System

Abstract

An object of the system according to the embodiment is to compare a body shape and a motion of a person to be imitated with a body shape and a motion of the user and propose an improvement plan.SOLUTION: A system includes a body shape conversion part, a comparison part, a notification part, and an improvement proposal part. A body shape conversion part converts the body shape of a person to be imitated into his / her own body shape. The comparison unit compares the body shapes converted by the body shape conversion unit. The notification unit notifies the comparison result obtained by the comparison unit by voice. The improvement proposal unit predicts a result obtained from an operation being performed and proposes an improvement proposal.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 is difficult to compare the body shape and movements of the person you want to imitate with your own body shape and movements, and there is room for improvement.

[0005] The system according to the embodiment aims to compare the body shape and movements of the person the user wants to imitate with the user's own body shape and movements, and to propose improvements. [Means for solving the problem]

[0006] The system according to the embodiment includes a body shape conversion unit, a comparison unit, a notification unit, and an improvement suggestion unit. The body shape conversion unit converts the body shape of a person the user wants to imitate into the user's own body shape. The comparison unit compares the body shapes converted by the body shape conversion unit. The notification unit notifies the user of the comparison result obtained by the comparison unit by voice. The improvement suggestion unit predicts the desired result from the current movement and proposes an improvement plan. [Effects of the Invention]

[0007] The system according to the embodiment can compare the body shape and movements of the person the user wants to imitate with the user's own body shape and movements, and can suggest improvements. [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) An app service according to an embodiment of the present invention is a system for easily and precisely comparing the movements of a person you want to imitate with your own. This system uses AI to convert the physique of the person you want to imitate into your own, allowing you to compare the results more clearly. The comparison results are communicated not only in text but also by voice, eliminating the need for confirmation each time. Furthermore, the app service has a function that predicts the desired results from the movements you are currently making and suggests improvement suggestions. This allows the app service to efficiently learn and improve the movements of the person you want to imitate. For example, in dance practice or sports training, you can improve your own movements by imitating the movements of a professional. Furthermore, voice notifications eliminate the need to constantly look at the screen while performing the movements, allowing you to practice efficiently.

[0029] An app service according to an embodiment includes a body shape conversion unit, a comparison unit, a notification unit, and an improvement suggestion unit. The body shape conversion unit converts the body shape of a person the user wants to imitate into the user's own body shape. For example, the generation AI inputs a video of the person the user wants to imitate, analyzes that person's body shape, and converts it into the user's own body shape. The generation AI can also convert the body shape of a professional dancer into the user's own body shape to clarify differences in their movements. The generation AI can also convert the body shape of an athlete during sports training into the user's own body shape to clarify differences in their movements. The comparison unit compares the body shapes converted by the body shape conversion unit. For example, the generation AI compares the movements of the person the user wants to imitate with the user's own movements based on the converted body shape. The generation AI can also visually display the comparison results to clarify differences in their movements. The generation AI can also quantify differences in their movements and provide the comparison results. The notification unit provides audio notification of the comparison results obtained by the comparison unit. For example, the generation AI can provide an audio notification such as, "Your right arm movement is slow. Move it a little faster." The generation AI can also provide voice notification such as, "Your step timing is off. Try moving a little faster." The generation AI can also provide voice notification such as, "Your swing angle is not enough. Try swinging wider." The improvement suggestion unit predicts the desired results from the movements being performed and proposes improvement suggestions. For example, the generation AI can analyze movements during sports training and propose specific improvement suggestions such as, "Your jump height is not high enough. Bend your knees more before jumping." The generation AI can also analyze movements during fitness instruction and propose specific improvement suggestions such as, "Your squat depth is not deep enough. Try squatting deeper." The generation AI can also analyze movements during dance practice and propose specific improvement suggestions such as, "Your step timing is off. Try moving a little faster." This allows the app service according to the embodiment to efficiently learn and improve the movements of the person it wants to imitate. For example, during dance practice or sports training, it is possible to improve one's own movements by imitating the movements of a professional.In addition, voice notifications allow you to practice efficiently without having to keep looking at the screen while you are doing the exercises.

[0030] The comparison unit can reproduce the movements of the person the user wants to imitate as a 3D model, allowing the user to freely change the viewpoint to check the movements. For example, the comparison unit can use generative AI to reproduce the movements of the person the user wants to imitate as a 3D model, allowing the user to freely change the viewpoint to check the movements. For example, the movements of a dancer can be reproduced as a 3D model, allowing the user to check the movements from various angles. The comparison unit can also use the 3D model to reproduce the movements of the person the user wants to imitate, allowing the user to freely change the viewpoint to check the movements. For example, the movements of an athlete can be reproduced as a 3D model, making the differences in the movements clear. The comparison unit can also use the 3D model to reproduce the movements of the person the user wants to imitate, allowing the user to freely change the viewpoint to check the movements. For example, the movements of a fitness instructor can be reproduced as a 3D model, making the differences in the movements clear. This allows the movements of the person the user wants to imitate to be checked from various angles.

[0031] The comparison unit can develop a specialized body shape transformation algorithm for each different sport or dance genre, enabling more specialized comparison. For example, the comparison unit develops a specialized body shape transformation algorithm for each different sport or dance genre, enabling more specialized comparison. For example, ballet and hip hop movements are each specializedly analyzed. The comparison unit can also develop a specialized body shape transformation algorithm for each sport or dance genre, enabling more specialized comparison. For example, soccer and basketball movements are each specialized analyzed. The comparison unit can also develop a specialized body shape transformation algorithm for each genre, enabling more specialized comparison. For example, yoga and Pilates movements are each specializedly analyzed. This enables more specialized comparison.

[0032] The notification unit can notify the comparison result not only by sound but also by vibration or light patterns, and provide visual or tactile feedback. For example, the notification unit uses a generation AI to notify the comparison result not only by sound but also by vibration or light patterns. For example, if the movement of the right arm is late, it will be notified by vibration. The notification unit can also notify the comparison result not only by sound but also by vibration or light patterns, and provide visual or tactile feedback. For example, if the timing of a step is late, it will be notified by a light pattern. The notification unit can also use a generation AI to notify the comparison result not only by sound but also by vibration or light patterns, and provide visual or tactile feedback. For example, if the jump height is insufficient, it will be notified by vibration. This makes it possible to provide visual and tactile feedback.

[0033] The notification unit may have a function that allows customization of the tone and speed of the voice to suit the user's preferences when providing voice notification. For example, the notification unit adds a function that allows customization of the tone and speed of the voice to suit the user's preferences when providing voice notification. For example, notification may be made in a slow tone. The notification unit also adds a function that allows customization of the tone and speed of the voice to suit the user's preferences when providing voice notification. For example, notification may be made in a high tone. The notification unit also adds a function that allows customization of the tone and speed of the voice to suit the user's preferences when providing voice notification. For example, notification may be made in a fast speed. This makes it possible to provide voice notification that suits the user's preferences.

[0034] The notification unit can make the voice notification multilingual so that it can also accommodate users who speak different languages. The notification unit, for example, makes the voice notification multilingual so that it can also accommodate users who speak different languages. For example, notifications are made in multiple languages ​​such as English, French, and Chinese. The notification unit also implements multilingual voice notifications so that it can also accommodate users who speak different languages. For example, it automatically switches the notification language according to the user's language setting. The notification unit also makes the voice notification multilingual so that it can also accommodate users who speak different languages. For example, it makes notifications in a language selected by the user. This makes it possible to accommodate users who speak different languages.

[0035] The notification unit can compare the content of the voice notification with the user's past motion data to provide feedback including the user's progress. For example, the notification unit compares the content of the voice notification with the user's past motion data to provide feedback including the user's progress. For example, the notification unit may notify the user that "Your motion has improved since the last time." The notification unit also compares the content of the voice notification with the user's past motion data to provide feedback including the user's progress. For example, the notification unit may notify the user that "Your jump height has increased since the last time." The notification unit also compares the content of the voice notification with the user's past motion data to provide feedback including the user's progress. For example, the notification unit may notify the user that "Your step timing has improved since the last time." This makes it possible to provide feedback including the user's progress.

[0036] The improvement suggestion unit can use the generation AI to analyze the user's movements in real time and provide a predicted movement and an improvement proposal as visual feedback. The improvement suggestion unit, for example, uses the generation AI to analyze the user's movements in real time and provide a predicted movement and an improvement proposal as visual feedback. For example, it predicts the height of a jump and visually shows how the knees should bend. The improvement suggestion unit also analyzes the user's movements in real time and provides a predicted movement and an improvement proposal as visual feedback. For example, it predicts the angle of a swing and visually shows the movement of the arms. The improvement suggestion unit also uses the generation AI to analyze the user's movements in real time and provide a predicted movement and an improvement proposal as visual feedback. For example, it predicts the timing of a step and visually shows the movement of the feet. This makes it possible to analyze the user's movements in real time and provide visual feedback.

[0037] The improvement suggestion unit can refer to the user's past motion data and provide individually optimized advice. For example, when proposing an improvement plan, the improvement suggestion unit refers to the user's past motion data and provides individually optimized advice. For example, it suggests how to bend the knees based on past jump data. The improvement suggestion unit also refers to the user's past motion data and provides individually optimized advice. For example, it suggests arm movements based on past swing data. The improvement suggestion unit also refers to the user's past motion data and provides individually optimized advice when proposing an improvement plan. For example, it suggests foot movements based on past step data. This makes it possible to provide individual advice based on the user's past motion data.

[0038] The improvement suggestion unit can provide improvement suggestions specialized for different sports or dance genres, thereby realizing more specialized advice. The improvement suggestion unit can, for example, provide improvement suggestions specialized for different sports or dance genres, thereby realizing more specialized advice. For example, ballet and hip hop movements can be analyzed professionally. The improvement suggestion unit can also provide improvement suggestions specialized for different sports or dance genres, thereby realizing more specialized advice. For example, soccer and basketball movements can be analyzed professionally. The improvement suggestion unit can also provide improvement suggestions specialized for different genres, thereby realizing more specialized advice. For example, yoga and Pilates movements can be analyzed professionally. This makes it possible to provide more specialized advice.

[0039] The improvement suggestion unit can customize the improvement suggestions according to the user's fitness level and health condition, and encourage improvements within a reasonable range. For example, the improvement suggestion unit customizes the improvement suggestions according to the user's fitness level and health condition, and encourages improvements within a reasonable range. For example, the improvement suggestion unit proposes improvement suggestions for beginners. The improvement suggestion unit also customizes the improvement suggestions according to the user's fitness level and health condition, and encourages improvements within a reasonable range. For example, the improvement suggestion unit proposes improvement suggestions for advanced users. The improvement suggestion unit also customizes the improvement suggestions according to the user's fitness level and health condition, and encourages improvements within a reasonable range. For example, the improvement suggestion unit proposes improvement suggestions according to the health condition. This makes it possible to make improvements according to the user's fitness level and health condition.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The comparison unit reproduces the movements of the person the user wants to imitate as a 3D model, allowing the user to freely change the viewpoint to check the movements. For example, the movements of a dancer can be reproduced as a 3D model, allowing the user to check the movements from various angles. The comparison unit also uses the 3D model to reproduce the movements of the person the user wants to imitate, allowing the user to freely change the viewpoint to check the movements. For example, the movements of an athlete can be reproduced as a 3D model, making the differences in the movements clear. The comparison unit also uses the 3D model to reproduce the movements of the person the user wants to imitate, allowing the user to freely change the viewpoint to check the movements. For example, the movements of a fitness instructor can be reproduced as a 3D model, making the differences in the movements clear. This allows the user to check the movements of the person the user wants to imitate from various angles.

[0042] The notification unit can notify the comparison results not only by sound but also by vibration or light patterns, and provide visual or tactile feedback. For example, if the movement of the right arm is late, it will be notified by vibration. The notification unit can also notify the comparison results not only by sound but also by vibration or light patterns, and provide visual or tactile feedback. For example, if the timing of a step is late, it will be notified by light patterns. The notification unit can also use generative AI to notify the comparison results not only by sound but also by vibration or light patterns, and provide visual or tactile feedback. For example, if the jump height is insufficient, it will be notified by vibration. This makes it possible to provide visual and tactile feedback.

[0043] The notification unit may have a function that allows the tone and speed of the voice to be customized to suit the user's preferences when providing voice notification. For example, the notification may be made in a slow tone. The notification unit may also add a function that allows the tone and speed of the voice to be customized to suit the user's preferences when providing voice notification. For example, the notification may be made in a high tone. The notification unit may also add a function that allows the tone and speed of the voice to be customized to suit the user's preferences when providing voice notification. For example, the notification may be made in a fast speed. This makes it possible to provide voice notification that suits the user's preferences.

[0044] The notification unit can make the voice notification multilingual so that it can accommodate users who speak different languages. For example, notifications can be made in multiple languages, such as English, French, and Chinese. The notification unit also implements multilingual voice notifications so that it can accommodate users who speak different languages. For example, it automatically switches the notification language according to the user's language setting. The notification unit also makes the voice notification multilingual so that it can accommodate users who speak different languages. For example, it can provide notifications in a language selected by the user. This makes it possible to accommodate users who speak different languages.

[0045] The notification unit can compare the content of the voice notification with the user's past motion data to provide feedback including the user's progress. For example, the notification unit may notify the user that "your motion has improved since the last time." The notification unit can also compare the content of the voice notification with the user's past motion data to provide feedback including the user's progress. For example, the notification unit may notify the user that "your jump height has increased since the last time." The notification unit can also compare the content of the voice notification with the user's past motion data to provide feedback including the user's progress. For example, the notification unit may notify the user that "your step timing has improved since the last time." This makes it possible to provide feedback including the user's progress.

[0046] The improvement suggestion unit can use the generation AI to analyze the user's movements in real time and provide predicted movements and improvement suggestions as visual feedback. For example, it can predict the height of a jump and visually show how to bend the knees. The improvement suggestion unit can also analyze the user's movements in real time and provide predicted movements and improvement suggestions as visual feedback. For example, it can predict the angle of a swing and visually show arm movement. The improvement suggestion unit can also use the generation AI to analyze the user's movements in real time and provide predicted movements and improvement suggestions as visual feedback. For example, it can predict the timing of a step and visually show foot movement. This makes it possible to analyze the user's movements in real time and provide visual feedback.

[0047] The improvement suggestion unit can refer to the user's past motion data and provide individually optimized advice. For example, it suggests how to bend the knees based on past jump data. The improvement suggestion unit also refers to the user's past motion data and provides individually optimized advice. For example, it suggests arm movements based on past swing data. The improvement suggestion unit also refers to the user's past motion data when proposing improvement suggestions and provides individually optimized advice. For example, it suggests foot movements based on past step data. This makes it possible to provide individual advice based on the user's past motion data.

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

[0049] Step 1: The body shape conversion unit converts the body shape of the person you want to imitate into your own body shape. For example, the generative AI takes a video of the person you want to imitate as input, analyzes their body shape, and converts it into your own body shape. The generative AI can also convert the body shapes of professional dancers or athletes into your own body shape, making the differences in their movements clearer. Step 2: The comparison unit compares the body shapes converted by the body shape conversion unit. For example, the generation AI can compare its own movements with the movements of the person it wants to imitate based on the converted body shapes, and visually display or quantify the differences in movements to clarify them. Step 3: The notification unit notifies the user of the comparison results obtained by the comparison unit by voice. For example, the generation AI may provide voice notifications such as, "Your right arm movement is slow. Move it a little faster," or "Your step timing is slow. Move it a little faster." Step 4: The improvement suggestion unit predicts the desired outcome from the movements being performed and proposes improvement suggestions. For example, the generative AI could analyze movements during sports training and suggest specific improvement suggestions such as, "Your jump isn't high enough. Bend your knees more before jumping," or "Your squat isn't deep enough. Squat deeper."

[0050] (Example 2) An app service according to an embodiment of the present invention is a system for easily and precisely comparing the movements of a person you want to imitate with your own. This system uses AI to convert the physique of the person you want to imitate into your own, allowing you to compare the results more clearly. The comparison results are communicated not only in text but also by voice, eliminating the need for confirmation each time. Furthermore, the app service has a function that predicts the desired results from the movements you are currently making and suggests improvement suggestions. This allows the app service to efficiently learn and improve the movements of the person you want to imitate. For example, in dance practice or sports training, you can improve your own movements by imitating the movements of a professional. Furthermore, voice notifications eliminate the need to constantly look at the screen while performing the movements, allowing you to practice efficiently.

[0051] An app service according to an embodiment includes a body shape conversion unit, a comparison unit, a notification unit, and an improvement suggestion unit. The body shape conversion unit converts the body shape of a person the user wants to imitate into the user's own body shape. For example, the generation AI inputs a video of the person the user wants to imitate, analyzes that person's body shape, and converts it into the user's own body shape. The generation AI can also convert the body shape of a professional dancer into the user's own body shape to clarify differences in their movements. The generation AI can also convert the body shape of an athlete during sports training into the user's own body shape to clarify differences in their movements. The comparison unit compares the body shapes converted by the body shape conversion unit. For example, the generation AI compares the movements of the person the user wants to imitate with the user's own movements based on the converted body shape. The generation AI can also visually display the comparison results to clarify differences in their movements. The generation AI can also quantify differences in their movements and provide the comparison results. The notification unit provides audio notification of the comparison results obtained by the comparison unit. For example, the generation AI can provide an audio notification such as, "Your right arm movement is slow. Move it a little faster." The generation AI can also provide voice notification such as, "Your step timing is off. Try moving a little faster." The generation AI can also provide voice notification such as, "Your swing angle is not enough. Try swinging wider." The improvement suggestion unit predicts the desired results from the movements being performed and proposes improvement suggestions. For example, the generation AI can analyze movements during sports training and propose specific improvement suggestions such as, "Your jump height is not high enough. Bend your knees more before jumping." The generation AI can also analyze movements during fitness instruction and propose specific improvement suggestions such as, "Your squat depth is not deep enough. Try squatting deeper." The generation AI can also analyze movements during dance practice and propose specific improvement suggestions such as, "Your step timing is off. Try moving a little faster." This allows the app service according to the embodiment to efficiently learn and improve the movements of the person it wants to imitate. For example, during dance practice or sports training, it is possible to improve one's own movements by imitating the movements of a professional.In addition, voice notifications allow you to practice efficiently without having to keep looking at the screen while you are doing the exercises.

[0052] The body transformation unit can transform not only the body shape of the person being imitated, but also their clothing and accessories to fit their own body shape. For example, using a generative AI, the body transformation unit can transform not only the body shape of the person being imitated, but also their clothing and accessories to fit their own body shape. For example, it can recreate the costumes and accessories of a professional dancer to fit their own body shape, allowing for a more realistic look at the differences in their movements. Furthermore, when the generative AI performs body transformation, the body transformation unit also takes into account the movement of the clothing and accessories, analyzing the differences in their movements in more detail. For example, it can recreate the swaying of a skirt or the movement of accessories to clarify the differences in their movements. Furthermore, the body transformation unit uses an emotion estimation function to estimate the emotional state of the person being imitated (e.g., tension, relaxation) and reflect that emotional state in their own body shape. For example, it can recreate a dancer's state of tension and compare it with their own movements. This allows for a more realistic look at the movements of the person being imitated.

[0053] The body transformation unit also takes into account the user's muscle movements and skeletal structure, allowing for a more detailed analysis of differences in movements. For example, when the generation AI performs body transformation, the body transformation unit also takes into account the user's muscle movements and skeletal structure. For example, it reproduces the muscle movements of a professional athlete and compares them with the user's own movements. The body transformation unit also analyzes muscle movements and skeletal structure to analyze differences in movements in more detail. For example, it reproduces the knee movements and foot position when jumping to clarify differences in movements. The body transformation unit also uses an emotion estimation function to estimate the emotional state of the person it is trying to imitate and reflects that emotional state in the user's own body shape. For example, it reproduces the state of concentration of an athlete and compares it with the user's own movements. This allows for a more detailed analysis of differences in movements.

[0054] The body shape transformation unit can use the emotion estimation function to estimate the emotional state of the person the user wants to imitate and reflect that emotional state in the user's own body shape. The body shape transformation unit, for example, uses the emotion estimation function to estimate the emotional state of the person the user wants to imitate and reflect that emotional state in the user's own body shape. For example, the body shape transformation unit recreates a dancer's relaxed state and compares it with the user's own movements. The body shape transformation unit can also use the emotion estimation function to estimate the emotional state of the person the user wants to imitate and reflect that emotional state in the user's own body shape. For example, the body shape transformation unit recreates an athlete's state of concentration and compares it with the user's own movements. The body shape transformation unit can also use the emotion estimation function to estimate the emotional state of the person the user wants to imitate and reflect that emotional state in the user's own body shape. For example, the body shape transformation unit recreates a fitness instructor's motivated state and compares it with the user's own movements. In this way, by reflecting the emotional state of the person the user wants to imitate, a more realistic comparison is possible.

[0055] The comparison unit can reproduce the movements of the person the user wants to imitate as a 3D model, allowing the user to freely change the viewpoint to check the movements. For example, the comparison unit can use generative AI to reproduce the movements of the person the user wants to imitate as a 3D model, allowing the user to freely change the viewpoint to check the movements. For example, the movements of a dancer can be reproduced as a 3D model, allowing the user to check the movements from various angles. The comparison unit can also use the 3D model to reproduce the movements of the person the user wants to imitate, allowing the user to freely change the viewpoint to check the movements. For example, the movements of an athlete can be reproduced as a 3D model, making the differences in the movements clear. The comparison unit can also use the 3D model to reproduce the movements of the person the user wants to imitate, allowing the user to freely change the viewpoint to check the movements. For example, the movements of a fitness instructor can be reproduced as a 3D model, making the differences in the movements clear. This allows the movements of the person the user wants to imitate to be checked from various angles.

[0056] The comparison unit can develop a specialized body shape transformation algorithm for each different sport or dance genre, enabling more specialized comparison. For example, the comparison unit develops a specialized body shape transformation algorithm for each different sport or dance genre, enabling more specialized comparison. For example, ballet and hip hop movements are each specializedly analyzed. The comparison unit can also develop a specialized body shape transformation algorithm for each sport or dance genre, enabling more specialized comparison. For example, soccer and basketball movements are each specialized analyzed. The comparison unit can also develop a specialized body shape transformation algorithm for each genre, enabling more specialized comparison. For example, yoga and Pilates movements are each specializedly analyzed. This enables more specialized comparison.

[0057] The notification unit can notify the comparison result not only by sound but also by vibration or light patterns, and provide visual or tactile feedback. For example, the notification unit uses a generation AI to notify the comparison result not only by sound but also by vibration or light patterns. For example, if the movement of the right arm is late, it will be notified by vibration. The notification unit can also notify the comparison result not only by sound but also by vibration or light patterns, and provide visual or tactile feedback. For example, if the timing of a step is late, it will be notified by a light pattern. The notification unit can also use a generation AI to notify the comparison result not only by sound but also by vibration or light patterns, and provide visual or tactile feedback. For example, if the jump height is insufficient, it will be notified by vibration. This makes it possible to provide visual and tactile feedback.

[0058] The notification unit may have a function that allows customization of the tone and speed of the voice to suit the user's preferences when providing voice notification. For example, the notification unit adds a function that allows customization of the tone and speed of the voice to suit the user's preferences when providing voice notification. For example, notification may be made in a slow tone. The notification unit also adds a function that allows customization of the tone and speed of the voice to suit the user's preferences when providing voice notification. For example, notification may be made in a high tone. The notification unit also adds a function that allows customization of the tone and speed of the voice to suit the user's preferences when providing voice notification. For example, notification may be made in a fast speed. This makes it possible to provide voice notification that suits the user's preferences.

[0059] The notification unit can use the emotion estimation function to provide a voice notification according to the user's emotional state and provide feedback that reduces stress. For example, the notification unit uses the emotion estimation function to provide a voice notification according to the user's emotional state and provide feedback that reduces stress. For example, the notification is made in a relaxed tone. Furthermore, the notification unit uses the emotion estimation function to provide a voice notification according to the user's emotional state and provide feedback that reduces stress. For example, the notification includes encouraging words. Furthermore, the notification unit uses the emotion estimation function to provide a voice notification according to the user's emotional state and provide feedback that reduces stress. For example, the notification includes positive words. This makes it possible to provide feedback according to the user's emotional state.

[0060] The notification unit can make the voice notification multilingual so that it can also accommodate users who speak different languages. The notification unit, for example, makes the voice notification multilingual so that it can also accommodate users who speak different languages. For example, notifications are made in multiple languages ​​such as English, French, and Chinese. The notification unit also implements multilingual voice notifications so that it can also accommodate users who speak different languages. For example, it automatically switches the notification language according to the user's language setting. The notification unit also makes the voice notification multilingual so that it can also accommodate users who speak different languages. For example, it makes notifications in a language selected by the user. This makes it possible to accommodate users who speak different languages.

[0061] The notification unit can compare the content of the voice notification with the user's past motion data to provide feedback including the user's progress. For example, the notification unit compares the content of the voice notification with the user's past motion data to provide feedback including the user's progress. For example, the notification unit may notify the user that "Your motion has improved since the last time." The notification unit also compares the content of the voice notification with the user's past motion data to provide feedback including the user's progress. For example, the notification unit may notify the user that "Your jump height has increased since the last time." The notification unit also compares the content of the voice notification with the user's past motion data to provide feedback including the user's progress. For example, the notification unit may notify the user that "Your step timing has improved since the last time." This makes it possible to provide feedback including the user's progress.

[0062] The notification unit can use the emotion estimation function to automatically adjust the timing of the voice notification so that the user can be most relaxed. The notification unit, for example, uses the emotion estimation function to automatically adjust the timing of the voice notification so that the user can be most relaxed. For example, a notification is made when the user is relaxed. The notification unit also uses the emotion estimation function to automatically adjust the timing of the voice notification so that the user can be most relaxed. For example, a notification is made when the user is concentrating. The notification unit also uses the emotion estimation function to automatically adjust the timing of the voice notification so that the user can be most relaxed. For example, a notification is made when the user is not feeling stressed. This makes it possible to provide the voice notification at a timing when the user can be most relaxed.

[0063] The improvement suggestion unit can use the generation AI to analyze the user's movements in real time and provide a predicted movement and an improvement proposal as visual feedback. The improvement suggestion unit, for example, uses the generation AI to analyze the user's movements in real time and provide a predicted movement and an improvement proposal as visual feedback. For example, it predicts the height of a jump and visually shows how the knees should bend. The improvement suggestion unit also analyzes the user's movements in real time and provides a predicted movement and an improvement proposal as visual feedback. For example, it predicts the angle of a swing and visually shows the movement of the arms. The improvement suggestion unit also uses the generation AI to analyze the user's movements in real time and provide a predicted movement and an improvement proposal as visual feedback. For example, it predicts the timing of a step and visually shows the movement of the feet. This makes it possible to analyze the user's movements in real time and provide visual feedback.

[0064] The improvement suggestion unit can refer to the user's past motion data and provide individually optimized advice. For example, when proposing an improvement plan, the improvement suggestion unit refers to the user's past motion data and provides individually optimized advice. For example, it suggests how to bend the knees based on past jump data. The improvement suggestion unit also refers to the user's past motion data and provides individually optimized advice. For example, it suggests arm movements based on past swing data. The improvement suggestion unit also refers to the user's past motion data and provides individually optimized advice when proposing an improvement plan. For example, it suggests foot movements based on past step data. This makes it possible to provide individual advice based on the user's past motion data.

[0065] The improvement suggestion unit can use the emotion estimation function to propose improvement suggestions based on the user's emotional state and provide advice for maintaining motivation. The improvement suggestion unit, for example, uses the emotion estimation function to propose improvement suggestions based on the user's emotional state and provide advice for maintaining motivation. For example, the improvement suggestion unit proposes improvement suggestions when the user is in a relaxed state. The improvement suggestion unit also uses the emotion estimation function to propose improvement suggestions based on the user's emotional state and provide advice for maintaining motivation. For example, the improvement suggestion unit proposes improvement suggestions when the user is highly concentrated. The improvement suggestion unit also uses the emotion estimation function to propose improvement suggestions based on the user's emotional state and provide advice for maintaining motivation. For example, the improvement suggestion unit proposes improvement suggestions when the user is in a low state of motivation. This makes it possible to provide improvement suggestions and advice based on the user's emotional state.

[0066] The improvement suggestion unit can provide improvement suggestions specialized for different sports or dance genres, thereby realizing more specialized advice. The improvement suggestion unit can, for example, provide improvement suggestions specialized for different sports or dance genres, thereby realizing more specialized advice. For example, ballet and hip hop movements can be analyzed professionally. The improvement suggestion unit can also provide improvement suggestions specialized for different sports or dance genres, thereby realizing more specialized advice. For example, soccer and basketball movements can be analyzed professionally. The improvement suggestion unit can also provide improvement suggestions specialized for different genres, thereby realizing more specialized advice. For example, yoga and Pilates movements can be analyzed professionally. This makes it possible to provide more specialized advice.

[0067] The improvement suggestion unit can customize the improvement suggestions according to the user's fitness level and health condition, and encourage improvements within a reasonable range. For example, the improvement suggestion unit customizes the improvement suggestions according to the user's fitness level and health condition, and encourages improvements within a reasonable range. For example, the improvement suggestion unit proposes improvement suggestions for beginners. The improvement suggestion unit also customizes the improvement suggestions according to the user's fitness level and health condition, and encourages improvements within a reasonable range. For example, the improvement suggestion unit proposes improvement suggestions for advanced users. The improvement suggestion unit also customizes the improvement suggestions according to the user's fitness level and health condition, and encourages improvements within a reasonable range. For example, the improvement suggestion unit proposes improvement suggestions according to the health condition. This makes it possible to make improvements according to the user's fitness level and health condition.

[0068] The improvement suggestion unit uses the emotion estimation function to propose improvement proposals at a timing when the user is most receptive, thereby reducing stress. The improvement suggestion unit, for example, uses the emotion estimation function to propose improvement proposals at a timing when the user is most receptive, thereby reducing stress. For example, the improvement proposals are proposed when the user is relaxed. The improvement suggestion unit also uses the emotion estimation function to propose improvement proposals at a timing when the user is most receptive, thereby reducing stress. For example, the improvement proposals are proposed when the user is concentrating. The improvement suggestion unit also uses the emotion estimation function to propose improvement proposals at a timing when the user is most receptive, thereby reducing stress. For example, the improvement proposals are proposed when the user is not feeling stressed. In this way, the improvement proposals are proposed at a timing when the user is most receptive, thereby reducing stress.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The comparison unit reproduces the movements of the person the user wants to imitate as a 3D model, allowing the user to freely change the viewpoint to check the movements. For example, the movements of a dancer can be reproduced as a 3D model, allowing the user to check the movements from various angles. The comparison unit also uses the 3D model to reproduce the movements of the person the user wants to imitate, allowing the user to freely change the viewpoint to check the movements. For example, the movements of an athlete can be reproduced as a 3D model, making the differences in the movements clear. The comparison unit also uses the 3D model to reproduce the movements of the person the user wants to imitate, allowing the user to freely change the viewpoint to check the movements. For example, the movements of a fitness instructor can be reproduced as a 3D model, making the differences in the movements clear. This allows the user to check the movements of the person the user wants to imitate from various angles.

[0071] The notification unit can notify the comparison results not only by sound but also by vibration or light patterns, and provide visual or tactile feedback. For example, if the movement of the right arm is late, it will be notified by vibration. The notification unit can also notify the comparison results not only by sound but also by vibration or light patterns, and provide visual or tactile feedback. For example, if the timing of a step is late, it will be notified by light patterns. The notification unit can also use generative AI to notify the comparison results not only by sound but also by vibration or light patterns, and provide visual or tactile feedback. For example, if the jump height is insufficient, it will be notified by vibration. This makes it possible to provide visual and tactile feedback.

[0072] The notification unit may have a function that allows the tone and speed of the voice to be customized to suit the user's preferences when providing voice notification. For example, the notification may be made in a slow tone. The notification unit may also add a function that allows the tone and speed of the voice to be customized to suit the user's preferences when providing voice notification. For example, the notification may be made in a high tone. The notification unit may also add a function that allows the tone and speed of the voice to be customized to suit the user's preferences when providing voice notification. For example, the notification may be made in a fast speed. This makes it possible to provide voice notification that suits the user's preferences.

[0073] The notification unit uses the emotion estimation function to provide a voice notification according to the user's emotional state and feedback to reduce stress. For example, the notification is made in a relaxed tone. The notification unit also uses the emotion estimation function to provide a voice notification according to the user's emotional state and feedback to reduce stress. For example, the notification includes encouraging words. The notification unit also uses the emotion estimation function to provide a voice notification according to the user's emotional state and feedback to reduce stress. For example, the notification includes positive words. This makes it possible to provide feedback according to the user's emotional state.

[0074] The notification unit can make the voice notification multilingual so that it can accommodate users who speak different languages. For example, notifications can be made in multiple languages, such as English, French, and Chinese. The notification unit also implements multilingual voice notifications so that it can accommodate users who speak different languages. For example, it automatically switches the notification language according to the user's language setting. The notification unit also makes the voice notification multilingual so that it can accommodate users who speak different languages. For example, it can provide notifications in a language selected by the user. This makes it possible to accommodate users who speak different languages.

[0075] The notification unit can compare the content of the voice notification with the user's past motion data to provide feedback including the user's progress. For example, the notification unit may notify the user that "your motion has improved since the last time." The notification unit can also compare the content of the voice notification with the user's past motion data to provide feedback including the user's progress. For example, the notification unit may notify the user that "your jump height has increased since the last time." The notification unit can also compare the content of the voice notification with the user's past motion data to provide feedback including the user's progress. For example, the notification unit may notify the user that "your step timing has improved since the last time." This makes it possible to provide feedback including the user's progress.

[0076] The notification unit can use the emotion estimation function to automatically adjust the timing of the voice notification to allow the user to feel most relaxed. For example, the notification is made when the user is relaxed. The notification unit also uses the emotion estimation function to automatically adjust the timing of the voice notification to allow the user to feel most relaxed. For example, the notification is made when the user is concentrating. The notification unit also uses the emotion estimation function to automatically adjust the timing of the voice notification to allow the user to feel most relaxed. For example, the notification is made when the user is not feeling stressed. This makes it possible to provide the voice notification at a timing when the user is most relaxed.

[0077] The improvement suggestion unit can use the generation AI to analyze the user's movements in real time and provide predicted movements and improvement suggestions as visual feedback. For example, it can predict the height of a jump and visually show how to bend the knees. The improvement suggestion unit can also analyze the user's movements in real time and provide predicted movements and improvement suggestions as visual feedback. For example, it can predict the angle of a swing and visually show arm movement. The improvement suggestion unit can also use the generation AI to analyze the user's movements in real time and provide predicted movements and improvement suggestions as visual feedback. For example, it can predict the timing of a step and visually show foot movement. This makes it possible to analyze the user's movements in real time and provide visual feedback.

[0078] The improvement suggestion unit can refer to the user's past motion data and provide individually optimized advice. For example, it suggests how to bend the knees based on past jump data. The improvement suggestion unit also refers to the user's past motion data and provides individually optimized advice. For example, it suggests arm movements based on past swing data. The improvement suggestion unit also refers to the user's past motion data when proposing improvement suggestions and provides individually optimized advice. For example, it suggests foot movements based on past step data. This makes it possible to provide individual advice based on the user's past motion data.

[0079] The improvement suggestion unit can use the emotion estimation function to suggest improvement suggestions based on the user's emotional state and provide advice for maintaining motivation. For example, it can suggest improvement suggestions when the user is in a relaxed state. The improvement suggestion unit also uses the emotion estimation function to suggest improvement suggestions based on the user's emotional state and provide advice for maintaining motivation. For example, it can suggest improvement suggestions when the user is highly concentrated. The improvement suggestion unit also uses the emotion estimation function to suggest improvement suggestions based on the user's emotional state and provide advice for maintaining motivation. For example, it can suggest improvement suggestions when the user is in a low state of motivation. This makes it possible to provide improvement suggestions and advice based on the user's emotional state.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The body shape conversion unit converts the body shape of the person you want to imitate into your own body shape. For example, the generative AI takes a video of the person you want to imitate as input, analyzes their body shape, and converts it into your own body shape. The generative AI can also convert the body shapes of professional dancers or athletes into your own body shape, making the differences in their movements clearer. Step 2: The comparison unit compares the body shapes converted by the body shape conversion unit. For example, the generation AI can compare its own movements with the movements of the person it wants to imitate based on the converted body shapes, and visually display or quantify the differences in movements to clarify them. Step 3: The notification unit notifies the user of the comparison results obtained by the comparison unit by voice. For example, the generation AI may provide voice notifications such as, "Your right arm movement is slow. Move it a little faster," or "Your step timing is slow. Move it a little faster." Step 4: The improvement suggestion unit predicts the desired outcome from the movements being performed and proposes improvement suggestions. For example, the generative AI could analyze movements during sports training and suggest specific improvement suggestions such as, "Your jump isn't high enough. Bend your knees more before jumping," or "Your squat isn't deep enough. Squat deeper."

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a body shape conversion unit that converts the body shape of the person to be imitated into one's own body shape; a comparison unit that compares the body shapes converted by the body shape conversion unit; a notification unit that notifies by voice of the comparison result obtained by the comparison unit; An improvement suggestion unit that predicts a desired result from an operation in progress and proposes an improvement plan. A system characterized by:

2. The body shape transformation unit is Not only the body shape of the person you want to imitate, but also the clothes and accessories are changed to fit your own body shape.

2. The system of claim 1.

3. The body shape transformation unit is Taking into account the user's muscle movements and skeletal structure, the differences in these movements are analyzed in more detail.

2. The system of claim 1.

4. The body shape transformation unit is The emotional state of the person to be imitated is estimated, and the emotional state is reflected in the body shape of the person to be imitated.

2. The system of claim 1.

5. The comparison section is The movements of the person the user wants to imitate are reproduced as a 3D model, allowing the user to freely change the viewpoint to check the movement.

2. The system of claim 1.

Images