by using a high-resolution camera.

[0068] The acquisition unit can estimate the player's emotions and adjust the timing of video acquisition based on the estimated player's emotions. The acquisition unit, for example, estimates the player's emotions and adjusts the timing of video acquisition based on the estimated player's emotions. For example, if the player is nervous, the acquisition unit can delay video acquisition until the player is relaxed. Also, if the player is concentrating, the acquisition unit can immediately acquire video to capture that moment. Furthermore, if the player is tired, the acquisition unit can acquire video after a break. In this way, more appropriate video can be acquired by adjusting the timing of video acquisition according to the player's emotions.

[0069] The acquisition unit can analyze a player's past form data and select the optimal camera angle. The acquisition unit, for example, analyzes a player's past form data and selects the optimal camera angle. For example, the acquisition unit identifies a player's weak points from the past data and selects an angle that emphasizes those points. The acquisition unit can also select an angle that captures a player's strong movements from the past data. Furthermore, the acquisition unit can select an angle that captures the player's overall balance based on the past data. In this way, effective footage can be obtained by selecting the optimal camera angle based on the past data.

[0070] The acquisition unit can dynamically adjust the frame rate according to the movement speed of the player when acquiring the video. For example, the acquisition unit dynamically adjusts the frame rate according to the movement speed of the player when acquiring the video. For example, the acquisition unit sets the frame rate high when the player is moving fast. The acquisition unit can also set the frame rate low when the player is moving slowly. Furthermore, the acquisition unit can adjust the frame rate in real time when the movement of the player changes. In this way, appropriate video can be acquired by adjusting the frame rate according to the movement speed.

[0071] The acquisition unit can estimate the emotions of the players and determine the priority of the videos to be acquired based on the estimated emotions of the players. The acquisition unit can, for example, estimate the emotions of the players and determine the priority of the videos to be acquired based on the estimated emotions of the players. For example, if the player is nervous, the acquisition unit can refrain from acquiring important videos until the player is relaxed. Also, if the player is concentrating, the acquisition unit can prioritize acquiring that moment. Furthermore, if the player is tired, the acquisition unit can prioritize acquiring videos after a break. In this way, by determining the priority of videos according to the emotions of the players, important videos can be acquired preferentially.

[0072] When acquiring video, the acquisition unit can prioritize acquiring highly relevant video by taking into account the geographical location information of the player. For example, when acquiring video, the acquisition unit prioritizes acquiring highly relevant video by taking into account the geographical location information of the player. For example, when a player is in a specific training area, the acquisition unit prioritizes acquiring video of that area. Furthermore, when a player is at a game venue, the acquisition unit can also prioritize acquiring video of the venue. Furthermore, when a player is traveling, the acquisition unit can also prioritize acquiring video of the player's destination. In this way, highly relevant video can be prioritized by taking into account the geographical location information.

[0073] The acquisition unit can analyze the social media activity of the player when acquiring the video and acquire related video. For example, the acquisition unit analyzes the social media activity of the player when acquiring the video and acquires related video. For example, if the player posts a specific training session on social media, the acquisition unit can prioritize acquiring video of that training session. Also, if the player posts highlights of a game, the acquisition unit can prioritize acquiring video of that game. Furthermore, if the player is participating in a specific event, the acquisition unit can prioritize acquiring video of that event. In this way, by analyzing social media activity, related video can be prioritized.

[0074] The analysis unit can estimate the player's emotions and adjust the accuracy of the analysis based on the estimated player's emotions. The analysis unit, for example, estimates the player's emotions and adjusts the accuracy of the analysis based on the estimated player's emotions. For example, if the player is nervous, the analysis unit can increase the accuracy of the analysis and provide detailed feedback. Furthermore, if the player is relaxed, the analysis unit can set the accuracy of the analysis to normal. Furthermore, if the player is tired, the analysis unit can lower the accuracy of the analysis and provide simple feedback. In this way, by adjusting the accuracy of the analysis according to the player's emotions, more appropriate feedback can be provided.

[0075] The analysis unit can optimize the analysis algorithm by referring to the player's past performance data during analysis. For example, the analysis unit can optimize the analysis algorithm by referring to the player's past performance data during analysis. For example, the analysis unit can identify the player's weak points from the past data and focus the analysis on those areas. The analysis unit can also reflect the player's strong moves from the past data in the analysis algorithm. Furthermore, the analysis unit can perform analysis that takes into account the overall balance based on the past data. In this way, the analysis algorithm can be optimized based on the past data, thereby improving the accuracy of the analysis.

[0076] The analysis unit can perform the analysis while taking into account the player's body shape and muscle movement. For example, the analysis unit performs the analysis while taking into account the player's body shape and muscle movement. For example, the analysis unit performs an analysis according to the player's body shape and proposes an optimal form. The analysis unit can also analyze the player's muscle movement and propose efficient movements. Furthermore, the analysis unit can comprehensively analyze the player's body shape and muscle movement and propose a balanced form. This allows for more appropriate analysis by taking into account the player's body shape and muscle movement.

[0077] The analysis unit can estimate the player's emotions and adjust the display method of the analysis results based on the estimated player's emotions. The analysis unit, for example, estimates the player's emotions and adjusts the display method of the analysis results based on the estimated player's emotions. For example, if the player is nervous, the analysis unit provides a simple, highly visible display method. If the player is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the player is in a hurry, the analysis unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method according to the player's emotions, more appropriate feedback can be provided.

[0078] The analysis unit can perform the analysis taking into account the geographical distribution of players. For example, when an athlete is training in a specific region, the analysis unit performs the analysis taking into account the characteristics of that region. In addition, when an athlete is training in different regions, the analysis unit can also perform the analysis taking into account the characteristics of each region. Furthermore, when an athlete is traveling, the analysis unit can also perform the analysis taking into account the characteristics of the region to which the athlete is traveling. In this way, by taking geographical distribution into account, more appropriate analysis is possible.

[0079] The analysis unit can improve the accuracy of the analysis by referring to literature related to the player during analysis. The analysis unit can improve the accuracy of the analysis by referring to literature related to the player during analysis, for example. For example, the analysis unit performs the analysis by referring to the latest research papers related to the player's training. The analysis unit can also perform the analysis by referring to past research papers related to the player's performance. Furthermore, the analysis unit can perform the analysis by referring to literature related to the player's body type and muscle movement. In this way, the accuracy of the analysis is improved by referring to related literature.

[0080] The generation unit can estimate the player's emotions and adjust the way in which advice is expressed based on the estimated player's emotions. The generation unit, for example, estimates the player's emotions and adjusts the way in which advice is expressed based on the estimated player's emotions. For example, if the player is nervous, the generation unit can provide advice in gentle words. Also, if the player is relaxed, the generation unit can provide detailed advice. Furthermore, if the player is in a hurry, the generation unit can provide concise and to-the-point advice. In this way, more appropriate advice can be provided by adjusting the way in which advice is expressed according to the player's emotions.

[0081] When generating advice, the generation unit can refer to the player's past training data to generate optimal advice. When generating advice, the generation unit, for example, refers to the player's past training data to generate optimal advice. For example, the generation unit can identify the player's weak points from the past data and generate advice to improve those areas. The generation unit can also generate advice to strengthen the player's strong movements from the past data. Furthermore, the generation unit can generate advice that takes into account the overall balance based on the past data. This enables effective training by generating optimal advice based on the past data.

[0082] When generating advice, the generation unit can customize the advice by taking into account the player's current physical condition and fatigue level. For example, when generating advice, the generation unit customizes the advice by taking into account the player's current physical condition and fatigue level. For example, if the player is tired, the generation unit generates advice encouraging the player to rest. Furthermore, if the player is in poor physical condition, the generation unit can also generate advice suggesting lighter training. Furthermore, if the player is in good physical condition, the generation unit can also generate advice suggesting harder training. This allows for more effective training by providing advice according to the player's physical condition and fatigue level.

[0083] The generation unit can estimate the player's emotions and determine the priority of advice based on the estimated player's emotions. The generation unit, for example, estimates the player's emotions and determines the priority of advice based on the estimated player's emotions. For example, if the player is nervous, the generation unit can prioritize advice to relax. Furthermore, if the player is relaxed, the generation unit can also prioritize detailed advice. Furthermore, if the player is in a hurry, the generation unit can prioritize concise and to the point advice. In this way, by determining the priority of advice according to the player's emotions, more appropriate advice can be provided.

[0084] The generation unit can generate optimal advice by taking into account the geographical location information of the player when generating advice. For example, the generation unit generates optimal advice by taking into account the geographical location information of the player when generating advice. For example, if the player is in a specific training area, the generation unit generates advice appropriate for that area. Also, if the player is at a game venue, the generation unit can generate advice appropriate for that venue. Furthermore, if the player is traveling, the generation unit can generate advice appropriate for the player's destination. In this way, by taking into account the geographical location information, more appropriate advice can be provided.

[0085] The generation unit can analyze the social media activity of the player and adjust the content of the advice when generating the advice. For example, the generation unit analyzes the social media activity of the player and adjusts the content of the advice when generating the advice. For example, if the player posts a specific training on social media, the generation unit can generate advice related to that training. Also, if the player posts highlights of a game, the generation unit can generate advice related to that game. Furthermore, if the player is participating in a specific event, the generation unit can generate advice related to that event. In this way, by analyzing social media activity, more relevant advice can be provided.

[0086] The providing unit can estimate the player's emotions and adjust the method of providing advice based on the estimated player's emotions. The providing unit, for example, estimates the player's emotions and adjusts the method of providing advice based on the estimated player's emotions. For example, if the player is nervous, the providing unit can provide advice in gentle words. Also, if the player is relaxed, the providing unit can provide detailed advice. Furthermore, if the player is in a hurry, the providing unit can provide concise advice that gets to the point. In this way, by adjusting the method of providing advice according to the player's emotions, more appropriate advice can be provided.

[0087] The providing unit can select the optimal method of providing advice by referring to the player's past feedback when providing advice. For example, the providing unit selects the optimal method of providing advice by referring to the player's past feedback when providing advice. For example, the providing unit prioritizes a method of providing advice that the player has preferred in the past. The providing unit can also avoid a method of providing advice that the player has avoided in the past. Furthermore, the providing unit can select the optimal method of providing advice based on the player's past feedback. In this way, more effective advice can be provided by selecting the optimal method of providing advice based on past feedback.

[0088] The providing unit can customize the means of providing advice according to the player's current environment and situation when providing advice. For example, the providing unit customizes the means of providing advice according to the player's current environment and situation when providing advice. For example, if the player is training outdoors, the providing unit can provide advice by voice. Also, if the player is training indoors, the providing unit can provide visual advice using AR glasses. Furthermore, if the player is on the move, the providing unit can provide advice via a smartphone. In this way, by customizing the means of providing advice according to the environment and situation, more appropriate advice can be provided.

[0089] The providing unit can estimate the player's emotions and adjust the timing of providing advice based on the estimated player's emotions. The providing unit, for example, estimates the player's emotions and adjusts the timing of providing advice based on the estimated player's emotions. For example, if the player is nervous, the providing unit delays providing advice until the player is relaxed. Also, if the player is concentrating, the providing unit can provide advice at that moment. Furthermore, if the player is tired, the providing unit can provide advice after a break. In this way, by adjusting the timing of providing advice according to the player's emotions, advice can be provided at more appropriate times.

[0090] The providing unit can select the optimal providing method by taking into consideration the device information of the player when providing advice. For example, the providing unit selects the optimal providing method by taking into consideration the device information of the player when providing advice. For example, if the player is using a smartphone, the providing unit can provide a display method that matches the screen size. Also, if the player is using a tablet, the providing unit can provide a display method that is optimized for a large screen. Furthermore, if the player is using a smartwatch, the providing unit can provide a display method that is simple and highly visible. In this way, more appropriate advice can be provided by taking into consideration the device information.

[0091] The providing unit can improve the accuracy of advice provision by referring to the player's related data when providing advice. The providing unit, for example, improves the accuracy of advice provision by referring to the player's related data when providing advice. For example, the providing unit provides optimal advice by referring to the player's past training data. The providing unit can also provide appropriate advice by referring to the player's current physical condition data. Furthermore, the providing unit can also provide effective advice by referring to the player's performance data. In this way, the accuracy of advice provision is improved by referring to the related data. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit captures footage of the player's form using the camera 42 of the smart device 14. The analysis unit analyzes the captured footage using the specific processing unit 290 of the data processing device 12 and identifies areas for improvement in the form. The generation unit generates specific advice based on the analysis results using the specific processing unit 290 of the data processing device 12. The provision unit displays visual advice through the output device 40 of the smart device 14 or provides audio advice through earphones. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit captures an image of the player's form using the camera 42 of the smart glasses 214. The analysis unit analyzes the captured image using the specific processing unit 290 of the data processing device 12 and identifies areas for improvement in the form. The generation unit generates specific advice based on the analysis results using the specific processing unit 290 of the data processing device 12. The provision unit displays visual advice on the display of the smart glasses 214 or provides audio advice through earphones. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit captures footage of the player's form using the camera 42 of the headset type terminal 314. The analysis unit analyzes the captured footage using the specific processing unit 290 of the data processing device 12 and identifies areas for improvement in the form. The generation unit generates specific advice based on the analysis results using the specific processing unit 290 of the data processing device 12. The provision unit displays visual advice on the display 343 of the headset type terminal 314 or provides audio advice through earphones. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit captures footage of the player's form using the camera 42 of the robot 414. The analysis unit analyzes the captured footage using the specific processing unit 290 of the data processing device 12 and identifies areas for improvement in the player's form. The generation unit generates specific advice based on the analysis results using the specific processing unit 290 of the data processing device 12. The provision unit provides advice via audio through the speaker 240 of the robot 414, or displays visual advice on the display of the robot 414.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The acquisition unit acquires video using a camera that captures the player's running form. The acquisition unit can, for example, capture the player's entire body movements using a high-resolution camera. For example, the acquisition unit can capture the player's entire body movements in detail using a camera while the player is running. The acquisition unit can also estimate the player's emotions and adjust the timing of video acquisition based on the estimated player's emotions. For example, if the player is nervous, the acquisition of video can be delayed until the player is relaxed. The analysis unit analyzes the video acquired by the acquisition unit. The analysis unit can, for example, use AI to analyze the player's movements in the video and identify areas for improvement in the player's running form. For example, the analysis unit can analyze the player's foot position and arm swing in the player's running form. The analysis unit can also estimate the player's emotions and adjust the accuracy of the analysis based on the estimated player's emotions. For example, if the player is nervous, the analysis accuracy can be improved to provide more detailed feedback. The generation unit generates advice based on the results of the analysis by the analysis unit. The generation unit generates specific advice using AI, for example. For example, the generation unit can generate specific advice such as "put your feet forward" as an area for improving your running form. The generation unit can also estimate the player's emotions and adjust the way the advice is presented based on the estimated player's emotions. For example, if the player is nervous, the generation unit can provide the advice in gentler terms. The provision unit provides the advice generated by the generation unit to the player. The provision unit can provide visual advice through, for example, AR glasses. The provision unit can also provide audio advice through earphones. For example, if the player is training outdoors, the provision unit can provide audio advice. This allows the form analysis system according to the embodiment to analyze the player's form in real time and provide immediate advice.

[0094] The providing unit can provide visual advice through the AR glasses. For example, the providing unit can display advice directly in the athlete's field of vision. The providing unit can also provide visual advice using animations and graphics. For example, the providing unit can use animations to show areas for improvement in the athlete's running form. Furthermore, the providing unit can visually show changes in the athlete's form in real time. This allows the athlete to correct their form in real time by providing visual advice.

[0095] The providing unit can provide advice by voice through earphones. For example, the providing unit can deliver advice directly to the player's ear. The providing unit can also customize the type and content of the audio. For example, the providing unit can adjust the tone and speed of the audio according to the player's preferences. Furthermore, the providing unit can change the content of the audio advice according to the player's training status. This allows the player to correct their form in real time by providing advice by voice.

[0096] The analysis unit can analyze the position of the feet or the way the arms swing in the athlete's running form. For example, the analysis unit analyzes the position of the feet in the athlete's running form. For example, the analysis unit performs an analysis based on the measurement points of the foot position and the measurement accuracy. The analysis unit can also analyze the way the arms swing in the athlete's running form. For example, the analysis unit performs an analysis based on the angle and speed of the arm swing. Furthermore, the analysis unit can analyze changes in the athlete's form in real time and provide immediate feedback. This enables detailed analysis of the running form.

[0097] The generation unit can generate specific advice, such as to improve the running form by positioning the feet forward. For example, the generation unit can analyze the athlete's running form and determine that the form can be improved by positioning the feet forward. The generation unit can also generate specific advice for other areas of improvement. For example, the generation unit can generate advice to improve the way the arms swing. Furthermore, the generation unit can customize the content of the advice depending on the athlete's training situation. In this way, the athlete's form can be effectively improved by providing specific areas of improvement.

[0098] The acquisition unit can capture the entire body movements of an athlete using a high-resolution camera. For example, the acquisition unit can capture the entire body movements of an athlete in detail using the camera while the athlete is running. The acquisition unit can also adjust the camera resolution and shooting conditions. For example, the acquisition unit can set the camera resolution to a high level to capture the athlete's movements in more detail. Furthermore, the acquisition unit can adjust the camera frame rate according to the athlete's movement speed. As a result, detailed images can be acquired by using a high-resolution camera.

[0099] The acquisition unit can estimate the player's emotions and adjust the timing of video acquisition based on the estimated player's emotions. For example, if the player is nervous, the acquisition unit can delay video acquisition until the player is relaxed. Also, if the player is concentrating, the acquisition unit can immediately acquire video to capture that moment. Furthermore, if the player is tired, the acquisition unit can acquire video after a break. In this way, more appropriate video can be acquired by adjusting the timing of video acquisition according to the player's emotions.

[0100] The acquisition unit can analyze a player's past form data and select the optimal camera angle. For example, the acquisition unit can identify a player's weak points from past data and select an angle that emphasizes those areas. The acquisition unit can also select an angle from past data that captures a player's strong movements. Furthermore, the acquisition unit can select an angle that captures the player's overall balance based on past data. In this way, effective footage can be obtained by selecting the optimal camera angle based on past data.

[0101] The acquisition unit can dynamically adjust the frame rate according to the movement speed of the player when acquiring the video. For example, the acquisition unit sets the frame rate high when the player is moving fast. The acquisition unit can also set the frame rate low when the player is moving slowly. Furthermore, the acquisition unit can adjust the frame rate in real time when the player's movement changes. This allows appropriate video to be acquired by adjusting the frame rate according to the movement speed.

[0102] The acquisition unit can estimate the emotions of the players and determine the priority of the videos to be acquired based on the estimated emotions of the players. For example, if the player is nervous, the acquisition unit can refrain from acquiring important videos until the player is relaxed. Also, if the player is concentrating, the acquisition unit can prioritize acquiring those moments. Furthermore, if the player is tired, the acquisition unit can prioritize acquiring videos after a break. In this way, by determining the priority of videos according to the emotions of the players, important videos can be acquired preferentially.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The acquisition unit acquires video footage using a camera that captures the player's form. The acquisition unit uses a high-resolution camera to capture the player's entire body movements in detail. The acquisition unit can also estimate the player's emotions and adjust the timing of video acquisition based on the estimated emotions. For example, if the player is nervous, it can delay video acquisition until the player is relaxed. Step 2: The analysis unit analyzes the video captured by the acquisition unit. The analysis unit uses AI to analyze the athlete's movements in the video and identify areas for improvement in form. For example, the analysis unit can analyze the athlete's foot position and arm swing in their running form. The analysis unit can also estimate the athlete's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the athlete is nervous, the analysis accuracy can be improved to provide more detailed feedback. Step 3: The generator generates advice based on the results of the analysis by the analyzer. The generator uses AI to generate specific advice. For example, it can generate specific advice such as "put your feet forward" to improve your running form. The generator can also estimate the athlete's emotions and adjust the way the advice is presented based on the estimated emotions. For example, if the athlete is nervous, it can provide advice in gentler words. Step 4: The providing unit provides the advice generated by the generating unit to the athlete. The providing unit can provide visual advice through AR glasses. It can also provide advice via audio through earphones. For example, if the athlete is training outdoors, advice can be provided via audio.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 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.

[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 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).

[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] 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.

[0149] 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.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] 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.

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

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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. An acquisition unit that acquires the player's form using a camera; an analysis unit that analyzes the video acquired by the acquisition unit; a generation unit that generates advice based on the results of the analysis by the analysis unit; a providing unit that provides the advice generated by the generating unit to the player. A system characterized by:

2. The providing unit Providing visual advice through AR glasses The system of claim 1 .

3. The providing unit Provides audio advice through earphones The system of claim 1 .

4. The analysis unit Analyzing the position of the feet or the swing of the arms in an athlete's running form The system of claim 1 .

5. The generation unit Generates specific advice on how to improve running form, such as putting your feet forward The system of claim 1 .

6. The acquisition unit Capturing the player's entire body movements using a camera with a specific resolution The system of claim 1 .

7. The acquisition unit Estimate the player's emotions and adjust the timing of video capture based on the estimated player's emotions. The system of claim 1 .

8. The acquisition unit Analyzing the player's past form data and selecting the optimal camera angle The system of claim 1 .

9. The acquisition unit When capturing video, the frame rate is dynamically adjusted according to the speed of the player's movements. The system of claim 1 .

Images