System

Abstract

An object of a system according to an embodiment is to realize sensory rehearsal of martial arts regardless of a place and support learning of a complicated form for advanced users.SOLUTION: A system according to an embodiment includes a generation AI, AR devices, and haptic devices. The generation AI analyzes the motion of the user. The AR devices provide visual feedback based on the movements analyzed by the generation AI. The haptic devices provide haptic feedback based on the movements analyzed by the generation AI.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] With conventional technology, it was difficult to realize martial arts training regardless of location, and there was a problem that advanced practitioners were particularly uneasy about mastering complex forms.

[0005] The system according to the embodiment aims to enable hands-on martial arts training regardless of location and to support the acquisition of complex forms for advanced practitioners. [Means for solving the problem]

[0006] A system according to an embodiment includes a generation AI, an AR device, and a haptic device. The generation AI analyzes a user's movements. The AR device provides visual feedback based on the movements analyzed by the generation AI. The haptic device provides haptic feedback based on the movements analyzed by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment allows for hands-on martial arts training regardless of location, and can support advanced practitioners in mastering complex forms. [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) The martial arts immersive training system according to an embodiment of the present invention utilizes generative AI, AR (augmented reality), and haptic sharing technology to realize martial arts immersive training regardless of location, such as at home. As a result, the martial arts immersive training system allows users to realize martial arts immersive training regardless of location, such as at home.

[0029] A martial arts training system according to an embodiment includes a generation AI, an AR device, and a haptic device. The generation AI analyzes a user's movements. For example, the generation AI allows the user to film their own movements with a camera, analyzes the video, compares it with basic forms, and provides guidance on correct movements. The generation AI can provide specific instructions, such as, "Your right hand is too low. Please raise it a little higher." The generation AI can also analyze the user's movements in real time, detect subtle deviations, and provide immediate corrections. For example, the generation AI can provide specific instructions, such as, "The angle of your right foot is inappropriate. Please turn it a little more inward." Furthermore, the generation AI can learn the user's past movement data, identify individual weaknesses and areas for improvement, and provide customized instruction. For example, the generation AI can provide guidance such as, "Based on past data, your left hand movement is unstable. Please do this to make it more stable." The AR device provides visual feedback based on the movements analyzed by the generation AI. For example, the user can wear the AR device and overlay virtual guidelines or models on their actual movements to confirm the correct form. AR devices can display a user's movements as a 3D model in real time, allowing the user to visually check the accuracy of their movements. For example, a user wears an AR device and their own movements are displayed as a 3D model. This allows the user to visually check the accuracy of their movements. Furthermore, AR devices can display a virtual coach or trainer and provide real-time guidance on the user's movements. For example, a user wears an AR device, and the virtual coach analyzes the user's movements and provides specific guidance. Haptic devices provide haptic feedback based on movements analyzed by generative AI. For example, when a user assumes correct posture, the haptic device gently vibrates to indicate the correct position. Haptic devices can provide real-time feedback on the user's movements and allow the user to correct even subtle deviations in their movements. For example, when a user assumes correct form, the device gently vibrates to indicate the correct position. This allows the user to correct even subtle deviations in their movements.Furthermore, the haptic device can learn the user's past movement data, identify individual weaknesses and areas for improvement, and provide customized feedback. For example, it can provide feedback such as, "Based on past data, your left hand movement is unstable. Please do this to make it more stable." In this way, the martial arts immersive training system according to the embodiment can analyze the user's movements and provide visual and haptic feedback, thereby enabling accurate movements.

[0030] The generative AI can analyze a user's movements in real time, detect subtle discrepancies, and instantly provide corrective instructions. For example, a user can film their own movements with a camera, analyze the video, compare it with basic movements, and provide guidance on correct movements. Specifically, it can provide specific instructions such as, "Your right hand is too low. Raise it a little higher." The generative AI can also analyze a user's movements in real time, detect subtle discrepancies, and instantly provide corrective instructions. For example, it can provide specific instructions such as, "The angle of your right foot is inappropriate. Point it a little more inward." The generative AI can also analyze a user's movements in real time, detect subtle discrepancies, and instantly provide corrective instructions. For example, it can provide specific instructions such as, "Your hips are too high. Lower them a little." This allows for accurate movement by correcting subtle discrepancies in real time.

[0031] The generative AI can learn from a user's past movement data, identify individual weaknesses and areas for improvement, and provide customized instruction. For example, the generative AI can save data on the user's past practice and provide personalized instruction based on that data. Specifically, it can provide instruction such as, "Based on past data, your left hand movement is unstable. Do this to make it more stable." The generative AI can also learn from a user's past movement data, identify individual weaknesses and areas for improvement, and provide customized instruction. For example, it can provide instruction such as, "Based on past data, your foot movement is slow. Do this to move faster." The generative AI can also learn from a user's past movement data, identify individual weaknesses and areas for improvement, and provide customized instruction. For example, it can provide instruction such as, "Based on past data, your hip movement is unnatural. Do this to move more naturally." This allows for effective training by identifying individual weaknesses and areas for improvement and providing customized instruction.

[0032] The generative AI can also analyze the movements of different martial arts and sports, making it usable as a general-purpose training tool. The generative AI can analyze the movements of not only martial arts such as karate, judo, and kendo, but also sports such as basketball and soccer. This allows users to use it for a variety of training. The generative AI can also analyze the movements of different martial arts and sports. For example, it can analyze the movements of not only martial arts such as karate, judo, and kendo, but also sports such as tennis and golf. This allows users to use it for a variety of training. The generative AI can also analyze the movements of different martial arts and sports. For example, it can analyze the movements of not only martial arts such as karate, judo, and kendo, but also sports such as volleyball and baseball. This allows users to use it for a variety of training. This allows users to use it for a variety of training. By supporting the movement analysis of different martial arts and sports, the generative AI can be used as a general-purpose training tool.

[0033] Generative AI can analyze the movements of multiple users simultaneously and be applied to group training and competition judging. Generative AI can analyze the movements of multiple users simultaneously. Specifically, in group training, it analyzes the forms practiced by multiple users simultaneously and provides individual feedback to each user. This can improve the training effectiveness of the entire group. Generative AI can also analyze the movements of multiple users simultaneously. Specifically, in competitions, it analyzes the forms practiced by multiple athletes simultaneously and provides fair judging. This allows for more accurate and efficient judging of competitions. Generative AI can also analyze the movements of multiple users simultaneously. Specifically, in online group training, it analyzes the forms practiced by multiple users simultaneously and provides individual feedback to each user. This enables effective training even in remote environments. By analyzing the movements of multiple users simultaneously, this can be applied to group training and competition judging.

[0034] The AR device can display the user's movements as a 3D model in real time, allowing the user to visually confirm the accuracy of the movements. For example, a user wears the AR device, and the user's movements are displayed as a 3D model. This allows the user to visually confirm the accuracy of the movements. The AR device can also display the user's movements as a 3D model in real time. For example, a user wears the AR device, and the user's movements are displayed as a 3D model. This allows the user to visually confirm the accuracy of the movements. The AR device can also display the user's movements as a 3D model in real time. For example, a user wears the AR device, and the user's movements are displayed as a 3D model. This allows the user to visually confirm the accuracy of the movements. This allows the user to visually confirm the accuracy of the movements by displaying the user's movements as a 3D model in real time.

[0035] The AR device can display a virtual coach or trainer and provide real-time guidance to the user's movements. The AR device can, for example, display a virtual coach or trainer and provide real-time guidance to the user's movements. Specifically, the user wears the AR device, and the virtual coach analyzes the user's movements and provides specific guidance. This allows the user to receive accurate guidance in real time. The AR device can also display a virtual coach or trainer and provide real-time guidance to the user's movements. Specifically, the user wears the AR device, and the virtual coach analyzes the user's movements and provides specific guidance. This allows the user to receive accurate guidance in real time. The AR device can also display a virtual coach or trainer and provide real-time guidance to the user's movements. Specifically, the user wears the AR device, and the virtual coach analyzes the user's movements and provides specific guidance. This allows the user to receive accurate guidance in real time. This allows the user to achieve accurate movements by displaying a virtual coach or trainer and providing guidance in real time.

[0036] The AR device can be used for training in different martial arts and sports, and can be used as a general-purpose training tool. The AR device can be used for training in not only martial arts such as karate, judo, and kendo, but also sports such as basketball and soccer. This allows users to use it for a variety of training. The AR device can also be used for training in different martial arts and sports. For example, the AR device can be used for training in not only martial arts such as karate, judo, and kendo, but also sports such as tennis and golf. This allows users to use it for a variety of training. The AR device can also be used for training in different martial arts and sports. For example, the AR device can be used for training in not only martial arts such as karate, judo, and kendo, but also sports such as volleyball and baseball. This allows users to use it for a variety of training. By being able to use it for training in different martial arts and sports, the AR device can be used as a general-purpose training tool.

[0037] The AR device can add a function that allows a user to record a user's movements and play them back later for use in self-evaluation or coaching. For example, the AR device adds a function that allows a user to record a user's movements and play them back later for use in self-evaluation or coaching. Specifically, the user can later play back video recorded during training to check their own movements. This makes self-evaluation and coaching easier. The AR device can also add a function that allows a user to record a user's movements and play them back later for use in self-evaluation or coaching. Specifically, the user can later play back video recorded during training to check their own movements. This makes self-evaluation and coaching easier. The AR device can also add a function that allows a user to record a user's movements and play them back later for use in self-evaluation or coaching. Specifically, the user can later play back video recorded during training to check their own movements. This makes self-evaluation and coaching easier.

[0038] The haptic device provides feedback to the user's movements in real time, allowing the user to correct even the slightest deviations in their movements. For example, when the user takes the correct form, the haptic device gently vibrates to indicate the correct position, allowing the user to correct even the slightest deviations in their movements. The haptic device also provides feedback to the user's movements in real time. Specifically, when the user takes the correct form, the device gently vibrates to indicate the correct position, allowing the user to correct even the slightest deviations in their movements. The haptic device also provides feedback to the user's movements in real time. Specifically, when the user takes the correct form, the device gently vibrates to indicate the correct position, allowing the user to correct even the slightest deviations in their movements.

[0039] The haptic device can learn the user's past motion data, identify individual weaknesses and areas for improvement, and provide customized feedback. For example, the haptic device stores data on the user's past practice and provides personalized feedback based on that data. Specifically, the haptic device can provide feedback such as, "Based on past data, your left hand movements are unstable. Please do this to make them more stable." The haptic device can also learn the user's past motion data, identify individual weaknesses and areas for improvement, and provide customized feedback. Specifically, the haptic device can provide feedback such as, "Based on past data, your foot movements are slow. Please do this to move them faster." The haptic device can also learn the user's past motion data, identify individual weaknesses and areas for improvement, and provide customized feedback. Specifically, the haptic device can provide feedback such as, "Based on past data, your hip movements are unnatural. Please do this to move them more naturally." In this way, the haptic device can learn the user's past motion data, identify individual weaknesses and areas for improvement, and provide customized feedback.

[0040] The haptic device can be used for training in different martial arts and sports, and can be used as a general-purpose training tool. The haptic device can be used for training in not only martial arts such as karate, judo, and kendo, but also sports such as basketball and soccer. This allows users to use it for a variety of training. The haptic device can also be used for training in different martial arts and sports. For example, the haptic device can be used for training in not only martial arts such as karate, judo, and kendo, but also sports such as tennis and golf. This allows users to use it for a variety of training. The haptic device can also be used for training in different martial arts and sports. For example, the haptic device can be used for training in not only martial arts such as karate, judo, and kendo, but also sports such as volleyball and baseball. This allows users to use it for a variety of training. By being able to use it for training in different martial arts and sports, the haptic device can be used as a general-purpose training tool.

[0041] A haptic device can analyze the movements of multiple users simultaneously and be applied to group training and competition judging. For example, a haptic device can analyze the movements of multiple users simultaneously. Specifically, in group training, the device analyzes the forms practiced by multiple users simultaneously and provides individual feedback to each user. This can improve the training effectiveness of the entire group. A haptic device can also analyze the movements of multiple users simultaneously. Specifically, in competitions, the device analyzes the forms practiced by multiple athletes simultaneously and provides fair judging. This can enable more accurate and efficient judging of competitions. A haptic device can also analyze the movements of multiple users simultaneously. Specifically, in online group training, the device analyzes the forms practiced by multiple users simultaneously and provides individual feedback to each user. This enables effective training even in remote environments. By analyzing the movements of multiple users simultaneously, the device can be applied to group training and competition judging.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The martial arts immersive training system can further include an audio feedback unit. The audio feedback unit provides real-time audio guidance for the user's movements. For example, when the user assumes the correct posture, audio feedback such as "Good posture" is provided. Also, if there is a discrepancy in the movement, specific audio instructions such as "Raise your right hand a little higher" can be provided. Furthermore, the audio feedback unit can learn the user's past movement data and provide customized guidance based on individual weaknesses and areas for improvement. For example, audio guidance such as "Based on past data, your left hand movement is unstable. Do this to make it more stable" can be provided.

[0044] The martial arts immersive training system can further include a biometric sensor unit. The biometric sensor unit acquires biometric information such as the user's heart rate and breathing rate in real time and monitors the effectiveness of the training. For example, if the user's heart rate becomes too high, the system can provide advice such as "Take a short break." Also, if the breathing rate remains stable, the system can provide feedback such as "That's a good pace." Furthermore, the biometric sensor unit can learn the user's past biometric data and suggest individual training plans. For example, the system can provide advice such as, "Based on your past data, you tend to have an elevated heart rate. Try this to relax more."

[0045] The martial arts immersive training system can further include an environmental sensor unit. The environmental sensor unit monitors the temperature, humidity, illuminance, etc. of the user's training environment in real time to provide an optimal training environment. For example, if the temperature is too high, it can provide advice such as "Please cool the room." If the illuminance is low, it can provide instructions such as "Please turn up the lights." Furthermore, the environmental sensor unit can learn the user's past environmental data and propose an individual environmental adjustment plan. For example, it can provide advice such as "Based on past data, you do not do well training in humid environments. Please do this to create a more comfortable environment."

[0046] The martial arts training experience system can also incorporate gamification elements. For example, users can earn points as they progress through training and use those points to purchase virtual items. It is also possible to introduce a system where users level up as their training progresses, unlocking new techniques and training menus. Furthermore, a ranking function that allows users to compete with other users can be added to increase motivation. For example, a system can be introduced whereby users who rank highly in the weekly rankings are awarded special titles and rewards. This makes training more enjoyable and helps maintain ongoing motivation.

[0047] The martial arts training experience system can also be equipped with social functions. For example, users can share their training results and send each other comments and advice. They can also post their training progress on social media and share it with friends and family. Furthermore, online group training sessions can be held and users can train together with other users. This allows users to enjoy training as part of a community without feeling lonely. For example, an online group training session can be held on the weekend, and all participants can practice the same routine, enhancing a sense of community.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The generative AI analyzes the user's movements. For example, the user can film their own movements with a camera, analyze the footage, compare it with basic movements, and provide guidance on correct movements. The generative AI can provide specific instructions, detect minute deviations in movement in real time, and provide immediate corrections. It can also study past movement data to identify individual weaknesses and areas for improvement and provide customized instruction. Step 2: The AR device provides visual feedback based on the movements analyzed by the generative AI. For example, the user can wear the AR device and check the accuracy of their movements by overlaying virtual guidelines or models on their actual movements. The AR device displays the user's movements as a 3D model in real time, allowing them to visually check the accuracy of their movements. In addition, a virtual coach or trainer can be displayed to provide real-time guidance on the user's movements. Step 3: The haptic device provides haptic feedback based on the movements analyzed by the generative AI. For example, when the user assumes the correct posture, the haptic device will gently vibrate to indicate the correct position. The haptic device provides real-time feedback on the user's movements and can correct even the slightest deviations. Furthermore, it learns from past movement data, identifies individual weaknesses and areas for improvement, and provides customized feedback.

[0050] (Example 2) The martial arts immersive training system according to an embodiment of the present invention utilizes generative AI, AR (augmented reality), and haptic sharing technology to realize martial arts immersive training regardless of location, such as at home. As a result, the martial arts immersive training system allows users to realize martial arts immersive training regardless of location, such as at home.

[0051] A martial arts training system according to an embodiment includes a generation AI, an AR device, and a haptic device. The generation AI analyzes a user's movements. For example, the generation AI allows the user to film their own movements with a camera, analyzes the video, compares it with basic forms, and provides guidance on correct movements. The generation AI can provide specific instructions, such as, "Your right hand is too low. Please raise it a little higher." The generation AI can also analyze the user's movements in real time, detect subtle deviations, and provide immediate corrections. For example, the generation AI can provide specific instructions, such as, "The angle of your right foot is inappropriate. Please turn it a little more inward." Furthermore, the generation AI can learn the user's past movement data, identify individual weaknesses and areas for improvement, and provide customized instruction. For example, the generation AI can provide guidance such as, "Based on past data, your left hand movement is unstable. Please do this to make it more stable." The AR device provides visual feedback based on the movements analyzed by the generation AI. For example, the user can wear the AR device and overlay virtual guidelines or models on their actual movements to confirm the correct form. AR devices can display a user's movements as a 3D model in real time, allowing the user to visually check the accuracy of their movements. For example, a user wears an AR device and their own movements are displayed as a 3D model. This allows the user to visually check the accuracy of their movements. Furthermore, AR devices can display a virtual coach or trainer and provide real-time guidance on the user's movements. For example, a user wears an AR device, and the virtual coach analyzes the user's movements and provides specific guidance. Haptic devices provide haptic feedback based on movements analyzed by generative AI. For example, when a user assumes correct posture, the haptic device gently vibrates to indicate the correct position. Haptic devices can provide real-time feedback on the user's movements and allow the user to correct even subtle deviations in their movements. For example, when a user assumes correct form, the device gently vibrates to indicate the correct position. This allows the user to correct even subtle deviations in their movements.Furthermore, the haptic device can learn the user's past movement data, identify individual weaknesses and areas for improvement, and provide customized feedback. For example, it can provide feedback such as, "Based on past data, your left hand movement is unstable. Please do this to make it more stable." In this way, the martial arts immersive training system according to the embodiment can analyze the user's movements and provide visual and haptic feedback, thereby enabling accurate movements.

[0052] The generative AI can analyze a user's movements in real time, detect subtle discrepancies, and instantly provide corrective instructions. For example, a user can film their own movements with a camera, analyze the video, compare it with basic movements, and provide guidance on correct movements. Specifically, it can provide specific instructions such as, "Your right hand is too low. Raise it a little higher." The generative AI can also analyze a user's movements in real time, detect subtle discrepancies, and instantly provide corrective instructions. For example, it can provide specific instructions such as, "The angle of your right foot is inappropriate. Point it a little more inward." The generative AI can also analyze a user's movements in real time, detect subtle discrepancies, and instantly provide corrective instructions. For example, it can provide specific instructions such as, "Your hips are too high. Lower them a little." This allows for accurate movement by correcting subtle discrepancies in real time.

[0053] The generative AI can learn from a user's past movement data, identify individual weaknesses and areas for improvement, and provide customized instruction. For example, the generative AI can save data on the user's past practice and provide personalized instruction based on that data. Specifically, it can provide instruction such as, "Based on past data, your left hand movement is unstable. Do this to make it more stable." The generative AI can also learn from a user's past movement data, identify individual weaknesses and areas for improvement, and provide customized instruction. For example, it can provide instruction such as, "Based on past data, your foot movement is slow. Do this to move faster." The generative AI can also learn from a user's past movement data, identify individual weaknesses and areas for improvement, and provide customized instruction. For example, it can provide instruction such as, "Based on past data, your hip movement is unnatural. Do this to move more naturally." This allows for effective training by identifying individual weaknesses and areas for improvement and providing customized instruction.

[0054] The generation AI can use the emotion estimation function to analyze the user's motivation and concentration level and provide encouragement and advice at the appropriate time. For example, the generation AI uses the emotion estimation function to analyze the user's motivation and concentration level. Specifically, it analyzes the user's facial expressions and voice and displays an encouraging message if it determines that motivation is declining. For example, it displays a message such as, "Keep up the good work! You'll almost get the perfect form." The generation AI also uses the emotion estimation function to analyze the user's motivation and concentration level. Specifically, it analyzes the user's facial expressions and voice and displays an encouraging message if it determines that concentration is declining. For example, it displays a message such as, "Keep focused. You'll almost get the right form." The generation AI also uses the emotion estimation function to analyze the user's motivation and concentration level. Specifically, it analyzes the user's facial expressions and voice and displays an encouraging message if it determines that motivation is declining. For example, it displays a message such as, "You can do it! Try a little harder." This allows the generation AI to analyze the user's motivation and concentration level and provide encouragement and advice at the appropriate time, thereby improving the effectiveness of training.

[0055] The generative AI can also analyze the movements of different martial arts and sports, making it usable as a general-purpose training tool. The generative AI can analyze the movements of not only martial arts such as karate, judo, and kendo, but also sports such as basketball and soccer. This allows users to use it for a variety of training. The generative AI can also analyze the movements of different martial arts and sports. For example, it can analyze the movements of not only martial arts such as karate, judo, and kendo, but also sports such as tennis and golf. This allows users to use it for a variety of training. The generative AI can also analyze the movements of different martial arts and sports. For example, it can analyze the movements of not only martial arts such as karate, judo, and kendo, but also sports such as volleyball and baseball. This allows users to use it for a variety of training. This allows users to use it for a variety of training. By supporting the movement analysis of different martial arts and sports, the generative AI can be used as a general-purpose training tool.

[0056] Generative AI can analyze the movements of multiple users simultaneously and be applied to group training and competition judging. Generative AI can analyze the movements of multiple users simultaneously. Specifically, in group training, it analyzes the forms practiced by multiple users simultaneously and provides individual feedback to each user. This can improve the training effectiveness of the entire group. Generative AI can also analyze the movements of multiple users simultaneously. Specifically, in competitions, it analyzes the forms practiced by multiple athletes simultaneously and provides fair judging. This allows for more accurate and efficient judging of competitions. Generative AI can also analyze the movements of multiple users simultaneously. Specifically, in online group training, it analyzes the forms practiced by multiple users simultaneously and provides individual feedback to each user. This enables effective training even in remote environments. By analyzing the movements of multiple users simultaneously, this can be applied to group training and competition judging.

[0057] The generation AI uses the emotion estimation function to automatically generate a training menu according to the user's emotional state, thereby meeting individual needs. For example, the generation AI uses the emotion estimation function to automatically generate a training menu according to the user's emotional state. Specifically, if the user is feeling stressed, it provides a training menu with a relaxing effect. For example, it automatically generates a menu that includes breathing techniques and stretching. The generation AI also uses the emotion estimation function to automatically generate a training menu according to the user's emotional state. Specifically, if the user wants to increase their motivation, it provides a challenging training menu. For example, it automatically generates a menu that includes high-intensity training and learning new techniques. The generation AI also uses the emotion estimation function to automatically generate a training menu according to the user's emotional state. Specifically, if the user is feeling fatigued, it provides a training menu that promotes recovery. For example, it automatically generates a menu that includes light exercise and relaxation. This allows the generation AI to automatically generate a training menu according to the user's emotional state, thereby meeting individual needs.

[0058] The AR device can display the user's movements as a 3D model in real time, allowing the user to visually confirm the accuracy of the movements. For example, a user wears the AR device, and the user's movements are displayed as a 3D model. This allows the user to visually confirm the accuracy of the movements. The AR device can also display the user's movements as a 3D model in real time. For example, a user wears the AR device, and the user's movements are displayed as a 3D model. This allows the user to visually confirm the accuracy of the movements. The AR device can also display the user's movements as a 3D model in real time. For example, a user wears the AR device, and the user's movements are displayed as a 3D model. This allows the user to visually confirm the accuracy of the movements. This allows the user to visually confirm the accuracy of the movements by displaying the user's movements as a 3D model in real time.

[0059] The AR device can display a virtual coach or trainer and provide real-time guidance to the user's movements. The AR device can, for example, display a virtual coach or trainer and provide real-time guidance to the user's movements. Specifically, the user wears the AR device, and the virtual coach analyzes the user's movements and provides specific guidance. This allows the user to receive accurate guidance in real time. The AR device can also display a virtual coach or trainer and provide real-time guidance to the user's movements. Specifically, the user wears the AR device, and the virtual coach analyzes the user's movements and provides specific guidance. This allows the user to receive accurate guidance in real time. The AR device can also display a virtual coach or trainer and provide real-time guidance to the user's movements. Specifically, the user wears the AR device, and the virtual coach analyzes the user's movements and provides specific guidance. This allows the user to receive accurate guidance in real time. This allows the user to achieve accurate movements by displaying a virtual coach or trainer and providing guidance in real time.

[0060] An AR device can use an emotion estimation function to provide visual feedback according to a user's emotional state, thereby maintaining motivation. For example, the AR device can use the emotion estimation function to provide visual feedback according to a user's emotional state. Specifically, if it determines that the user is losing motivation, it displays an encouraging message or a positive visual effect. This allows the user to maintain motivation. The AR device can also use the emotion estimation function to provide visual feedback according to a user's emotional state. Specifically, if it determines that the user is lacking concentration, it displays a visual effect or advice that encourages concentration. This allows the user to maintain concentration. The AR device can also use the emotion estimation function to provide visual feedback according to a user's emotional state. Specifically, if it determines that the user is feeling stressed, it displays a visual effect or message that has a relaxing effect. This allows the user to reduce stress and maintain motivation. By providing visual feedback according to a user's emotional state, it is possible to maintain motivation.

[0061] The AR device can be used for training in different martial arts and sports, and can be used as a general-purpose training tool. The AR device can be used for training in not only martial arts such as karate, judo, and kendo, but also sports such as basketball and soccer. This allows users to use it for a variety of training. The AR device can also be used for training in different martial arts and sports. For example, the AR device can be used for training in not only martial arts such as karate, judo, and kendo, but also sports such as tennis and golf. This allows users to use it for a variety of training. The AR device can also be used for training in different martial arts and sports. For example, the AR device can be used for training in not only martial arts such as karate, judo, and kendo, but also sports such as volleyball and baseball. This allows users to use it for a variety of training. By being able to use it for training in different martial arts and sports, the AR device can be used as a general-purpose training tool.

[0062] The AR device can add a function that allows a user to record a user's movements and play them back later for use in self-evaluation or coaching. For example, the AR device adds a function that allows a user to record a user's movements and play them back later for use in self-evaluation or coaching. Specifically, the user can later play back video recorded during training to check their own movements. This makes self-evaluation and coaching easier. The AR device can also add a function that allows a user to record a user's movements and play them back later for use in self-evaluation or coaching. Specifically, the user can later play back video recorded during training to check their own movements. This makes self-evaluation and coaching easier. The AR device can also add a function that allows a user to record a user's movements and play them back later for use in self-evaluation or coaching. Specifically, the user can later play back video recorded during training to check their own movements. This makes self-evaluation and coaching easier.

[0063] The AR device uses an emotion estimation function to display visual effects and animations according to the user's emotional state, thereby improving the enjoyment of training. For example, the AR device uses the emotion estimation function to display visual effects and animations according to the user's emotional state. Specifically, if it is determined that the user is enjoying themselves, it displays positive effects and animations. This can improve the enjoyment of training. The AR device also uses the emotion estimation function to display visual effects and animations according to the user's emotional state. Specifically, if it is determined that the user is concentrating, it displays effects and animations that encourage concentration. This can improve the effectiveness of training. The AR device also uses the emotion estimation function to display visual effects and animations according to the user's emotional state. Specifically, if it is determined that the user is tired, it displays effects and animations that have a relaxing effect. This can reduce the user's fatigue and improve the enjoyment of training. For example, the AR device uses the emotion estimation function to display visual effects and animations according to the user's emotional state, thereby improving the enjoyment of training.

[0064] The haptic device provides feedback to the user's movements in real time, allowing the user to correct even the slightest deviations in their movements. For example, when the user takes the correct form, the haptic device gently vibrates to indicate the correct position, allowing the user to correct even the slightest deviations in their movements. The haptic device also provides feedback to the user's movements in real time. Specifically, when the user takes the correct form, the device gently vibrates to indicate the correct position, allowing the user to correct even the slightest deviations in their movements. The haptic device also provides feedback to the user's movements in real time. Specifically, when the user takes the correct form, the device gently vibrates to indicate the correct position, allowing the user to correct even the slightest deviations in their movements.

[0065] The haptic device can learn the user's past motion data, identify individual weaknesses and areas for improvement, and provide customized feedback. For example, the haptic device stores data on the user's past practice and provides personalized feedback based on that data. Specifically, the haptic device can provide feedback such as, "Based on past data, your left hand movements are unstable. Please do this to make them more stable." The haptic device can also learn the user's past motion data, identify individual weaknesses and areas for improvement, and provide customized feedback. Specifically, the haptic device can provide feedback such as, "Based on past data, your foot movements are slow. Please do this to move them faster." The haptic device can also learn the user's past motion data, identify individual weaknesses and areas for improvement, and provide customized feedback. Specifically, the haptic device can provide feedback such as, "Based on past data, your hip movements are unnatural. Please do this to move them more naturally." In this way, the haptic device can learn the user's past motion data, identify individual weaknesses and areas for improvement, and provide customized feedback.

[0066] The haptic device uses an emotion estimation function to provide haptic feedback according to the user's emotional state, thereby maintaining motivation. For example, the haptic device uses the emotion estimation function to provide haptic feedback according to the user's emotional state. Specifically, if it is determined that the user is losing motivation, the haptic device gently vibrates to provide encouraging feedback. This allows the user to maintain motivation. The haptic device also uses the emotion estimation function to provide haptic feedback according to the user's emotional state. Specifically, if it is determined that the user is lacking concentration, the haptic device gently vibrates to provide feedback that encourages concentration. This allows the user to maintain concentration. The haptic device also uses the emotion estimation function to provide haptic feedback according to the user's emotional state. Specifically, if it is determined that the user is feeling stressed, the haptic device gently vibrates to provide feedback that has a relaxing effect. This allows the user's stress to be reduced and motivation to be maintained. By providing haptic feedback according to the user's emotional state, motivation can be maintained.

[0067] The haptic device can be used for training in different martial arts and sports, and can be used as a general-purpose training tool. The haptic device can be used for training in not only martial arts such as karate, judo, and kendo, but also sports such as basketball and soccer. This allows users to use it for a variety of training. The haptic device can also be used for training in different martial arts and sports. For example, the haptic device can be used for training in not only martial arts such as karate, judo, and kendo, but also sports such as tennis and golf. This allows users to use it for a variety of training. The haptic device can also be used for training in different martial arts and sports. For example, the haptic device can be used for training in not only martial arts such as karate, judo, and kendo, but also sports such as volleyball and baseball. This allows users to use it for a variety of training. By being able to use it for training in different martial arts and sports, the haptic device can be used as a general-purpose training tool.

[0068] A haptic device can analyze the movements of multiple users simultaneously and be applied to group training and competition judging. For example, a haptic device can analyze the movements of multiple users simultaneously. Specifically, in group training, the device analyzes the forms practiced by multiple users simultaneously and provides individual feedback to each user. This can improve the training effectiveness of the entire group. A haptic device can also analyze the movements of multiple users simultaneously. Specifically, in competitions, the device analyzes the forms practiced by multiple athletes simultaneously and provides fair judging. This can enable more accurate and efficient judging of competitions. A haptic device can also analyze the movements of multiple users simultaneously. Specifically, in online group training, the device analyzes the forms practiced by multiple users simultaneously and provides individual feedback to each user. This enables effective training even in remote environments. By analyzing the movements of multiple users simultaneously, the device can be applied to group training and competition judging.

[0069] The haptic device uses an emotion estimation function to provide haptic feedback according to the user's emotional state, thereby improving the enjoyment of training. The haptic device, for example, uses the emotion estimation function to provide haptic feedback according to the user's emotional state. Specifically, if it is determined that the user is enjoying themselves, the haptic device gently vibrates to provide positive feedback. This makes it possible to improve the enjoyment of training. The haptic device also uses the emotion estimation function to provide haptic feedback according to the user's emotional state. Specifically, if it is determined that the user is concentrating, the haptic device gently vibrates to provide feedback that encourages concentration. This makes it possible to improve the effectiveness of training. The haptic device also uses the emotion estimation function to provide haptic feedback according to the user's emotional state. Specifically, if it is determined that the user is tired, the haptic device gently vibrates to provide feedback that has a relaxing effect. This reduces the user's fatigue and improves the enjoyment of training. The haptic device provides haptic feedback according to the user's emotional state, thereby improving the enjoyment of training.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The martial arts immersive training system can further include an audio feedback unit. The audio feedback unit provides real-time audio guidance for the user's movements. For example, when the user assumes the correct posture, audio feedback such as "Good posture" is provided. Also, if there is a discrepancy in the movement, specific audio instructions such as "Raise your right hand a little higher" can be provided. Furthermore, the audio feedback unit can learn the user's past movement data and provide customized guidance based on individual weaknesses and areas for improvement. For example, audio guidance such as "Based on past data, your left hand movement is unstable. Do this to make it more stable" can be provided.

[0072] The martial arts immersive training system can further include a biometric sensor unit. The biometric sensor unit acquires biometric information such as the user's heart rate and breathing rate in real time and monitors the effectiveness of the training. For example, if the user's heart rate becomes too high, the system can provide advice such as "Take a short break." Also, if the breathing rate remains stable, the system can provide feedback such as "That's a good pace." Furthermore, the biometric sensor unit can learn the user's past biometric data and suggest individual training plans. For example, the system can provide advice such as, "Based on your past data, you tend to have an elevated heart rate. Try this to relax more."

[0073] The martial arts immersive training system can further include an environmental sensor unit. The environmental sensor unit monitors the temperature, humidity, illuminance, etc. of the user's training environment in real time to provide an optimal training environment. For example, if the temperature is too high, it can provide advice such as "Please cool the room." If the illuminance is low, it can provide instructions such as "Please turn up the lights." Furthermore, the environmental sensor unit can learn the user's past environmental data and propose an individual environmental adjustment plan. For example, it can provide advice such as "Based on past data, you do not do well training in humid environments. Please do this to create a more comfortable environment."

[0074] The martial arts training experience system can also incorporate gamification elements. For example, users can earn points as they progress through training and use those points to purchase virtual items. It is also possible to introduce a system where users level up as their training progresses, unlocking new techniques and training menus. Furthermore, a ranking function that allows users to compete with other users can be added to increase motivation. For example, a system can be introduced whereby users who rank highly in the weekly rankings are awarded special titles and rewards. This makes training more enjoyable and helps maintain ongoing motivation.

[0075] The martial arts training experience system can also be equipped with social functions. For example, users can share their training results and send each other comments and advice. They can also post their training progress on social media and share it with friends and family. Furthermore, online group training sessions can be held and users can train together with other users. This allows users to enjoy training as part of a community without feeling lonely. For example, an online group training session can be held on the weekend, and all participants can practice the same routine, enhancing a sense of community.

[0076] The martial arts immersive training system can use the emotion estimation function to automatically select music according to the user's emotional state. For example, if the user feels like relaxing, it can play music that has a relaxing effect. If the user feels like increasing motivation, it can play up-tempo music. Furthermore, if the user feels like concentrating, it can play music that has the effect of increasing concentration. In this way, by providing music according to the user's emotional state, it is possible to improve the effectiveness of training.

[0077] The martial arts immersive training system can use the emotion estimation function to automatically match a training partner according to the user's emotional state. For example, if a user wants to increase their motivation, they can be matched with a similarly motivated user. If a user wants to relax, they can be matched with a training partner that has a relaxing effect. Furthermore, if a user wants to concentrate, they can be matched with a training partner that has the effect of increasing concentration. This makes it possible to improve the effectiveness of training by providing a training partner that suits the user's emotional state.

[0078] The martial arts immersive training system can use the emotion estimation function to set training goals according to the user's emotional state. For example, if the user is feeling stressed, a training goal that has a relaxing effect can be set. If the user wants to increase their motivation, a challenging training goal can be set. Furthermore, if the user wants to concentrate, a training goal that has the effect of increasing concentration can be set. In this way, by providing training goals according to the user's emotional state, the effectiveness of training can be improved.

[0079] The martial arts immersive training system can use the emotion estimation function to automatically adjust the training schedule according to the user's emotional state. For example, if the user feels tired, the training intensity can be reduced. If the user is energetic, the training intensity can be increased. Furthermore, if the user is feeling stressed, training that has a relaxing effect can be incorporated into the schedule. In this way, by providing a training schedule according to the user's emotional state, the effectiveness of the training can be improved.

[0080] The martial arts immersive training system can use the emotion estimation function to provide training rewards according to the user's emotional state. For example, if the user feels like increasing their motivation, a special reward can be provided according to the level of training achievement. If the user feels like relaxing, a reward with a relaxing effect can be provided. Furthermore, if the user feels like concentrating, a reward with the effect of increasing concentration can be provided. In this way, by providing training rewards according to the user's emotional state, the effectiveness of training can be improved.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The generative AI analyzes the user's movements. For example, the user can film their own movements with a camera, analyze the footage, compare it with basic movements, and provide guidance on correct movements. The generative AI can provide specific instructions, detect minute deviations in movement in real time, and provide immediate corrections. It can also study past movement data to identify individual weaknesses and areas for improvement and provide customized instruction. Step 2: The AR device provides visual feedback based on the movements analyzed by the generative AI. For example, the user can wear the AR device and check the accuracy of their movements by overlaying virtual guidelines or models on their actual movements. The AR device displays the user's movements as a 3D model in real time, allowing them to visually check the accuracy of their movements. In addition, a virtual coach or trainer can be displayed to provide real-time guidance on the user's movements. Step 3: The haptic device provides haptic feedback based on the movements analyzed by the generative AI. For example, when the user assumes the correct posture, the haptic device will gently vibrate to indicate the correct position. The haptic device provides real-time feedback on the user's movements and can correct even the slightest deviations. Furthermore, it learns from past movement data, identifies individual weaknesses and areas for improvement, and provides customized feedback.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. Generative AI and AR devices and a haptic device; The generated AI is Analyzing user movements, The AR device includes: providing visual feedback based on the movement analyzed by the generating AI; The haptic device comprises: Providing haptic feedback based on the movements analyzed by the generative AI A system characterized by:

2. The generated AI is Analyze the user's movements in real time, detect minute discrepancies in movement, and immediately issue correction instructions 2. The system of claim 1.

3. The generated AI is Learns from the user's past performance data, identifies individual weaknesses and areas for improvement, and provides customized instruction 2. The system of claim 1.

4. The generated AI is Analyze the user's motivation and concentration level and provide encouragement and advice at the appropriate time 2. The system of claim 1.

5. The generated AI is It will also be compatible with motion analysis for different martial arts and sports, making it usable as a general-purpose training tool.

2. The system of claim 1.

Images