System

Abstract

An object of a system according to an embodiment is to allow an athlete to efficiently find a form optimal for the athlete.SOLUTION: A system includes a moving image photographing part, an analysis part, a collection part, and a generation part. The moving image capturing unit captures a form of a player. The analysis unit analyzes the moving image captured by the moving image capturing unit. The collection unit collects motion data of professionals around the world. The generation part generates an optimum form on the basis of the data analyzed by the analysis part and the data collected by the collection part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for athletes to find their optimal form, which takes time and effort.

[0005] The system according to the embodiment aims to enable athletes to efficiently find their own optimal form. [Means for solving the problem]

[0006] The system according to the embodiment includes a video capture unit, an analysis unit, a collection unit, and a generation unit. The video capture unit captures the player's form. The analysis unit analyzes the video captured by the video capture unit. The collection unit collects motion data from professionals around the world. The generation unit generates an optimal form based on the data analyzed by the analysis unit and the data collected by the collection unit. [Effects of the Invention]

[0007] The system according to the embodiment allows athletes to efficiently find their own optimal form. [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 is a system that helps athletes learn correct form and improve their performance in matches. This form analysis system combines generative AI and AI to efficiently extract an athlete's form, training methods, and areas for improvement, and provides the optimal form. This allows the form analysis system to efficiently analyze an athlete's form and provide the optimal form.

[0029] A form analysis system according to an embodiment includes a video capture unit, an analysis unit, a collection unit, and a generation unit. The video capture unit captures a player's form. For example, the video capture unit uses a high-resolution camera to record the player's movements in detail. The video capture unit can also capture footage from different angles. The analysis unit analyzes the video captured by the video capture unit. For example, the analysis unit uses a generation AI to analyze the player's movements in the video and extract form characteristics. The collection unit collects movement data from professionals around the world. For example, the collection unit collects movement data from professional players via the Internet. The generation unit generates an optimal form based on the data analyzed by the analysis unit and the data collected by the collection unit. For example, the generation unit uses a generation AI to suggest an optimal form. This allows the form analysis system according to an embodiment to efficiently analyze a player's form and provide an optimal form.

[0030] The video recording unit records a player's movements using 3D motion capture technology, enabling more detailed motion analysis. For example, the video recording unit records a player's movements using 3D motion capture technology and analyzes the movement and angle of each joint in detail. For example, it accurately captures the movement of the knees and ankles during running. The video recording unit also uses 3D motion capture technology to convert a player's movements into a 3D model in real time and accurately reproduces the movements. For example, it analyzes the trajectory of the bat during a swing in detail. The video recording unit also works in conjunction with motion analysis software to automatically identify areas for improvement in movements based on the 3D motion capture data. For example, it analyzes the landing motion during a jump and suggests ways to reduce strain on the knees. This allows for detailed analysis of a player's movements.

[0031] The video recording unit measures the muscle movement and load of the player in real time, which can be used to improve form. The video recording unit, for example, uses an electromyogram (EMG) sensor to measure the muscle movement of the player in real time, which can be used to improve form. For example, the video recording unit analyzes the activity of the quadriceps during sprinting. The video recording unit also uses a wearable device to collect data in real time to measure muscle movement and load. For example, the video recording unit analyzes the load on the back muscles during weightlifting. The video recording unit also attaches sensors to the player's body to measure muscle movement and load, and records the muscle activity during movement in detail. For example, the video recording unit analyzes the movement of the shoulder muscles during a tennis serve. This allows for a detailed analysis of the muscle movement and load of the player.

[0032] The video capture unit uses a drone to capture video and can simultaneously record a player's form from different angles. For example, the video capture unit uses a drone to capture a player's movements from above and record their form from different angles. For example, a soccer dribbling motion can be analyzed from above. The video capture unit can also use multiple drones to capture a player's movements from all directions in 360 degrees. For example, a basketball shooting form can be analyzed from multiple angles. The video capture unit can also use a drone to track and capture a player's movements and perform a detailed analysis of the movements. For example, a marathon runner's running form can be tracked and captured. This makes it possible to record a player's form from different angles in detail.

[0033] The video recording unit can measure the athlete's heart rate and breathing rate while recording and analyze the relationship between their physical condition and form. The video recording unit can, for example, use a heart rate monitor to measure the athlete's heart rate in real time while recording and use this information to analyze their form. For example, the relationship between their heart rate and form while running can be analyzed. The video recording unit can also use a breathing sensor to measure the athlete's breathing rate while recording and use this information to analyze their form. For example, the relationship between their breathing pattern while swimming can be analyzed. The video recording unit can also simultaneously measure their heart rate and breathing rate to perform a detailed analysis of the relationship between their physical condition and form. For example, the video recording unit can analyze fluctuations in heart rate and breathing rate while cycling and identify areas for improvement in their form. This allows for a detailed analysis of the relationship between the athlete's physical condition and form.

[0034] When analyzing a video, the generative AI can break down a player's movements and analyze the timing and speed of each movement in detail. For example, the generative AI breaks down a player's movements in a video frame by frame and analyzes the timing and speed of each movement in detail. For example, it analyzes the foot movements during a sprint frame by frame. The generative AI also uses a motion analysis algorithm to break down a player's movements and quantify the timing and speed of each movement. For example, it analyzes a tennis serve and measures the speed of each step. The generative AI also breaks down a player's movements in a video in real time and analyzes the timing and speed of each movement. For example, it analyzes a basketball dribbling movement in real time. This allows for a detailed analysis of a player's movements.

[0035] The generation AI can add a function to compare a player's past form data and evaluate their progress. For example, the generation AI analyzes a player's past form data and compares it with their current form to evaluate their progress. For example, it identifies areas for improvement in their running form by comparing it with past data. The generation AI also evaluates a player's progress based on past form data and suggests areas for improvement. For example, it compares the progress of their swing form with past data to evaluate it. The generation AI also adds a function to analyze a player's past form data in real time and evaluate their progress. For example, it compares the progress of their jump height and distance with past data to evaluate it. This makes it possible to evaluate a player's progress.

[0036] The generating AI can provide comprehensive advice to improve form by taking into account the player's nutritional status and sleep data. For example, the generating AI can analyze the player's nutritional status and sleep data and provide comprehensive advice to improve form. For example, it can analyze the impact of improving nutritional balance on form. The generating AI can also provide specific advice to improve form based on nutritional status and sleep data. For example, it can analyze the impact of lack of sleep on form and suggest improvement measures. The generating AI can also analyze the player's nutritional status and sleep data in real time and provide comprehensive advice to improve form. For example, it can analyze the impact of the timing of nutritional supplementation on form. This makes it possible to provide advice that takes into account the player's nutritional status and sleep data.

[0037] The generation AI can collect motion data of professional athletes in real time and provide a form that reflects the latest trends. The generation AI, for example, collects motion data of professional athletes in real time and provides a form that reflects the latest trends. For example, it analyzes the latest trends in running form. The generation AI can also provide a form that reflects the latest trends based on the motion data of professional athletes. For example, it analyzes the latest trends in swing form. The generation AI can also collect motion data of professional athletes in real time and provide a form that reflects the latest trends. For example, it analyzes the latest trends in jumping form. This makes it possible to provide a form that reflects the latest trends.

[0038] The generation AI can add a function to analyze the movement data of professional athletes and evaluate the energy efficiency of each movement. For example, the generation AI can analyze the movement data of professional athletes and evaluate the energy efficiency of each movement. For example, it can analyze the energy efficiency of running form and suggest the optimal form. The generation AI can also add a function to evaluate the energy efficiency of each movement based on the movement data of professional athletes. For example, it can analyze the energy efficiency of swing form and suggest areas for improvement. The generation AI can also add a function to analyze the movement data of professional athletes in real time and evaluate the energy efficiency of each movement. For example, it can analyze the energy efficiency of jumping form and suggest the optimal form. This makes it possible to evaluate the energy efficiency of each movement.

[0039] Generative AI can collect motion data from professional athletes of different sports and extract the optimal common motion patterns. For example, generative AI collects motion data from professional athletes of different sports and extracts the optimal common motion patterns. For example, it analyzes the commonalities between a baseball swing form and a golf swing form. Generative AI can also extract the optimal common motion patterns based on the motion data of professional athletes of different sports. For example, it analyzes the commonalities between a soccer kicking motion and a rugby kicking motion. Generative AI can also collect motion data from professional athletes of different sports in real time and extract the optimal common motion patterns. For example, it analyzes the commonalities between a basketball shooting form and a handball shooting form. This makes it possible to extract the optimal common motion patterns between different sports.

[0040] The generation AI can provide a customized training plan based on the motion data of professional athletes, tailored to the individual goals of the athletes. For example, the generation AI analyzes the motion data of professional athletes and provides a customized training plan based on the individual goals of the athletes. For example, it proposes a specific training menu for an athlete aiming to improve his / her running form. The generation AI also provides a training plan based on the motion data of professional athletes, tailored to the individual goals of the athletes. For example, it proposes a specific practice menu for an athlete aiming to improve his / her swing form. The generation AI also analyzes the motion data of professional athletes in real time and provides a customized training plan based on the individual goals of the athletes. For example, it proposes a specific training menu for an athlete aiming to improve his / her jumping form. This makes it possible to provide a training plan tailored to the individual goals of the athletes.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The video recording unit can capture a player's movements with an infrared camera, enabling detailed movement analysis even in dark places or at night. For example, a player's running form at night can be captured with an infrared camera, and the details of the movement can be analyzed. The video recording unit can also use an infrared camera to record in detail movements during training in dark indoor places. For example, weightlifting movements in dark places can be analyzed. The video recording unit can also use an infrared camera to monitor a player's movements in real time, and identify areas for improvement in movements in dark places. For example, dribbling movements during soccer practice at night can be analyzed. This makes it possible to analyze a player's movements in detail even in dark places or at night.

[0043] The video recording unit can capture the player's movements with a thermal imaging camera and analyze muscle temperature changes. For example, the thermal imaging camera can capture muscle temperature changes during sprinting to analyze the load on the muscles. The video recording unit can also use the thermal imaging camera to monitor muscle temperature changes during the player's movements in real time. For example, it can analyze muscle temperature changes during weightlifting. The video recording unit can also use the thermal imaging camera to record the player's movements in detail and identify areas for improvement based on muscle temperature changes. For example, it can analyze temperature changes in the shoulder muscles during a tennis serve. This allows for detailed analysis of muscle temperature changes.

[0044] The video recording unit can measure the player's movements with an ultrasonic sensor and analyze the joint movements in detail. For example, the ultrasonic sensor can measure the movement of the knee joint while running and analyze the details of the movement. The video recording unit can also use the ultrasonic sensor to monitor the joint movements of the player while they are moving in real time. For example, the movement of the elbow joint while weightlifting can be analyzed. The video recording unit can also use the ultrasonic sensor to record the player's movements in detail and identify areas for improvement in the movements based on the joint movements. For example, the movement of the shoulder joint during a tennis serve can be analyzed. This allows for a detailed analysis of the joint movements.

[0045] The video recording unit can measure the athlete's movements with a laser sensor and analyze the speed and distance of the movements in detail. For example, the speed of movements during sprinting can be measured with the laser sensor and the details of the movements can be analyzed. The video recording unit can also use the laser sensor to monitor the speed and distance of the athlete's movements in real time. For example, the speed of movements during weightlifting can be analyzed. The video recording unit can also use the laser sensor to record the athlete's movements in detail and identify areas for improvement in movements based on the speed and distance. For example, the speed of movements during a tennis serve can be analyzed. This allows the speed and distance of movements to be analyzed in detail.

[0046] The video recording unit can measure the player's movements using a magnetic sensor and analyze the direction and angle of the movements in detail. For example, the magnetic sensor can measure the direction of movement during a sprint and analyze the details of the movement. The video recording unit can also use the magnetic sensor to monitor the direction and angle of the player's movements in real time. For example, the angle of movement during weightlifting can be analyzed. The video recording unit can also use the magnetic sensor to record the player's movements in detail and identify areas for improvement in the movements based on the direction and angle. For example, the direction of movement during a tennis serve can be analyzed. This allows the direction and angle of movement to be analyzed in detail.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The video recording unit records the player's form. For example, the video recording unit can use a high-resolution camera to record the player's movements in detail and take pictures from different angles. Step 2: The analysis unit analyzes the video captured by the video capture unit. For example, the analysis unit uses a generation AI to analyze the player's movements in the video and extract form characteristics. Step 3: The collection unit collects motion data of professionals around the world. For example, the collection unit collects motion data of professional athletes via the Internet. Step 4: The generation unit generates an optimal form based on the data analyzed by the analysis unit and the data collected by the collection unit. For example, the generation unit uses a generation AI to suggest an optimal form.

[0049] (Example 2) A form analysis system according to an embodiment of the present invention is a system that helps athletes learn correct form and improve their performance in matches. This form analysis system combines generative AI and AI to efficiently extract an athlete's form, training methods, and areas for improvement, and provides the optimal form. This allows the form analysis system to efficiently analyze an athlete's form and provide the optimal form.

[0050] A form analysis system according to an embodiment includes a video capture unit, an analysis unit, a collection unit, and a generation unit. The video capture unit captures a player's form. For example, the video capture unit uses a high-resolution camera to record the player's movements in detail. The video capture unit can also capture footage from different angles. The analysis unit analyzes the video captured by the video capture unit. For example, the analysis unit uses a generation AI to analyze the player's movements in the video and extract form characteristics. The collection unit collects movement data from professionals around the world. For example, the collection unit collects movement data from professional players via the Internet. The generation unit generates an optimal form based on the data analyzed by the analysis unit and the data collected by the collection unit. For example, the generation unit uses a generation AI to suggest an optimal form. This allows the form analysis system according to an embodiment to efficiently analyze a player's form and provide an optimal form.

[0051] The video recording unit records a player's movements using 3D motion capture technology, enabling more detailed motion analysis. For example, the video recording unit records a player's movements using 3D motion capture technology and analyzes the movement and angle of each joint in detail. For example, it accurately captures the movement of the knees and ankles during running. The video recording unit also uses 3D motion capture technology to convert a player's movements into a 3D model in real time and accurately reproduces the movements. For example, it analyzes the trajectory of the bat during a swing in detail. The video recording unit also works in conjunction with motion analysis software to automatically identify areas for improvement in movements based on the 3D motion capture data. For example, it analyzes the landing motion during a jump and suggests ways to reduce strain on the knees. This allows for detailed analysis of a player's movements.

[0052] The video recording unit measures the muscle movement and load of the player in real time, which can be used to improve form. The video recording unit, for example, uses an electromyogram (EMG) sensor to measure the muscle movement of the player in real time, which can be used to improve form. For example, the video recording unit analyzes the activity of the quadriceps during sprinting. The video recording unit also uses a wearable device to collect data in real time to measure muscle movement and load. For example, the video recording unit analyzes the load on the back muscles during weightlifting. The video recording unit also attaches sensors to the player's body to measure muscle movement and load, and records the muscle activity during movement in detail. For example, the video recording unit analyzes the movement of the shoulder muscles during a tennis serve. This allows for a detailed analysis of the muscle movement and load of the player.

[0053] The video recording unit uses the emotion estimation function to analyze the emotional state of the player while being filmed, and can evaluate the impact of stress and tension on their form. For example, the video recording unit analyzes the facial expressions of the player while being filmed, and uses the emotion estimation function to evaluate the level of stress and tension. For example, the video recording unit analyzes the emotional state of the player during warm-ups before a match. The video recording unit also analyzes the audio of the player while being filmed, and uses the emotion estimation function to evaluate the emotional state. For example, the video recording unit analyzes the tone and tempo of the player's voice during practice to measure the level of tension. The video recording unit also analyzes the player's biometric data (heart rate and galvanic skin response) while being filmed, and uses the emotion estimation function to evaluate the emotional state. For example, the video recording unit monitors the stress level during a match in real time. This makes it possible to analyze the player's emotional state and evaluate the impact on their form.

[0054] The video capture unit uses a drone to capture video and can simultaneously record a player's form from different angles. For example, the video capture unit uses a drone to capture a player's movements from above and record their form from different angles. For example, a soccer dribbling motion can be analyzed from above. The video capture unit can also use multiple drones to capture a player's movements from all directions in 360 degrees. For example, a basketball shooting form can be analyzed from multiple angles. The video capture unit can also use a drone to track and capture a player's movements and perform a detailed analysis of the movements. For example, a marathon runner's running form can be tracked and captured. This makes it possible to record a player's form from different angles in detail.

[0055] The video recording unit can measure the athlete's heart rate and breathing rate while recording and analyze the relationship between their physical condition and form. The video recording unit can, for example, use a heart rate monitor to measure the athlete's heart rate in real time while recording and use this information to analyze their form. For example, the relationship between their heart rate and form while running can be analyzed. The video recording unit can also use a breathing sensor to measure the athlete's breathing rate while recording and use this information to analyze their form. For example, the relationship between their breathing pattern while swimming can be analyzed. The video recording unit can also simultaneously measure their heart rate and breathing rate to perform a detailed analysis of the relationship between their physical condition and form. For example, the video recording unit can analyze fluctuations in heart rate and breathing rate while cycling and identify areas for improvement in their form. This allows for a detailed analysis of the relationship between the athlete's physical condition and form.

[0056] The video recording unit can use the emotion estimation function to monitor the emotions of the player being filmed in real time and provide music or messages to elicit positive emotions. For example, the video recording unit analyzes the facial expressions of the player being filmed and monitors their emotions in real time using the emotion estimation function. For example, it analyzes the emotional state of the player during practice and provides music to elicit positive emotions. The video recording unit also analyzes the voice of the player being filmed and monitors their emotional state using the emotion estimation function. For example, it analyzes the tone of the player's voice during practice and provides a positive message. The video recording unit also analyzes the biometric data of the player being filmed (heart rate and galvanic skin response) and monitors their emotional state using the emotion estimation function. For example, it analyzes the stress level during practice and provides music to relax them. In this way, it is possible to monitor the emotions of the player in real time and elicit positive emotions.

[0057] When analyzing a video, the generative AI can break down a player's movements and analyze the timing and speed of each movement in detail. For example, the generative AI breaks down a player's movements in a video frame by frame and analyzes the timing and speed of each movement in detail. For example, it analyzes the foot movements during a sprint frame by frame. The generative AI also uses a motion analysis algorithm to break down a player's movements and quantify the timing and speed of each movement. For example, it analyzes a tennis serve and measures the speed of each step. The generative AI also breaks down a player's movements in a video in real time and analyzes the timing and speed of each movement. For example, it analyzes a basketball dribbling movement in real time. This allows for a detailed analysis of a player's movements.

[0058] The generation AI can add a function to compare a player's past form data and evaluate their progress. For example, the generation AI analyzes a player's past form data and compares it with their current form to evaluate their progress. For example, it identifies areas for improvement in their running form by comparing it with past data. The generation AI also evaluates a player's progress based on past form data and suggests areas for improvement. For example, it compares the progress of their swing form with past data to evaluate it. The generation AI also adds a function to analyze a player's past form data in real time and evaluate their progress. For example, it compares the progress of their jump height and distance with past data to evaluate it. This makes it possible to evaluate a player's progress.

[0059] The generation AI can use the emotion estimation function to provide feedback to improve a player's motivation based on the analysis results. For example, the generation AI can estimate a player's emotional state based on video analysis results and provide feedback to improve motivation. For example, it can provide positive feedback during practice. The generation AI can also use the emotion estimation function to provide specific advice to improve a player's motivation based on the analysis results. For example, it can provide a positive message such as, "This move is very good." The generation AI can also monitor a player's emotional state in real time based on the video analysis results and provide feedback to improve motivation. For example, it can provide an encouraging message to a player during practice. This makes it possible to provide feedback to improve a player's motivation.

[0060] The generating AI can provide comprehensive advice to improve form by taking into account the player's nutritional status and sleep data. For example, the generating AI can analyze the player's nutritional status and sleep data and provide comprehensive advice to improve form. For example, it can analyze the impact of improving nutritional balance on form. The generating AI can also provide specific advice to improve form based on nutritional status and sleep data. For example, it can analyze the impact of lack of sleep on form and suggest improvement measures. The generating AI can also analyze the player's nutritional status and sleep data in real time and provide comprehensive advice to improve form. For example, it can analyze the impact of the timing of nutritional supplementation on form. This makes it possible to provide advice that takes into account the player's nutritional status and sleep data.

[0061] The generation AI can use the emotion estimation function to propose a training plan that takes into account the player's emotional state based on the analysis results. For example, the generation AI can estimate the player's emotional state based on video analysis results and propose a training plan that takes into account the player's emotional state. For example, it can propose a low-stress training menu. The generation AI can also use the emotion estimation function to provide a specific training plan that takes into account the player's emotional state based on the analysis results. For example, it can propose a training menu that has a relaxing effect. The generation AI can also monitor the player's emotional state in real time based on the video analysis results and propose a training plan that takes into account the player's emotional state. For example, it can propose a training menu that elicits positive emotions. This makes it possible to propose a training plan that takes into account the player's emotional state.

[0062] The generation AI can collect motion data of professional athletes in real time and provide a form that reflects the latest trends. The generation AI, for example, collects motion data of professional athletes in real time and provides a form that reflects the latest trends. For example, it analyzes the latest trends in running form. The generation AI can also provide a form that reflects the latest trends based on the motion data of professional athletes. For example, it analyzes the latest trends in swing form. The generation AI can also collect motion data of professional athletes in real time and provide a form that reflects the latest trends. For example, it analyzes the latest trends in jumping form. This makes it possible to provide a form that reflects the latest trends.

[0063] The generation AI can add a function to analyze the movement data of professional athletes and evaluate the energy efficiency of each movement. For example, the generation AI can analyze the movement data of professional athletes and evaluate the energy efficiency of each movement. For example, it can analyze the energy efficiency of running form and suggest the optimal form. The generation AI can also add a function to evaluate the energy efficiency of each movement based on the movement data of professional athletes. For example, it can analyze the energy efficiency of swing form and suggest areas for improvement. The generation AI can also add a function to analyze the movement data of professional athletes in real time and evaluate the energy efficiency of each movement. For example, it can analyze the energy efficiency of jumping form and suggest the optimal form. This makes it possible to evaluate the energy efficiency of each movement.

[0064] The generative AI can use the emotion estimation function to analyze the emotional state of professional athletes and evaluate the impact of emotions on performance. For example, the generative AI can analyze the motion data and emotional state of professional athletes to evaluate the impact of emotions on performance. For example, it can analyze the impact of stress during a match on running form. The generative AI can also use the emotion estimation function to analyze the emotional state of professional athletes and evaluate the impact of emotions on performance. For example, it can analyze the impact of tension on swing form. The generative AI can also analyze the motion data and emotional state of professional athletes in real time to evaluate the impact of emotions on performance. For example, it can analyze the impact of positive emotions on jumping form. This makes it possible to evaluate the impact of emotions on performance.

[0065] Generative AI can collect motion data from professional athletes of different sports and extract the optimal common motion patterns. For example, generative AI collects motion data from professional athletes of different sports and extracts the optimal common motion patterns. For example, it analyzes the commonalities between a baseball swing form and a golf swing form. Generative AI can also extract the optimal common motion patterns based on the motion data of professional athletes of different sports. For example, it analyzes the commonalities between a soccer kicking motion and a rugby kicking motion. Generative AI can also collect motion data from professional athletes of different sports in real time and extract the optimal common motion patterns. For example, it analyzes the commonalities between a basketball shooting form and a handball shooting form. This makes it possible to extract the optimal common motion patterns between different sports.

[0066] The generation AI can provide a customized training plan based on the motion data of professional athletes, tailored to the individual goals of the athletes. For example, the generation AI analyzes the motion data of professional athletes and provides a customized training plan based on the individual goals of the athletes. For example, it proposes a specific training menu for an athlete aiming to improve his / her running form. The generation AI also provides a training plan based on the motion data of professional athletes, tailored to the individual goals of the athletes. For example, it proposes a specific practice menu for an athlete aiming to improve his / her swing form. The generation AI also analyzes the motion data of professional athletes in real time and provides a customized training plan based on the individual goals of the athletes. For example, it proposes a specific training menu for an athlete aiming to improve his / her jumping form. This makes it possible to provide a training plan tailored to the individual goals of the athletes.

[0067] The generation AI can use the emotion estimation function to take into account the emotional state of a professional player and provide feedback to maintain the player's motivation. For example, the generation AI can analyze the movement data and emotional state of a professional player and provide feedback to maintain the player's motivation. For example, it can provide positive feedback during practice. The generation AI can also use the emotion estimation function to analyze the emotional state of a professional player and provide specific advice to maintain the player's motivation. For example, it can provide a positive message such as, "This movement is very good." The generation AI can also analyze the movement data and emotional state of a professional player in real time and provide feedback to maintain the player's motivation. For example, it can provide an encouraging message to a player during practice. This makes it possible to provide feedback to maintain the player's motivation.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The video recording unit can capture a player's movements with an infrared camera, enabling detailed movement analysis even in dark places or at night. For example, a player's running form at night can be captured with an infrared camera, and the details of the movement can be analyzed. The video recording unit can also use an infrared camera to record in detail movements during training in dark indoor places. For example, weightlifting movements in dark places can be analyzed. The video recording unit can also use an infrared camera to monitor a player's movements in real time, and identify areas for improvement in movements in dark places. For example, dribbling movements during soccer practice at night can be analyzed. This makes it possible to analyze a player's movements in detail even in dark places or at night.

[0070] The video recording unit can capture the player's movements with a thermal imaging camera and analyze muscle temperature changes. For example, the thermal imaging camera can capture muscle temperature changes during sprinting to analyze the load on the muscles. The video recording unit can also use the thermal imaging camera to monitor muscle temperature changes during the player's movements in real time. For example, it can analyze muscle temperature changes during weightlifting. The video recording unit can also use the thermal imaging camera to record the player's movements in detail and identify areas for improvement based on muscle temperature changes. For example, it can analyze temperature changes in the shoulder muscles during a tennis serve. This allows for detailed analysis of muscle temperature changes.

[0071] The video recording unit can measure the player's movements with an ultrasonic sensor and analyze the joint movements in detail. For example, the ultrasonic sensor can measure the movement of the knee joint while running and analyze the details of the movement. The video recording unit can also use the ultrasonic sensor to monitor the joint movements of the player while they are moving in real time. For example, the movement of the elbow joint while weightlifting can be analyzed. The video recording unit can also use the ultrasonic sensor to record the player's movements in detail and identify areas for improvement in the movements based on the joint movements. For example, the movement of the shoulder joint during a tennis serve can be analyzed. This allows for a detailed analysis of the joint movements.

[0072] The video recording unit can measure the athlete's movements with a laser sensor and analyze the speed and distance of the movements in detail. For example, the speed of movements during sprinting can be measured with the laser sensor and the details of the movements can be analyzed. The video recording unit can also use the laser sensor to monitor the speed and distance of the athlete's movements in real time. For example, the speed of movements during weightlifting can be analyzed. The video recording unit can also use the laser sensor to record the athlete's movements in detail and identify areas for improvement in movements based on the speed and distance. For example, the speed of movements during a tennis serve can be analyzed. This allows the speed and distance of movements to be analyzed in detail.

[0073] The video recording unit can measure the player's movements using a magnetic sensor and analyze the direction and angle of the movements in detail. For example, the magnetic sensor can measure the direction of movement during a sprint and analyze the details of the movement. The video recording unit can also use the magnetic sensor to monitor the direction and angle of the player's movements in real time. For example, the angle of movement during weightlifting can be analyzed. The video recording unit can also use the magnetic sensor to record the player's movements in detail and identify areas for improvement in the movements based on the direction and angle. For example, the direction of movement during a tennis serve can be analyzed. This allows the direction and angle of movement to be analyzed in detail.

[0074] The video recording unit can use the emotion estimation function to analyze the player's emotional state and adjust the training intensity based on the emotion. For example, the video recording unit can analyze the player's facial expression, evaluate the player's stress level using the emotion estimation function, and adjust the training intensity. The video recording unit can also analyze the player's voice, evaluate the player's emotional state using the emotion estimation function, and adjust the training intensity. For example, the video recording unit can analyze the player's tone of voice during practice and adjust the training intensity according to the player's level of tension. The video recording unit can also analyze the player's biometric data (heart rate and galvanic skin response), evaluate the player's emotional state using the emotion estimation function, and adjust the training intensity. For example, the video recording unit can adjust the training intensity according to the player's stress level during a game. This makes it possible to adjust the training intensity based on the player's emotional state.

[0075] The video recording unit can use the emotion estimation function to analyze the player's emotional state and customize the training content based on the emotion. For example, the video recording unit can analyze the player's facial expression, evaluate the player's stress level using the emotion estimation function, and customize the training content. The video recording unit can also analyze the player's voice, evaluate the player's emotional state using the emotion estimation function, and customize the training content. For example, the video recording unit can analyze the player's tone of voice during practice and customize the training content according to the player's level of tension. The video recording unit can also analyze the player's biometric data (heart rate and galvanic skin response), evaluate the player's emotional state using the emotion estimation function, and customize the training content. For example, the video recording unit can customize the training content according to the player's stress level during a game. This makes it possible to customize the training content based on the player's emotional state.

[0076] The video recording unit can use the emotion estimation function to analyze the player's emotional state and adjust the timing of training based on the emotion. For example, the video recording unit can analyze the player's facial expression, evaluate the player's stress level using the emotion estimation function, and adjust the timing of training. The video recording unit can also analyze the player's voice, evaluate the player's emotional state using the emotion estimation function, and adjust the timing of training. For example, the video recording unit can analyze the player's tone of voice during practice and adjust the timing of training based on the level of tension. The video recording unit can also analyze the player's biometric data (heart rate and galvanic skin response), evaluate the player's emotional state using the emotion estimation function, and adjust the timing of training. For example, the video recording unit can adjust the timing of training based on the player's stress level during a game. This makes it possible to adjust the timing of training based on the player's emotional state.

[0077] The video recording unit can use the emotion estimation function to analyze the player's emotional state and adjust the training environment based on the emotion. For example, the video recording unit can analyze the player's facial expression, evaluate the player's stress level using the emotion estimation function, and adjust the training environment. The video recording unit can also analyze the player's voice, evaluate the player's emotional state using the emotion estimation function, and adjust the training environment. For example, the video recording unit can analyze the player's tone of voice during practice and adjust the training environment according to the player's level of tension. The video recording unit can also analyze the player's biometric data (heart rate and galvanic skin response), evaluate the player's emotional state using the emotion estimation function, and adjust the training environment. For example, the video recording unit can adjust the training environment according to the player's stress level during a game. In this way, the training environment can be adjusted based on the player's emotional state.

[0078] The video recording unit can use the emotion estimation function to analyze the player's emotional state and set training goals based on the emotions. For example, the video recording unit can analyze the player's facial expressions, evaluate the player's stress level using the emotion estimation function, and set training goals. The video recording unit can also analyze the player's voice, evaluate the player's emotional state using the emotion estimation function, and set training goals. For example, the video recording unit can analyze the player's tone of voice during practice and set training goals based on the player's level of tension. The video recording unit can also analyze the player's biometric data (heart rate and galvanic skin response), evaluate the player's emotional state using the emotion estimation function, and set training goals. For example, the video recording unit can set training goals based on the player's stress level during a game. In this way, training goals can be set based on the player's emotional state.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The video recording unit records the player's form. For example, the video recording unit can use a high-resolution camera to record the player's movements in detail and take pictures from different angles. Step 2: The analysis unit analyzes the video captured by the video capture unit. For example, the analysis unit uses a generation AI to analyze the player's movements in the video and extract form characteristics. Step 3: The collection unit collects motion data of professionals around the world. For example, the collection unit collects motion data of professional athletes via the Internet. Step 4: The generation unit generates an optimal form based on the data analyzed by the analysis unit and the data collected by the collection unit. For example, the generation unit uses a generation AI to suggest an optimal form.

[0081] 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.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0110] 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.

[0111] 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.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The 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.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] In the robot 414, 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 robot 414 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.

[0126] 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.

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A videography department that records the players' form, an analysis unit that analyzes the video captured by the video capture unit; A collection department that collects professional motion data from around the world; a generation unit that generates an optimal form based on the data analyzed by the analysis unit and the data collected by the collection unit. A system characterized by:

2. The video shooting unit The player's movements are recorded using 3D motion capture technology, enabling more detailed movement analysis.

2. The system of claim 1.

3. The video shooting unit Measure the muscle movement and load of the athlete in real time to help improve form 2. The system of claim 1.

4. The video shooting unit Analyzing the athlete's emotional state during filming and assessing the impact of stress and tension on their form 2. The system of claim 1.

5. The video shooting unit A drone is used to shoot video and simultaneously record the form from different angles.

2. The system of claim 1.

Images