System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to photograph his / her motion using a smartphone; means for the smartphone to transmit the photographed video to a server; means for the server to diagnose the motion using a AI module that analyzes the received video; means for the server to notify the user of the diagnostic result and improvement proposals; and means for the user to visually and audibly provide motion guidance through a display device worn by the user.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] Current golf practice and lessons are difficult for many golfers and newcomers due to the travel time to the practice range and the cost of lessons. Furthermore, there are few opportunities to receive proper instruction, making it difficult to practice efficiently. For those who are trying to improve their golf game despite their busy schedules, there is a demand for an environment where they can receive high-quality instruction without the constraints of time or place. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system that uses AI linked to a smartphone camera to allow a user to capture their own movements anywhere, such as at home, and transmit the captured images to a server. This system includes the following components.

[0006] A means for users to film themselves using their smartphones.

[0007] A means for the device to send captured video to a server.

[0008] A means of diagnosing operation using an AI module that analyzes the video received by the server.

[0009] A means by which the server notifies the user of diagnostic results and improvement suggestions.

[0010] A means of providing visual and audio guidance through a display device worn by the user.

[0011] Furthermore, users can ask the chatbot questions, which will then compare them with past diagnostic data to generate appropriate answers, which will then be displayed to the user on the device, allowing users to practice efficiently at home and receive professional guidance.

[0012] "User" refers to an individual who uses the system to film their own movements and receive a diagnosis.

[0013] "Device" refers to electronic devices such as smartphones and tablets used by users.

[0014] "Server" refers to a remote computer system that receives, analyzes, and manages data sent by users.

[0015] "AI module" refers to artificial intelligence software that runs within the server, analyzes received video data, and performs operational diagnosis.

[0016] "Movement diagnosis" refers to the process of evaluating the user's movements and suggesting areas for improvement based on video data analyzed by the AI ​​module.

[0017] "Diagnosis results" refers to the results of behavior diagnosis by the AI ​​module, and is information that includes problems with the user's behavior and measures for improvement.

[0018] "Improvement suggestions" refer to specific methods of improving operation provided to users based on the diagnostic results.

[0019] "Notification" refers to information about diagnostic results and improvement suggestions sent from the server to the terminal.

[0020] "Display device" refers to a device worn by a user to receive visual and audio feedback.

[0021] A "chatbot" is a program that automatically generates appropriate answers to questions from users based on past diagnostic data, etc.

[0022] "Video data" refers to video files of actions captured by a user using a device. [Brief explanation of the drawings]

[0023] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0024] 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.

[0025] First, the terms used in the following description will be explained.

[0026] 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, a 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), and an APU (Accelerated Processing Unit).

[0027] 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.

[0028] 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.

[0029] 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), Bluetooth (registered trademark), etc.

[0030] 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."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 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.

[0034] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0035] 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.

[0036] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0037] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0038] 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.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 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.

[0041] 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.

[0042] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0043] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0044] This invention is a system that allows users to film their movements at home or anywhere, and then AI analyzes the footage to diagnose the movements and suggest improvements, providing feedback to the user through a display device. Below, we will explain in natural language the program processing of specific embodiments for implementing the invention.

[0045] 1. Initial system setup

[0046] First, the user installs a dedicated app on their smartphone, launches the app, and creates an account. They enter the information required to create the account (e.g., name, email address, password, etc.), and the device sends that information to the server. The server stores the received information in a database and generates a new user ID. The server then sends a registration completion notification to the device and displays a registration completion message to the user.

[0047] 2. Record and upload your action

[0048] To record their movements, users use the camera function on their smartphone. By tapping the recording button in the dedicated app, the device's camera function is activated and the user can record their own movements. Once recording is complete, the video data is temporarily saved on the device. Then, when the user taps the upload button, the device compresses the video data and sends it to the server.

[0049] 3. AI-based operation diagnosis

[0050] The server passes the received video data to the AI ​​module for analysis. The AI ​​module analyzes the video and evaluates each point of the user's movement (e.g., backswing, impact position, follow-through, etc.). As a result of the evaluation, it generates problems with the user's movement and areas for improvement.

[0051] 4. Notification of diagnosis results

[0052] The results are stored on a server and linked to the user's account, allowing them to view the results through the app.

[0053] 5. Feedback via display devices

[0054] The user wears the display device and connects it to a smartphone. The device checks the connection status and sends a login request to the server. After logging in, the server sends the diagnosis results and improvement suggestions to the device, which are then displayed on the display device. The display device then provides visual guidelines and detailed audio advice.

[0055] 6. Detailed advice from chatbots

[0056] The user uses the chatbot function to input a question. The device sends the question to the server. The chatbot on the server compares the question with past diagnostic results and existing databases to generate an appropriate answer. The generated answer is sent to the device and displayed to the user.

[0057] Specific examples

[0058] For example, a beginner user wants to improve their golf swing, so they can use the app to film their movements at home. The video is sent to a server, where an AI module diagnoses the user's backswing as being too high. The diagnosis and specific suggestions for improvement (e.g., "Keep your shoulders low during the backswing") are provided to the user through a display device. Furthermore, if the user asks the chatbot, "What's the trick to stabilizing your backswing?" the chatbot will display an answer such as, "Adjusting the position of your grip will increase stability."

[0059] In this way, users can improve their performance efficiently at home.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] The user installs and launches the dedicated app on their smartphone, and enters their name, email address, and password on the account registration screen.

[0063] Step 2:

[0064] The terminal sends the entered user information to the server.

[0065] Step 3:

[0066] The server stores the received information in a database, generates a new user ID, sends the generated ID to the device, and returns a registration completion notification.

[0067] Step 4:

[0068] The device will display a registration completion message to the user.

[0069] Step 5:

[0070] When the user taps the "Swing Photo" button in the app, the device's camera function is activated and the movement is captured.

[0071] Step 6:

[0072] The device temporarily stores the captured video data.

[0073] Step 7:

[0074] When the user taps the "Upload" button, the device compresses the video data and sends it to the server.

[0075] Step 8:

[0076] The server passes the received video data to the AI ​​module.

[0077] Step 9:

[0078] AI analyzes the video data and evaluates each point of the movement (e.g., backswing, impact position, follow-through, etc.).

[0079] Step 10:

[0080] The AI ​​generates evaluation results and improvement points and returns them to the server.

[0081] Step 11:

[0082] The server stores the diagnostic results and improvement suggestions in a database and links them to the user's account.

[0083] Step 12:

[0084] When a user wears a display device and connects it wirelessly to a smartphone, the device checks the connection status and sends a login request to the server.

[0085] Step 13:

[0086] The server authenticates the user's login and sends the latest diagnostic results and improvement suggestions to the device.

[0087] Step 14:

[0088] The terminal displays the received diagnostic results and improvement suggestions on a display device.

[0089] Step 15:

[0090] The AI ​​glasses provide users with visual feedback (e.g., backswing guidelines) and audio advice (e.g., "Rotate your shoulders more").

[0091] Step 16:

[0092] Users use the chatbot function within the app to enter questions.

[0093] Step 17:

[0094] The device sends the question to the server.

[0095] Step 18:

[0096] The server passes the question to the chatbot, which compares it with past diagnostic data and generates an appropriate answer.

[0097] Step 19:

[0098] The server sends the generated answer to the terminal, which displays the answer to the user.

[0099] This process allows users to efficiently capture, diagnose, and improve their own movements and receive expert feedback.

[0100] Example 1

[0101] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0102] The goal is to provide a system that allows users to film their own movements and receive effective diagnosis and guidance on how to improve them without the help of a specialist. In particular, there is a need for a system that can be easily used at home or anywhere, that efficiently processes video data, and that provides accurate feedback.

[0103] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0104] In this invention, the server includes a means for transmitting video data captured by the terminal to the server, a means for the server to perform a motion diagnosis using an AI module that analyzes the received video, and a means for the server to store the diagnosis results and improvement suggestions in a database. This allows users to film their own motion using their smartphones and send the video to the server. The server also performs a motion diagnosis using the AI ​​module, and the diagnosis results and improvement suggestions are quickly provided to the user. This realizes a system that allows users to receive accurate feedback to improve their motion at home or anywhere.

[0105] "User" refers to an individual who uses the system to film their own movements and receive diagnostic results and suggestions for improvement.

[0106] A "smartphone" refers to a portable information terminal device that can communicate via a mobile phone network and can install and use a variety of applications.

[0107] "Terminal" refers to a smartphone or other mobile information terminal device used by a user.

[0108] A "server" refers to a computer system that processes data and provides services in response to requests from other terminals via a network.

[0109] "Video data" refers to video files taken by users using smartphones or other devices.

[0110] "AI module" refers to a software component that uses artificial intelligence algorithms to analyze received video data and perform operational diagnosis.

[0111] "Behavioral diagnosis" refers to the process of analyzing user behavior and identifying problems and areas for improvement.

[0112] "Diagnosis results" refer to the evaluation of the user's behavior and specific problems obtained through behavior diagnosis.

[0113] "Improvement suggestions" refer to specific advice and guidance to improve the user's behavior based on the diagnostic results.

[0114] "Database" refers to a collection of data that is stored in an organized manner and managed so that information can be searched and used efficiently.

[0115] "Display device" refers to a hardware device worn by a user to receive visual and audio information.

[0116] A "chatbot" is a program that automatically responds to questions from users.

[0117] "Movement instruction" refers to the process of showing users specific ways to improve their movements.

[0118] MODE FOR CARRYING OUT THE INVENTION

[0119] This invention is a system that allows users to film their movements at home or anywhere, and then AI analyzes the footage to diagnose the movements and suggest improvements, providing feedback to the user through a display device. Specific embodiments for implementing the invention are described below.

[0120] 1. Initial system setup

[0121] First, the user installs a dedicated application on their smartphone, launches the application, and creates an account. They enter the information required to create the account (e.g., name, email address, password, etc.), and the device sends that information to the server. The server stores the received information in a database and generates a new user ID. The server then sends a registration completion notification to the device and displays a registration completion message to the user.

[0122] 2. Record and upload your action

[0123] To record their movements, users use the camera function of their smartphone. By tapping the recording button in the dedicated application, the device's camera function is activated and the user can record their own movements. Once recording is complete, the video data is temporarily saved on the device. Then, when the user taps the upload button, the device compresses the video data and sends it to the server.

[0124] 3. AI-based operation diagnosis

[0125] The server passes the received video data to the AI ​​module for analysis. The AI ​​module analyzes the video and evaluates each point of the user's movement (e.g., backswing, impact position, follow-through, etc.). As a result of the evaluation, it generates problems with the user's movement and areas for improvement.

[0126] 4. Notification of diagnosis results

[0127] The results are stored on a server and linked to the user's account, allowing them to view the results through the application.

[0128] 5. Feedback via display devices

[0129] The user wears the display device and connects it to a smartphone. The device checks the connection status and sends a login request to the server. After logging in, the server sends the diagnosis results and improvement suggestions to the device, which are then displayed on the display device. The display device then provides visual guidelines and detailed audio advice.

[0130] 6. Detailed advice from chatbots

[0131] The user uses the chatbot function to input a question. The device sends the question to the server. The chatbot on the server compares the question with past diagnostic results and existing databases to generate an appropriate answer. The generated answer is sent to the device and displayed to the user.

[0132] Specific examples

[0133] For example, a beginner user wants to improve their golf swing, so they can use a dedicated application to film their movements at home. The video is sent to a server, where an AI module diagnoses that their backswing is too high. The diagnosis and specific suggestions for improvement (e.g., "Keep your shoulders low during the backswing") are provided to the user via a display device. Furthermore, if the user asks the chatbot, "What's the trick to stabilizing your backswing?" the chatbot will respond with an answer such as, "Adjusting the grip position will increase stability." In this way, users can efficiently improve their movements from the comfort of their own homes.

[0134] Prompt Sentence Examples

[0135] "What should I do if my backswing in my golf swing is too high? I'd also like some tips for stabilizing my movement."

[0136] Thus, the present invention provides a system that allows users to film their actions at home or anywhere, analyze the footage, and receive feedback.

[0137] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0138] Step 1:

[0139] The user installs a dedicated app on their smartphone, launches the app, and creates an account. The user enters information such as their name, email address, and password, and the device sends that information to the server. Input data: name, email address, password. The server stores the received information in a database and generates a new user ID. Output data: new user ID. The server sends a registration completion notification to the device and displays a registration completion message to the user.

[0140] Step 2:

[0141] Users use their smartphone camera to record their movements. When they tap the recording button in the dedicated app, the device's camera function is activated and they can record their own movements. Recorded data: video data. Once recording is complete, the video is temporarily saved on the device. After that, the user taps the upload button, and the device compresses the video data. Output data: compressed video data. The device sends this compressed video data to the server.

[0142] Step 3:

[0143] The server passes the received video data to the AI ​​module for analysis. Input data: Compressed video data. The AI ​​module analyzes the video and evaluates each point of the user's movement. Examples: backswing, impact position, follow-through. As a result of the evaluation, it generates problems with the user's movement and areas for improvement. Output data: Movement evaluation results.

[0144] Step 4:

[0145] The server stores the diagnostic results and improvement suggestions in a database. Input data: behavior evaluation results, user ID. The diagnostic results are stored on the server and linked to the user's account.

[0146] Step 5:

[0147] The user wears the display device and connects it to a smartphone. The device checks the connection status and sends a login request to the server. Input data: user ID, password. After logging in, the server sends the diagnosis results and improvement suggestions to the device, which are displayed on the display device. Output data: diagnosis results, improvement suggestions. The display device provides visual guidelines and detailed audio advice.

[0148] Step 6:

[0149] The user uses the chatbot function to input a question. Input data: Question content. The device sends the question to the server. The chatbot on the server compares the question content with past diagnostic results and existing databases to generate an appropriate answer. Output data: Answer. The generated answer is sent to the device and displayed to the user.

[0150] As an example of specific operations, if a beginner user wants to improve their golf swing, they can use this system to film their movements at home. The video is sent to a server, where the AI ​​module diagnoses that their backswing is too high and notifies the user via a display device with specific suggestions for improvement, such as "keeping your shoulders low during the backswing." Furthermore, if the user asks the chatbot, "What's the trick to stabilizing your backswing?" the chatbot will respond with, "Adjusting the position of your grip will increase stability."

[0151] (Application example 1)

[0152] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0153] Robots operating in modern production facilities must operate efficiently, but they are prone to degradation and malfunctions. To solve this problem, regular monitoring and optimization are necessary. However, conventional methods require specialized engineers and large-scale systems, resulting in high operational costs. It is also difficult to check smooth operation or receive appropriate improvement suggestions quickly.

[0154] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0155] In this invention, the server includes a means for a user to film their own movements using an information terminal, a means for the terminal to transmit the filmed video to a data center, and a means for the server to perform operational diagnosis using an artificial intelligence module that analyzes the video received. This allows factory managers to monitor and optimize the robot's operations. Furthermore, the server provides diagnostic results and improvement suggestions visually and audibly through a display device, enabling operational problems to be resolved quickly and efficiently.

[0156] A "user" is an entity that uses the system to film their behavior, receive a diagnosis, and receive suggestions for improvement.

[0157] An "information terminal" is an electronic device that a user uses to record their actions, and examples include smartphones and tablets.

[0158] "Means for taking photographs" refers to a mechanism for recording actions as video using the camera function of an information terminal.

[0159] A "terminal" is an electronic device that has the ability to capture images and send or receive data.

[0160] A "data center" is a facility equipped with servers and infrastructure for storing and processing data.

[0161] A "server" is a central computing device that analyzes received video and generates diagnostic results and improvement suggestions.

[0162] The "artificial intelligence module" is a group of programs placed inside the server that analyzes video data and performs operational diagnosis.

[0163] "Motion diagnosis" is the process of analyzing captured footage to identify problems and areas for improvement in movement.

[0164] "Improvement suggestions" are specific advice to encourage improvements in movement based on the results of movement diagnosis.

[0165] "Means of notification" refers to the method by which the server communicates the diagnosis results and improvement suggestions to the user.

[0166] A "display device" is a device that allows the user to visually and audibly confirm the diagnostic results and improvement suggestions.

[0167] "Means for providing visual and audio guidance" refers to a system that uses text, graphics, or audio to inform the user of improvements to their movements through a display device.

[0168] A "factory manager" is a person responsible for monitoring and optimizing the operation of robots within a factory.

[0169] An "automatic dialogue program" is software that provides appropriate answers to questions from users based on past diagnostic data.

[0170] "Means for reducing" refers to the process of reducing the file size of video data.

[0171] The "information recording device" is a facility for storing diagnostic results and improvement proposals as data for a long period of time.

[0172] To implement this invention, the following configuration and procedures are required.

[0173] First, a user installs a dedicated application on their information terminal, then launches the application to create an account. To create an account, they enter information such as their name, email address, and password, and the terminal sends that information to the server. The server stores the received information in a database and generates a new user ID. The server then notifies the terminal that registration is complete and displays a message to the user indicating this.

[0174] Next, the user must record their movements. To do so, the user uses the camera function of their information device. By tapping the capture button within the app, the device's camera function is activated and the user can record their movements. Once the recording is complete, the video data is temporarily stored on the device. After that, the user taps the upload button, and the device compresses the video data and sends it to a data center. The OpenCV library is generally used for this compression. The compressed video data is then collected and stored in a data center.

[0175] The server then passes the received video data to an artificial intelligence module for analysis. The AI ​​module analyzes the video using deep learning frameworks such as TensorFlow and PyTorch. This analysis evaluates each aspect of the user's movements (for example, the timing of the robot's movements and posture biases). As a result of the evaluation, it generates problems with the movement and suggestions for improvement. The diagnosis results and improvement suggestions are stored on the server and linked to the user's account.

[0176] When a user wears the display device and connects it to an information terminal, the terminal checks the connection status and sends a login request to the server. After logging in, the server sends the diagnosis results and improvement suggestions to the terminal, which are then displayed on the display device. This display device is often a smart glass or head-mounted display. The display device provides visual guidelines and detailed audio advice, allowing the user to improve their performance effectively and quickly.

[0177] Users can also get detailed advice using an automated dialogue program. When a user inputs a question using the automated dialogue program function, the terminal sends the question to the server. The automated dialogue program in the server compares the question with past diagnostic results and existing databases to generate an appropriate answer. The generated answer is sent to the terminal and displayed to the user. Examples of such prompt sentences include, "What can I do to make the robot's movements smoother?" and "Please tell me how to improve the current speed of the robot."

[0178] For example, a factory manager puts on smart glasses, launches an application, and films the robot's movements. The footage is sent to a data center, where an AI module diagnoses the robot's slow movement speed and provides improvement suggestions, such as periodically adding grease to the gears, via a display device. Furthermore, if the manager asks the automated dialogue program, "What is the most efficient way to add grease?", detailed advice, such as the recommendation to use a specific brand or tool, can be received.

[0179] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0180] Step 1:

[0181] The user installs a dedicated application on their information terminal and creates an account. The user enters the required information, such as name, email address, and password, and the terminal sends this information to the server. The input data is the name, email address, and password, and the output data is the new user ID. The server saves the received information in a database, generates a new user ID, and sends a notification of registration completion to the terminal.

[0182] Step 2:

[0183] The user uses the camera function of the information device to record their movements. When the user taps the camera button in the app, the device's camera function is activated and the user records their movements. The input data is the camera image, and the output data is the recorded video file. After recording is complete, the video data is temporarily saved on the device.

[0184] Step 3:

[0185] When the user taps the upload button, the device compresses the video data and sends it to the data center. The OpenCV library is used for compression. The input data is the captured video file, and the output data is the compressed video data. The compressed data is sent to the data center and stored.

[0186] Step 4:

[0187] The server passes the received video data to an artificial intelligence module for analysis. The AI ​​module analyzes the video using deep learning frameworks such as TensorFlow and PyTorch. The input data is compressed video data, and the output data is operational diagnosis results and improvement suggestions. The analysis results are stored on the server and linked to the user's account.

[0188] Step 5:

[0189] When a user wears the display device and connects it to an information terminal, the terminal checks the connection status and sends a login request to the server. The input data is the user ID, and the output data is an authentication token. After logging in, the server sends the diagnosis results and improvement suggestions to the terminal, which are then displayed on the display device. The display device, which can be smart glasses or a head-mounted display, provides visual guidelines and detailed audio advice.

[0190] Step 6:

[0191] When a user inputs a question using the automated dialogue program, the terminal sends the question to the server. The input data is the user's question, and the output data is the generated answer. The automated dialogue program on the server compares the question with past diagnostic results and existing databases to generate an appropriate answer. The generated answer is sent to the terminal and displayed to the user.

[0192] As a specific example, a factory manager puts on smart glasses, launches an application, and films the robot's movements. The footage is sent to a data center, where an AI module diagnoses the robot's slow movement speed and provides improvement suggestions, such as periodically adding grease to the gears, via a display device. Furthermore, if the manager asks the automated dialogue program, "What is the most efficient way to add grease?", detailed advice, such as the recommendation to use a specific brand or tool, can be received.

[0193] Examples of prompts include "How can I make my robot move more smoothly?" and "Tell me how to improve the current speed of my robot."

[0194] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0195] This invention is a system in which a user films their movements at home or anywhere, and AI analyzes the footage to diagnose the movements and suggest improvements, providing feedback to the user through a display device. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotional state and adjust the feedback based on that. Below, we will explain in natural language the program processing of a specific embodiment of the present invention.

[0196] 1. Initial system setup

[0197] First, the user installs a dedicated app on their smartphone, launches the app, and creates an account. They enter the information required to create the account (e.g., name, email address, password, etc.), and the device sends that information to the server. The server stores the received information in a database and generates a new user ID. The server then sends a registration completion notification to the device and displays a registration completion message to the user.

[0198] 2. Record and upload your action

[0199] To record their movements, users use the camera function on their smartphone. By tapping the recording button in the dedicated app, the device's camera function is activated and the user can record their own movements. Once recording is complete, the video data is temporarily saved on the device. Then, when the user taps the upload button, the device compresses the video data and sends it to the server.

[0200] 3. AI-based operation diagnosis

[0201] The server passes the received video data to the AI ​​module for analysis. The AI ​​module analyzes the video and evaluates each point of the user's movement (e.g., backswing, impact position, follow-through, etc.). As a result of the evaluation, it generates problems with the user's movement and areas for improvement.

[0202] 4. Notification of diagnosis results

[0203] The results are stored on a server and linked to the user's account, allowing them to view the results through the app.

[0204] 5. Feedback via display devices

[0205] The user wears the display device and connects it to a smartphone. The device checks the connection status and sends a login request to the server. After logging in, the server sends the diagnosis results and improvement suggestions to the device, which are then displayed on the display device. The display device then provides visual guidelines and detailed audio advice.

[0206] 6. Leveraging Emotional Engines

[0207] As the user practices and checks their movements, the display device they wear and the microphone and camera on their smartphone capture their voice and facial expressions. This data is sent to a server via the device, and the emotion engine on the server analyzes their emotional state from their voice and facial expressions. Based on the analysis results, the tone and content of the feedback is adjusted. For example, if the user is nervous, advice to help them relax is provided.

[0208] 7. Detailed advice from chatbots

[0209] The user uses the chatbot function to input a question. The device sends the question to the server. The chatbot on the server compares the question with past diagnostic results and existing databases to generate an appropriate answer. The generated answer is sent to the device and displayed to the user.

[0210] Specific examples

[0211] For example, a beginner user wants to improve their golf swing and uses the app to film their movements at home. The video is sent to a server, where an AI module diagnoses the user's backswing as being too high. The diagnosis and specific suggestions for improvement (e.g., "Keep your shoulders low during the backswing") are provided to the user via a display device. The emotion engine recognizes that the user is tense during practice and provides advice to encourage relaxation. If the user asks the chatbot, "What's the trick to stabilizing your backswing?", the chatbot will display an answer such as, "Adjusting the position of your grip will increase stability."

[0212] In this way, users can improve their performance efficiently at home and receive flexible feedback based on their emotional state.

[0213] The processing flow will be explained below.

[0214] Step 1:

[0215] The user installs and launches the dedicated app on their smartphone, and enters their name, email address, and password on the account registration screen.

[0216] Step 2:

[0217] The terminal sends the entered user information to the server.

[0218] Step 3:

[0219] The server stores the received information in a database, generates a new user ID, sends the generated ID to the device, and returns a registration completion notification.

[0220] Step 4:

[0221] The device will display a registration completion message to the user.

[0222] Step 5:

[0223] When the user taps the "Swing Photo" button in the app, the device's camera function is activated and the movement is captured.

[0224] Step 6:

[0225] The device temporarily stores the captured video data.

[0226] Step 7:

[0227] When the user taps the "Upload" button, the device compresses the video data and sends it to the server.

[0228] Step 8:

[0229] The server passes the received video data to the AI ​​module.

[0230] Step 9:

[0231] AI analyzes the video data and evaluates each point of the movement (e.g., backswing, impact position, follow-through, etc.).

[0232] Step 10:

[0233] The AI ​​generates evaluation results and improvement points and returns them to the server.

[0234] Step 11:

[0235] The server stores the diagnostic results and improvement suggestions in a database and links them to the user's account.

[0236] Step 12:

[0237] When a user wears a display device and connects it wirelessly to a smartphone, the device checks the connection status and sends a login request to the server.

[0238] Step 13:

[0239] The server authenticates the user's login and sends the latest diagnostic results and improvement suggestions to the device.

[0240] Step 14:

[0241] The terminal displays the received diagnostic results and improvement suggestions on a display device.

[0242] Step 15:

[0243] The AI ​​glasses provide users with visual feedback (e.g., backswing guidelines) and audio advice (e.g., "Rotate your shoulders more").

[0244] Step 16:

[0245] While the user is practicing, the microphone and camera on the display device or smartphone captures the user's voice and facial expressions.

[0246] Step 17:

[0247] The device transmits the captured voice and facial expression data to the server.

[0248] Step 18:

[0249] The emotion engine in the server analyzes the user's emotional state from voice and facial expression data.

[0250] Step 19:

[0251] The server then adjusts the tone and content of the feedback based on the analysis results, for example generating advice to relax if the user is feeling tense.

[0252] Step 20:

[0253] The server sends the adjusted feedback to the terminal, which provides it to the user through a display device.

[0254] Step 21:

[0255] Users use the chatbot function within the app to enter questions.

[0256] Step 22:

[0257] The device sends the question to the server.

[0258] Step 23:

[0259] The server passes the question to the chatbot, which compares it with past diagnostic data and generates an appropriate answer.

[0260] Step 24:

[0261] The server sends the generated answer to the terminal, which displays the answer to the user.

[0262] This series of processes allows users to efficiently capture, diagnose, and improve their own movements, and not only receive professional feedback, but also flexible feedback based on their emotional state.

[0263] Example 2

[0264] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0265] Conventional motion analysis systems have the problem that the feedback users receive is static and cannot appropriately adapt to the user's emotional state or training environment at any given time. It is also difficult to receive detailed advice in real time based on past diagnostic data. This can result in users not getting the full benefits they need. Furthermore, there is a lack of efficient data processing when filming movements and uploading data.

[0266] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0267] In this invention, the server includes an engine that can capture the user's voice and facial expressions and analyze their emotional state, and also includes a means for adjusting feedback based on the analysis results, a means for the user to ask the interactive agent questions, a means for the interactive agent to compare the questions with past diagnostic data and generate an appropriate response, a means for the terminal to compress the video data and transmit it to the processing device, a means for the processing device to store the received video data in association with user identification information, and a means for the processing device to store the diagnostic results and improvement suggestions in a database. This allows the user to receive flexible feedback based on their emotions in real time and detailed advice based on past data. Furthermore, efficient data processing can shorten the time from imaging to diagnosis.

[0268] "User" means a person who utilizes the system to receive performance diagnosis and feedback.

[0269] A "mobile communication terminal" refers to a portable information processing device, such as a smartphone or tablet, that can connect to the Internet via wireless communication.

[0270] A "processor" is a computer system that analyzes and stores received data.

[0271] An "artificial intelligence module" is a software program that uses machine learning algorithms to analyze video data and perform motion assessment and diagnosis.

[0272] "Display device" means a device for providing the user with visual and audio diagnostic results and suggestions for improvement.

[0273] The "engine" is a software module that analyzes the user's voice and facial expression data to recognize their emotional state.

[0274] An "interactive agent" is a program that uses natural language processing to provide appropriate answers to users' questions.

[0275] "User identification information" means information used to identify a user (e.g., user ID).

[0276] A "database" is a data management system for storing data such as diagnostic results and improvement proposals.

[0277] MODE FOR CARRYING OUT THE INVENTION

[0278] This invention is a system in which a user films their own movements at home or anywhere, and AI analyzes the footage to diagnose the movements and suggest improvements, providing feedback to the user through a display device. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotional state and adjust the feedback based on that. Specific embodiments for implementing the invention are described below.

[0279] 1. System Configuration

[0280] This system mainly consists of a mobile communication terminal, a server, and a display device. The mobile communication terminal is a portable information processing device such as a smartphone or tablet, on which the user installs a dedicated app. The server is a computer system that analyzes and stores video data and diagnostic results. The display device is a device that provides the user with operational diagnostic results and improvement suggestions visually and audibly.

[0281] 2. Install the dedicated app and create an account

[0282] First, the user installs a dedicated app (e.g., MotionAnalysisApp) on their smartphone, launches the app, and creates an account. The user enters the information required to create an account (e.g., name, email address, password, etc.), and the device sends that information to the server. The server stores the received information in a database (e.g., MySQL database) and generates a new user ID. The server then sends a registration completion notification to the device and displays a registration completion message to the user.

[0283] 3. Recording the movement and sending the video data

[0284] When a user taps the capture button in the app, the device's camera function is activated, allowing the user to record their own movements. Once recording is complete, the video data is temporarily stored on the device. When the user taps the upload button, the device compresses the video data (e.g., using the zlib library) and sends it to the server. The server receives the video data and stores it in association with user identification information.

[0285] 4. AI-based behavior analysis and diagnosis

[0286] The server passes the received video data to an AI module (e.g., TensorFlow). The AI ​​module analyzes the video and evaluates each point of the user's movement (e.g., backswing, impact position, follow-through, etc.). As a result of the evaluation, it generates problems with the user's movement and areas for improvement. The server saves the diagnostic results in a database (e.g., a PostgreSQL database) and links them to the user's account.

[0287] 5. Providing Feedback

[0288] The user wears a display device (e.g., Oculus Quest) and connects it to a smartphone. The device checks the connection status and sends a login request to the server. The server sends the diagnosis results and improvement suggestions to the device, which are then displayed on the display device. The display device provides visual guidelines and detailed audio advice.

[0289] 6. Leveraging Emotional Engines

[0290] As the user practices and checks their movements, the display device they wear and the microphone and camera on their smartphone capture their voice and facial expressions. This data is sent to a server via the device, and an emotion engine (e.g., Microsoft Azure Emotion API) on the server analyzes their emotional state from their voice and facial expressions. Based on the analysis results, the tone and content of the feedback is adjusted. For example, if the user is nervous, advice to help them relax is provided.

[0291] 7. Chatbots provide detailed advice

[0292] When a user uses the chatbot function (e.g., Dialogflow) to input a question, the device sends the question to the server. The chatbot on the server analyzes the question and generates an appropriate answer by comparing it with past diagnostic data and existing databases. The generated answer is sent to the device and displayed to the user.

[0293] Specific examples

[0294] For example, a beginner user wants to improve their golf swing, so they use the app to film their movements at home. The video is sent to a server, where an AI module diagnoses their swing as "too high." The diagnosis and specific suggestions for improvement (e.g., "Keep your shoulders low during the backswing") are provided to the user through a display device. If the emotion engine recognizes that the user is tense while practicing, the advice to "relax" is displayed. Similarly, if the user asks the chatbot, "What's the trick to stabilizing your backswing?", the chatbot will respond with, "Adjusting the position of your grip will increase stability."

[0295] This invention allows users to improve their movements efficiently and effectively at home or anywhere they like, and also allows them to receive flexible feedback based on their emotional state, allowing them to receive advice tailored to their individual needs.

[0296] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0297] Step 1:

[0298] The user installs a dedicated app on their smartphone. They launch the app and enter their name, email address, and password on the sign-up screen. The entered information is sent to the server. The server saves the entered information in a database, generates a new user ID, and sends a registration completion notification to the device.

[0299] Input: User's name, email address, password

[0300] Output: New user ID, registration completion notification

[0301] Specific operation: The user installs "MotionAnalysisApp" on their iPhone and enters the name "Yamada Taro", email address "taro@example.com", and password "password123".

[0302] Step 2:

[0303] The user taps the recording button in the app. The device's camera function is activated, and the user records their own movements. When recording is complete, the video data is temporarily saved on the device. When the upload button is tapped, the device compresses the video data and sends it to the server. The server receives the video data and saves it in association with the user's identification information.

[0304] Input: Video data of user movements

[0305] Output: Compressed video data, video data associated with user identification information

[0306] Specific operation: The user films their golf swing, compresses the video data using the zlib library, and transmits it.

[0307] Step 3:

[0308] The server passes the received video data to the AI ​​module, which analyzes the video and evaluates each aspect of the user's movements. As a result of the evaluation, it generates problems with the movements and areas for improvement. The server stores the diagnostic results in a database and links them to the user's account.

[0309] Input: User's compressed video data

[0310] Output: Performance evaluation results, diagnostic results and improvements

[0311] Specific behavior: The server passes the data to TensorFlow, which evaluates it as "the backswing is too high."

[0312] Step 4:

[0313] The user wears the display device and connects it to a smartphone. The device checks the connection status and sends a login request to the server. The server sends the diagnosis results and improvement suggestions to the device, which are then displayed on the display device. The display device provides visual guidelines and audio advice.

[0314] Input: Diagnostic results, improvement suggestions

[0315] Output: Display content (guidelines, advice) on the display device

[0316] Specific actions: The user puts on the Oculus Quest and sees the improvement suggestions.

[0317] Step 5:

[0318] While the user practices, the microphone and camera on the display device or smartphone captures their voice and facial expressions. This data is sent to a server via the device and analyzed by an emotion engine. Based on this analysis, the tone and content of the feedback is adjusted.

[0319] Input: User's voice data, facial expression data

[0320] Output: Emotional state analysis results, adjusted feedback content

[0321] What it does: It detects when the user is tense while practicing and displays the message "Relax."

[0322] Step 6:

[0323] The user uses the chatbot function to input a question. The device sends the question to the server. The chatbot on the server analyzes the question, compares it with past diagnostic data and existing databases, and generates an appropriate answer. The generated answer is sent to the device and displayed to the user.

[0324] Input: User's question

[0325] Output: The generated answer

[0326] Specific action: The user asks, "What's the trick to stabilizing your backswing?" and the answer displayed is, "Adjusting the grip position will increase stability."

[0327] In this way, specific input data is given at each processing step, processing is carried out based on that data, and output data is obtained.

[0328] (Application example 2)

[0329] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0330] Conventional motion diagnosis systems have difficulty not only analyzing a user's motion but also providing feedback that takes into account the user's emotional state. In particular, in industrial settings, such as in factory robot operations, efficient motion improvement and mental care for workers are important. The present invention aims to provide a system that not only diagnoses motion but also recognizes the user's emotional state and provides feedback tailored to each individual situation.

[0331] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0332] In this invention, the server includes: a means for a user to film their own movements using a smartphone; a means for the device to send the filmed video to the server; a means for the server to perform a movement diagnosis using an AI module that analyzes the received video; a means for the server to notify the user of the diagnosis results and improvement suggestions; a means for providing visual and audio movement guidance through a display device worn by the user; an emotion analysis means for recognizing the user's emotional state and adjusting the tone and content of feedback; and a means for providing advice based on the emotion analysis results. This enables not only efficient movement improvement but also flexible feedback that takes into consideration the user's mental health.

[0333] "User" refers to a person who uses a smartphone or display device to film their own actions and receive feedback.

[0334] A "smartphone" is a portable information terminal that allows users to capture their actions.

[0335] "Terminal" refers to a device that transmits video data to a server and displays diagnostic results.

[0336] A "server" refers to a computer system that receives video data sent from a terminal and performs analysis and provides feedback.

[0337] An "AI module" is software and hardware that includes artificial intelligence that performs operational diagnosis within a server.

[0338] "Motion diagnosis" refers to the process of analyzing video data captured by the user and identifying problems and areas for improvement in motion.

[0339] "Diagnosis results" refer to the analysis results of the user's behavior generated after the AI ​​module performs behavior diagnosis.

[0340] "Improvement suggestions" are specific advice provided to users on how to improve their behavior based on the diagnostic results.

[0341] "Display device" refers to a display device worn by a user to receive visual and audio instructions and feedback.

[0342] "Emotion analysis" refers to the process of analyzing a user's voice and facial expressions to recognize their emotional state.

[0343] "Emotion analysis means" refers to hardware and software for analyzing the user's emotional state.

[0344] A "chatbot" is an automated dialogue system that responds to a user's text-based questions by comparing them with past diagnostic data and generating appropriate answers.

[0345] "Video data" refers to video data generated when a user films themselves in action.

[0346] "Database" refers to an information storage system in which the server stores diagnostic results and improvement proposals, and can be referenced as needed.

[0347] In the system for realizing the present invention, the following hardware and software are used to process and analyze various data.

[0348] Hardware used

[0349] Smartphone

[0350] Head-mounted displays (HMDs) or other display devices

[0351] Camera module

[0352] Software used

[0353] OpenCV (video capture and processing)

[0354] Requests (API communication)

[0355] EmotionRecognition Library (a virtual emotion analysis library)

[0356] AI module (motion analysis software)

[0357] Detailed system processing explanation

[0358] 1. Initial Setup:

[0359] First, the user installs a dedicated application on their smartphone and launches it. The user creates an account and enters the required information (name, email address, password, etc.). The device sends this information to the server, which then generates a new user ID. The generated user ID is stored in a database, and a notification of registration completion is sent to the device.

[0360] 2. Record and upload the action:

[0361] Users use the smartphone's camera function to record their own movements. When they tap the recording button, the camera starts up and records the user's movements in real time. When recording is finished, the video data is temporarily saved on the device, and then when the user taps the upload button, the video data is compressed and sent to the server.

[0362] 3. AI-based operational diagnosis:

[0363] The server passes the received video data to the AI ​​module for analysis. The AI ​​module analyzes the video and evaluates each aspect of the user's movements (e.g., angle, timing, speed, etc.). As a result of the evaluation, it generates problems with the user's movements and areas for improvement.

[0364] 4. Notification of diagnostic results:

[0365] The diagnostic results are stored in a database on the server and linked to the user's account. This allows the user to view the results through a dedicated application. Visual and audio feedback is provided through a display device (such as an HMD) worn by the user.

[0366] 5. Leveraging the Emotion Engine:

[0367] As the user practices while checking the feedback, the microphone and camera on the smartphone or the display device they wear captures the user's voice and facial expressions. This data is sent to a server via the device, and the emotion engine in the server analyzes the user's emotional state from the voice and facial expressions. Based on the analysis results, the tone and content of the feedback are adjusted.

[0368] Specific examples

[0369] For example, if a factory worker wants to check the accuracy of a robot arm's operation, he or she picks up a smartphone and films the robot's movements. The video is sent to a server, and the AI ​​module diagnoses that the robot's arm is off by about 2 degrees. The diagnosis and specific improvement suggestions (e.g., "Raise the arm height slightly") are provided through the HMD. Furthermore, the emotion engine recognizes that the worker appears nervous and provides advice such as "Take a deep breath before starting work."

[0370] Prompt Sentence Examples

[0371] "Please tell me if the arm's movement is accurate with respect to the target."

[0372] "What is an efficient way to improve robot behavior?"

[0373] In this way, this system can simultaneously achieve efficient operational improvement and mental care for workers.

[0374] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0375] Step 1:

[0376] Initial Settings:

[0377] The user installs a dedicated application on their smartphone, launches the application and creates an account.

[0378] Input: User information such as name, email address, and password.

[0379] Data processing: The terminal sends the entered information to the server.

[0380] Output: The server generates a new user ID, saves it in the database, and sends a registration completion notification to the terminal.

[0381] Step 2:

[0382] Filming and uploading the action:

[0383] The user uses the smartphone camera to record their own movements.

[0384] Input: Video data captured by the user's camera.

[0385] Data processing: The device temporarily stores the video data, and when the user taps the upload button, the video data is compressed and sent to the server.

[0386] Output: The compressed video data is uploaded to the server.

[0387] Step 3:

[0388] AI-powered diagnostics:

[0389] The server passes the received video data to the AI ​​module for analysis.

[0390] Input: Video data received by the server.

[0391] Data processing: The AI ​​module analyzes the video and evaluates each aspect of the user's movements (e.g., angle, timing, speed, etc.).

[0392] Output: As an evaluation result, problems and improvements in the user's behavior are generated.

[0393] Step 4:

[0394] Notification of diagnostic results:

[0395] The server stores the diagnostic results in a database and links them to the user's account, which can then be viewed through a dedicated application.

[0396] Input: Operational diagnostic results from the AI ​​module.

[0397] Data processing: The server stores the diagnostic results in a database and notifies the user.

[0398] Output: The diagnostic results and specific improvement suggestions are displayed on the user's display device (e.g., HMD).

[0399] Step 5:

[0400] Leveraging the Emotion Engine:

[0401] As the user practices while checking the feedback, the microphone and camera on the display device worn by the user or the smartphone captures the user's voice and facial expressions.

[0402] Input: Captured voice and facial expression data.

[0403] Data processing: The data is sent to a server via the device, and the emotion engine in the server analyzes the emotional state from the voice and facial expressions.

[0404] Output: The tone and content of feedback is adjusted based on emotional state.

[0405] Step 6:

[0406] Chatbot advice provided:

[0407] Users can use the chatbot function to enter questions.

[0408] Input: A text-based question entered by the user.

[0409] Data processing: The device sends a question to the server, and the chatbot on the server compares it with past diagnostic data and generates an appropriate answer.

[0410] Output: The generated answer is sent to the terminal and displayed to the user.

[0411] 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.

[0412] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0413] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0414] [Second embodiment]

[0415] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0416] 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.

[0417] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0418] 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.

[0419] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0420] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0421] 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.

[0422] 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.

[0423] 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 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.

[0424] 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.

[0425] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0426] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0427] This invention is a system that allows users to film their movements at home or anywhere, and then AI analyzes the footage to diagnose the movements and suggest improvements, providing feedback to the user through a display device. Below, we will explain in natural language the program processing of specific embodiments for implementing the invention.

[0428] 1. Initial system setup

[0429] First, the user installs a dedicated app on their smartphone, launches the app, and creates an account. They enter the information required to create the account (e.g., name, email address, password, etc.), and the device sends that information to the server. The server stores the received information in a database and generates a new user ID. The server then sends a registration completion notification to the device and displays a registration completion message to the user.

[0430] 2. Record and upload your action

[0431] To record their movements, users use the camera function on their smartphone. By tapping the recording button in the dedicated app, the device's camera function is activated and the user can record their own movements. Once recording is complete, the video data is temporarily saved on the device. Then, when the user taps the upload button, the device compresses the video data and sends it to the server.

[0432] 3. AI-based operation diagnosis

[0433] The server passes the received video data to the AI ​​module for analysis. The AI ​​module analyzes the video and evaluates each point of the user's movement (e.g., backswing, impact position, follow-through, etc.). As a result of the evaluation, it generates problems with the user's movement and areas for improvement.

[0434] 4. Notification of diagnosis results

[0435] The results are stored on a server and linked to the user's account, allowing them to view the results through the app.

[0436] 5. Feedback via display devices

[0437] The user wears the display device and connects it to a smartphone. The device checks the connection status and sends a login request to the server. After logging in, the server sends the diagnosis results and improvement suggestions to the device, which are then displayed on the display device. The display device then provides visual guidelines and detailed audio advice.

[0438] 6. Detailed advice from chatbots

[0439] The user uses the chatbot function to input a question. The device sends the question to the server. The chatbot on the server compares the question with past diagnostic results and existing databases to generate an appropriate answer. The generated answer is sent to the device and displayed to the user.

[0440] Specific examples

[0441] For example, a beginner user wants to improve their golf swing, so they can use the app to film their movements at home. The video is sent to a server, where an AI module diagnoses the user's backswing as being too high. The diagnosis and specific suggestions for improvement (e.g., "Keep your shoulders low during the backswing") are provided to the user through a display device. Furthermore, if the user asks the chatbot, "What's the trick to stabilizing your backswing?" the chatbot will display an answer such as, "Adjusting the position of your grip will increase stability."

[0442] In this way, users can improve their performance efficiently at home.

[0443] The processing flow will be explained below.

[0444] Step 1:

[0445] The user installs and launches the dedicated app on their smartphone, and enters their name, email address, and password on the account registration screen.

[0446] Step 2:

[0447] The terminal sends the entered user information to the server.

[0448] Step 3:

[0449] The server stores the received information in a database, generates a new user ID, sends the generated ID to the device, and returns a registration completion notification.

[0450] Step 4:

[0451] The device will display a registration completion message to the user.

[0452] Step 5:

[0453] When the user taps the "Swing Photo" button in the app, the device's camera function is activated and the movement is captured.

[0454] Step 6:

[0455] The device temporarily stores the captured video data.

[0456] Step 7:

[0457] When the user taps the "Upload" button, the device compresses the video data and sends it to the server.

[0458] Step 8:

[0459] The server passes the received video data to the AI ​​module.

[0460] Step 9:

[0461] AI analyzes the video data and evaluates each point of the movement (e.g., backswing, impact position, follow-through, etc.).

[0462] Step 10:

[0463] The AI ​​generates evaluation results and improvement points and returns them to the server.

[0464] Step 11:

[0465] The server stores the diagnostic results and improvement suggestions in a database and links them to the user's account.

[0466] Step 12:

[0467] When a user wears a display device and connects it wirelessly to a smartphone, the device checks the connection status and sends a login request to the server.

[0468] Step 13:

[0469] The server authenticates the user's login and sends the latest diagnostic results and improvement suggestions to the device.

[0470] Step 14:

[0471] The terminal displays the received diagnostic results and improvement suggestions on a display device.

[0472] Step 15:

[0473] The AI ​​glasses provide users with visual feedback (e.g., backswing guidelines) and audio advice (e.g., "Rotate your shoulders more").

[0474] Step 16:

[0475] Users use the chatbot function within the app to enter questions.

[0476] Step 17:

[0477] The device sends the question to the server.

[0478] Step 18:

[0479] The server passes the question to the chatbot, which compares it with past diagnostic data and generates an appropriate answer.

[0480] Step 19:

[0481] The server sends the generated answer to the terminal, which displays the answer to the user.

[0482] This process allows users to efficiently capture, diagnose, and improve their own movements and receive expert feedback.

[0483] Example 1

[0484] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0485] The goal is to provide a system that allows users to film their own movements and receive effective diagnosis and guidance on how to improve them without the help of a specialist. In particular, there is a need for a system that can be easily used at home or anywhere, that efficiently processes video data, and that provides accurate feedback.

[0486] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0487] In this invention, the server includes a means for transmitting video data captured by the terminal to the server, a means for the server to perform a motion diagnosis using an AI module that analyzes the received video, and a means for the server to store the diagnosis results and improvement suggestions in a database. This allows users to film their own motion using their smartphones and send the video to the server. The server also performs a motion diagnosis using the AI ​​module, and the diagnosis results and improvement suggestions are quickly provided to the user. This realizes a system that allows users to receive accurate feedback to improve their motion at home or anywhere.

[0488] "User" refers to an individual who uses the system to film their own movements and receive diagnostic results and suggestions for improvement.

[0489] A "smartphone" refers to a portable information terminal device that can communicate via a mobile phone network and can install and use a variety of applications.

[0490] "Terminal" refers to a smartphone or other mobile information terminal device used by a user.

[0491] A "server" refers to a computer system that processes data and provides services in response to requests from other terminals via a network.

[0492] "Video data" refers to video files taken by users using smartphones or other devices.

[0493] "AI module" refers to a software component that uses artificial intelligence algorithms to analyze received video data and perform operational diagnosis.

[0494] "Behavioral diagnosis" refers to the process of analyzing user behavior and identifying problems and areas for improvement.

[0495] "Diagnosis results" refer to the evaluation of the user's behavior and specific problems obtained through behavior diagnosis.

[0496] "Improvement suggestions" refer to specific advice and guidance to improve the user's behavior based on the diagnostic results.

[0497] "Database" refers to a collection of data that is stored in an organized manner and managed so that information can be searched and used efficiently.

[0498] "Display device" refers to a hardware device worn by a user to receive visual and audio information.

[0499] A "chatbot" is a program that automatically responds to questions from users.

[0500] "Movement instruction" refers to the process of showing users specific ways to improve their movements.

[0501] MODE FOR CARRYING OUT THE INVENTION

[0502] This invention is a system that allows users to film their movements at home or anywhere, and then AI analyzes the footage to diagnose the movements and suggest improvements, providing feedback to the user through a display device. Specific embodiments for implementing the invention are described below.

[0503] 1. Initial system setup

[0504] First, the user installs a dedicated application on their smartphone, launches the application, and creates an account. They enter the information required to create the account (e.g., name, email address, password, etc.), and the device sends that information to the server. The server stores the received information in a database and generates a new user ID. The server then sends a registration completion notification to the device and displays a registration completion message to the user.

[0505] 2. Record and upload your action

[0506] To record their movements, users use the camera function of their smartphone. By tapping the recording button in the dedicated application, the device's camera function is activated and the user can record their own movements. Once recording is complete, the video data is temporarily saved on the device. Then, when the user taps the upload button, the device compresses the video data and sends it to the server.

[0507] 3. AI-based operation diagnosis

[0508] The server passes the received video data to the AI ​​module for analysis. The AI ​​module analyzes the video and evaluates each point of the user's movement (e.g., backswing, impact position, follow-through, etc.). As a result of the evaluation, it generates problems with the user's movement and areas for improvement.

[0509] 4. Notification of diagnosis results

[0510] The results are stored on a server and linked to the user's account, allowing them to view the results through the application.

[0511] 5. Feedback via display devices

[0512] The user wears the display device and connects it to a smartphone. The device checks the connection status and sends a login request to the server. After logging in, the server sends the diagnosis results and improvement suggestions to the device, which are then displayed on the display device. The display device then provides visual guidelines and detailed audio advice.

[0513] 6. Detailed advice from chatbots

[0514] The user uses the chatbot function to input a question. The device sends the question to the server. The chatbot on the server compares the question with past diagnostic results and existing databases to generate an appropriate answer. The generated answer is sent to the device and displayed to the user.

[0515] Specific examples

[0516] For example, a beginner user wants to improve their golf swing, so they can use a dedicated application to film their movements at home. The video is sent to a server, where an AI module diagnoses that their backswing is too high. The diagnosis and specific suggestions for improvement (e.g., "Keep your shoulders low during the backswing") are provided to the user via a display device. Furthermore, if the user asks the chatbot, "What's the trick to stabilizing your backswing?" the chatbot will respond with an answer such as, "Adjusting the grip position will increase stability." In this way, users can efficiently improve their movements from the comfort of their own homes.

[0517] Prompt Sentence Examples

[0518] "What should I do if my backswing in my golf swing is too high? I'd also like some tips for stabilizing my movement."

[0519] Thus, the present invention provides a system that allows users to film their actions at home or anywhere, analyze the footage, and receive feedback.

[0520] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0521] Step 1:

[0522] The user installs a dedicated app on their smartphone, launches the app, and creates an account. The user enters information such as their name, email address, and password, and the device sends that information to the server. Input data: name, email address, password. The server stores the received information in a database and generates a new user ID. Output data: new user ID. The server sends a registration completion notification to the device and displays a registration completion message to the user.

[0523] Step 2:

[0524] Users use their smartphone camera to record their movements. When they tap the recording button in the dedicated app, the device's camera function is activated and they can record their own movements. Recorded data: video data. Once recording is complete, the video is temporarily saved on the device. After that, the user taps the upload button, and the device compresses the video data. Output data: compressed video data. The device sends this compressed video data to the server.

[0525] Step 3:

[0526] The server passes the received video data to the AI ​​module for analysis. Input data: Compressed video data. The AI ​​module analyzes the video and evaluates each point of the user's movement. Examples: backswing, impact position, follow-through. As a result of the evaluation, it generates problems with the user's movement and areas for improvement. Output data: Movement evaluation results.

[0527] Step 4:

[0528] The server stores the diagnostic results and improvement suggestions in a database. Input data: behavior evaluation results, user ID. The diagnostic results are stored on the server and linked to the user's account.

[0529] Step 5:

[0530] The user wears the display device and connects it to a smartphone. The device checks the connection status and sends a login request to the server. Input data: user ID, password. After logging in, the server sends the diagnosis results and improvement suggestions to the device, which are displayed on the display device. Output data: diagnosis results, improvement suggestions. The display device provides visual guidelines and detailed audio advice.

[0531] Step 6:

[0532] The user uses the chatbot function to input a question. Input data: Question content. The device sends the question to the server. The chatbot on the server compares the question content with past diagnostic results and existing databases to generate an appropriate answer. Output data: Answer. The generated answer is sent to the device and displayed to the user.

[0533] As an example of specific operations, if a beginner user wants to improve their golf swing, they can use this system to film their movements at home. The video is sent to a server, where the AI ​​module diagnoses that their backswing is too high and notifies the user via a display device with specific suggestions for improvement, such as "keeping your shoulders low during the backswing." Furthermore, if the user asks the chatbot, "What's the trick to stabilizing your backswing?" the chatbot will respond with, "Adjusting the position of your grip will increase stability."

[0534] (Application example 1)

[0535] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0536] Robots operating in modern production facilities must operate efficiently, but they are prone to degradation and malfunctions. To solve this problem, regular monitoring and optimization are necessary. However, conventional methods require specialized engineers and large-scale systems, resulting in high operational costs. It is also difficult to check smooth operation or receive appropriate improvement suggestions quickly.

[0537] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0538] In this invention, the server includes a means for a user to film their own movements using an information terminal, a means for the terminal to transmit the filmed video to a data center, and a means for the server to perform operational diagnosis using an artificial intelligence module that analyzes the video received. This allows factory managers to monitor and optimize the robot's operations. Furthermore, the server provides diagnostic results and improvement suggestions visually and audibly through a display device, enabling operational problems to be resolved quickly and efficiently.

[0539] A "user" is an entity that uses the system to film their behavior, receive a diagnosis, and receive suggestions for improvement.

[0540] An "information terminal" is an electronic device that a user uses to record their actions, and examples include smartphones and tablets.

[0541] "Means for taking photographs" refers to a mechanism for recording actions as video using the camera function of an information terminal.

[0542] A "terminal" is an electronic device that has the ability to capture images and send or receive data.

[0543] A "data center" is a facility equipped with servers and infrastructure for storing and processing data.

[0544] A "server" is a central computing device that analyzes received video and generates diagnostic results and improvement suggestions.

[0545] The "artificial intelligence module" is a group of programs placed inside the server that analyzes video data and performs operational diagnosis.

[0546] "Motion diagnosis" is the process of analyzing captured footage to identify problems and areas for improvement in movement.

[0547] "Improvement suggestions" are specific advice to encourage improvements in movement based on the results of movement diagnosis.

[0548] "Means of notification" refers to the method by which the server communicates the diagnosis results and improvement suggestions to the user.

[0549] A "display device" is a device that allows the user to visually and audibly confirm the diagnostic results and improvement suggestions.

[0550] "Means for providing visual and audio guidance" refers to a system that uses text, graphics, or audio to inform the user of improvements to their movements through a display device.

[0551] A "factory manager" is a person responsible for monitoring and optimizing the operation of robots within a factory.

[0552] An "automatic dialogue program" is software that provides appropriate answers to questions from users based on past diagnostic data.

[0553] "Means for reducing" refers to the process of reducing the file size of video data.

[0554] The "information recording device" is a facility for storing diagnostic results and improvement proposals as data for a long period of time.

[0555] To implement this invention, the following configuration and procedures are required.

[0556] First, a user installs a dedicated application on their information terminal, then launches the application to create an account. To create an account, they enter information such as their name, email address, and password, and the terminal sends that information to the server. The server stores the received information in a database and generates a new user ID. The server then notifies the terminal that registration is complete and displays a message to the user indicating this.

[0557] Next, the user must record their movements. To do so, the user uses the camera function of their information device. By tapping the capture button within the app, the device's camera function is activated and the user can record their movements. Once the recording is complete, the video data is temporarily stored on the device. After that, the user taps the upload button, and the device compresses the video data and sends it to a data center. The OpenCV library is generally used for this compression. The compressed video data is then collected and stored in a data center.

[0558] The server then passes the received video data to an artificial intelligence module for analysis. The AI ​​module analyzes the video using deep learning frameworks such as TensorFlow and PyTorch. This analysis evaluates each aspect of the user's movements (for example, the timing of the robot's movements and posture biases). As a result of the evaluation, it generates problems with the movement and suggestions for improvement. The diagnosis results and improvement suggestions are stored on the server and linked to the user's account.

[0559] When a user wears the display device and connects it to an information terminal, the terminal checks the connection status and sends a login request to the server. After logging in, the server sends the diagnosis results and improvement suggestions to the terminal, which are then displayed on the display device. This display device is often a smart glass or head-mounted display. The display device provides visual guidelines and detailed audio advice, allowing the user to improve their performance effectively and quickly.

[0560] Users can also get detailed advice using an automated dialogue program. When a user inputs a question using the automated dialogue program function, the terminal sends the question to the server. The automated dialogue program in the server compares the question with past diagnostic results and existing databases to generate an appropriate answer. The generated answer is sent to the terminal and displayed to the user. Examples of such prompt sentences include, "What can I do to make the robot's movements smoother?" and "Please tell me how to improve the current speed of the robot."

[0561] For example, a factory manager puts on smart glasses, launches an application, and films the robot's movements. The footage is sent to a data center, where an AI module diagnoses the robot's slow movement speed and provides improvement suggestions, such as periodically adding grease to the gears, via a display device. Furthermore, if the manager asks the automated dialogue program, "What is the most efficient way to add grease?", detailed advice, such as the recommendation to use a specific brand or tool, can be received.

[0562] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0563] Step 1:

[0564] The user installs a dedicated application on their information terminal and creates an account. The user enters the required information, such as name, email address, and password, and the terminal sends this information to the server. The input data is the name, email address, and password, and the output data is the new user ID. The server saves the received information in a database, generates a new user ID, and sends a notification of registration completion to the terminal.

[0565] Step 2:

[0566] The user uses the camera function of the information device to record their movements. When the user taps the camera button in the app, the device's camera function is activated and the user records their movements. The input data is the camera image, and the output data is the recorded video file. After recording is complete, the video data is temporarily saved on the device.

[0567] Step 3:

[0568] When the user taps the upload button, the device compresses the video data and sends it to the data center. The OpenCV library is used for compression. The input data is the captured video file, and the output data is the compressed video data. The compressed data is sent to the data center and stored.

[0569] Step 4:

[0570] The server passes the received video data to an artificial intelligence module for analysis. The AI ​​module analyzes the video using deep learning frameworks such as TensorFlow and PyTorch. The input data is compressed video data, and the output data is operational diagnosis results and improvement suggestions. The analysis results are stored on the server and linked to the user's account.

[0571] Step 5:

[0572] When a user wears the display device and connects it to an information terminal, the terminal checks the connection status and sends a login request to the server. The input data is the user ID, and the output data is an authentication token. After logging in, the server sends the diagnosis results and improvement suggestions to the terminal, which are then displayed on the display device. The display device, which can be smart glasses or a head-mounted display, provides visual guidelines and detailed audio advice.

[0573] Step 6:

[0574] When a user inputs a question using the automated dialogue program, the terminal sends the question to the server. The input data is the user's question, and the output data is the generated answer. The automated dialogue program on the server compares the question with past diagnostic results and existing databases to generate an appropriate answer. The generated answer is sent to the terminal and displayed to the user.

[0575] As a specific example, a factory manager puts on smart glasses, launches an application, and films the robot's movements. The footage is sent to a data center, where an AI module diagnoses the robot's slow movement speed and provides improvement suggestions, such as periodically adding grease to the gears, via a display device. Furthermore, if the manager asks the automated dialogue program, "What is the most efficient way to add grease?", detailed advice, such as the recommendation to use a specific brand or tool, can be received.

[0576] Examples of prompts include "How can I make my robot move more smoothly?" and "Tell me how to improve the current speed of my robot."

[0577] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0578] This invention is a system in which a user films their movements at home or anywhere, and AI analyzes the footage to diagnose the movements and suggest improvements, providing feedback to the user through a display device. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotional state and adjust the feedback based on that. Below, we will explain in natural language the program processing of a specific embodiment of the present invention.

[0579] 1. Initial system setup

[0580] First, the user installs a dedicated app on their smartphone, launches the app, and creates an account. They enter the information required to create the account (e.g., name, email address, password, etc.), and the device sends that information to the server. The server stores the received information in a database and generates a new user ID. The server then sends a registration completion notification to the device and displays a registration completion message to the user.

[0581] 2. Record and upload your action

[0582] To record their movements, users use the camera function on their smartphone. By tapping the recording button in the dedicated app, the device's camera function is activated and the user can record their own movements. Once recording is complete, the video data is temporarily saved on the device. Then, when the user taps the upload button, the device compresses the video data and sends it to the server.

[0583] 3. AI-based operation diagnosis

[0584] The server passes the received video data to the AI ​​module for analysis. The AI ​​module analyzes the video and evaluates each point of the user's movement (e.g., backswing, impact position, follow-through, etc.). As a result of the evaluation, it generates problems with the user's movement and areas for improvement.

[0585] 4. Notification of diagnosis results

[0586] The results are stored on a server and linked to the user's account, allowing them to view the results through the app.

[0587] 5. Feedback via display devices

[0588] The user wears the display device and connects it to a smartphone. The device checks the connection status and sends a login request to the server. After logging in, the server sends the diagnosis results and improvement suggestions to the device, which are then displayed on the display device. The display device then provides visual guidelines and detailed audio advice.

[0589] 6. Leveraging Emotional Engines

[0590] As the user practices and checks their movements, the display device they wear and the microphone and camera on their smartphone capture their voice and facial expressions. This data is sent to a server via the device, and the emotion engine on the server analyzes their emotional state from their voice and facial expressions. Based on the analysis results, the tone and content of the feedback is adjusted. For example, if the user is nervous, advice to help them relax is provided.

[0591] 7. Detailed advice from chatbots

[0592] The user uses the chatbot function to input a question. The device sends the question to the server. The chatbot on the server compares the question with past diagnostic results and existing databases to generate an appropriate answer. The generated answer is sent to the device and displayed to the user.

[0593] Specific examples

[0594] For example, a beginner user wants to improve their golf swing and uses the app to film their movements at home. The video is sent to a server, where an AI module diagnoses the user's backswing as being too high. The diagnosis and specific suggestions for improvement (e.g., "Keep your shoulders low during the backswing") are provided to the user via a display device. The emotion engine recognizes that the user is tense during practice and provides advice to encourage relaxation. If the user asks the chatbot, "What's the trick to stabilizing your backswing?", the chatbot will display an answer such as, "Adjusting the position of your grip will increase stability."

[0595] In this way, users can improve their performance efficiently at home and receive flexible feedback based on their emotional state.

[0596] The processing flow will be explained below.

[0597] Step 1:

[0598] The user installs and launches the dedicated app on their smartphone, and enters their name, email address, and password on the account registration screen.

[0599] Step 2:

[0600] The terminal sends the entered user information to the server.

[0601] Step 3:

[0602] The server stores the received information in a database, generates a new user ID, sends the generated ID to the device, and returns a registration completion notification.

[0603] Step 4:

[0604] The device will display a registration completion message to the user.

[0605] Step 5:

[0606] When the user taps the "Swing Photo" button in the app, the device's camera function is activated and the movement is captured.

[0607] Step 6:

[0608] The device temporarily stores the captured video data.

[0609] Step 7:

[0610] When the user taps the "Upload" button, the device compresses the video data and sends it to the server.

[0611] Step 8:

[0612] The server passes the received video data to the AI ​​module.

[0613] Step 9:

[0614] AI analyzes the video data and evaluates each point of the movement (e.g., backswing, impact position, follow-through, etc.).

[0615] Step 10:

[0616] The AI ​​generates evaluation results and improvement points and returns them to the server.

[0617] Step 11:

[0618] The server stores the diagnostic results and improvement suggestions in a database and links them to the user's account.

[0619] Step 12:

[0620] When a user wears a display device and connects it wirelessly to a smartphone, the device checks the connection status and sends a login request to the server.

[0621] Step 13:

[0622] The server authenticates the user's login and sends the latest diagnostic results and improvement suggestions to the device.

[0623] Step 14:

[0624] The terminal displays the received diagnostic results and improvement suggestions on a display device.

[0625] Step 15:

[0626] The AI ​​glasses provide users with visual feedback (e.g., backswing guidelines) and audio advice (e.g., "Rotate your shoulders more").

[0627] Step 16:

[0628] While the user is practicing, the microphone and camera on the display device or smartphone captures the user's voice and facial expressions.

[0629] Step 17:

[0630] The device transmits the captured voice and facial expression data to the server.

[0631] Step 18:

[0632] The emotion engine in the server analyzes the user's emotional state from voice and facial expression data.

[0633] Step 19:

[0634] The server then adjusts the tone and content of the feedback based on the analysis results, for example generating advice to relax if the user is feeling tense.

[0635] Step 20:

[0636] The server sends the adjusted feedback to the terminal, which provides it to the user through a display device.

[0637] Step 21:

[0638] Users use the chatbot function within the app to enter questions.

[0639] Step 22:

[0640] The device sends the question to the server.

[0641] Step 23:

[0642] The server passes the question to the chatbot, which compares it with past diagnostic data and generates an appropriate answer.

[0643] Step 24:

[0644] The server sends the generated answer to the terminal, which displays the answer to the user.

[0645] This series of processes allows users to efficiently capture, diagnose, and improve their own movements, and not only receive professional feedback, but also flexible feedback based on their emotional state.

[0646] Example 2

[0647] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0648] Conventional motion analysis systems have the problem that the feedback users receive is static and cannot appropriately adapt to the user's emotional state or training environment at any given time. It is also difficult to receive detailed advice in real time based on past diagnostic data. This can result in users not getting the full benefits they need. Furthermore, there is a lack of efficient data processing when filming movements and uploading data.

[0649] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0650] In this invention, the server includes an engine that can capture the user's voice and facial expressions and analyze their emotional state, and also includes a means for adjusting feedback based on the analysis results, a means for the user to ask the interactive agent questions, a means for the interactive agent to compare the questions with past diagnostic data and generate an appropriate response, a means for the terminal to compress the video data and transmit it to the processing device, a means for the processing device to store the received video data in association with user identification information, and a means for the processing device to store the diagnostic results and improvement suggestions in a database. This allows the user to receive flexible feedback based on their emotions in real time and detailed advice based on past data. Furthermore, efficient data processing can shorten the time from imaging to diagnosis.

[0651] "User" means a person who utilizes the system to receive performance diagnosis and feedback.

[0652] A "mobile communication terminal" refers to a portable information processing device, such as a smartphone or tablet, that can connect to the Internet via wireless communication.

[0653] A "processor" is a computer system that analyzes and stores received data.

[0654] An "artificial intelligence module" is a software program that uses machine learning algorithms to analyze video data and perform motion assessment and diagnosis.

[0655] "Display device" means a device for providing the user with visual and audio diagnostic results and suggestions for improvement.

[0656] The "engine" is a software module that analyzes the user's voice and facial expression data to recognize their emotional state.

[0657] An "interactive agent" is a program that uses natural language processing to provide appropriate answers to users' questions.

[0658] "User identification information" means information used to identify a user (e.g., user ID).

[0659] A "database" is a data management system for storing data such as diagnostic results and improvement proposals.

[0660] MODE FOR CARRYING OUT THE INVENTION

[0661] This invention is a system in which a user films their own movements at home or anywhere, and AI analyzes the footage to diagnose the movements and suggest improvements, providing feedback to the user through a display device. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotional state and adjust the feedback based on that. Specific embodiments for implementing the invention are described below.

[0662] 1. System Configuration

[0663] This system mainly consists of a mobile communication terminal, a server, and a display device. The mobile communication terminal is a portable information processing device such as a smartphone or tablet, on which the user installs a dedicated app. The server is a computer system that analyzes and stores video data and diagnostic results. The display device is a device that provides the user with operational diagnostic results and improvement suggestions visually and audibly.

[0664] 2. Install the dedicated app and create an account

[0665] First, the user installs a dedicated app (e.g., MotionAnalysisApp) on their smartphone, launches the app, and creates an account. The user enters the information required to create an account (e.g., name, email address, password, etc.), and the device sends that information to the server. The server stores the received information in a database (e.g., MySQL database) and generates a new user ID. The server then sends a registration completion notification to the device and displays a registration completion message to the user.

[0666] 3. Recording the movement and sending the video data

[0667] When a user taps the capture button in the app, the device's camera function is activated, allowing the user to record their own movements. Once recording is complete, the video data is temporarily stored on the device. When the user taps the upload button, the device compresses the video data (e.g., using the zlib library) and sends it to the server. The server receives the video data and stores it in association with user identification information.

[0668] 4. AI-based behavior analysis and diagnosis

[0669] The server passes the received video data to an AI module (e.g., TensorFlow). The AI ​​module analyzes the video and evaluates each point of the user's movement (e.g., backswing, impact position, follow-through, etc.). As a result of the evaluation, it generates problems with the user's movement and areas for improvement. The server saves the diagnostic results in a database (e.g., a PostgreSQL database) and links them to the user's account.

[0670] 5. Providing Feedback

[0671] The user wears a display device (e.g., Oculus Quest) and connects it to a smartphone. The device checks the connection status and sends a login request to the server. The server sends the diagnosis results and improvement suggestions to the device, which are then displayed on the display device. The display device provides visual guidelines and detailed audio advice.

[0672] 6. Leveraging Emotional Engines

[0673] As the user practices and checks their movements, the display device they wear and the microphone and camera on their smartphone capture their voice and facial expressions. This data is sent to a server via the device, and an emotion engine (e.g., Microsoft Azure Emotion API) on the server analyzes their emotional state from their voice and facial expressions. Based on the analysis results, the tone and content of the feedback is adjusted. For example, if the user is nervous, advice to help them relax is provided.

[0674] 7. Chatbots provide detailed advice

[0675] When a user uses the chatbot function (e.g., Dialogflow) to input a question, the device sends the question to the server. The chatbot on the server analyzes the question and generates an appropriate answer by comparing it with past diagnostic data and existing databases. The generated answer is sent to the device and displayed to the user.

[0676] Specific examples

[0677] For example, a beginner user wants to improve their golf swing, so they use the app to film their movements at home. The video is sent to a server, where an AI module diagnoses their swing as "too high." The diagnosis and specific suggestions for improvement (e.g., "Keep your shoulders low during the backswing") are provided to the user through a display device. If the emotion engine recognizes that the user is tense while practicing, the advice to "relax" is displayed. Similarly, if the user asks the chatbot, "What's the trick to stabilizing your backswing?", the chatbot will respond with, "Adjusting the position of your grip will increase stability."

[0678] This invention allows users to improve their movements efficiently and effectively at home or anywhere they like, and also allows them to receive flexible feedback based on their emotional state, allowing them to receive advice tailored to their individual needs.

[0679] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0680] Step 1:

[0681] The user installs a dedicated app on their smartphone. They launch the app and enter their name, email address, and password on the sign-up screen. The entered information is sent to the server. The server saves the entered information in a database, generates a new user ID, and sends a registration completion notification to the device.

[0682] Input: User's name, email address, password

[0683] Output: New user ID, registration completion notification

[0684] Specific operation: The user installs "MotionAnalysisApp" on their iPhone and enters the name "Yamada Taro", email address "taro@example.com", and password "password123".

[0685] Step 2:

[0686] The user taps the recording button in the app. The device's camera function is activated, and the user records their own movements. When recording is complete, the video data is temporarily saved on the device. When the upload button is tapped, the device compresses the video data and sends it to the server. The server receives the video data and saves it in association with the user's identification information.

[0687] Input: Video data of user movements

[0688] Output: Compressed video data, video data associated with user identification information

[0689] Specific operation: The user films their golf swing, compresses the video data using the zlib library, and transmits it.

[0690] Step 3:

[0691] The server passes the received video data to the AI ​​module, which analyzes the video and evaluates each aspect of the user's movements. As a result of the evaluation, it generates problems with the movements and areas for improvement. The server stores the diagnostic results in a database and links them to the user's account.

[0692] Input: User's compressed video data

[0693] Output: Performance evaluation results, diagnostic results and improvements

[0694] Specific behavior: The server passes the data to TensorFlow, which evaluates it as "the backswing is too high."

[0695] Step 4:

[0696] The user wears the display device and connects it to a smartphone. The device checks the connection status and sends a login request to the server. The server sends the diagnosis results and improvement suggestions to the device, which are then displayed on the display device. The display device provides visual guidelines and audio advice.

[0697] Input: Diagnostic results, improvement suggestions

[0698] Output: Display content (guidelines, advice) on the display device

[0699] Specific actions: The user puts on the Oculus Quest and sees the improvement suggestions.

[0700] Step 5:

[0701] While the user practices, the microphone and camera on the display device or smartphone captures their voice and facial expressions. This data is sent to a server via the device and analyzed by an emotion engine. Based on this analysis, the tone and content of the feedback is adjusted.

[0702] Input: User's voice data, facial expression data

[0703] Output: Emotional state analysis results, adjusted feedback content

[0704] What it does: It detects when the user is tense while practicing and displays the message "Relax."

[0705] Step 6:

[0706] The user uses the chatbot function to input a question. The device sends the question to the server. The chatbot on the server analyzes the question, compares it with past diagnostic data and existing databases, and generates an appropriate answer. The generated answer is sent to the device and displayed to the user.

[0707] Input: User's question

[0708] Output: The generated answer

[0709] Specific action: The user asks, "What's the trick to stabilizing your backswing?" and the answer displayed is, "Adjusting the grip position will increase stability."

[0710] In this way, specific input data is given at each processing step, processing is carried out based on that data, and output data is obtained.

[0711] (Application example 2)

[0712] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0713] Conventional motion diagnosis systems have difficulty not only analyzing a user's motion but also providing feedback that takes into account the user's emotional state. In particular, in industrial settings, such as in factory robot operations, efficient motion improvement and mental care for workers are important. The present invention aims to provide a system that not only diagnoses motion but also recognizes the user's emotional state and provides feedback tailored to each individual situation.

[0714] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0715] In this invention, the server includes: a means for a user to film their own movements using a smartphone; a means for the device to send the filmed video to the server; a means for the server to perform a movement diagnosis using an AI module that analyzes the received video; a means for the server to notify the user of the diagnosis results and improvement suggestions; a means for providing visual and audio movement guidance through a display device worn by the user; an emotion analysis means for recognizing the user's emotional state and adjusting the tone and content of feedback; and a means for providing advice based on the emotion analysis results. This enables not only efficient movement improvement but also flexible feedback that takes into consideration the user's mental health.

[0716] "User" refers to a person who uses a smartphone or display device to film their own actions and receive feedback.

[0717] A "smartphone" is a portable information terminal that allows users to capture their actions.

[0718] "Terminal" refers to a device that transmits video data to a server and displays diagnostic results.

[0719] A "server" refers to a computer system that receives video data sent from a terminal and performs analysis and provides feedback.

[0720] An "AI module" is software and hardware that includes artificial intelligence that performs operational diagnosis within a server.

[0721] "Motion diagnosis" refers to the process of analyzing video data captured by the user and identifying problems and areas for improvement in motion.

[0722] "Diagnosis results" refer to the analysis results of the user's behavior generated after the AI ​​module performs behavior diagnosis.

[0723] "Improvement suggestions" are specific advice provided to users on how to improve their behavior based on the diagnostic results.

[0724] "Display device" refers to a display device worn by a user to receive visual and audio instructions and feedback.

[0725] "Emotion analysis" refers to the process of analyzing a user's voice and facial expressions to recognize their emotional state.

[0726] "Emotion analysis means" refers to hardware and software for analyzing the user's emotional state.

[0727] A "chatbot" is an automated dialogue system that responds to a user's text-based questions by comparing them with past diagnostic data and generating appropriate answers.

[0728] "Video data" refers to video data generated when a user films themselves in action.

[0729] "Database" refers to an information storage system in which the server stores diagnostic results and improvement proposals, and can be referenced as needed.

[0730] In the system for realizing the present invention, the following hardware and software are used to process and analyze various data.

[0731] Hardware used

[0732] Smartphone

[0733] Head-mounted displays (HMDs) or other display devices

[0734] Camera module

[0735] Software used

[0736] OpenCV (video capture and processing)

[0737] Requests (API communication)

[0738] EmotionRecognition Library (a virtual emotion analysis library)

[0739] AI module (motion analysis software)

[0740] Detailed system processing explanation

[0741] 1. Initial Setup:

[0742] First, the user installs a dedicated application on their smartphone and launches it. The user creates an account and enters the required information (name, email address, password, etc.). The device sends this information to the server, which then generates a new user ID. The generated user ID is stored in a database, and a notification of registration completion is sent to the device.

[0743] 2. Record and upload the action:

[0744] Users use the smartphone's camera function to record their own movements. When they tap the recording button, the camera starts up and records the user's movements in real time. When recording is finished, the video data is temporarily saved on the device, and then when the user taps the upload button, the video data is compressed and sent to the server.

[0745] 3. AI-based operational diagnosis:

[0746] The server passes the received video data to the AI ​​module for analysis. The AI ​​module analyzes the video and evaluates each aspect of the user's movements (e.g., angle, timing, speed, etc.). As a result of the evaluation, it generates problems with the user's movements and areas for improvement.

[0747] 4. Notification of diagnostic results:

[0748] The diagnostic results are stored in a database on the server and linked to the user's account. This allows the user to view the results through a dedicated application. Visual and audio feedback is provided through a display device (such as an HMD) worn by the user.

[0749] 5. Leveraging the Emotion Engine:

[0750] As the user practices while checking the feedback, the microphone and camera on the smartphone or the display device they wear captures the user's voice and facial expressions. This data is sent to a server via the device, and the emotion engine in the server analyzes the user's emotional state from the voice and facial expressions. Based on the analysis results, the tone and content of the feedback are adjusted.

[0751] Specific examples

[0752] For example, if a factory worker wants to check the accuracy of a robot arm's operation, he or she picks up a smartphone and films the robot's movements. The video is sent to a server, and the AI ​​module diagnoses that the robot's arm is off by about 2 degrees. The diagnosis and specific improvement suggestions (e.g., "Raise the arm height slightly") are provided through the HMD. Furthermore, the emotion engine recognizes that the worker appears nervous and provides advice such as "Take a deep breath before starting work."

[0753] Prompt Sentence Examples

[0754] "Please tell me if the arm's movement is accurate with respect to the target."

[0755] "What is an efficient way to improve robot behavior?"

[0756] In this way, this system can simultaneously achieve efficient operational improvement and mental care for workers.

[0757] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0758] Step 1:

[0759] Initial Settings:

[0760] The user installs a dedicated application on their smartphone, launches the application and creates an account.

[0761] Input: User information such as name, email address, and password.

[0762] Data processing: The terminal sends the entered information to the server.

[0763] Output: The server generates a new user ID, saves it in the database, and sends a registration completion notification to the terminal.

[0764] Step 2:

[0765] Filming and uploading the action:

[0766] The user uses the smartphone camera to record their own movements.

[0767] Input: Video data captured by the user's camera.

[0768] Data processing: The device temporarily stores the video data, and when the user taps the upload button, the video data is compressed and sent to the server.

[0769] Output: The compressed video data is uploaded to the server.

[0770] Step 3:

[0771] AI-powered diagnostics:

[0772] The server passes the received video data to the AI ​​module for analysis.

[0773] Input: Video data received by the server.

[0774] Data processing: The AI ​​module analyzes the video and evaluates each aspect of the user's movements (e.g., angle, timing, speed, etc.).

[0775] Output: As an evaluation result, problems and improvements in the user's behavior are generated.

[0776] Step 4:

[0777] Notification of diagnostic results:

[0778] The server stores the diagnostic results in a database and links them to the user's account, which can then be viewed through a dedicated application.

[0779] Input: Operational diagnostic results from the AI ​​module.

[0780] Data processing: The server stores the diagnostic results in a database and notifies the user.

[0781] Output: The diagnostic results and specific improvement suggestions are displayed on the user's display device (e.g., HMD).

[0782] Step 5:

[0783] Leveraging the Emotion Engine:

[0784] As the user practices while checking the feedback, the microphone and camera on the display device worn by the user or the smartphone captures the user's voice and facial expressions.

[0785] Input: Captured voice and facial expression data.

[0786] Data processing: The data is sent to a server via the device, and the emotion engine in the server analyzes the emotional state from the voice and facial expressions.

[0787] Output: The tone and content of feedback is adjusted based on emotional state.

[0788] Step 6:

[0789] Chatbot advice provided:

[0790] Users can use the chatbot function to enter questions.

[0791] Input: A text-based question entered by the user.

[0792] Data processing: The device sends a question to the server, and the chatbot on the server compares it with past diagnostic data and generates an appropriate answer.

[0793] Output: The generated answer is sent to the terminal and displayed to the user.

[0794] 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.

[0795] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0796] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0797] [Third embodiment]

[0798] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0799] 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.

[0800] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0801] 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.

[0802] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0803] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0804] 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.

[0805] 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.

[0806] 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 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.

[0807] 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.

[0808] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0809] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0810] This invention is a system that allows users to film their movements at home or anywhere, and then AI analyzes the footage to diagnose the movements and suggest improvements, providing feedback to the user through a display device. Below, we will explain in natural language the program processing of specific embodiments for implementing the invention.

[0811] 1. Initial system setup

[0812] First, the user installs a dedicated app on their smartphone, launches the app, and creates an account. They enter the information required to create the account (e.g., name, email address, password, etc.), and the device sends that information to the server. The server stores the received information in a database and generates a new user ID. The server then sends a registration completion notification to the device and displays a registration completion message to the user.

[0813] 2. Record and upload your action

[0814] To record their movements, users use the camera function on their smartphone. By tapping the recording button in the dedicated app, the device's camera function is activated and the user can record their own movements. Once recording is complete, the video data is temporarily saved on the device. Then, when the user taps the upload button, the device compresses the video data and sends it to the server.

[0815] 3. AI-based operation diagnosis

[0816] The server passes the received video data to the AI ​​module for analysis. The AI ​​module analyzes the video and evaluates each point of the user's movement (e.g., backswing, impact position, follow-through, etc.). As a result of the evaluation, it generates problems with the user's movement and areas for improvement.

[0817] 4. Notification of diagnosis results

[0818] The results are stored on a server and linked to the user's account, allowing them to view the results through the app.

[0819] 5. Feedback via display devices

[0820] The user wears the display device and connects it to a smartphone. The device checks the connection status and sends a login request to the server. After logging in, the server sends the diagnosis results and improvement suggestions to the device, which are then displayed on the display device. The display device then provides visual guidelines and detailed audio advice.

[0821] 6. Detailed advice from chatbots

[0822] The user uses the chatbot function to input a question. The device sends the question to the server. The chatbot on the server compares the question with past diagnostic results and existing databases to generate an appropriate answer. The generated answer is sent to the device and displayed to the user.

[0823] Specific examples

[0824] For example, a beginner user wants to improve their golf swing, so they can use the app to film their movements at home. The video is sent to a server, where an AI module diagnoses the user's backswing as being too high. The diagnosis and specific suggestions for improvement (e.g., "Keep your shoulders low during the backswing") are provided to the user through a display device. Furthermore, if the user asks the chatbot, "What's the trick to stabilizing your backswing?" the chatbot will display an answer such as, "Adjusting the position of your grip will increase stability."

[0825] In this way, users can improve their performance efficiently at home.

[0826] The processing flow will be explained below.

[0827] Step 1:

[0828] The user installs and launches the dedicated app on their smartphone, and enters their name, email address, and password on the account registration screen.

[0829] Step 2:

[0830] The terminal sends the entered user information to the server.

[0831] Step 3:

[0832] The server stores the received information in a database, generates a new user ID, sends the generated ID to the device, and returns a registration completion notification.

[0833] Step 4:

[0834] The device will display a registration completion message to the user.

[0835] Step 5:

[0836] When the user taps the "Swing Photo" button in the app, the device's camera function is activated and the movement is captured.

[0837] Step 6:

[0838] The device temporarily stores the captured video data.

[0839] Step 7:

[0840] When the user taps the "Upload" button, the device compresses the video data and sends it to the server.

[0841] Step 8:

[0842] The server passes the received video data to the AI ​​module.

[0843] Step 9:

[0844] AI analyzes the video data and evaluates each point of the movement (e.g., backswing, impact position, follow-through, etc.).

[0845] Step 10:

[0846] The AI ​​generates evaluation results and improvement points and returns them to the server.

[0847] Step 11:

[0848] The server stores the diagnostic results and improvement suggestions in a database and links them to the user's account.

[0849] Step 12:

[0850] When a user wears a display device and connects it wirelessly to a smartphone, the device checks the connection status and sends a login request to the server.

[0851] Step 13:

[0852] The server authenticates the user's login and sends the latest diagnostic results and improvement suggestions to the device.

[0853] Step 14:

[0854] The terminal displays the received diagnostic results and improvement suggestions on a display device.

[0855] Step 15:

[0856] The AI ​​glasses provide users with visual feedback (e.g., backswing guidelines) and audio advice (e.g., "Rotate your shoulders more").

[0857] Step 16:

[0858] Users use the chatbot function within the app to enter questions.

[0859] Step 17:

[0860] The device sends the question to the server.

[0861] Step 18:

[0862] The server passes the question to the chatbot, which compares it with past diagnostic data and generates an appropriate answer.

[0863] Step 19:

[0864] The server sends the generated answer to the terminal, which displays the answer to the user.

[0865] This process allows users to efficiently capture, diagnose, and improve their own movements and receive expert feedback.

[0866] Example 1

[0867] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0868] The goal is to provide a system that allows users to film their own movements and receive effective diagnosis and guidance on how to improve them without the help of a specialist. In particular, there is a need for a system that can be easily used at home or anywhere, that efficiently processes video data, and that provides accurate feedback.

[0869] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0870] In this invention, the server includes a means for transmitting video data captured by the terminal to the server, a means for the server to perform a motion diagnosis using an AI module that analyzes the received video, and a means for the server to store the diagnosis results and improvement suggestions in a database. This allows users to film their own motion using their smartphones and send the video to the server. The server also performs a motion diagnosis using the AI ​​module, and the diagnosis results and improvement suggestions are quickly provided to the user. This realizes a system that allows users to receive accurate feedback to improve their motion at home or anywhere.

[0871] "User" refers to an individual who uses the system to film their own movements and receive diagnostic results and suggestions for improvement.

[0872] A "smartphone" refers to a portable information terminal device that can communicate via a mobile phone network and can install and use a variety of applications.

[0873] "Terminal" refers to a smartphone or other mobile information terminal device used by a user.

[0874] A "server" refers to a computer system that processes data and provides services in response to requests from other terminals via a network.

[0875] "Video data" refers to video files taken by users using smartphones or other devices.

[0876] "AI module" refers to a software component that uses artificial intelligence algorithms to analyze received video data and perform operational diagnosis.

[0877] "Behavioral diagnosis" refers to the process of analyzing user behavior and identifying problems and areas for improvement.

[0878] "Diagnosis results" refer to the evaluation of the user's behavior and specific problems obtained through behavior diagnosis.

[0879] "Improvement suggestions" refer to specific advice and guidance to improve the user's behavior based on the diagnostic results.

[0880] "Database" refers to a collection of data that is stored in an organized manner and managed so that information can be searched and used efficiently.

[0881] "Display device" refers to a hardware device worn by a user to receive visual and audio information.

[0882] A "chatbot" is a program that automatically responds to questions from users.

[0883] "Movement instruction" refers to the process of showing users specific ways to improve their movements.

[0884] MODE FOR CARRYING OUT THE INVENTION

[0885] This invention is a system that allows users to film their movements at home or anywhere, and then AI analyzes the footage to diagnose the movements and suggest improvements, providing feedback to the user through a display device. Specific embodiments for implementing the invention are described below.

[0886] 1. Initial system setup

[0887] First, the user installs a dedicated application on their smartphone, launches the application, and creates an account. They enter the information required to create the account (e.g., name, email address, password, etc.), and the device sends that information to the server. The server stores the received information in a database and generates a new user ID. The server then sends a registration completion notification to the device and displays a registration completion message to the user.

[0888] 2. Record and upload your action

[0889] To record their movements, users use the camera function of their smartphone. By tapping the recording button in the dedicated application, the device's camera function is activated and the user can record their own movements. Once recording is complete, the video data is temporarily saved on the device. Then, when the user taps the upload button, the device compresses the video data and sends it to the server.

[0890] 3. AI-based operation diagnosis

[0891] The server passes the received video data to the AI ​​module for analysis. The AI ​​module analyzes the video and evaluates each point of the user's movement (e.g., backswing, impact position, follow-through, etc.). As a result of the evaluation, it generates problems with the user's movement and areas for improvement.

[0892] 4. Notification of diagnosis results

[0893] The results are stored on a server and linked to the user's account, allowing them to view the results through the application.

[0894] 5. Feedback via display devices

[0895] The user wears the display device and connects it to a smartphone. The device checks the connection status and sends a login request to the server. After logging in, the server sends the diagnosis results and improvement suggestions to the device, which are then displayed on the display device. The display device then provides visual guidelines and detailed audio advice.

[0896] 6. Detailed advice from chatbots

[0897] The user uses the chatbot function to input a question. The device sends the question to the server. The chatbot on the server compares the question with past diagnostic results and existing databases to generate an appropriate answer. The generated answer is sent to the device and displayed to the user.

[0898] Specific examples

[0899] For example, a beginner user wants to improve their golf swing, so they can use a dedicated application to film their movements at home. The video is sent to a server, where an AI module diagnoses that their backswing is too high. The diagnosis and specific suggestions for improvement (e.g., "Keep your shoulders low during the backswing") are provided to the user via a display device. Furthermore, if the user asks the chatbot, "What's the trick to stabilizing your backswing?" the chatbot will respond with an answer such as, "Adjusting the grip position will increase stability." In this way, users can efficiently improve their movements from the comfort of their own homes.

[0900] Prompt Sentence Examples

[0901] "What should I do if my backswing in my golf swing is too high? I'd also like some tips for stabilizing my movement."

[0902] Thus, the present invention provides a system that allows users to film their actions at home or anywhere, analyze the footage, and receive feedback.

[0903] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0904] Step 1:

[0905] The user installs a dedicated app on their smartphone, launches the app, and creates an account. The user enters information such as their name, email address, and password, and the device sends that information to the server. Input data: name, email address, password. The server stores the received information in a database and generates a new user ID. Output data: new user ID. The server sends a registration completion notification to the device and displays a registration completion message to the user.

[0906] Step 2:

[0907] Users use their smartphone camera to record their movements. When they tap the recording button in the dedicated app, the device's camera function is activated and they can record their own movements. Recorded data: video data. Once recording is complete, the video is temporarily saved on the device. After that, the user taps the upload button, and the device compresses the video data. Output data: compressed video data. The device sends this compressed video data to the server.

[0908] Step 3:

[0909] The server passes the received video data to the AI ​​module for analysis. Input data: Compressed video data. The AI ​​module analyzes the video and evaluates each point of the user's movement. Examples: backswing, impact position, follow-through. As a result of the evaluation, it generates problems with the user's movement and areas for improvement. Output data: Movement evaluation results.

[0910] Step 4:

[0911] The server stores the diagnostic results and improvement suggestions in a database. Input data: behavior evaluation results, user ID. The diagnostic results are stored on the server and linked to the user's account.

[0912] Step 5:

[0913] The user wears the display device and connects it to a smartphone. The device checks the connection status and sends a login request to the server. Input data: user ID, password. After logging in, the server sends the diagnosis results and improvement suggestions to the device, which are displayed on the display device. Output data: diagnosis results, improvement suggestions. The display device provides visual guidelines and detailed audio advice.

[0914] Step 6:

[0915] The user uses the chatbot function to input a question. Input data: Question content. The device sends the question to the server. The chatbot on the server compares the question content with past diagnostic results and existing databases to generate an appropriate answer. Output data: Answer. The generated answer is sent to the device and displayed to the user.

[0916] As an example of specific operations, if a beginner user wants to improve their golf swing, they can use this system to film their movements at home. The video is sent to a server, where the AI ​​module diagnoses that their backswing is too high and notifies the user via a display device with specific suggestions for improvement, such as "keeping your shoulders low during the backswing." Furthermore, if the user asks the chatbot, "What's the trick to stabilizing your backswing?" the chatbot will respond with, "Adjusting the position of your grip will increase stability."

[0917] (Application example 1)

[0918] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0919] Robots operating in modern production facilities must operate efficiently, but they are prone to degradation and malfunctions. To solve this problem, regular monitoring and optimization are necessary. However, conventional methods require specialized engineers and large-scale systems, resulting in high operational costs. It is also difficult to check smooth operation or receive appropriate improvement suggestions quickly.

[0920] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0921] In this invention, the server includes a means for a user to film their own movements using an information terminal, a means for the terminal to transmit the filmed video to a data center, and a means for the server to perform operational diagnosis using an artificial intelligence module that analyzes the video received. This allows factory managers to monitor and optimize the robot's operations. Furthermore, the server provides diagnostic results and improvement suggestions visually and audibly through a display device, enabling operational problems to be resolved quickly and efficiently.

[0922] A "user" is an entity that uses the system to film their behavior, receive a diagnosis, and receive suggestions for improvement.

[0923] An "information terminal" is an electronic device that a user uses to record their actions, and examples include smartphones and tablets.

[0924] "Means for taking photographs" refers to a mechanism for recording actions as video using the camera function of an information terminal.

[0925] A "terminal" is an electronic device that has the ability to capture images and send or receive data.

[0926] A "data center" is a facility equipped with servers and infrastructure for storing and processing data.

[0927] A "server" is a central computing device that analyzes received video and generates diagnostic results and improvement suggestions.

[0928] The "artificial intelligence module" is a group of programs placed inside the server that analyzes video data and performs operational diagnosis.

[0929] "Motion diagnosis" is the process of analyzing captured footage to identify problems and areas for improvement in movement.

[0930] "Improvement suggestions" are specific advice to encourage improvements in movement based on the results of movement diagnosis.

[0931] "Means of notification" refers to the method by which the server communicates the diagnosis results and improvement suggestions to the user.

[0932] A "display device" is a device that allows the user to visually and audibly confirm the diagnostic results and improvement suggestions.

[0933] "Means for providing visual and audio guidance" refers to a system that uses text, graphics, or audio to inform the user of improvements to their movements through a display device.

[0934] A "factory manager" is a person responsible for monitoring and optimizing the operation of robots within a factory.

[0935] An "automatic dialogue program" is software that provides appropriate answers to questions from users based on past diagnostic data.

[0936] "Means for reducing" refers to the process of reducing the file size of video data.

[0937] The "information recording device" is a facility for storing diagnostic results and improvement proposals as data for a long period of time.

[0938] To implement this invention, the following configuration and procedures are required.

[0939] First, a user installs a dedicated application on their information terminal, then launches the application to create an account. To create an account, they enter information such as their name, email address, and password, and the terminal sends that information to the server. The server stores the received information in a database and generates a new user ID. The server then notifies the terminal that registration is complete and displays a message to the user indicating this.

[0940] Next, the user must record their movements. To do so, the user uses the camera function of their information device. By tapping the capture button within the app, the device's camera function is activated and the user can record their movements. Once the recording is complete, the video data is temporarily stored on the device. After that, the user taps the upload button, and the device compresses the video data and sends it to a data center. The OpenCV library is generally used for this compression. The compressed video data is then collected and stored in a data center.

[0941] The server then passes the received video data to an artificial intelligence module for analysis. The AI ​​module analyzes the video using deep learning frameworks such as TensorFlow and PyTorch. This analysis evaluates each aspect of the user's movements (for example, the timing of the robot's movements and posture biases). As a result of the evaluation, it generates problems with the movement and suggestions for improvement. The diagnosis results and improvement suggestions are stored on the server and linked to the user's account.

[0942] When a user wears the display device and connects it to an information terminal, the terminal checks the connection status and sends a login request to the server. After logging in, the server sends the diagnosis results and improvement suggestions to the terminal, which are then displayed on the display device. This display device is often a smart glass or head-mounted display. The display device provides visual guidelines and detailed audio advice, allowing the user to improve their performance effectively and quickly.

[0943] Users can also get detailed advice using an automated dialogue program. When a user inputs a question using the automated dialogue program function, the terminal sends the question to the server. The automated dialogue program in the server compares the question with past diagnostic results and existing databases to generate an appropriate answer. The generated answer is sent to the terminal and displayed to the user. Examples of such prompt sentences include, "What can I do to make the robot's movements smoother?" and "Please tell me how to improve the current speed of the robot."

[0944] For example, a factory manager puts on smart glasses, launches an application, and films the robot's movements. The footage is sent to a data center, where an AI module diagnoses the robot's slow movement speed and provides improvement suggestions, such as periodically adding grease to the gears, via a display device. Furthermore, if the manager asks the automated dialogue program, "What is the most efficient way to add grease?", detailed advice, such as the recommendation to use a specific brand or tool, can be received.

[0945] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0946] Step 1:

[0947] The user installs a dedicated application on their information terminal and creates an account. The user enters the required information, such as name, email address, and password, and the terminal sends this information to the server. The input data is the name, email address, and password, and the output data is the new user ID. The server saves the received information in a database, generates a new user ID, and sends a notification of registration completion to the terminal.

[0948] Step 2:

[0949] The user uses the camera function of the information device to record their movements. When the user taps the camera button in the app, the device's camera function is activated and the user records their movements. The input data is the camera image, and the output data is the recorded video file. After recording is complete, the video data is temporarily saved on the device.

[0950] Step 3:

[0951] When the user taps the upload button, the device compresses the video data and sends it to the data center. The OpenCV library is used for compression. The input data is the captured video file, and the output data is the compressed video data. The compressed data is sent to the data center and stored.

[0952] Step 4:

[0953] The server passes the received video data to an artificial intelligence module for analysis. The AI ​​module analyzes the video using deep learning frameworks such as TensorFlow and PyTorch. The input data is compressed video data, and the output data is operational diagnosis results and improvement suggestions. The analysis results are stored on the server and linked to the user's account.

[0954] Step 5:

[0955] When a user wears the display device and connects it to an information terminal, the terminal checks the connection status and sends a login request to the server. The input data is the user ID, and the output data is an authentication token. After logging in, the server sends the diagnosis results and improvement suggestions to the terminal, which are then displayed on the display device. The display device, which can be smart glasses or a head-mounted display, provides visual guidelines and detailed audio advice.

[0956] Step 6:

[0957] When a user inputs a question using the automated dialogue program, the terminal sends the question to the server. The input data is the user's question, and the output data is the generated answer. The automated dialogue program on the server compares the question with past diagnostic results and existing databases to generate an appropriate answer. The generated answer is sent to the terminal and displayed to the user.

[0958] As a specific example, a factory manager puts on smart glasses, launches an application, and films the robot's movements. The footage is sent to a data center, where an AI module diagnoses the robot's slow movement speed and provides improvement suggestions, such as periodically adding grease to the gears, via a display device. Furthermore, if the manager asks the automated dialogue program, "What is the most efficient way to add grease?", detailed advice, such as the recommendation to use a specific brand or tool, can be received.

[0959] Examples of prompts include "How can I make my robot move more smoothly?" and "Tell me how to improve the current speed of my robot."

[0960] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0961] This invention is a system in which a user films their movements at home or anywhere, and AI analyzes the footage to diagnose the movements and suggest improvements, providing feedback to the user through a display device. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotional state and adjust the feedback based on that. Below, we will explain in natural language the program processing of a specific embodiment of the present invention.

[0962] 1. Initial system setup

[0963] First, the user installs a dedicated app on their smartphone, launches the app, and creates an account. They enter the information required to create the account (e.g., name, email address, password, etc.), and the device sends that information to the server. The server stores the received information in a database and generates a new user ID. The server then sends a registration completion notification to the device and displays a registration completion message to the user.

[0964] 2. Record and upload your action

[0965] To record their movements, users use the camera function on their smartphone. By tapping the recording button in the dedicated app, the device's camera function is activated and the user can record their own movements. Once recording is complete, the video data is temporarily saved on the device. Then, when the user taps the upload button, the device compresses the video data and sends it to the server.

[0966] 3. AI-based operation diagnosis

[0967] The server passes the received video data to the AI ​​module for analysis. The AI ​​module analyzes the video and evaluates each point of the user's movement (e.g., backswing, impact position, follow-through, etc.). As a result of the evaluation, it generates problems with the user's movement and areas for improvement.

[0968] 4. Notification of diagnosis results

[0969] The results are stored on a server and linked to the user's account, allowing them to view the results through the app.

[0970] 5. Feedback via display devices

[0971] The user wears the display device and connects it to a smartphone. The device checks the connection status and sends a login request to the server. After logging in, the server sends the diagnosis results and improvement suggestions to the device, which are then displayed on the display device. The display device then provides visual guidelines and detailed audio advice.

[0972] 6. Leveraging Emotional Engines

[0973] As the user practices and checks their movements, the display device they wear and the microphone and camera on their smartphone capture their voice and facial expressions. This data is sent to a server via the device, and the emotion engine on the server analyzes their emotional state from their voice and facial expressions. Based on the analysis results, the tone and content of the feedback is adjusted. For example, if the user is nervous, advice to help them relax is provided.

[0974] 7. Detailed advice from chatbots

[0975] The user uses the chatbot function to input a question. The device sends the question to the server. The chatbot on the server compares the question with past diagnostic results and existing databases to generate an appropriate answer. The generated answer is sent to the device and displayed to the user.

[0976] Specific examples

[0977] For example, a beginner user wants to improve their golf swing and uses the app to film their movements at home. The video is sent to a server, where an AI module diagnoses the user's backswing as being too high. The diagnosis and specific suggestions for improvement (e.g., "Keep your shoulders low during the backswing") are provided to the user via a display device. The emotion engine recognizes that the user is tense during practice and provides advice to encourage relaxation. If the user asks the chatbot, "What's the trick to stabilizing your backswing?", the chatbot will display an answer such as, "Adjusting the position of your grip will increase stability."

[0978] In this way, users can improve their performance efficiently at home and receive flexible feedback based on their emotional state.

[0979] The processing flow will be explained below.

[0980] Step 1:

[0981] The user installs and launches the dedicated app on their smartphone, and enters their name, email address, and password on the account registration screen.

[0982] Step 2:

[0983] The terminal sends the entered user information to the server.

[0984] Step 3:

[0985] The server stores the received information in a database, generates a new user ID, sends the generated ID to the device, and returns a registration completion notification.

[0986] Step 4:

[0987] The device will display a registration completion message to the user.

[0988] Step 5:

[0989] When the user taps the "Swing Photo" button in the app, the device's camera function is activated and the movement is captured.

[0990] Step 6:

[0991] The device temporarily stores the captured video data.

[0992] Step 7:

[0993] When the user taps the "Upload" button, the device compresses the video data and sends it to the server.

[0994] Step 8:

[0995] The server passes the received video data to the AI ​​module.

[0996] Step 9:

[0997] AI analyzes the video data and evaluates each point of the movement (e.g., backswing, impact position, follow-through, etc.).

[0998] Step 10:

[0999] The AI ​​generates evaluation results and improvement points and returns them to the server.

[1000] Step 11:

[1001] The server stores the diagnostic results and improvement suggestions in a database and links them to the user's account.

[1002] Step 12:

[1003] When a user wears a display device and connects it wirelessly to a smartphone, the device checks the connection status and sends a login request to the server.

[1004] Step 13:

[1005] The server authenticates the user's login and sends the latest diagnostic results and improvement suggestions to the device.

[1006] Step 14:

[1007] The terminal displays the received diagnostic results and improvement suggestions on a display device.

[1008] Step 15:

[1009] The AI ​​glasses provide users with visual feedback (e.g., backswing guidelines) and audio advice (e.g., "Rotate your shoulders more").

[1010] Step 16:

[1011] While the user is practicing, the microphone and camera on the display device or smartphone captures the user's voice and facial expressions.

[1012] Step 17:

[1013] The device transmits the captured voice and facial expression data to the server.

[1014] Step 18:

[1015] The emotion engine in the server analyzes the user's emotional state from voice and facial expression data.

[1016] Step 19:

[1017] The server then adjusts the tone and content of the feedback based on the analysis results, for example generating advice to relax if the user is feeling tense.

[1018] Step 20:

[1019] The server sends the adjusted feedback to the terminal, which provides it to the user through a display device.

[1020] Step 21:

[1021] Users use the chatbot function within the app to enter questions.

[1022] Step 22:

[1023] The device sends the question to the server.

[1024] Step 23:

[1025] The server passes the question to the chatbot, which compares it with past diagnostic data and generates an appropriate answer.

[1026] Step 24:

[1027] The server sends the generated answer to the terminal, which displays the answer to the user.

[1028] This series of processes allows users to efficiently capture, diagnose, and improve their own movements, and not only receive professional feedback, but also flexible feedback based on their emotional state.

[1029] Example 2

[1030] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1031] Conventional motion analysis systems have the problem that the feedback users receive is static and cannot appropriately adapt to the user's emotional state or training environment at any given time. It is also difficult to receive detailed advice in real time based on past diagnostic data. This can result in users not getting the full benefits they need. Furthermore, there is a lack of efficient data processing when filming movements and uploading data.

[1032] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1033] In this invention, the server includes an engine that can capture the user's voice and facial expressions and analyze their emotional state, and also includes a means for adjusting feedback based on the analysis results, a means for the user to ask the interactive agent questions, a means for the interactive agent to compare the questions with past diagnostic data and generate an appropriate response, a means for the terminal to compress the video data and transmit it to the processing device, a means for the processing device to store the received video data in association with user identification information, and a means for the processing device to store the diagnostic results and improvement suggestions in a database. This allows the user to receive flexible feedback based on their emotions in real time and detailed advice based on past data. Furthermore, efficient data processing can shorten the time from imaging to diagnosis.

[1034] "User" means a person who utilizes the system to receive performance diagnosis and feedback.

[1035] A "mobile communication terminal" refers to a portable information processing device, such as a smartphone or tablet, that can connect to the Internet via wireless communication.

[1036] A "processor" is a computer system that analyzes and stores received data.

[1037] An "artificial intelligence module" is a software program that uses machine learning algorithms to analyze video data and perform motion assessment and diagnosis.

[1038] "Display device" means a device for providing the user with visual and audio diagnostic results and suggestions for improvement.

[1039] The "engine" is a software module that analyzes the user's voice and facial expression data to recognize their emotional state.

[1040] An "interactive agent" is a program that uses natural language processing to provide appropriate answers to users' questions.

[1041] "User identification information" means information used to identify a user (e.g., user ID).

[1042] A "database" is a data management system for storing data such as diagnostic results and improvement proposals.

[1043] MODE FOR CARRYING OUT THE INVENTION

[1044] This invention is a system in which a user films their own movements at home or anywhere, and AI analyzes the footage to diagnose the movements and suggest improvements, providing feedback to the user through a display device. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotional state and adjust the feedback based on that. Specific embodiments for implementing the invention are described below.

[1045] 1. System Configuration

[1046] This system mainly consists of a mobile communication terminal, a server, and a display device. The mobile communication terminal is a portable information processing device such as a smartphone or tablet, on which the user installs a dedicated app. The server is a computer system that analyzes and stores video data and diagnostic results. The display device is a device that provides the user with operational diagnostic results and improvement suggestions visually and audibly.

[1047] 2. Install the dedicated app and create an account

[1048] First, the user installs a dedicated app (e.g., MotionAnalysisApp) on their smartphone, launches the app, and creates an account. The user enters the information required to create an account (e.g., name, email address, password, etc.), and the device sends that information to the server. The server stores the received information in a database (e.g., MySQL database) and generates a new user ID. The server then sends a registration completion notification to the device and displays a registration completion message to the user.

[1049] 3. Recording the movement and sending the video data

[1050] When a user taps the capture button in the app, the device's camera function is activated, allowing the user to record their own movements. Once recording is complete, the video data is temporarily stored on the device. When the user taps the upload button, the device compresses the video data (e.g., using the zlib library) and sends it to the server. The server receives the video data and stores it in association with user identification information.

[1051] 4. AI-based behavior analysis and diagnosis

[1052] The server passes the received video data to an AI module (e.g., TensorFlow). The AI ​​module analyzes the video and evaluates each point of the user's movement (e.g., backswing, impact position, follow-through, etc.). As a result of the evaluation, it generates problems with the user's movement and areas for improvement. The server saves the diagnostic results in a database (e.g., a PostgreSQL database) and links them to the user's account.

[1053] 5. Providing Feedback

[1054] The user wears a display device (e.g., Oculus Quest) and connects it to a smartphone. The device checks the connection status and sends a login request to the server. The server sends the diagnosis results and improvement suggestions to the device, which are then displayed on the display device. The display device provides visual guidelines and detailed audio advice.

[1055] 6. Leveraging Emotional Engines

[1056] As the user practices and checks their movements, the display device they wear and the microphone and camera on their smartphone capture their voice and facial expressions. This data is sent to a server via the device, and an emotion engine (e.g., Microsoft Azure Emotion API) on the server analyzes their emotional state from their voice and facial expressions. Based on the analysis results, the tone and content of the feedback is adjusted. For example, if the user is nervous, advice to help them relax is provided.

[1057] 7. Chatbots provide detailed advice

[1058] When a user uses the chatbot function (e.g., Dialogflow) to input a question, the device sends the question to the server. The chatbot on the server analyzes the question and generates an appropriate answer by comparing it with past diagnostic data and existing databases. The generated answer is sent to the device and displayed to the user.

[1059] Specific examples

[1060] For example, a beginner user wants to improve their golf swing, so they use the app to film their movements at home. The video is sent to a server, where an AI module diagnoses their swing as "too high." The diagnosis and specific suggestions for improvement (e.g., "Keep your shoulders low during the backswing") are provided to the user through a display device. If the emotion engine recognizes that the user is tense while practicing, the advice to "relax" is displayed. Similarly, if the user asks the chatbot, "What's the trick to stabilizing your backswing?", the chatbot will respond with, "Adjusting the position of your grip will increase stability."

[1061] This invention allows users to improve their movements efficiently and effectively at home or anywhere they like, and also allows them to receive flexible feedback based on their emotional state, allowing them to receive advice tailored to their individual needs.

[1062] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1063] Step 1:

[1064] The user installs a dedicated app on their smartphone. They launch the app and enter their name, email address, and password on the sign-up screen. The entered information is sent to the server. The server saves the entered information in a database, generates a new user ID, and sends a registration completion notification to the device.

[1065] Input: User's name, email address, password

[1066] Output: New user ID, registration completion notification

[1067] Specific operation: The user installs "MotionAnalysisApp" on their iPhone and enters the name "Yamada Taro", email address "taro@example.com", and password "password123".

[1068] Step 2:

[1069] The user taps the recording button in the app. The device's camera function is activated, and the user records their own movements. When recording is complete, the video data is temporarily saved on the device. When the upload button is tapped, the device compresses the video data and sends it to the server. The server receives the video data and saves it in association with the user's identification information.

[1070] Input: Video data of user movements

[1071] Output: Compressed video data, video data associated with user identification information

[1072] Specific operation: The user films their golf swing, compresses the video data using the zlib library, and transmits it.

[1073] Step 3:

[1074] The server passes the received video data to the AI ​​module, which analyzes the video and evaluates each aspect of the user's movements. As a result of the evaluation, it generates problems with the movements and areas for improvement. The server stores the diagnostic results in a database and links them to the user's account.

[1075] Input: User's compressed video data

[1076] Output: Performance evaluation results, diagnostic results and improvements

[1077] Specific behavior: The server passes the data to TensorFlow, which evaluates it as "the backswing is too high."

[1078] Step 4:

[1079] The user wears the display device and connects it to a smartphone. The device checks the connection status and sends a login request to the server. The server sends the diagnosis results and improvement suggestions to the device, which are then displayed on the display device. The display device provides visual guidelines and audio advice.

[1080] Input: Diagnostic results, improvement suggestions

[1081] Output: Display content (guidelines, advice) on the display device

[1082] Specific actions: The user puts on the Oculus Quest and sees the improvement suggestions.

[1083] Step 5:

[1084] While the user practices, the microphone and camera on the display device or smartphone captures their voice and facial expressions. This data is sent to a server via the device and analyzed by an emotion engine. Based on this analysis, the tone and content of the feedback is adjusted.

[1085] Input: User's voice data, facial expression data

[1086] Output: Emotional state analysis results, adjusted feedback content

[1087] What it does: It detects when the user is tense while practicing and displays the message "Relax."

[1088] Step 6:

[1089] The user uses the chatbot function to input a question. The device sends the question to the server. The chatbot on the server analyzes the question, compares it with past diagnostic data and existing databases, and generates an appropriate answer. The generated answer is sent to the device and displayed to the user.

[1090] Input: User's question

[1091] Output: The generated answer

[1092] Specific action: The user asks, "What's the trick to stabilizing your backswing?" and the answer displayed is, "Adjusting the grip position will increase stability."

[1093] In this way, specific input data is given at each processing step, processing is carried out based on that data, and output data is obtained.

[1094] (Application example 2)

[1095] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1096] Conventional motion diagnosis systems have difficulty not only analyzing a user's motion but also providing feedback that takes into account the user's emotional state. In particular, in industrial settings, such as in factory robot operations, efficient motion improvement and mental care for workers are important. The present invention aims to provide a system that not only diagnoses motion but also recognizes the user's emotional state and provides feedback tailored to each individual situation.

[1097] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1098] In this invention, the server includes: a means for a user to film their own movements using a smartphone; a means for the device to send the filmed video to the server; a means for the server to perform a movement diagnosis using an AI module that analyzes the received video; a means for the server to notify the user of the diagnosis results and improvement suggestions; a means for providing visual and audio movement guidance through a display device worn by the user; an emotion analysis means for recognizing the user's emotional state and adjusting the tone and content of feedback; and a means for providing advice based on the emotion analysis results. This enables not only efficient movement improvement but also flexible feedback that takes into consideration the user's mental health.

[1099] "User" refers to a person who uses a smartphone or display device to film their own actions and receive feedback.

[1100] A "smartphone" is a portable information terminal that allows users to capture their actions.

[1101] "Terminal" refers to a device that transmits video data to a server and displays diagnostic results.

[1102] A "server" refers to a computer system that receives video data sent from a terminal and performs analysis and provides feedback.

[1103] An "AI module" is software and hardware that includes artificial intelligence that performs operational diagnosis within a server.

[1104] "Motion diagnosis" refers to the process of analyzing video data captured by the user and identifying problems and areas for improvement in motion.

[1105] "Diagnosis results" refer to the analysis results of the user's behavior generated after the AI ​​module performs behavior diagnosis.

[1106] "Improvement suggestions" are specific advice provided to users on how to improve their behavior based on the diagnostic results.

[1107] "Display device" refers to a display device worn by a user to receive visual and audio instructions and feedback.

[1108] "Emotion analysis" refers to the process of analyzing a user's voice and facial expressions to recognize their emotional state.

[1109] "Emotion analysis means" refers to hardware and software for analyzing the user's emotional state.

[1110] A "chatbot" is an automated dialogue system that responds to a user's text-based questions by comparing them with past diagnostic data and generating appropriate answers.

[1111] "Video data" refers to video data generated when a user films themselves in action.

[1112] "Database" refers to an information storage system in which the server stores diagnostic results and improvement proposals, and can be referenced as needed.

[1113] In the system for realizing the present invention, the following hardware and software are used to process and analyze various data.

[1114] Hardware used

[1115] Smartphone

[1116] Head-mounted displays (HMDs) or other display devices

[1117] Camera module

[1118] Software used

[1119] OpenCV (video capture and processing)

[1120] Requests (API communication)

[1121] EmotionRecognition Library (a virtual emotion analysis library)

[1122] AI module (motion analysis software)

[1123] Detailed system processing explanation

[1124] 1. Initial Setup:

[1125] First, the user installs a dedicated application on their smartphone and launches it. The user creates an account and enters the required information (name, email address, password, etc.). The device sends this information to the server, which then generates a new user ID. The generated user ID is stored in a database, and a notification of registration completion is sent to the device.

[1126] 2. Record and upload the action:

[1127] Users use the smartphone's camera function to record their own movements. When they tap the recording button, the camera starts up and records the user's movements in real time. When recording is finished, the video data is temporarily saved on the device, and then when the user taps the upload button, the video data is compressed and sent to the server.

[1128] 3. AI-based operational diagnosis:

[1129] The server passes the received video data to the AI ​​module for analysis. The AI ​​module analyzes the video and evaluates each aspect of the user's movements (e.g., angle, timing, speed, etc.). As a result of the evaluation, it generates problems with the user's movements and areas for improvement.

[1130] 4. Notification of diagnostic results:

[1131] The diagnostic results are stored in a database on the server and linked to the user's account. This allows the user to view the results through a dedicated application. Visual and audio feedback is provided through a display device (such as an HMD) worn by the user.

[1132] 5. Leveraging the Emotion Engine:

[1133] As the user practices while checking the feedback, the microphone and camera on the smartphone or the display device they wear captures the user's voice and facial expressions. This data is sent to a server via the device, and the emotion engine in the server analyzes the user's emotional state from the voice and facial expressions. Based on the analysis results, the tone and content of the feedback are adjusted.

[1134] Specific examples

[1135] For example, if a factory worker wants to check the accuracy of a robot arm's operation, he or she picks up a smartphone and films the robot's movements. The video is sent to a server, and the AI ​​module diagnoses that the robot's arm is off by about 2 degrees. The diagnosis and specific improvement suggestions (e.g., "Raise the arm height slightly") are provided through the HMD. Furthermore, the emotion engine recognizes that the worker appears nervous and provides advice such as "Take a deep breath before starting work."

[1136] Prompt Sentence Examples

[1137] "Please tell me if the arm's movement is accurate with respect to the target."

[1138] "What is an efficient way to improve robot behavior?"

[1139] In this way, this system can simultaneously achieve efficient operational improvement and mental care for workers.

[1140] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1141] Step 1:

[1142] Initial Settings:

[1143] The user installs a dedicated application on their smartphone, launches the application and creates an account.

[1144] Input: User information such as name, email address, and password.

[1145] Data processing: The terminal sends the entered information to the server.

[1146] Output: The server generates a new user ID, saves it in the database, and sends a registration completion notification to the terminal.

[1147] Step 2:

[1148] Filming and uploading the action:

[1149] The user uses the smartphone camera to record their own movements.

[1150] Input: Video data captured by the user's camera.

[1151] Data processing: The device temporarily stores the video data, and when the user taps the upload button, the video data is compressed and sent to the server.

[1152] Output: The compressed video data is uploaded to the server.

[1153] Step 3:

[1154] AI-powered diagnostics:

[1155] The server passes the received video data to the AI ​​module for analysis.

[1156] Input: Video data received by the server.

[1157] Data processing: The AI ​​module analyzes the video and evaluates each aspect of the user's movements (e.g., angle, timing, speed, etc.).

[1158] Output: As an evaluation result, problems and improvements in the user's behavior are generated.

[1159] Step 4:

[1160] Notification of diagnostic results:

[1161] The server stores the diagnostic results in a database and links them to the user's account, which can then be viewed through a dedicated application.

[1162] Input: Operational diagnostic results from the AI ​​module.

[1163] Data processing: The server stores the diagnostic results in a database and notifies the user.

[1164] Output: The diagnostic results and specific improvement suggestions are displayed on the user's display device (e.g., HMD).

[1165] Step 5:

[1166] Leveraging the Emotion Engine:

[1167] As the user practices while checking the feedback, the microphone and camera on the display device worn by the user or the smartphone captures the user's voice and facial expressions.

[1168] Input: Captured voice and facial expression data.

[1169] Data processing: The data is sent to a server via the device, and the emotion engine in the server analyzes the emotional state from the voice and facial expressions.

[1170] Output: The tone and content of feedback is adjusted based on emotional state.

[1171] Step 6:

[1172] Chatbot advice provided:

[1173] Users can use the chatbot function to enter questions.

[1174] Input: A text-based question entered by the user.

[1175] Data processing: The device sends a question to the server, and the chatbot on the server compares it with past diagnostic data and generates an appropriate answer.

[1176] Output: The generated answer is sent to the terminal and displayed to the user.

[1177] 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.

[1178] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1179] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1180] [Fourth embodiment]

[1181] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1182] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1183] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[1184] 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.

[1185] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1186] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1187] 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.

[1188] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1189] 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.

[1190] 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 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.

[1191] 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.

[1192] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1193] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1194] This invention is a system that allows users to film their movements at home or anywhere, and then AI analyzes the footage to diagnose the movements and suggest improvements, providing feedback to the user through a display device. Below, we will explain in natural language the program processing of specific embodiments for implementing the invention.

[1195] 1. Initial system setup

[1196] First, the user installs a dedicated app on their smartphone, launches the app, and creates an account. They enter the information required to create the account (e.g., name, email address, password, etc.), and the device sends that information to the server. The server stores the received information in a database and generates a new user ID. The server then sends a registration completion notification to the device and displays a registration completion message to the user.

[1197] 2. Record and upload your action

[1198] To record their movements, users use the camera function on their smartphone. By tapping the recording button in the dedicated app, the device's camera function is activated and the user can record their own movements. Once recording is complete, the video data is temporarily saved on the device. Then, when the user taps the upload button, the device compresses the video data and sends it to the server.

[1199] 3. AI-based operation diagnosis

[1200] The server passes the received video data to the AI ​​module for analysis. The AI ​​module analyzes the video and evaluates each point of the user's movement (e.g., backswing, impact position, follow-through, etc.). As a result of the evaluation, it generates problems with the user's movement and areas for improvement.

[1201] 4. Notification of diagnosis results

[1202] The results are stored on a server and linked to the user's account, allowing them to view the results through the app.

[1203] 5. Feedback via display devices

[1204] The user wears the display device and connects it to a smartphone. The device checks the connection status and sends a login request to the server. After logging in, the server sends the diagnosis results and improvement suggestions to the device, which are then displayed on the display device. The display device then provides visual guidelines and detailed audio advice.

[1205] 6. Detailed advice from chatbots

[1206] The user uses the chatbot function to input a question. The device sends the question to the server. The chatbot on the server compares the question with past diagnostic results and existing databases to generate an appropriate answer. The generated answer is sent to the device and displayed to the user.

[1207] Specific examples

[1208] For example, a beginner user wants to improve their golf swing, so they can use the app to film their movements at home. The video is sent to a server, where an AI module diagnoses the user's backswing as being too high. The diagnosis and specific suggestions for improvement (e.g., "Keep your shoulders low during the backswing") are provided to the user through a display device. Furthermore, if the user asks the chatbot, "What's the trick to stabilizing your backswing?" the chatbot will display an answer such as, "Adjusting the position of your grip will increase stability."

[1209] In this way, users can improve their performance efficiently at home.

[1210] The processing flow will be explained below.

[1211] Step 1:

[1212] The user installs and launches the dedicated app on their smartphone, and enters their name, email address, and password on the account registration screen.

[1213] Step 2:

[1214] The terminal sends the entered user information to the server.

[1215] Step 3:

[1216] The server stores the received information in a database, generates a new user ID, sends the generated ID to the device, and returns a registration completion notification.

[1217] Step 4:

[1218] The device will display a registration completion message to the user.

[1219] Step 5:

[1220] When the user taps the "Swing Photo" button in the app, the device's camera function is activated and the movement is captured.

[1221] Step 6:

[1222] The device temporarily stores the captured video data.

[1223] Step 7:

[1224] When the user taps the "Upload" button, the device compresses the video data and sends it to the server.

[1225] Step 8:

[1226] The server passes the received video data to the AI ​​module.

[1227] Step 9:

[1228] AI analyzes the video data and evaluates each point of the movement (e.g., backswing, impact position, follow-through, etc.).

[1229] Step 10:

[1230] The AI ​​generates evaluation results and improvement points and returns them to the server.

[1231] Step 11:

[1232] The server stores the diagnostic results and improvement suggestions in a database and links them to the user's account.

[1233] Step 12:

[1234] When a user wears a display device and connects it wirelessly to a smartphone, the device checks the connection status and sends a login request to the server.

[1235] Step 13:

[1236] The server authenticates the user's login and sends the latest diagnostic results and improvement suggestions to the device.

[1237] Step 14:

[1238] The terminal displays the received diagnostic results and improvement suggestions on a display device.

[1239] Step 15:

[1240] The AI ​​glasses provide users with visual feedback (e.g., backswing guidelines) and audio advice (e.g., "Rotate your shoulders more").

[1241] Step 16:

[1242] Users use the chatbot function within the app to enter questions.

[1243] Step 17:

[1244] The device sends the question to the server.

[1245] Step 18:

[1246] The server passes the question to the chatbot, which compares it with past diagnostic data and generates an appropriate answer.

[1247] Step 19:

[1248] The server sends the generated answer to the terminal, which displays the answer to the user.

[1249] This process allows users to efficiently capture, diagnose, and improve their own movements and receive expert feedback.

[1250] Example 1

[1251] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1252] The goal is to provide a system that allows users to film their own movements and receive effective diagnosis and guidance on how to improve them without the help of a specialist. In particular, there is a need for a system that can be easily used at home or anywhere, that efficiently processes video data, and that provides accurate feedback.

[1253] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1254] In this invention, the server includes a means for transmitting video data captured by the terminal to the server, a means for the server to perform a motion diagnosis using an AI module that analyzes the received video, and a means for the server to store the diagnosis results and improvement suggestions in a database. This allows users to film their own motion using their smartphones and send the video to the server. The server also performs a motion diagnosis using the AI ​​module, and the diagnosis results and improvement suggestions are quickly provided to the user. This realizes a system that allows users to receive accurate feedback to improve their motion at home or anywhere.

[1255] "User" refers to an individual who uses the system to film their own movements and receive diagnostic results and suggestions for improvement.

[1256] A "smartphone" refers to a portable information terminal device that can communicate via a mobile phone network and can install and use a variety of applications.

[1257] "Terminal" refers to a smartphone or other mobile information terminal device used by a user.

[1258] A "server" refers to a computer system that processes data and provides services in response to requests from other terminals via a network.

[1259] "Video data" refers to video files taken by users using smartphones or other devices.

[1260] "AI module" refers to a software component that uses artificial intelligence algorithms to analyze received video data and perform operational diagnosis.

[1261] "Behavioral diagnosis" refers to the process of analyzing user behavior and identifying problems and areas for improvement.

[1262] "Diagnosis results" refer to the evaluation of the user's behavior and specific problems obtained through behavior diagnosis.

[1263] "Improvement suggestions" refer to specific advice and guidance to improve the user's behavior based on the diagnostic results.

[1264] "Database" refers to a collection of data that is stored in an organized manner and managed so that information can be searched and used efficiently.

[1265] "Display device" refers to a hardware device worn by a user to receive visual and audio information.

[1266] A "chatbot" is a program that automatically responds to questions from users.

[1267] "Movement instruction" refers to the process of showing users specific ways to improve their movements.

[1268] MODE FOR CARRYING OUT THE INVENTION

[1269] This invention is a system that allows users to film their movements at home or anywhere, and then AI analyzes the footage to diagnose the movements and suggest improvements, providing feedback to the user through a display device. Specific embodiments for implementing the invention are described below.

[1270] 1. Initial system setup

[1271] First, the user installs a dedicated application on their smartphone, launches the application, and creates an account. They enter the information required to create the account (e.g., name, email address, password, etc.), and the device sends that information to the server. The server stores the received information in a database and generates a new user ID. The server then sends a registration completion notification to the device and displays a registration completion message to the user.

[1272] 2. Record and upload your action

[1273] To record their movements, users use the camera function of their smartphone. By tapping the recording button in the dedicated application, the device's camera function is activated and the user can record their own movements. Once recording is complete, the video data is temporarily saved on the device. Then, when the user taps the upload button, the device compresses the video data and sends it to the server.

[1274] 3. AI-based operation diagnosis

[1275] The server passes the received video data to the AI ​​module for analysis. The AI ​​module analyzes the video and evaluates each point of the user's movement (e.g., backswing, impact position, follow-through, etc.). As a result of the evaluation, it generates problems with the user's movement and areas for improvement.

[1276] 4. Notification of diagnosis results

[1277] The results are stored on a server and linked to the user's account, allowing them to view the results through the application.

[1278] 5. Feedback via display devices

[1279] The user wears the display device and connects it to a smartphone. The device checks the connection status and sends a login request to the server. After logging in, the server sends the diagnosis results and improvement suggestions to the device, which are then displayed on the display device. The display device then provides visual guidelines and detailed audio advice.

[1280] 6. Detailed advice from chatbots

[1281] The user uses the chatbot function to input a question. The device sends the question to the server. The chatbot on the server compares the question with past diagnostic results and existing databases to generate an appropriate answer. The generated answer is sent to the device and displayed to the user.

[1282] Specific examples

[1283] For example, a beginner user wants to improve their golf swing, so they can use a dedicated application to film their movements at home. The video is sent to a server, where an AI module diagnoses that their backswing is too high. The diagnosis and specific suggestions for improvement (e.g., "Keep your shoulders low during the backswing") are provided to the user via a display device. Furthermore, if the user asks the chatbot, "What's the trick to stabilizing your backswing?" the chatbot will respond with an answer such as, "Adjusting the grip position will increase stability." In this way, users can efficiently improve their movements from the comfort of their own homes.

[1284] Prompt Sentence Examples

[1285] "What should I do if my backswing in my golf swing is too high? I'd also like some tips for stabilizing my movement."

[1286] Thus, the present invention provides a system that allows users to film their actions at home or anywhere, analyze the footage, and receive feedback.

[1287] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1288] Step 1:

[1289] The user installs a dedicated app on their smartphone, launches the app, and creates an account. The user enters information such as their name, email address, and password, and the device sends that information to the server. Input data: name, email address, password. The server stores the received information in a database and generates a new user ID. Output data: new user ID. The server sends a registration completion notification to the device and displays a registration completion message to the user.

[1290] Step 2:

[1291] Users use their smartphone camera to record their movements. When they tap the recording button in the dedicated app, the device's camera function is activated and they can record their own movements. Recorded data: video data. Once recording is complete, the video is temporarily saved on the device. After that, the user taps the upload button, and the device compresses the video data. Output data: compressed video data. The device sends this compressed video data to the server.

[1292] Step 3:

[1293] The server passes the received video data to the AI ​​module for analysis. Input data: Compressed video data. The AI ​​module analyzes the video and evaluates each point of the user's movement. Examples: backswing, impact position, follow-through. As a result of the evaluation, it generates problems with the user's movement and areas for improvement. Output data: Movement evaluation results.

[1294] Step 4:

[1295] The server stores the diagnostic results and improvement suggestions in a database. Input data: behavior evaluation results, user ID. The diagnostic results are stored on the server and linked to the user's account.

[1296] Step 5:

[1297] The user wears the display device and connects it to a smartphone. The device checks the connection status and sends a login request to the server. Input data: user ID, password. After logging in, the server sends the diagnosis results and improvement suggestions to the device, which are displayed on the display device. Output data: diagnosis results, improvement suggestions. The display device provides visual guidelines and detailed audio advice.

[1298] Step 6:

[1299] The user uses the chatbot function to input a question. Input data: Question content. The device sends the question to the server. The chatbot on the server compares the question content with past diagnostic results and existing databases to generate an appropriate answer. Output data: Answer. The generated answer is sent to the device and displayed to the user.

[1300] As an example of specific operations, if a beginner user wants to improve their golf swing, they can use this system to film their movements at home. The video is sent to a server, where the AI ​​module diagnoses that their backswing is too high and notifies the user via a display device with specific suggestions for improvement, such as "keeping your shoulders low during the backswing." Furthermore, if the user asks the chatbot, "What's the trick to stabilizing your backswing?" the chatbot will respond with, "Adjusting the position of your grip will increase stability."

[1301] (Application example 1)

[1302] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1303] Robots operating in modern production facilities must operate efficiently, but they are prone to degradation and malfunctions. To solve this problem, regular monitoring and optimization are necessary. However, conventional methods require specialized engineers and large-scale systems, resulting in high operational costs. It is also difficult to check smooth operation or receive appropriate improvement suggestions quickly.

[1304] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1305] In this invention, the server includes a means for a user to film their own movements using an information terminal, a means for the terminal to transmit the filmed video to a data center, and a means for the server to perform operational diagnosis using an artificial intelligence module that analyzes the video received. This allows factory managers to monitor and optimize the robot's operations. Furthermore, the server provides diagnostic results and improvement suggestions visually and audibly through a display device, enabling operational problems to be resolved quickly and efficiently.

[1306] A "user" is an entity that uses the system to film their behavior, receive a diagnosis, and receive suggestions for improvement.

[1307] An "information terminal" is an electronic device that a user uses to record their actions, and examples include smartphones and tablets.

[1308] "Means for taking photographs" refers to a mechanism for recording actions as video using the camera function of an information terminal.

[1309] A "terminal" is an electronic device that has the ability to capture images and send or receive data.

[1310] A "data center" is a facility equipped with servers and infrastructure for storing and processing data.

[1311] A "server" is a central computing device that analyzes received video and generates diagnostic results and improvement suggestions.

[1312] The "artificial intelligence module" is a group of programs placed inside the server that analyzes video data and performs operational diagnosis.

[1313] "Motion diagnosis" is the process of analyzing captured footage to identify problems and areas for improvement in movement.

[1314] "Improvement suggestions" are specific advice to encourage improvements in movement based on the results of movement diagnosis.

[1315] "Means of notification" refers to the method by which the server communicates the diagnosis results and improvement suggestions to the user.

[1316] A "display device" is a device that allows the user to visually and audibly confirm the diagnostic results and improvement suggestions.

[1317] "Means for providing visual and audio guidance" refers to a system that uses text, graphics, or audio to inform the user of improvements to their movements through a display device.

[1318] A "factory manager" is a person responsible for monitoring and optimizing the operation of robots within a factory.

[1319] An "automatic dialogue program" is software that provides appropriate answers to questions from users based on past diagnostic data.

[1320] "Means for reducing" refers to the process of reducing the file size of video data.

[1321] The "information recording device" is a facility for storing diagnostic results and improvement proposals as data for a long period of time.

[1322] To implement this invention, the following configuration and procedures are required.

[1323] First, a user installs a dedicated application on their information terminal, then launches the application to create an account. To create an account, they enter information such as their name, email address, and password, and the terminal sends that information to the server. The server stores the received information in a database and generates a new user ID. The server then notifies the terminal that registration is complete and displays a message to the user indicating this.

[1324] Next, the user must record their movements. To do so, the user uses the camera function of their information device. By tapping the capture button within the app, the device's camera function is activated and the user can record their movements. Once the recording is complete, the video data is temporarily stored on the device. After that, the user taps the upload button, and the device compresses the video data and sends it to a data center. The OpenCV library is generally used for this compression. The compressed video data is then collected and stored in a data center.

[1325] The server then passes the received video data to an artificial intelligence module for analysis. The AI ​​module analyzes the video using deep learning frameworks such as TensorFlow and PyTorch. This analysis evaluates each aspect of the user's movements (for example, the timing of the robot's movements and posture biases). As a result of the evaluation, it generates problems with the movement and suggestions for improvement. The diagnosis results and improvement suggestions are stored on the server and linked to the user's account.

[1326] When a user wears the display device and connects it to an information terminal, the terminal checks the connection status and sends a login request to the server. After logging in, the server sends the diagnosis results and improvement suggestions to the terminal, which are then displayed on the display device. This display device is often a smart glass or head-mounted display. The display device provides visual guidelines and detailed audio advice, allowing the user to improve their performance effectively and quickly.

[1327] Users can also get detailed advice using an automated dialogue program. When a user inputs a question using the automated dialogue program function, the terminal sends the question to the server. The automated dialogue program in the server compares the question with past diagnostic results and existing databases to generate an appropriate answer. The generated answer is sent to the terminal and displayed to the user. Examples of such prompt sentences include, "What can I do to make the robot's movements smoother?" and "Please tell me how to improve the current speed of the robot."

[1328] For example, a factory manager puts on smart glasses, launches an application, and films the robot's movements. The footage is sent to a data center, where an AI module diagnoses the robot's slow movement speed and provides improvement suggestions, such as periodically adding grease to the gears, via a display device. Furthermore, if the manager asks the automated dialogue program, "What is the most efficient way to add grease?", detailed advice, such as the recommendation to use a specific brand or tool, can be received.

[1329] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1330] Step 1:

[1331] The user installs a dedicated application on their information terminal and creates an account. The user enters the required information, such as name, email address, and password, and the terminal sends this information to the server. The input data is the name, email address, and password, and the output data is the new user ID. The server saves the received information in a database, generates a new user ID, and sends a notification of registration completion to the terminal.

[1332] Step 2:

[1333] The user uses the camera function of the information device to record their movements. When the user taps the camera button in the app, the device's camera function is activated and the user records their movements. The input data is the camera image, and the output data is the recorded video file. After recording is complete, the video data is temporarily saved on the device.

[1334] Step 3:

[1335] When the user taps the upload button, the device compresses the video data and sends it to the data center. The OpenCV library is used for compression. The input data is the captured video file, and the output data is the compressed video data. The compressed data is sent to the data center and stored.

[1336] Step 4:

[1337] The server passes the received video data to an artificial intelligence module for analysis. The AI ​​module analyzes the video using deep learning frameworks such as TensorFlow and PyTorch. The input data is compressed video data, and the output data is operational diagnosis results and improvement suggestions. The analysis results are stored on the server and linked to the user's account.

[1338] Step 5:

[1339] When a user wears the display device and connects it to an information terminal, the terminal checks the connection status and sends a login request to the server. The input data is the user ID, and the output data is an authentication token. After logging in, the server sends the diagnosis results and improvement suggestions to the terminal, which are then displayed on the display device. The display device, which can be smart glasses or a head-mounted display, provides visual guidelines and detailed audio advice.

[1340] Step 6:

[1341] When a user inputs a question using the automated dialogue program, the terminal sends the question to the server. The input data is the user's question, and the output data is the generated answer. The automated dialogue program on the server compares the question with past diagnostic results and existing databases to generate an appropriate answer. The generated answer is sent to the terminal and displayed to the user.

[1342] As a specific example, a factory manager puts on smart glasses, launches an application, and films the robot's movements. The footage is sent to a data center, where an AI module diagnoses the robot's slow movement speed and provides improvement suggestions, such as periodically adding grease to the gears, via a display device. Furthermore, if the manager asks the automated dialogue program, "What is the most efficient way to add grease?", detailed advice, such as the recommendation to use a specific brand or tool, can be received.

[1343] Examples of prompts include "How can I make my robot move more smoothly?" and "Tell me how to improve the current speed of my robot."

[1344] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1345] This invention is a system in which a user films their movements at home or anywhere, and AI analyzes the footage to diagnose the movements and suggest improvements, providing feedback to the user through a display device. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotional state and adjust the feedback based on that. Below, we will explain in natural language the program processing of a specific embodiment of the present invention.

[1346] 1. Initial system setup

[1347] First, the user installs a dedicated app on their smartphone, launches the app, and creates an account. They enter the information required to create the account (e.g., name, email address, password, etc.), and the device sends that information to the server. The server stores the received information in a database and generates a new user ID. The server then sends a registration completion notification to the device and displays a registration completion message to the user.

[1348] 2. Record and upload your action

[1349] To record their movements, users use the camera function on their smartphone. By tapping the recording button in the dedicated app, the device's camera function is activated and the user can record their own movements. Once recording is complete, the video data is temporarily saved on the device. Then, when the user taps the upload button, the device compresses the video data and sends it to the server.

[1350] 3. AI-based operation diagnosis

[1351] The server passes the received video data to the AI ​​module for analysis. The AI ​​module analyzes the video and evaluates each point of the user's movement (e.g., backswing, impact position, follow-through, etc.). As a result of the evaluation, it generates problems with the user's movement and areas for improvement.

[1352] 4. Notification of diagnosis results

[1353] The results are stored on a server and linked to the user's account, allowing them to view the results through the app.

[1354] 5. Feedback via display devices

[1355] The user wears the display device and connects it to a smartphone. The device checks the connection status and sends a login request to the server. After logging in, the server sends the diagnosis results and improvement suggestions to the device, which are then displayed on the display device. The display device then provides visual guidelines and detailed audio advice.

[1356] 6. Leveraging Emotional Engines

[1357] As the user practices and checks their movements, the display device they wear and the microphone and camera on their smartphone capture their voice and facial expressions. This data is sent to a server via the device, and the emotion engine on the server analyzes their emotional state from their voice and facial expressions. Based on the analysis results, the tone and content of the feedback is adjusted. For example, if the user is nervous, advice to help them relax is provided.

[1358] 7. Detailed advice from chatbots

[1359] The user uses the chatbot function to input a question. The device sends the question to the server. The chatbot on the server compares the question with past diagnostic results and existing databases to generate an appropriate answer. The generated answer is sent to the device and displayed to the user.

[1360] Specific examples

[1361] For example, a beginner user wants to improve their golf swing and uses the app to film their movements at home. The video is sent to a server, where an AI module diagnoses the user's backswing as being too high. The diagnosis and specific suggestions for improvement (e.g., "Keep your shoulders low during the backswing") are provided to the user via a display device. The emotion engine recognizes that the user is tense during practice and provides advice to encourage relaxation. If the user asks the chatbot, "What's the trick to stabilizing your backswing?", the chatbot will display an answer such as, "Adjusting the position of your grip will increase stability."

[1362] In this way, users can improve their performance efficiently at home and receive flexible feedback based on their emotional state.

[1363] The processing flow will be explained below.

[1364] Step 1:

[1365] The user installs and launches the dedicated app on their smartphone, and enters their name, email address, and password on the account registration screen.

[1366] Step 2:

[1367] The terminal sends the entered user information to the server.

[1368] Step 3:

[1369] The server stores the received information in a database, generates a new user ID, sends the generated ID to the device, and returns a registration completion notification.

[1370] Step 4:

[1371] The device will display a registration completion message to the user.

[1372] Step 5:

[1373] When the user taps the "Swing Photo" button in the app, the device's camera function is activated and the movement is captured.

[1374] Step 6:

[1375] The device temporarily stores the captured video data.

[1376] Step 7:

[1377] When the user taps the "Upload" button, the device compresses the video data and sends it to the server.

[1378] Step 8:

[1379] The server passes the received video data to the AI ​​module.

[1380] Step 9:

[1381] AI analyzes the video data and evaluates each point of the movement (e.g., backswing, impact position, follow-through, etc.).

[1382] Step 10:

[1383] The AI ​​generates evaluation results and improvement points and returns them to the server.

[1384] Step 11:

[1385] The server stores the diagnostic results and improvement suggestions in a database and links them to the user's account.

[1386] Step 12:

[1387] When a user wears a display device and connects it wirelessly to a smartphone, the device checks the connection status and sends a login request to the server.

[1388] Step 13:

[1389] The server authenticates the user's login and sends the latest diagnostic results and improvement suggestions to the device.

[1390] Step 14:

[1391] The terminal displays the received diagnostic results and improvement suggestions on a display device.

[1392] Step 15:

[1393] The AI ​​glasses provide users with visual feedback (e.g., backswing guidelines) and audio advice (e.g., "Rotate your shoulders more").

[1394] Step 16:

[1395] While the user is practicing, the microphone and camera on the display device or smartphone captures the user's voice and facial expressions.

[1396] Step 17:

[1397] The device transmits the captured voice and facial expression data to the server.

[1398] Step 18:

[1399] The emotion engine in the server analyzes the user's emotional state from voice and facial expression data.

[1400] Step 19:

[1401] The server then adjusts the tone and content of the feedback based on the analysis results, for example generating advice to relax if the user is feeling tense.

[1402] Step 20:

[1403] The server sends the adjusted feedback to the terminal, which provides it to the user through a display device.

[1404] Step 21:

[1405] Users use the chatbot function within the app to enter questions.

[1406] Step 22:

[1407] The device sends the question to the server.

[1408] Step 23:

[1409] The server passes the question to the chatbot, which compares it with past diagnostic data and generates an appropriate answer.

[1410] Step 24:

[1411] The server sends the generated answer to the terminal, which displays the answer to the user.

[1412] This series of processes allows users to efficiently capture, diagnose, and improve their own movements, and not only receive professional feedback, but also flexible feedback based on their emotional state.

[1413] Example 2

[1414] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1415] Conventional motion analysis systems have the problem that the feedback users receive is static and cannot appropriately adapt to the user's emotional state or training environment at any given time. It is also difficult to receive detailed advice in real time based on past diagnostic data. This can result in users not getting the full benefits they need. Furthermore, there is a lack of efficient data processing when filming movements and uploading data.

[1416] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1417] In this invention, the server includes an engine that can capture the user's voice and facial expressions and analyze their emotional state, and also includes a means for adjusting feedback based on the analysis results, a means for the user to ask the interactive agent questions, a means for the interactive agent to compare the questions with past diagnostic data and generate an appropriate response, a means for the terminal to compress the video data and transmit it to the processing device, a means for the processing device to store the received video data in association with user identification information, and a means for the processing device to store the diagnostic results and improvement suggestions in a database. This allows the user to receive flexible feedback based on their emotions in real time and detailed advice based on past data. Furthermore, efficient data processing can shorten the time from imaging to diagnosis.

[1418] "User" means a person who utilizes the system to receive performance diagnosis and feedback.

[1419] A "mobile communication terminal" refers to a portable information processing device, such as a smartphone or tablet, that can connect to the Internet via wireless communication.

[1420] A "processor" is a computer system that analyzes and stores received data.

[1421] An "artificial intelligence module" is a software program that uses machine learning algorithms to analyze video data and perform motion assessment and diagnosis.

[1422] "Display device" means a device for providing the user with visual and audio diagnostic results and suggestions for improvement.

[1423] The "engine" is a software module that analyzes the user's voice and facial expression data to recognize their emotional state.

[1424] An "interactive agent" is a program that uses natural language processing to provide appropriate answers to users' questions.

[1425] "User identification information" means information used to identify a user (e.g., user ID).

[1426] A "database" is a data management system for storing data such as diagnostic results and improvement proposals.

[1427] MODE FOR CARRYING OUT THE INVENTION

[1428] This invention is a system in which a user films their own movements at home or anywhere, and AI analyzes the footage to diagnose the movements and suggest improvements, providing feedback to the user through a display device. Furthermore, by combining it with an emotion engine, it is possible to recognize the user's emotional state and adjust the feedback based on that. Specific embodiments for implementing the invention are described below.

[1429] 1. System Configuration

[1430] This system mainly consists of a mobile communication terminal, a server, and a display device. The mobile communication terminal is a portable information processing device such as a smartphone or tablet, on which the user installs a dedicated app. The server is a computer system that analyzes and stores video data and diagnostic results. The display device is a device that provides the user with operational diagnostic results and improvement suggestions visually and audibly.

[1431] 2. Install the dedicated app and create an account

[1432] First, the user installs a dedicated app (e.g., MotionAnalysisApp) on their smartphone, launches the app, and creates an account. The user enters the information required to create an account (e.g., name, email address, password, etc.), and the device sends that information to the server. The server stores the received information in a database (e.g., MySQL database) and generates a new user ID. The server then sends a registration completion notification to the device and displays a registration completion message to the user.

[1433] 3. Recording the movement and sending the video data

[1434] When a user taps the capture button in the app, the device's camera function is activated, allowing the user to record their own movements. Once recording is complete, the video data is temporarily stored on the device. When the user taps the upload button, the device compresses the video data (e.g., using the zlib library) and sends it to the server. The server receives the video data and stores it in association with user identification information.

[1435] 4. AI-based behavior analysis and diagnosis

[1436] The server passes the received video data to an AI module (e.g., TensorFlow). The AI ​​module analyzes the video and evaluates each point of the user's movement (e.g., backswing, impact position, follow-through, etc.). As a result of the evaluation, it generates problems with the user's movement and areas for improvement. The server saves the diagnostic results in a database (e.g., a PostgreSQL database) and links them to the user's account.

[1437] 5. Providing Feedback

[1438] The user wears a display device (e.g., Oculus Quest) and connects it to a smartphone. The device checks the connection status and sends a login request to the server. The server sends the diagnosis results and improvement suggestions to the device, which are then displayed on the display device. The display device provides visual guidelines and detailed audio advice.

[1439] 6. Leveraging Emotional Engines

[1440] As the user practices and checks their movements, the display device they wear and the microphone and camera on their smartphone capture their voice and facial expressions. This data is sent to a server via the device, and an emotion engine (e.g., Microsoft Azure Emotion API) on the server analyzes their emotional state from their voice and facial expressions. Based on the analysis results, the tone and content of the feedback is adjusted. For example, if the user is nervous, advice to help them relax is provided.

[1441] 7. Chatbots provide detailed advice

[1442] When a user uses the chatbot function (e.g., Dialogflow) to input a question, the device sends the question to the server. The chatbot on the server analyzes the question and generates an appropriate answer by comparing it with past diagnostic data and existing databases. The generated answer is sent to the device and displayed to the user.

[1443] Specific examples

[1444] For example, a beginner user wants to improve their golf swing, so they use the app to film their movements at home. The video is sent to a server, where an AI module diagnoses their swing as "too high." The diagnosis and specific suggestions for improvement (e.g., "Keep your shoulders low during the backswing") are provided to the user through a display device. If the emotion engine recognizes that the user is tense while practicing, the advice to "relax" is displayed. Similarly, if the user asks the chatbot, "What's the trick to stabilizing your backswing?", the chatbot will respond with, "Adjusting the position of your grip will increase stability."

[1445] This invention allows users to improve their movements efficiently and effectively at home or anywhere they like, and also allows them to receive flexible feedback based on their emotional state, allowing them to receive advice tailored to their individual needs.

[1446] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1447] Step 1:

[1448] The user installs a dedicated app on their smartphone. They launch the app and enter their name, email address, and password on the sign-up screen. The entered information is sent to the server. The server saves the entered information in a database, generates a new user ID, and sends a registration completion notification to the device.

[1449] Input: User's name, email address, password

[1450] Output: New user ID, registration completion notification

[1451] Specific operation: The user installs "MotionAnalysisApp" on their iPhone and enters the name "Yamada Taro", email address "taro@example.com", and password "password123".

[1452] Step 2:

[1453] The user taps the recording button in the app. The device's camera function is activated, and the user records their own movements. When recording is complete, the video data is temporarily saved on the device. When the upload button is tapped, the device compresses the video data and sends it to the server. The server receives the video data and saves it in association with the user's identification information.

[1454] Input: Video data of user movements

[1455] Output: Compressed video data, video data associated with user identification information

[1456] Specific operation: The user films their golf swing, compresses the video data using the zlib library, and transmits it.

[1457] Step 3:

[1458] The server passes the received video data to the AI ​​module, which analyzes the video and evaluates each aspect of the user's movements. As a result of the evaluation, it generates problems with the movements and areas for improvement. The server stores the diagnostic results in a database and links them to the user's account.

[1459] Input: User's compressed video data

[1460] Output: Performance evaluation results, diagnostic results and improvements

[1461] Specific behavior: The server passes the data to TensorFlow, which evaluates it as "the backswing is too high."

[1462] Step 4:

[1463] The user wears the display device and connects it to a smartphone. The device checks the connection status and sends a login request to the server. The server sends the diagnosis results and improvement suggestions to the device, which are then displayed on the display device. The display device provides visual guidelines and audio advice.

[1464] Input: Diagnostic results, improvement suggestions

[1465] Output: Display content (guidelines, advice) on the display device

[1466] Specific actions: The user puts on the Oculus Quest and sees the improvement suggestions.

[1467] Step 5:

[1468] While the user practices, the microphone and camera on the display device or smartphone captures their voice and facial expressions. This data is sent to a server via the device and analyzed by an emotion engine. Based on this analysis, the tone and content of the feedback is adjusted.

[1469] Input: User's voice data, facial expression data

[1470] Output: Emotional state analysis results, adjusted feedback content

[1471] What it does: It detects when the user is tense while practicing and displays the message "Relax."

[1472] Step 6:

[1473] The user uses the chatbot function to input a question. The device sends the question to the server. The chatbot on the server analyzes the question, compares it with past diagnostic data and existing databases, and generates an appropriate answer. The generated answer is sent to the device and displayed to the user.

[1474] Input: User's question

[1475] Output: The generated answer

[1476] Specific action: The user asks, "What's the trick to stabilizing your backswing?" and the answer displayed is, "Adjusting the grip position will increase stability."

[1477] In this way, specific input data is given at each processing step, processing is carried out based on that data, and output data is obtained.

[1478] (Application example 2)

[1479] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1480] Conventional motion diagnosis systems have difficulty not only analyzing a user's motion but also providing feedback that takes into account the user's emotional state. In particular, in industrial settings, such as in factory robot operations, efficient motion improvement and mental care for workers are important. The present invention aims to provide a system that not only diagnoses motion but also recognizes the user's emotional state and provides feedback tailored to each individual situation.

[1481] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1482] In this invention, the server includes: a means for a user to film their own movements using a smartphone; a means for the device to send the filmed video to the server; a means for the server to perform a movement diagnosis using an AI module that analyzes the received video; a means for the server to notify the user of the diagnosis results and improvement suggestions; a means for providing visual and audio movement guidance through a display device worn by the user; an emotion analysis means for recognizing the user's emotional state and adjusting the tone and content of feedback; and a means for providing advice based on the emotion analysis results. This enables not only efficient movement improvement but also flexible feedback that takes into consideration the user's mental health.

[1483] "User" refers to a person who uses a smartphone or display device to film their own actions and receive feedback.

[1484] A "smartphone" is a portable information terminal that allows users to capture their actions.

[1485] "Terminal" refers to a device that transmits video data to a server and displays diagnostic results.

[1486] A "server" refers to a computer system that receives video data sent from a terminal and performs analysis and provides feedback.

[1487] An "AI module" is software and hardware that includes artificial intelligence that performs operational diagnosis within a server.

[1488] "Motion diagnosis" refers to the process of analyzing video data captured by the user and identifying problems and areas for improvement in motion.

[1489] "Diagnosis results" refer to the analysis results of the user's behavior generated after the AI ​​module performs behavior diagnosis.

[1490] "Improvement suggestions" are specific advice provided to users on how to improve their behavior based on the diagnostic results.

[1491] "Display device" refers to a display device worn by a user to receive visual and audio instructions and feedback.

[1492] "Emotion analysis" refers to the process of analyzing a user's voice and facial expressions to recognize their emotional state.

[1493] "Emotion analysis means" refers to hardware and software for analyzing the user's emotional state.

[1494] A "chatbot" is an automated dialogue system that responds to a user's text-based questions by comparing them with past diagnostic data and generating appropriate answers.

[1495] "Video data" refers to video data generated when a user films themselves in action.

[1496] "Database" refers to an information storage system in which the server stores diagnostic results and improvement proposals, and can be referenced as needed.

[1497] In the system for realizing the present invention, the following hardware and software are used to process and analyze various data.

[1498] Hardware used

[1499] Smartphone

[1500] Head-mounted displays (HMDs) or other display devices

[1501] Camera module

[1502] Software used

[1503] OpenCV (video capture and processing)

[1504] Requests (API communication)

[1505] EmotionRecognition Library (a virtual emotion analysis library)

[1506] AI module (motion analysis software)

[1507] Detailed system processing explanation

[1508] 1. Initial Setup:

[1509] First, the user installs a dedicated application on their smartphone and launches it. The user creates an account and enters the required information (name, email address, password, etc.). The device sends this information to the server, which then generates a new user ID. The generated user ID is stored in a database, and a notification of registration completion is sent to the device.

[1510] 2. Record and upload the action:

[1511] Users use the smartphone's camera function to record their own movements. When they tap the recording button, the camera starts up and records the user's movements in real time. When recording is finished, the video data is temporarily saved on the device, and then when the user taps the upload button, the video data is compressed and sent to the server.

[1512] 3. AI-based operational diagnosis:

[1513] The server passes the received video data to the AI ​​module for analysis. The AI ​​module analyzes the video and evaluates each aspect of the user's movements (e.g., angle, timing, speed, etc.). As a result of the evaluation, it generates problems with the user's movements and areas for improvement.

[1514] 4. Notification of diagnostic results:

[1515] The diagnostic results are stored in a database on the server and linked to the user's account. This allows the user to view the results through a dedicated application. Visual and audio feedback is provided through a display device (such as an HMD) worn by the user.

[1516] 5. Leveraging the Emotion Engine:

[1517] As the user practices while checking the feedback, the microphone and camera on the smartphone or the display device they wear captures the user's voice and facial expressions. This data is sent to a server via the device, and the emotion engine in the server analyzes the user's emotional state from the voice and facial expressions. Based on the analysis results, the tone and content of the feedback are adjusted.

[1518] Specific examples

[1519] For example, if a factory worker wants to check the accuracy of a robot arm's operation, he or she picks up a smartphone and films the robot's movements. The video is sent to a server, and the AI ​​module diagnoses that the robot's arm is off by about 2 degrees. The diagnosis and specific improvement suggestions (e.g., "Raise the arm height slightly") are provided through the HMD. Furthermore, the emotion engine recognizes that the worker appears nervous and provides advice such as "Take a deep breath before starting work."

[1520] Prompt Sentence Examples

[1521] "Please tell me if the arm's movement is accurate with respect to the target."

[1522] "What is an efficient way to improve robot behavior?"

[1523] In this way, this system can simultaneously achieve efficient operational improvement and mental care for workers.

[1524] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1525] Step 1:

[1526] Initial Settings:

[1527] The user installs a dedicated application on their smartphone, launches the application and creates an account.

[1528] Input: User information such as name, email address, and password.

[1529] Data processing: The terminal sends the entered information to the server.

[1530] Output: The server generates a new user ID, saves it in the database, and sends a registration completion notification to the terminal.

[1531] Step 2:

[1532] Filming and uploading the action:

[1533] The user uses the smartphone camera to record their own movements.

[1534] Input: Video data captured by the user's camera.

[1535] Data processing: The device temporarily stores the video data, and when the user taps the upload button, the video data is compressed and sent to the server.

[1536] Output: The compressed video data is uploaded to the server.

[1537] Step 3:

[1538] AI-powered diagnostics:

[1539] The server passes the received video data to the AI ​​module for analysis.

[1540] Input: Video data received by the server.

[1541] Data processing: The AI ​​module analyzes the video and evaluates each aspect of the user's movements (e.g., angle, timing, speed, etc.).

[1542] Output: As an evaluation result, problems and improvements in the user's behavior are generated.

[1543] Step 4:

[1544] Notification of diagnostic results:

[1545] The server stores the diagnostic results in a database and links them to the user's account, which can then be viewed through a dedicated application.

[1546] Input: Operational diagnostic results from the AI ​​module.

[1547] Data processing: The server stores the diagnostic results in a database and notifies the user.

[1548] Output: The diagnostic results and specific improvement suggestions are displayed on the user's display device (e.g., HMD).

[1549] Step 5:

[1550] Leveraging the Emotion Engine:

[1551] As the user practices while checking the feedback, the microphone and camera on the display device worn by the user or the smartphone captures the user's voice and facial expressions.

[1552] Input: Captured voice and facial expression data.

[1553] Data processing: The data is sent to a server via the device, and the emotion engine in the server analyzes the emotional state from the voice and facial expressions.

[1554] Output: The tone and content of feedback is adjusted based on emotional state.

[1555] Step 6:

[1556] Chatbot advice provided:

[1557] Users can use the chatbot function to enter questions.

[1558] Input: A text-based question entered by the user.

[1559] Data processing: The device sends a question to the server, and the chatbot on the server compares it with past diagnostic data and generates an appropriate answer.

[1560] Output: The generated answer is sent to the terminal and displayed to the user.

[1561] 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.

[1562] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1563] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1564] 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.

[1565] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1566] 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.

[1567] 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).

[1568] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1569] 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."

[1570] 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.

[1571] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1572] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1573] 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.

[1574] 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.

[1575] 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.

[1576] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1577] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1578] 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.

[1579] 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.

[1580] 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.

[1581] 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.

[1582] The following is further disclosed regarding the above embodiment.

[1583] (Claim 1)

[1584] A means for users to film their movements using a smartphone;

[1585] A means for transmitting the captured video by the terminal to a server;

[1586] A means for performing operational diagnosis using an AI module that analyzes the video received by the server;

[1587] A means for the server to notify the user of the diagnosis results and improvement suggestions;

[1588] A means for providing visual and audio instructions through a display device worn by the user;

[1589] A system including:

[1590] (Claim 2)

[1591] A way for users to ask questions to the chatbot, and

[1592] A means for the chatbot to compare past diagnostic data and generate appropriate answers;

[1593] means for the device to display the generated answer to the user;

[1594] The system of claim 1 further comprising:

[1595] (Claim 3)

[1596] A means for the terminal to compress video data and transmit it to a server;

[1597] A means for storing the video data received by the server in association with a user ID;

[1598] A means for the server to store the diagnosis results and improvement suggestions in a database;

[1599] The system of claim 1 further comprising:

[1600] "Example 1"

[1601] (Claim 1)

[1602] A means for users to film their movements using a smartphone;

[1603] A means for transmitting the captured video by the terminal to a server;

[1604] A means for compressing video data by the terminal;

[1605] A means for performing operational diagnosis using an AI module that analyzes the video received by the server;

[1606] A means for storing the video data received by the server in association with a user ID;

[1607] A means for the server to store the diagnosis results and improvement suggestions in a database;

[1608] A means for the server to notify the user of the diagnosis results and improvement suggestions;

[1609] A means for providing visual and audio instructions through a display device worn by the user;

[1610] A system including:

[1611] (Claim 2)

[1612] A way for users to ask questions to the chatbot, and

[1613] A means for the chatbot to compare past diagnostic data and generate appropriate answers;

[1614] means for the device to display the generated answer to the user;

[1615] The system of claim 1 further comprising:

[1616] (Claim 3)

[1617] A means for transmitting captured video data by the terminal to a server;

[1618] A means for performing operational diagnosis using an AI module that analyzes the video data received by the server;

[1619] A means for the server to store the diagnosis results and improvement suggestions in a database;

[1620] The system of claim 1 further comprising:

[1621] "Application Example 1"

[1622] (Claim 1)

[1623] A means for a user to photograph their own actions using an information terminal;

[1624] A means for transmitting the captured video from the terminal to a data center;

[1625] A means for performing operational diagnosis using an artificial intelligence module that analyzes the video received by the server;

[1626] A means for the server to notify the user of the diagnosis results and improvement suggestions;

[1627] means for providing visual and audio instructions through a user-worn display device;

[1628] A means for factory managers to monitor and optimize the robot's operation;

[1629] A system including:

[1630] (Claim 2)

[1631] means for a user to ask a question to the automated dialogue program;

[1632] A means for an automated dialogue program to compare the data with past diagnostic data and generate an appropriate response;

[1633] means for the device to display the generated answer to the user;

[1634] The system of claim 1 further comprising:

[1635] (Claim 3)

[1636] A means for the terminal to reduce the video data and transmit it to a data center;

[1637] a means for storing the video data received by the server in association with user identification information;

[1638] a means for the server to store the diagnostic results and improvement suggestions in an information recording device;

[1639] The system of claim 1 further comprising:

[1640] "Example 2: Combining Emotion Engines"

[1641] (Claim 1)

[1642] A means for a user to photograph his / her own actions using a mobile communication terminal;

[1643] A means for transmitting the captured image to a processing device by the terminal;

[1644] A means for performing operational diagnosis using an artificial intelligence module that analyzes the video received by the processing device;

[1645] a means for the processing device to notify the user of the diagnosis results and suggestions for improvement;

[1646] means for providing visual and audio instructions through a user-worn display device;

[1647] means for adjusting feedback based on the results of the analysis, including an engine capable of capturing the user's voice and facial expressions and analyzing their emotional state;

[1648] A system including:

[1649] (Claim 2)

[1650] a means for the user to ask questions of the conversational agent;

[1651] A means for the conversational agent to compare the past diagnostic data and generate an appropriate answer;

[1652] means for the device to display the generated answer to the user;

[1653] 10. The system of claim 1, comprising:

[1654] (Claim 3)

[1655] A means for compressing video data in the terminal and transmitting the data to a processing device;

[1656] means for storing the video data received by the processing device in association with user identification information;

[1657] a means for the processing device to store the diagnostic results and improvement suggestions in a database;

[1658] 10. The system of claim 1, comprising:

[1659] "Application example 2 when combining emotion engines"

[1660] (Claim 1)

[1661] A means for users to film their movements using a smartphone;

[1662] A means for transmitting the captured video by the terminal to a server;

[1663] A means for performing operational diagnosis using an AI module that analyzes the video received by the server;

[1664] A means for the server to notify the user of the diagnosis results and improvement suggestions;

[1665] A means for providing visual and audio guidance through a display device worn by the user;

[1666] an emotion analysis means for recognizing the user's emotional state and adjusting the tone and content of the feedback, and a means for providing advice based on the emotion analysis results;

[1667] A system including:

[1668] (Claim 2)

[1669] A way for users to ask questions to the chatbot, and

[1670] A means for the chatbot to compare past diagnostic data and generate appropriate answers;

[1671] means for the device to display the generated answer to the user;

[1672] The system of claim 1 further comprising:

[1673] (Claim 3)

[1674] A means for the terminal to compress video data and transmit it to a server;

[1675] A means for storing the video data received by the server in association with a user ID;

[1676] A means for the server to store the diagnosis results and improvement suggestions in a database;

[1677] The system of claim 1 further comprising: [Explanation of symbols]

[1678] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to film their movements using a smartphone; A means for transmitting the captured video by the terminal to a server; A means for performing operational diagnosis using an AI module that analyzes the video received by the server; A means for the server to notify the user of the diagnosis results and improvement suggestions; A means for providing visual and audio instructions through a display device worn by the user; A system including:

2. A way for users to ask questions to the chatbot, and A means for the chatbot to compare past diagnostic data and generate appropriate answers; means for the device to display the generated answer to the user; The system of claim 1 further comprising:

3. A means for the terminal to compress video data and transmit it to a server; A means for storing the video data received by the server in association with a user ID; A means for the server to store the diagnosis results and improvement suggestions in a database; The system of claim 1 further comprising:

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