System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving authorization information from a user and issuing an authorization token; AI system means for analyzing video uploaded by the user and evaluating muscles; and means for generating and providing training feedback to the user based on results of the AI system evaluation.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] The main challenges facing bodybuilding enthusiasts are the lack of in-person competitions and objective evaluation. Due to the current social situation, gyms and competitions have been canceled, making it difficult for users to compare their skills and training results with others. Furthermore, while online interactions have increased, it is difficult to maintain proper feedback and motivation. This invention aims to solve these challenges and provide a fair evaluation and international competition experience from the comfort of your own home. [Means for solving the problem]

[0005] The present invention provides a system consisting of a user authentication, an AI judgment system, a generative AI support system, an online community platform, and a real-time contest module. Specifically, the system includes the following means:

[0006] 1. A means of accepting authentication information from the user and issuing an authentication token

[0007] 2. AI system means for analyzing video data uploaded by users and evaluating muscles

[0008] 3. A means to generate training feedback based on the evaluation results of the AI ​​system and provide it to users

[0009] Furthermore, the following means are also included:

[0010] 4. A method to use AI to generate personalized training and nutrition plans based on user-entered health information and training goals.

[0011] 5. Providing a community function for users to interact with other users, and using generative AI to analyze interaction data and provide appropriate advice and information

[0012] This allows users to receive fair assessments, implement efficient training plans, and stay motivated while interacting with other enthusiasts, all from the comfort of their own home.

[0013] "User authentication" is the process of a user entering information required to access a system and verifying that the information is valid.

[0014] An "authentication token" is a digital key that proves that a user has been successfully authenticated and identifies the user in subsequent sessions.

[0015] "Video data" refers to video files and image files that users take and upload to the system, which record their training performance and physical condition.

[0016] The "AI Judgment System" is a system that uses artificial intelligence technology to analyze video data and evaluate muscle development and training form.

[0017] "Analysis results" refer to the evaluation scores and feedback information generated by the AI ​​Judgment System after analyzing the video data.

[0018] "Generative AI" refers to artificial intelligence that automatically generates personalized training and nutrition plans based on user input data.

[0019] "Training feedback" refers to advice and evaluation results provided to users based on the analysis results of the AI ​​Judgment System and the Generative AI.

[0020] An "online community platform" is a digital space where users can interact and share information with other users, including chat and forum functions.

[0021] The "Real-time Contest Module" is a system that allows users to compete with other users in real time through live streaming and receive fair evaluations. [Brief explanation of the drawings]

[0022] [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

[0023] 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.

[0024] First, the terms used in the following description will be explained.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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."

[0043] The embodiment of this invention is a system consisting of a user authentication, AI judgment system, generative AI support system, online community platform, and real-time contest module. The main components and their specific operations will be described for the program processing of this system.

[0044] User authentication

[0045] 1. User: First, the user accesses the application from a terminal and enters their username and password.

[0046] 2. Terminal: Sends username and password to server.

[0047] 3. Server: Checks the received authentication information against a database and issues an authentication token if the user has valid credentials.

[0048] 4. Server: Sends an authentication token back to the device, starting the user's session.

[0049] AI Judgment System

[0050] 1. User: The user shoots training footage and uploads it to the system.

[0051] 2. Terminal: Sends the uploaded video data to the server.

[0052] 3. Server: Passes the video data to the AI ​​Judgment System.

[0053] 4. AI system: The AI ​​system analyzes video data and evaluates muscle development and form.

[0054] 5. Server: Obtains the evaluation results and prepares them as feedback to be given to the user.

[0055] 6. Server: Sends feedback and scores to the device and displays them to the user.

[0056] Generative AI Support System

[0057] 1. User: Enter health information and training goals on the device.

[0058] 2. Terminal: Sends input data to the server.

[0059] 3. Server: Passes input data to the generation AI.

[0060] 4. Generative AI: Generative AI analyzes user data and generates optimal training and nutrition plans.

[0061] 5. Server: Takes the generated plan and formats it to be returned to the user.

[0062] 6. Server: Sends the personalized plan to the device and displays it to the user.

[0063] Online Community Platform

[0064] 1. User: Accesses chats and forums and posts messages.

[0065] 2. Terminal: Sends the user's message to the server.

[0066] 3. Server: Stores the message in a database and adds it to the associated thread.

[0067] 4. Server: Notifies other participants that a new message has been posted.

[0068] 5. Generative AI: Analyzes interaction data and provides appropriate advice and relevant information to users.

[0069] 6. Server: Providing advice and information to users, posting in forums and chats.

[0070] Real-time Contest Module

[0071] 1. User: Enter the real-time contest and start live streaming at the specified time.

[0072] 2. Terminal: Sends live video to the server in real time.

[0073] 3. Server: Passes the received video data to the AI ​​Judgment System in real time.

[0074] 4. AI system: Analyzes live footage in real time and evaluates user performance.

[0075] 5. Server: Updates the analysis results in real time and displays the contest scores instantly.

[0076] 6. Server: Display the scores in real time on the contest page and communicate them to all users.

[0077] Specific examples

[0078] For example, when user A wants to have his / her training performance evaluated, the process is as follows.

[0079] 1. User A logs in to the application and uploads a training video.

[0080] 2. The server sends the video data to the AI ​​Judgment System, which analyzes the video and calculates the score.

[0081] 3. The server generates a rating score and feedback and provides it to User A.

[0082] Furthermore, when user B uses the community function to interact with other users, the following occurs.

[0083] 1. User B accesses the forum and posts a question or training result.

[0084] 2. The server saves the post in a database and notifies other users.

[0085] 3. Along with replies from other users, advice provided by the generating AI will also be displayed.

[0086] The present invention can be implemented in the above-described manner, and will become a very useful system for many bodybuilding enthusiasts.

[0087] The processing flow will be explained below.

[0088] User authentication process steps

[0089] Step 1:

[0090] User: Accesses the application from a terminal and enters a username and password.

[0091] Step 2:

[0092] Terminal: Sends the entered username and password to the server.

[0093] Step 3:

[0094] Server: Checks the received authentication information against a database.

[0095] Step 4:

[0096] Server: If the user is found to be valid, generate an authentication token.

[0097] Step 5:

[0098] Server: Returns the authentication token to the device and starts the user session.

[0099] AI Judgment System Processing Steps

[0100] Step 1:

[0101] User: Films training footage and uploads it through the application.

[0102] Step 2:

[0103] Terminal: Sends video data to the server.

[0104] Step 3:

[0105] Server: Passes the received video data to the AI ​​Judgment System.

[0106] Step 4:

[0107] AI system: Analyzes video data and evaluates muscle development and form.

[0108] Step 5:

[0109] AI system: Calculates a score as an analysis result and generates feedback.

[0110] Step 6:

[0111] Server: Sends the generated feedback and scores to the device.

[0112] Step 7:

[0113] Device: Shows feedback and score to the user.

[0114] Processing steps of the generative AI support system

[0115] Step 1:

[0116] User: Enter health information and training goals.

[0117] Step 2:

[0118] Terminal: Sends the entered data to the server.

[0119] Step 3:

[0120] Server: Passes the received data to the generation AI.

[0121] Step 4:

[0122] Generative AI: Analyzes user data and generates optimal training and nutrition plans.

[0123] Step 5:

[0124] Server: Formats the generated plan for delivery to the user.

[0125] Step 6:

[0126] Server: Sends the personalized plan to the device and displays it to the user.

[0127] Online community platform processing steps

[0128] Step 1:

[0129] Users: Post messages in forums and chats.

[0130] Step 2:

[0131] Terminal: Sends the user's messages to the server.

[0132] Step 3:

[0133] Server: Stores received messages in a database and adds them to the associated thread.

[0134] Step 4:

[0135] Server: Notifies other users of new messages.

[0136] Step 5:

[0137] Generative AI: Analyzes interaction data and provides appropriate advice and information.

[0138] Step 6:

[0139] Server: Displays advice and information provided by the generated AI to the user.

[0140] Processing steps of the real-time contest module

[0141] Step 1:

[0142] User: Enter a real-time contest and start live streaming.

[0143] Step 2:

[0144] Terminal: Sends live video to the server in real time.

[0145] Step 3:

[0146] Server: Passes the received video data to the AI ​​Judgment System in real time.

[0147] Step 4:

[0148] AI system: Analyzes live footage in real time and evaluates performance.

[0149] Step 5:

[0150] Server: Updates analysis results in real time and displays contest scores instantly.

[0151] Step 6:

[0152] Device: The scores will be displayed in real time on the contest page and communicated to all users.

[0153] Example 1

[0154] 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."

[0155] Conventional training support systems are limited to functions such as user authentication, video data analysis, and training feedback generation, and lack functions such as providing personalized training and nutrition plans, analyzing user performance in real time, and providing interaction and advice between users. Furthermore, there are no integrated systems that combine these functions, which creates the issue of low user convenience when using multi-functional systems.

[0156] 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.

[0157] In this invention, the server includes means for receiving authentication information from a user and issuing an authentication token, an artificial intelligence system for analyzing video data uploaded by the user and evaluating the muscles, and means for generating training feedback based on the evaluation results of the artificial intelligence system and providing it to the user, thereby enabling user authentication, analysis of video data, and generation of training feedback.

[0158] In addition, in this invention, the server includes a generating artificial intelligence means for generating optimal training plans and nutrition plans based on user data, a means for analyzing and evaluating live streaming performed by the user in real time, and a generating artificial intelligence means for providing a community function for users to interact with other users and analyzing the interaction data to provide advice and information. This makes it possible to comprehensively realize multiple functions such as generating and providing personalized training plans and nutrition plans, analyzing performance in real time, and interacting with users and providing advice.

[0159] "User authentication" is the process of verifying whether a user is a legitimate user based on the authentication information provided by the user.

[0160] An "authentication token" is a digital certificate issued after successful user authentication and used to securely manage a user's session.

[0161] "Video data" refers to video and image data that users shoot and upload.

[0162] The "artificial intelligence system" analyzes video data and other data, mimicking human judgment to evaluate muscle development and form.

[0163] "Training feedback" refers to advice and evaluations about training provided to users based on the results of analysis by the artificial intelligence system.

[0164] "Generative AI" is AI that generates personalized training and nutrition plans based on user data.

[0165] "Live streaming" is a method for users to broadcast video in real time and share the process with other users or systems in real time.

[0166] "Real-time analysis" refers to the process of instantly analyzing data received during live streaming and providing results in real time.

[0167] The "community function" is a system that provides functions such as chat and forums for users to interact with each other.

[0168] "Advice and information" refers to content provided by artificial intelligence or other users to help users solve problems or improve performance.

[0169] MODE FOR CARRYING OUT THE INVENTION

[0170] This invention is a system that includes user authentication, analysis using artificial intelligence, generative AI support, online community functionality, and a real-time contest module. Specific embodiments for implementing this invention will be described below.

[0171] User Authentication

[0172] The user accesses the application from a terminal and enters their username and password.

[0173] The terminal sends the user name and password to the server.

[0174] The server checks the received credentials against a database (e.g. MySQL) by comparing the username in a user table with the hashed password.

[0175] If authentication is successful, the server generates a JSON Web Token (JWT) and returns it to the device.

[0176] Once the authentication token is received, the user's session begins.

[0177] AI Judgment System

[0178] Users film their training videos and upload them to the system.

[0179] The terminal transmits the uploaded video data to the server.

[0180] The server passes the video data to an artificial intelligence system, which uses AI models based on TensorFlow and PyTorch.

[0181] The AI ​​system analyzes the video data to assess muscle development and form, for example by detecting the positions of key joints in each frame of the video and calculating an evaluation score based on their movements.

[0182] The server takes the evaluation results and formats them to provide feedback to the user, which may take the form of textual advice or a score.

[0183] The server sends the feedback and score to the device and displays it to the user.

[0184] Generative AI Support System

[0185] Users input their health information and training goals through the terminal.

[0186] The terminal transmits the input data to the server.

[0187] The server passes the input data to the generative AI, which uses GPT-4 as its generative AI model.

[0188] The generative AI analyzes user data and generates personalized training and nutrition plans based on prompts, such as "I weigh 70 kg and am 175 cm tall. I want to gain 5 kg of muscle in one month."

[0189] The server takes the generated plan and formats it for delivery to the user.

[0190] The server sends the personalized plan to the terminal and displays it to the user.

[0191] Online Community Platform

[0192] Users access chats and forums and post messages.

[0193] The terminal sends the user's message to the server.

[0194] The server stores the message in a database (e.g. PostgreSQL) and adds it to the associated thread.

[0195] The server notifies other users that a new message has been posted.

[0196] Generative AI analyzes interaction data and provides appropriate advice and related information to users. It analyzes past posts and prompts to generate appropriate answers.

[0197] The server provides advice and information to users and posts to forums and chats.

[0198] Real-time Contest Module

[0199] Users enter a real-time contest and begin live streaming at a designated time.

[0200] The terminal transmits live video to the server in real time.

[0201] The server passes the received video data to the artificial intelligence system in real time.

[0202] The AI ​​system analyzes live video in real time and evaluates the user's performance, with the results being sent to the server each time.

[0203] The server updates the analysis results in real time and displays the contest scores instantly.

[0204] The server displays the scores in real time on the contest page and communicates them to all users.

[0205] As a specific example, when user A wants to have his / her training performance evaluated, the following procedure is carried out.

[0206] 1. User A logs in to the application and uploads a training video.

[0207] 2. The server sends the video data to an AI system, which analyzes the video and calculates a score. Specifically, the AI ​​detects the positions of key joints and calculates an evaluation score based on their movements.

[0208] 3. The server generates a rating score and feedback and provides it to User A.

[0209] Furthermore, when user B uses the community function to interact with other users, the following occurs.

[0210] 1. User B accesses the forum and posts a question or training result.

[0211] 2. The server saves the post in a database and notifies other users.

[0212] 3. Along with replies from other users, the AI ​​will also provide advice based on a prompt such as, "I'm having trouble with my training lately. Do you have any advice?"

[0213] This system allows many bodybuilding enthusiasts to train effectively, interact through the community, and compete against each other in real-time contests.

[0214] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0215] User Authentication

[0216] Step 1:

[0217] The user accesses the application from a terminal and enters their username and password.

[0218] Input: Username, Password

[0219] Output: Data input to the terminal

[0220] In this step, the user enters their authentication information.

[0221] Step 2:

[0222] The device sends the username and password to the server.

[0223] Input: Username, Password

[0224] Output: Authentication information to the server

[0225] The terminal sends the input information to the server in the appropriate format.

[0226] Step 3:

[0227] The server checks the received authentication information against a database.

[0228] Input: Credentials

[0229] Output: Authentication success or failure

[0230] It is matched against a user table in a database (e.g., MySQL), and if authentication is successful, a JWT is generated.

[0231] Step 4:

[0232] The server issues an authentication token (JWT) and sends it to the device.

[0233] Input: Authentication success or failure

[0234] Output: Authentication token

[0235] If authentication is successful, a JWT is generated and sent back to the device.

[0236] Step 5:

[0237] The device receives the authentication token and starts the user's session.

[0238] Input: Authentication token

[0239] Output: Authenticated session

[0240] Session management begins, allowing the user to operate within the application.

[0241] AI Judgment System

[0242] Step 1:

[0243] The user takes a training video and uploads it to the system.

[0244] Input: Training footage

[0245] Output: Video data to the device

[0246] The user records their own training and prepares to send it to the system.

[0247] Step 2:

[0248] The terminal transmits the uploaded video data to the server.

[0249] Input: Training video data

[0250] Output: Video data to the server

[0251] The video data is transmitted from the terminal to the server.

[0252] Step 3:

[0253] The server passes the video data to an artificial intelligence system.

[0254] Input: Training video data

[0255] Output: Video data to an artificial intelligence system

[0256] The server sends the data to an AI model using TensorFlow or PyTorch.

[0257] Step 4:

[0258] An artificial intelligence system analyzes the video data and evaluates muscle development and form.

[0259] Input: Training video data

[0260] Output: Evaluation score, analysis results

[0261] The AI ​​system detects key joint points, analyzes movements and calculates an evaluation score.

[0262] Step 5:

[0263] The server obtains the evaluation results and prepares them as feedback.

[0264] Input: Evaluation score, analysis results

[0265] Output: trimmed feedback

[0266] The server uses the analysis results to generate feedback to provide to the user.

[0267] Step 6:

[0268] The server sends feedback and scores to the device and displays them to the user.

[0269] Input: Feedback, Rating Score

[0270] Output: Data displayed to the user

[0271] The feedback is formatted and displayed to the user.

[0272] Generative AI Support System

[0273] Step 1:

[0274] The user inputs health information and training goals from the device.

[0275] Input: Health information, training goals

[0276] Output: Data input to the terminal

[0277] Users enter their health information and training goals.

[0278] Step 2:

[0279] The terminal sends the input data to the server.

[0280] Input: Health information, training goals

[0281] Output: Input data to the server

[0282] This data is sent to the server.

[0283] Step 3:

[0284] The server passes the input data to the generation AI.

[0285] Input: Health information, training goals

[0286] Output: Data to the generative AI

[0287] The server passes the data to the generation AI (such as GPT-4).

[0288] Step 4:

[0289] Generative AI analyzes user data and generates the optimal plan.

[0290] Input: Health information, training goals

[0291] Output: Optimal training and nutrition plans

[0292] For example, a plan is generated based on the prompt statement, "My weight is 70 kg and my height is 175 cm. I want to gain 5 kg of muscle in one month."

[0293] Step 5:

[0294] The server takes the generated plan and formats it to return to the user.

[0295] Input: Best Plan

[0296] Output: Cleaned plan format

[0297] The generated plan is then trimmed and prepared in a format that can be presented to the user.

[0298] Step 6:

[0299] The server sends the personalized plan to the device and displays it to the user.

[0300] Input: Trimmed plan format

[0301] Output: Data displayed to the user

[0302] Users will be able to view the best plan on their device.

[0303] Online Community Platform

[0304] Step 1:

[0305] A user accesses a chat or forum and posts a message.

[0306] Input: message

[0307] Output: Posted data to the terminal

[0308] Users create and post messages within the community.

[0309] Step 2:

[0310] The terminal sends the user's message to the server.

[0311] Input: message

[0312] Output: Message data to the server

[0313] It is sent from the terminal to the server.

[0314] Step 3:

[0315] The server saves the message in a database and adds it to the associated thread.

[0316] Input: Message data

[0317] Output: Save information to database

[0318] Messages are stored in a database using PostgreSQL or similar.

[0319] Step 4:

[0320] The server notifies other users that a new message has been posted.

[0321] Input: New message information

[0322] Output: Notification information

[0323] Notify other users that you have posted something new.

[0324] Step 5:

[0325] Generative AI analyzes interaction data and provides appropriate advice and relevant information.

[0326] Input: AC data

[0327] Output: Advice, related information

[0328] Generative AI analyzes and generates appropriate advice and information.

[0329] Step 6:

[0330] The server provides advice and information to users and posts to forums and chats.

[0331] Input: Advice, Information

[0332] Output: Data provided to the user

[0333] The advice and information provided is displayed to the user.

[0334] Real-time Contest Module

[0335] Step 1:

[0336] A user enters a real-time contest and starts live streaming at a specified time.

[0337] Input: Entry information, live video

[0338] Output: Live video data from the device

[0339] A user participates in a contest and starts live streaming.

[0340] Step 2:

[0341] The terminal transmits live video to the server in real time.

[0342] Input: Live video data

[0343] Output: Live video data to server

[0344] The video is transmitted in real time.

[0345] Step 3:

[0346] The server passes the received video data to the artificial intelligence system in real time.

[0347] Input: Live video data

[0348] Output: Data to an artificial intelligence system

[0349] The server passes it to the AI ​​system.

[0350] Step 4:

[0351] An artificial intelligence system analyzes live footage and evaluates user performance.

[0352] Input: Live video data

[0353] Output: Evaluation score, analysis results

[0354] Real-time analysis is performed.

[0355] Step 5:

[0356] The server updates the analysis results in real time and displays the contest scores instantly.

[0357] Input: Evaluation score, analysis results

[0358] Output: Real-time score display data

[0359] The server will display the score instantly.

[0360] Step 6:

[0361] The server displays the scores in real time on the contest page and communicates them to all users.

[0362] Input: Real-time score display data

[0363] Output: Display score to user

[0364] Scores are displayed to all users in real time.

[0365] (Application example 1)

[0366] 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."

[0367] Conventional factory robot management systems have had the problem of making it difficult to grasp the robot's operating efficiency and areas for improvement in real time and provide personalized work plans.In addition, there were delays in sharing information and providing advice between operators, making it difficult to contribute to improving productivity throughout the factory.

[0368] 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.

[0369] In this invention, the server includes: means for accepting authentication information from a user and issuing an authentication token; artificial intelligence means for analyzing video data uploaded by the user and evaluating the robot's performance; means for generating operational feedback based on the AI's evaluation results and providing it to the user; means for providing a community function, analyzing interaction data, and providing appropriate advice and information; and means for analyzing video data in real time, evaluating the robot's performance, and providing the evaluation results to the user. This makes it possible to grasp the robot's operational efficiency and areas for improvement in real time and provide corresponding feedback. It also enables effective information sharing between operators and rapid advice provision, contributing to improved productivity throughout the factory.

[0370] "User" refers to a person who uses the system to operate and manage it.

[0371] "Authentication information" refers to information such as a username and password that a user uses to log in to a system.

[0372] An "authentication token" refers to temporary data that is issued after verifying user authentication and is used to maintain a session and control access.

[0373] "Video data" refers to video and still image data captured using cameras and sensors.

[0374] "Artificial intelligence" refers to computer programs that use machine learning and deep learning to analyze data and make evaluations and judgments.

[0375] "Operational feedback" refers to advice on improving work and efficiency provided to users based on the results of analysis by systems and artificial intelligence.

[0376] "Community functions" refer to functions such as chat and forums that allow users to share information and interact with each other.

[0377] "Interaction data" refers to data such as messages, posts, and comments exchanged by users through community functions.

[0378] "Advice" refers to recommendations or suggestions provided to users by a system or artificial intelligence.

[0379] "Real-time" refers to processing that occurs simultaneously with real time.

[0380] "Movement evaluation" refers to a system or artificial intelligence determining and evaluating the efficiency and accuracy of a specific movement based on video data.

[0381] The system for realizing this invention consists of five main components: user authentication, behavior evaluation using artificial intelligence, generative AI support system, community function, and real-time evaluation function.

[0382] The system includes a means for accepting authentication information from a user and issuing an authentication token. Specifically, the user accesses an application from a terminal and enters a username and password. The terminal sends this authentication information to a server, which then collates the received authentication information with a database to perform authentication. If authentication is successful, the server generates an authentication token and returns it to the terminal.

[0383] The system then includes an AI that analyzes the video data uploaded by the user and evaluates its performance. The user takes a video of the factory robot in operation and uploads it to the system. The terminal sends this video data to a server, which then passes it on to the AI. The AI ​​analyzes the video and evaluates the robot's operating efficiency and areas for improvement. The evaluation results are sent to the server and provided to the user as feedback.

[0384] The system also includes a generative AI support system. Users input operational information and goals from their devices, which then send the data to a server. The server passes the data to the generative AI, which then generates optimal work and maintenance plans. The generated plans are then provided to the user via the server.

[0385] The system also includes a community function. Users can access chat rooms and forums and post messages. The device sends the message to the server, which stores it in a database. At the same time, related users are notified that a new message has been posted. The generative AI analyzes the interaction data and provides appropriate advice and information.

[0386] Finally, the system includes a function for analyzing video data in real time and evaluating the user's actions. When a user uploads a video in real time, the server immediately passes the video data to an artificial intelligence system for evaluation. The evaluation results are displayed in real time and provided to the user.

[0387] The hardware used includes industrial PCs, communication infrastructure, AI-enabled cameras, and robot controllers, while the software used is Python, PyTorch / TensorFlow, Django / Flask, and PostgreSQL / MySQL.

[0388] As a concrete example, consider the case where a user uploads a video of a factory robot in operation and the AI ​​evaluates that operation. The evaluation results are fed back to the user, and the generation AI generates an optimal work plan. Examples of prompt sentences include "Please evaluate this video and provide feedback on the robot's work efficiency" and "Please analyze the robot's recent work log and generate the optimal work plan for the next time."

[0389] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0390] Step 1:

[0391] A user accesses an application from a terminal and enters a username and password. The entered authentication information is sent from the terminal to the server in the form of a user ID and password. The server compares the received authentication information with a database to confirm that the user is a legitimate user. If authentication is successful, the server generates an authentication token and returns this token to the terminal. This starts the user's session.

[0392] Input: User ID, Password

[0393] Output: Authentication token

[0394] Step 2:

[0395] The user takes a video of the factory robot in operation and uploads it to the system. The device sends this video data to the server. The server receives the video data and passes it on to the AI ​​system. The AI ​​system analyzes the video data and evaluates the robot's operating efficiency and areas for improvement. The evaluation results are sent back to the server as a series of numerical and text data.

[0396] Input: Motion video data

[0397] Output: Evaluation results (numerical data, text data)

[0398] Step 3:

[0399] The server generates operational feedback based on the evaluation results returned by the AI ​​system. Specifically, specific advice is generated based on the analysis results of the AI ​​model regarding areas for improving the robot's operational efficiency and the need for maintenance. The generated feedback is formatted in a way that is easy for the user to understand and sent to the terminal.

[0400] Input: Evaluation results (numeric data, text data)

[0401] Output: Operational feedback (text data)

[0402] Step 4:

[0403] The user inputs operational information and goals from a terminal. This input data is sent to the server as operational information (e.g., robot work logs) and goals (e.g., improving work efficiency). The server passes this data to the generative AI, and the generative AI model generates optimal work and maintenance plans. The generated plans are provided to the user via the server.

[0404] Input: Operational information, target

[0405] Output: Optimal work plan, maintenance plan (text data)

[0406] Step 5:

[0407] A user accesses a chat or forum and posts a message. The device sends the posted message to the server, which stores the message in a database and adds it to the relevant thread. It also notifies other users that a new message has been posted. The server uses generative AI to analyze the interaction data and provide appropriate advice and information. This advice and information is also posted to the forum or chat.

[0408] Input: Message (text data)

[0409] Output: Stored messages, notifications, and advice (text data)

[0410] Step 6:

[0411] Users upload live streaming of factory robot operations in real time. The terminal transmits the live video to a server in real time, and the server passes the received video data to an AI system in real time. The AI ​​system analyzes the live video in real time and evaluates the robot's operating efficiency and areas for improvement. The evaluation results are immediately sent back to the server, which displays them to the user in real time.

[0412] Input: Live video data

[0413] Output: Real-time evaluation results (numerical data, text data)

[0414] 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.

[0415] The embodiment of this invention is a system consisting of a user authentication, AI judgment system, generative AI support system, online community platform, real-time contest module, and emotion engine that recognizes user emotions. The program processing of this system will be explained below with each component and its specific operation.

[0416] User authentication

[0417] 1. User: First, the user accesses the application from a terminal and enters their username and password.

[0418] 2. Terminal: Sends username and password to server.

[0419] 3. Server: Checks the received credentials against a database.

[0420] 4. Server: If the user has valid credentials, it generates an authentication token and sends it back to the device, thereby starting the user session.

[0421] AI Judgment System

[0422] 1. User: Films training footage and uploads it through the platform.

[0423] 2. Terminal: Sends video data to the server.

[0424] 3. Server: Passes the video data to the AI ​​Judgment System.

[0425] 4. AI system: Analyzes video data and evaluates muscle development and form.

[0426] 5. Server: Obtains the analysis results and prepares them as feedback to be given to the user.

[0427] 6. Server: Sends feedback and scores to the device and displays them to the user.

[0428] Generative AI Support System

[0429] 1. User: Enter your health information and training goals.

[0430] 2. Terminal: Sends input data to the server.

[0431] 3. Server: Passes input data to the generation AI.

[0432] 4. Generative AI: Analyzes user data and generates optimal training and nutrition plans.

[0433] 5. Server: Prepares the generated plan and prepares it for delivery to the user.

[0434] 6. Server: Sends the personalized plan to the device and displays it to the user.

[0435] Online Community Platform

[0436] 1. Users: Post messages in forums and chats.

[0437] 2. Terminal: Sends the user's message to the server.

[0438] 3. Server: Stores received messages in a database and adds them to the associated thread.

[0439] 4. Server: Notifies other users of new messages.

[0440] 5. Generative AI: Analyzes interaction data and provides appropriate advice and information.

[0441] 6. Server: Displays advice and information provided by the generative AI to the user.

[0442] Real-time Contest Module

[0443] 1. User: Enter a real-time contest and start live streaming.

[0444] 2. Terminal: Sends live video to the server in real time.

[0445] 3. Server: Passes video data to the AI ​​Judgment System in real time.

[0446] 4. AI system: Analyzes live footage in real time and evaluates performance.

[0447] 5. Server: Updates the analysis results in real time and displays the contest scores instantly.

[0448] 6. Terminal: The scores will be displayed in real time on the contest page and communicated to all users.

[0449] Incorporating an emotion engine

[0450] 1. User: Provides video and audio data for sentiment analysis.

[0451] 2. Terminal: Sends the provided data to the server.

[0452] 3. Server: Passes video and audio data to the emotion engine.

[0453] 4. Emotion engine: Analyzes data and recognizes the user's emotional state.

[0454] 5. Server: Passes the recognized emotional state to the generative AI.

[0455] 6. Generative AI: Generate personalized training plans and feedback based on your emotional state.

[0456] 7. Server: Provides emotion-based feedback to users.

[0457] Specific examples

[0458] For example, when user A wants to have his / her training performance evaluated, the process is as follows:

[0459] 1. User A logs in to the application and uploads a training video.

[0460] 2. The server sends the video data to the AI ​​Judgment System, which then evaluates the muscles and checks the form.

[0461] 3. The server generates a rating score and feedback and provides it to User A.

[0462] Furthermore, when user B uses the community function to interact with other users, the following occurs.

[0463] 1. User B accesses the forum and starts chatting and posting.

[0464] 2. The server saves the post in a database and notifies other users.

[0465] 3. The generating AI analyzes the content of the interaction and provides useful advice to User B.

[0466] Furthermore, when using the emotion engine, the flow of how user C receives emotion-based feedback is as follows.

[0467] 1. User C uploads video and audio data for the emotion engine.

[0468] 2. The server passes the data to the emotion engine to recognize the emotional state.

[0469] 3. Based on the recognized emotional state, the generative AI generates appropriate feedback and provides it to user C.

[0470] In this way, each module works together to provide comprehensive support to the user.

[0471] The processing flow will be explained below.

[0472] User authentication process steps

[0473] Step 1:

[0474] User: Accesses the application from a terminal and enters a username and password.

[0475] Step 2:

[0476] Terminal: Sends the entered username and password to the server.

[0477] Step 3:

[0478] Server: Checks the received authentication information against a database.

[0479] Step 4:

[0480] Server: If the user is found to be valid, generate an authentication token.

[0481] Step 5:

[0482] Server: Returns the authentication token to the device and starts the user session.

[0483] AI Judgment System Processing Steps

[0484] Step 1:

[0485] User: Films training footage and uploads it through the application.

[0486] Step 2:

[0487] Terminal: Sends video data to the server.

[0488] Step 3:

[0489] Server: Passes the received video data to the AI ​​Judgment System.

[0490] Step 4:

[0491] AI system: Analyzes video data and evaluates muscle development and form.

[0492] Step 5:

[0493] AI system: Calculates a score as an analysis result and generates feedback.

[0494] Step 6:

[0495] Server: Sends the generated feedback and scores to the device.

[0496] Step 7:

[0497] Device: Shows feedback and score to the user.

[0498] Processing steps of the generative AI support system

[0499] Step 1:

[0500] User: Enter health information and training goals.

[0501] Step 2:

[0502] Terminal: Sends the entered data to the server.

[0503] Step 3:

[0504] Server: Passes the received data to the generation AI.

[0505] Step 4:

[0506] Generative AI: Analyzes user data and generates optimal training and nutrition plans.

[0507] Step 5:

[0508] Server: Formats the generated plan for delivery to the user.

[0509] Step 6:

[0510] Server: Sends the personalized plan to the device and displays it to the user.

[0511] Online community platform processing steps

[0512] Step 1:

[0513] Users: Post messages in forums and chats.

[0514] Step 2:

[0515] Terminal: Sends the user's messages to the server.

[0516] Step 3:

[0517] Server: Stores received messages in a database and adds them to the associated thread.

[0518] Step 4:

[0519] Server: Notifies other users of new messages.

[0520] Step 5:

[0521] Generative AI: Analyzes interaction data and provides appropriate advice and information.

[0522] Step 6:

[0523] Server: Displays advice and information provided by the generated AI to the user.

[0524] Processing steps of the real-time contest module

[0525] Step 1:

[0526] User: Enter a real-time contest and start live streaming.

[0527] Step 2:

[0528] Terminal: Sends live video to the server in real time.

[0529] Step 3:

[0530] Server: Passes video data to the AI ​​Judgment System in real time.

[0531] Step 4:

[0532] AI system: Analyzes live footage in real time and evaluates performance.

[0533] Step 5:

[0534] Server: Updates analysis results in real time and displays contest scores instantly.

[0535] Step 6:

[0536] Device: The scores will be displayed in real time on the contest page and communicated to all users.

[0537] Incorporating an emotion engine

[0538] Step 1:

[0539] User: Provides video and audio data for sentiment analysis.

[0540] Step 2:

[0541] Terminal: Sends the provided data to the server.

[0542] Step 3:

[0543] Server: Passes video and audio data to the emotion engine.

[0544] Step 4:

[0545] Emotion engine: Analyzes data and recognizes the user's emotional state.

[0546] Step 5:

[0547] Server: Passes the recognized emotional state to the generation AI.

[0548] Step 6:

[0549] Generative AI: Generates personalized training plans and feedback based on your emotional state.

[0550] Step 7:

[0551] Server: Provides emotion-based feedback to users.

[0552] Example 2

[0553] 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."

[0554] Conventional training support systems have had the problem of being unable to effectively evaluate users' training performance and form, and are unable to provide appropriate feedback. Furthermore, feedback based on the user's emotions and real-time performance evaluation are insufficient, making it difficult to maximize the effectiveness of training. Furthermore, there has been a lack of systems that allow users to effectively maintain motivation and share information through interactions with other users.

[0555] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting authentication information from a user and issuing an authentication token, artificial intelligence system means for analyzing video data uploaded by the user and evaluating muscles and checking training form, means for generating training feedback based on the evaluation results of the artificial intelligence system and providing it to the user, means for generating and providing feedback based on the user's emotions using an emotion engine that analyzes the user's emotional state, and real-time contest module means for analyzing the video data in real time and instantly displaying contest scores. This makes it possible to provide personalized feedback based on the user's training performance and emotional state, enabling real-time performance evaluation and effective information exchange with other users and maintaining motivation.

[0556] An "authentication token" is a unique identifier generated based on a user's authentication information, and is used to maintain a user session and ensure secure communication.

[0557] An "artificial intelligence system" is a system that uses machine learning and deep learning technologies to analyze data and automatically make judgments and evaluations that humans would normally make.

[0558] An "emotion engine" is a system that analyzes video and audio data to recognize a user's emotional state, and outputs the category and certainty of the emotional state.

[0559] The "Real-time Contest Module" is a system that analyzes live streaming footage from users in real time and instantly evaluates their performance and displays their scores.

[0560] "Health Information" refers to data related to personal health such as weight, age, height, and health condition provided by a user.

[0561] A "training goal" refers to a specific exercise or health goal set by a user, such as improving muscle strength, losing weight, or improving endurance.

[0562] "Feedback" refers to providing evaluations and advice on a user's behavior and performance in the form of text messages, graphs, etc.

[0563] "Personalized training plan" refers to an individually optimized training menu and nutrition plan generated based on an individual user's health information and training goals.

[0564] A "community function" is a function that provides an online platform such as a chat or forum for users to interact with other users and share information.

[0565] "Generative AI" refers to artificial intelligence technology that uses natural language processing and deep learning techniques to automatically generate appropriate training plans and advice based on input data from users.

[0566] This invention relates to a system consisting of a user authentication, AI judgment system, generative AI support system, online community platform, real-time contest module, and emotion engine that recognizes user emotions. Specific embodiments of the system are described below.

[0567] User authentication

[0568] The program for this system begins with user authentication. The user accesses the application using a device such as a smartphone or PC and enters their username and password. The device sends this information to the server using HTTPS communication. The server compares the received authentication information with a database (for example, MySQL), and if the information is correct, generates an authentication token (JWT: JSON Web Token). This authentication token is used to maintain the user session and is sent back to the device.

[0569] AI Judgment System

[0570] Next, the user moves on to the step of uploading the training video they have filmed. The user films the training video using a smartphone or other device and uploads the video data through the application. The device sends the video data to a server, which stores it in cloud storage (e.g., AWS S3). This video data is then passed from the server to an AI judgment system. The AI ​​judgment system analyzes the video data using deep learning models such as TensorFlow and evaluates the level of muscle development and training form. The evaluation results are sent back to the server, which uses them to generate training feedback and provide it to the user.

[0571] Generative AI Support System

[0572] Users can also input health information (age, weight, goals, etc.) and training goals within the application. This information is sent from the device to a server, which passes the input data to a generation AI. This generation AI analyzes the user data and generates optimal training and nutrition plans, using models such as GPT-4. The server then formats the generated plans, sends them to the device, and displays them to the user.

[0573] Online Community Platform

[0574] Furthermore, users can interact with other users through forums and chats. When a user posts a message, it is sent from the device to the server, where it is stored in a database. After being saved, other users are notified in real time. The generative AI analyzes this interaction data, generates appropriate advice and information, and provides it to users through the server.

[0575] Real-time Contest Module

[0576] Users can also enter real-time contests. Users start live streaming and send the video data from their device to the server in real time. The server passes the video data to the AI ​​Judgment System, where it is analyzed in real time. The analysis results are immediately sent back to the server, and the contest score is updated in real time. This updated score is displayed on the contest page from the device and communicated to all users.

[0577] Emotion Engine

[0578] The emotion engine analyzes the video and audio data provided by the user to recognize the user's emotional state. The user uploads video and audio data for emotion analysis and sends it to the server via their device. The server passes this data to the emotion engine, which analyzes the emotional state. The analysis results are passed to the generation AI, which generates a personalized training plan and feedback based on the results. This is also provided to the user via the server.

[0579] Examples of specific examples and prompts

[0580] For example, if User A wants to have their training performance evaluated, the process would be as follows:

[0581] 1. User A logs in to the application and uploads a training video.

[0582] 2. The server sends the video data to the AI ​​Judgment System, which then evaluates the muscles and checks the form.

[0583] 3. The server generates a rating score and feedback and provides it to User A.

[0584] Example prompt sentence:

[0585] "The user is 30 years old, weighs 70kg, and wants to improve their muscle mass. Please generate the optimal training plan for this user."

[0586] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0587] Step 1: User authentication

[0588] 1. Input: The user enters their username and password into the terminal.

[0589] 2. Operation: The device sends the username and password to the server using HTTPS communication.

[0590] 3. Data processing: The server queries the database for the received authentication information and verifies the username and password.

[0591] 4. Output: If there is a match, the server generates an authentication token (JWT) and sends it back to the device, which stores the received JWT.

[0592] Step 2: Upload your training footage

[0593] 1. Input: The user uses the device to select the training video data (MP4 format) they have taken and press the upload button.

[0594] 2. Operation: The device converts the video data into the appropriate format and sends it to the server.

[0595] 3. Data processing: The server receives the video data and stores it in cloud storage.

[0596] 4. Output: The server confirms that the video data has been saved and saves the path to the video data.

[0597] Step 3: Analysis by AI Judgment System

[0598] 1. Input: The server obtains the path to the stored video data and passes it to the AI ​​Judgment System.

[0599] 2. Operation: To analyze the video data, the AI ​​Judgment System loads a TensorFlow model and inputs the data.

[0600] 3. Data processing: Using deep learning models, we run algorithms to evaluate muscle development and training form.

[0601] 4. Output: Analysis results are generated and sent back to the server, including scores and feedback comments.

[0602] Step 4: Plan generation by generative AI support system

[0603] 1. Input: The user inputs health information (age, weight, etc.) and training goals into the device.

[0604] 2. Operation: The terminal sends the input data to the server, which generates a prompt and passes it to the generation AI.

[0605] 3. Data processing: The generative AI analyzes the data based on the prompts and generates optimal training and nutrition plans.

[0606] 4. Output: The generated plan is sent back to the server, which formats it appropriately (e.g. HTML or JSON) and sends it to the device for display.

[0607] Step 5: Online community features

[0608] 1. Input: A user types a message into a forum or chat and presses the post button.

[0609] 2. Operation: The device sends the post content to the server.

[0610] 3. Data processing: The server saves the message in a database, adds it to the associated thread, and notifies other users via WebSocket that a new post has been made.

[0611] 4. Output: The message is displayed in real time to other users viewing the forum or chat.

[0612] Step 6: Real-time contest

[0613] 1. Enter: A user starts a live stream and participates in a real-time contest.

[0614] 2. Operation: The device transmits live video to the server in real time.

[0615] 3. Data processing: The server passes the video data to the AI ​​Judgment System in real time for analysis.

[0616] 4. Output: The analysis results are displayed in real time, and the contest scores are updated instantly, allowing users to check the results in real time.

[0617] Step 7: Feedback from the Emotion Engine

[0618] 1. Input: User uploads video and audio data for sentiment analysis.

[0619] 2. Operation: The device compresses the data and sends it to the server, which then passes it to the emotion engine.

[0620] 3. Data processing: The emotion engine analyzes video and audio data and runs algorithms to recognize the user's emotional state.

[0621] 4. Output: The recognized emotional state is passed to the generation AI, which generates feedback based on the results and provides it to the user via the server.

[0622] (Application example 2)

[0623] 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."

[0624] In modern manufacturing, factory work efficiency, part quality control, real-time performance evaluation, and operator stress management are key issues. Conventional systems can only achieve these functions individually, so integrated and efficient solutions are required. Furthermore, there is a lack of means to share knowledge between engineers and operators and improve work efficiency, making it difficult to improve overall productivity.

[0625] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for accepting authentication information from a user and issuing an authentication token; an AI system for analyzing video data uploaded by the user and evaluating parts; means for generating and providing work feedback to the user based on the AI ​​system's evaluation results; a community function for users to interact with other users, analyzing interaction data using the generating AI and providing appropriate advice and information; means for analyzing the real-time performance of equipment operated by the user and displaying scores; and means for analyzing the user's facial expressions and voice data to recognize their emotional state and provide advice and feedback based on the results. This enables integrated management of work efficiency and part quality control, real-time performance evaluation, and operator stress management within the factory. Furthermore, productivity is improved through knowledge sharing among engineers and work efficiency contest functions.

[0626] "User" refers to an individual or corporation that uses the system.

[0627] "Authentication information" refers to information (such as a user name and password) used to identify a user and verify their legitimacy.

[0628] An "authentication token" is a unique string of characters issued after successful authentication to maintain the authenticated state of a user.

[0629] "Video data" refers to video information that a user captures using a device such as a camera and uploads to the system.

[0630] "Part" refers to a material or part of a product used in the manufacturing process.

[0631] An "AI system" refers to a system that uses artificial intelligence to analyze and evaluate video data.

[0632] "Work feedback" refers to improvements and advice provided based on the evaluation results of the AI ​​system.

[0633] "Community function" refers to a platform for users to share information and interact with each other.

[0634] "Generative AI" refers to artificial intelligence that automatically generates optimal plans and advice based on user information and data.

[0635] "Real-time performance" refers to the ability to evaluate the current operational efficiency and results of the equipment operated by the user over time.

[0636] "Facial expression data" refers to information obtained by capturing the user's facial expressions using a camera or other device.

[0637] "Voice data" refers to acoustic information obtained using the user's voice.

[0638] "Emotional state" refers to a user's current psychological and emotional state.

[0639] "Advice" refers to suggestions for improvement or guidance regarding the user's work or status.

[0640] "Feedback" refers to the evaluation results and comments provided to the user by the system.

[0641] MODE FOR CARRYING OUT THE INVENTION

[0642] This invention is a system that improves factory work efficiency and integrates parts quality control, real-time performance evaluation, and operator emotion management. The system consists of the following components:

[0643] 1. User Authentication

[0644] The server accepts authentication information from the user and issues an authentication token, which controls access to the system and allows only authorized users to perform operations.

[0645] 2. AI Judgment System

[0646] The server receives the video data uploaded by the user and passes it to the AI ​​system, which then analyzes the video data and performs an accurate evaluation of the parts, enabling it to identify defective products in real time and improve the quality of the production line.

[0647] 3. Real-time feedback

[0648] The server generates work feedback based on the AI ​​system's evaluation results and provides it to the user in real time, allowing the user to immediately understand areas for improvement and work more efficiently.

[0649] 4. Generative AI Support System

[0650] The server receives the operational information and work goals entered by the user and passes them to the generation AI. The generation AI analyzes the user data and generates personalized work plans and improvement plans. The server provides the generated plans to the user, helping to improve work efficiency.

[0651] 5. Online Community Platforms

[0652] The server provides a community function that allows users to interact with other users. Furthermore, it uses generative AI to analyze interaction data and provide appropriate advice and information. This enables knowledge sharing and problem solving between users quickly.

[0653] 6. Real-time Contest Module

[0654] The server analyzes the real-time performance of the devices operated by users and displays a score, allowing users to compete against each other in terms of work efficiency and quality.

[0655] 7. Emotion Engine

[0656] The server receives the user's facial expressions and voice data and passes it to the emotion engine. The emotion engine analyzes the data and recognizes the user's emotional state. The results are passed to the generative AI, which generates appropriate feedback and advice based on the user's emotional state. The server provides this to the user, supporting the operator's stress management.

[0657] Examples:

[0658] For example, when a factory operator checks the quality of a part, the process goes as follows: The user enters their authentication information to log in to the system and uploads video data of the part to the server. The server passes the video data to the AI ​​system, which then evaluates the part. Based on the results, the server generates an evaluation score and work feedback, which are provided to the user.

[0659] To interact with other engineers using the community function, users access the forum and post questions or questions. The server stores the posts in a database and notifies other users. The generative AI analyzes the content of the interactions and provides useful advice to the user.

[0660] Example prompt sentence:

[0661] Create a Python program to authenticate by entering a username and password. The following conditions must be met:

[0662] 1. User name and password are obtained via standard input.

[0663] 2. Passwords are hashed using SHA-256 hashing.

[0664] 3. The hashed password is checked against a dictionary database to verify it is correct.

[0665] 4. Displays the authentication success or failure result.

[0666] This enables work efficiency within the factory, part quality control, real-time performance evaluation, and operator stress management.

[0667] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0668] Step 1:

[0669] A user accesses an application and enters authentication information (user name and password) from a terminal. The terminal sends this authentication information to a server.

[0670] Input: Username, Password

[0671] Output: Authentication information sent to the server

[0672] How it works: The user enters authentication information using the keyboard and clicks the send button on the terminal. The terminal receives the authentication information and sends it to the server.

[0673] Step 2:

[0674] The server checks the received authentication information against existing user information in its database, and if authentication is successful, generates an authentication token and returns it to the terminal.

[0675] Input: Credentials

[0676] Output: Authentication token or authentication failure message

[0677] How it works: The server authenticates the user by comparing the received authentication information with the hashed password stored in its database. If authentication is successful, it generates an authentication token and returns it to the device.

[0678] Step 3:

[0679] After the user logs in to the system, the image data of the part is uploaded from the terminal to the server.

[0680] Input: Video data

[0681] Output: Video data sent to the server

[0682] Operation: The user takes video data and sends it to the server using the device's upload function.

[0683] Step 4:

[0684] The server passes the received video data to the AI ​​Judgment System, which analyzes the video data and evaluates the parts.

[0685] Input: Video data

[0686] Output: Part evaluation results (score, feedback)

[0687] How it works: The server passes the video data to the AI ​​Judgment System, which then analyzes the video and evaluates the condition of the parts. The evaluation results are generated in the form of a score and feedback.

[0688] Step 5:

[0689] The server generates work feedback based on the evaluation results received from the AI ​​judgment system and provides it to the user.

[0690] Input: Evaluation result

[0691] Output: Work feedback

[0692] How it works: The server formats the evaluation results received from the AI ​​system and provides them to the user in an easy-to-understand format.

[0693] Step 6:

[0694] The user inputs operational information and work goals from a terminal and sends them to the server, which then passes the information to the generation AI.

[0695] Input: Operational information, work goals

[0696] Output: Operational information and work goals sent to the server

[0697] How it works: The user enters the necessary information and sends it from their device to the server, which then passes it on to the generation AI.

[0698] Step 7:

[0699] The generation AI analyzes user data and generates personalized work plans and improvement plans, which are then sent back to the server.

[0700] Input: Operational information, work goals

[0701] Output: personalized work plan, improvement plan

[0702] How it works: The generative AI analyzes the input data, generates optimal work plans and improvement plans, and sends them back to the server.

[0703] Step 8:

[0704] The server provides the generated work plan to the user, who can then view and execute the work plan through their terminal.

[0705] Input: Personalized work plan, improvement plan

[0706] Output: Work plan and improvement plan provided to the user

[0707] How it works: The server formats the plan received from the generation AI and sends it to the user's device. The user receives it and applies it to their actual work.

[0708] Step 9:

[0709] Users can use the community function to post messages and questions to interact with other users, and the device sends that data to the server.

[0710] Input: message, question

[0711] Output: Community data sent to the server

[0712] How it works: A user uses the forum or chat feature to send messages or questions to other users. The device sends that data to the server.

[0713] Step 10:

[0714] The server stores community data, which is analyzed by the AI ​​generator, who then provides appropriate advice and information, which the server then displays to the user.

[0715] Input: Community Data

[0716] Output: Advice, Information

[0717] Operation: The generation AI analyzes the content of the interaction, generates useful advice for the user, and displays it to the user via the server.

[0718] Step 11:

[0719] The user sends real-time performance data of the equipment they operate from their device to the server, which then passes the data to the AI ​​Judgment System for performance analysis.

[0720] Input: Performance data

[0721] Output: Performance evaluation

[0722] How it works: Users send performance data of their operating equipment from their devices to the server, which then passes the data to the AI ​​system, where it is analyzed in real time.

[0723] Step 12:

[0724] The server displays the evaluated performance results in real time to the user, allowing comparison between users.

[0725] Input: Performance Rating

[0726] Output: Performance score displayed

[0727] How it works: The server updates the evaluation results received from the AI ​​system in real time and displays them to the user, allowing users to compete against each other on work efficiency and quality.

[0728] Step 13:

[0729] The user's facial expression and voice data are sent from the device to the server, which then passes the data to the emotion engine to analyze the user's emotional state.

[0730] Input: facial expression data, voice data

[0731] Output: Emotional state

[0732] How it works: The user uses a camera and microphone to input facial and voice data into the device, which is then sent to the server, which then passes the data to the emotion engine for analysis.

[0733] Step 14:

[0734] The emotion engine passes the emotional state based on the analysis results to the generative AI, which then generates optimal feedback and advice, which the server then provides to the user.

[0735] Input: Emotional state

[0736] Output: Emotion-based feedback and advice

[0737] How it works: The emotion engine analyzes facial expressions and voice data to recognize the user's emotional state. The generative AI then generates feedback and advice based on that information, which the server then provides to the user.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] [Second embodiment]

[0742] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0743] 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.

[0744] 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).

[0745] 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.

[0746] 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.

[0747] 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).

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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."

[0754] The embodiment of this invention is a system consisting of a user authentication, AI judgment system, generative AI support system, online community platform, and real-time contest module. The main components and their specific operations will be described for the program processing of this system.

[0755] User authentication

[0756] 1. User: First, the user accesses the application from a terminal and enters their username and password.

[0757] 2. Terminal: Sends username and password to server.

[0758] 3. Server: Checks the received authentication information against a database and issues an authentication token if the user has valid credentials.

[0759] 4. Server: Sends an authentication token back to the device, starting the user's session.

[0760] AI Judgment System

[0761] 1. User: The user shoots training footage and uploads it to the system.

[0762] 2. Terminal: Sends the uploaded video data to the server.

[0763] 3. Server: Passes the video data to the AI ​​Judgment System.

[0764] 4. AI system: The AI ​​system analyzes video data and evaluates muscle development and form.

[0765] 5. Server: Obtains the evaluation results and prepares them as feedback to be given to the user.

[0766] 6. Server: Sends feedback and scores to the device and displays them to the user.

[0767] Generative AI Support System

[0768] 1. User: Enter health information and training goals on the device.

[0769] 2. Terminal: Sends input data to the server.

[0770] 3. Server: Passes input data to the generation AI.

[0771] 4. Generative AI: Generative AI analyzes user data and generates optimal training and nutrition plans.

[0772] 5. Server: Takes the generated plan and formats it to be returned to the user.

[0773] 6. Server: Sends the personalized plan to the device and displays it to the user.

[0774] Online Community Platform

[0775] 1. User: Accesses chats and forums and posts messages.

[0776] 2. Terminal: Sends the user's message to the server.

[0777] 3. Server: Stores the message in a database and adds it to the associated thread.

[0778] 4. Server: Notifies other participants that a new message has been posted.

[0779] 5. Generative AI: Analyzes interaction data and provides appropriate advice and relevant information to users.

[0780] 6. Server: Providing advice and information to users, posting in forums and chats.

[0781] Real-time Contest Module

[0782] 1. User: Enter the real-time contest and start live streaming at the specified time.

[0783] 2. Terminal: Sends live video to the server in real time.

[0784] 3. Server: Passes the received video data to the AI ​​Judgment System in real time.

[0785] 4. AI system: Analyzes live footage in real time and evaluates user performance.

[0786] 5. Server: Updates the analysis results in real time and displays the contest scores instantly.

[0787] 6. Server: Display the scores in real time on the contest page and communicate them to all users.

[0788] Specific examples

[0789] For example, when user A wants to have his / her training performance evaluated, the process is as follows.

[0790] 1. User A logs in to the application and uploads a training video.

[0791] 2. The server sends the video data to the AI ​​Judgment System, which analyzes the video and calculates the score.

[0792] 3. The server generates a rating score and feedback and provides it to User A.

[0793] Furthermore, when user B uses the community function to interact with other users, the following occurs.

[0794] 1. User B accesses the forum and posts a question or training result.

[0795] 2. The server saves the post in a database and notifies other users.

[0796] 3. Along with replies from other users, advice provided by the generating AI will also be displayed.

[0797] The present invention can be implemented in the above-described manner, and will become a very useful system for many bodybuilding enthusiasts.

[0798] The processing flow will be explained below.

[0799] User authentication process steps

[0800] Step 1:

[0801] User: Accesses the application from a terminal and enters a username and password.

[0802] Step 2:

[0803] Terminal: Sends the entered username and password to the server.

[0804] Step 3:

[0805] Server: Checks the received authentication information against a database.

[0806] Step 4:

[0807] Server: If the user is found to be valid, generate an authentication token.

[0808] Step 5:

[0809] Server: Returns the authentication token to the device and starts the user session.

[0810] AI Judgment System Processing Steps

[0811] Step 1:

[0812] User: Films training footage and uploads it through the application.

[0813] Step 2:

[0814] Terminal: Sends video data to the server.

[0815] Step 3:

[0816] Server: Passes the received video data to the AI ​​Judgment System.

[0817] Step 4:

[0818] AI system: Analyzes video data and evaluates muscle development and form.

[0819] Step 5:

[0820] AI system: Calculates a score as an analysis result and generates feedback.

[0821] Step 6:

[0822] Server: Sends the generated feedback and scores to the device.

[0823] Step 7:

[0824] Device: Shows feedback and score to the user.

[0825] Processing steps of the generative AI support system

[0826] Step 1:

[0827] User: Enter health information and training goals.

[0828] Step 2:

[0829] Terminal: Sends the entered data to the server.

[0830] Step 3:

[0831] Server: Passes the received data to the generation AI.

[0832] Step 4:

[0833] Generative AI: Analyzes user data and generates optimal training and nutrition plans.

[0834] Step 5:

[0835] Server: Formats the generated plan for delivery to the user.

[0836] Step 6:

[0837] Server: Sends the personalized plan to the device and displays it to the user.

[0838] Online community platform processing steps

[0839] Step 1:

[0840] Users: Post messages in forums and chats.

[0841] Step 2:

[0842] Terminal: Sends the user's messages to the server.

[0843] Step 3:

[0844] Server: Stores received messages in a database and adds them to the associated thread.

[0845] Step 4:

[0846] Server: Notifies other users of new messages.

[0847] Step 5:

[0848] Generative AI: Analyzes interaction data and provides appropriate advice and information.

[0849] Step 6:

[0850] Server: Displays advice and information provided by the generated AI to the user.

[0851] Processing steps of the real-time contest module

[0852] Step 1:

[0853] User: Enter a real-time contest and start live streaming.

[0854] Step 2:

[0855] Terminal: Sends live video to the server in real time.

[0856] Step 3:

[0857] Server: Passes the received video data to the AI ​​Judgment System in real time.

[0858] Step 4:

[0859] AI system: Analyzes live footage in real time and evaluates performance.

[0860] Step 5:

[0861] Server: Updates analysis results in real time and displays contest scores instantly.

[0862] Step 6:

[0863] Device: The scores will be displayed in real time on the contest page and communicated to all users.

[0864] Example 1

[0865] 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."

[0866] Conventional training support systems are limited to functions such as user authentication, video data analysis, and training feedback generation, and lack functions such as providing personalized training and nutrition plans, analyzing user performance in real time, and providing interaction and advice between users. Furthermore, there are no integrated systems that combine these functions, which creates the issue of low user convenience when using multi-functional systems.

[0867] 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.

[0868] In this invention, the server includes means for receiving authentication information from a user and issuing an authentication token, an artificial intelligence system for analyzing video data uploaded by the user and evaluating the muscles, and means for generating training feedback based on the evaluation results of the artificial intelligence system and providing it to the user, thereby enabling user authentication, analysis of video data, and generation of training feedback.

[0869] In addition, in this invention, the server includes a generating artificial intelligence means for generating optimal training plans and nutrition plans based on user data, a means for analyzing and evaluating live streaming performed by the user in real time, and a generating artificial intelligence means for providing a community function for users to interact with other users and analyzing the interaction data to provide advice and information. This makes it possible to comprehensively realize multiple functions such as generating and providing personalized training plans and nutrition plans, analyzing performance in real time, and interacting with users and providing advice.

[0870] "User authentication" is the process of verifying whether a user is a legitimate user based on the authentication information provided by the user.

[0871] An "authentication token" is a digital certificate issued after successful user authentication and used to securely manage a user's session.

[0872] "Video data" refers to video and image data that users shoot and upload.

[0873] The "artificial intelligence system" analyzes video data and other data, mimicking human judgment to evaluate muscle development and form.

[0874] "Training feedback" refers to advice and evaluations about training provided to users based on the results of analysis by the artificial intelligence system.

[0875] "Generative AI" is AI that generates personalized training and nutrition plans based on user data.

[0876] "Live streaming" is a method for users to broadcast video in real time and share the process with other users or systems in real time.

[0877] "Real-time analysis" refers to the process of instantly analyzing data received during live streaming and providing results in real time.

[0878] The "community function" is a system that provides functions such as chat and forums for users to interact with each other.

[0879] "Advice and information" refers to content provided by artificial intelligence or other users to help users solve problems or improve performance.

[0880] MODE FOR CARRYING OUT THE INVENTION

[0881] This invention is a system that includes user authentication, analysis using artificial intelligence, generative AI support, online community functionality, and a real-time contest module. Specific embodiments for implementing this invention will be described below.

[0882] User Authentication

[0883] The user accesses the application from a terminal and enters their username and password.

[0884] The terminal sends the user name and password to the server.

[0885] The server checks the received credentials against a database (e.g. MySQL) by comparing the username in a user table with the hashed password.

[0886] If authentication is successful, the server generates a JSON Web Token (JWT) and returns it to the device.

[0887] Once the authentication token is received, the user's session begins.

[0888] AI Judgment System

[0889] Users film their training videos and upload them to the system.

[0890] The terminal transmits the uploaded video data to the server.

[0891] The server passes the video data to an artificial intelligence system, which uses AI models based on TensorFlow and PyTorch.

[0892] The AI ​​system analyzes the video data to assess muscle development and form, for example by detecting the positions of key joints in each frame of the video and calculating an evaluation score based on their movements.

[0893] The server takes the evaluation results and formats them to provide feedback to the user, which may take the form of textual advice or a score.

[0894] The server sends the feedback and score to the device and displays it to the user.

[0895] Generative AI Support System

[0896] Users input their health information and training goals through the terminal.

[0897] The terminal transmits the input data to the server.

[0898] The server passes the input data to the generative AI, which uses GPT-4 as its generative AI model.

[0899] The generative AI analyzes user data and generates personalized training and nutrition plans based on prompts, such as "I weigh 70 kg and am 175 cm tall. I want to gain 5 kg of muscle in one month."

[0900] The server takes the generated plan and formats it for delivery to the user.

[0901] The server sends the personalized plan to the terminal and displays it to the user.

[0902] Online Community Platform

[0903] Users access chats and forums and post messages.

[0904] The terminal sends the user's message to the server.

[0905] The server stores the message in a database (e.g. PostgreSQL) and adds it to the associated thread.

[0906] The server notifies other users that a new message has been posted.

[0907] Generative AI analyzes interaction data and provides appropriate advice and related information to users. It analyzes past posts and prompts to generate appropriate answers.

[0908] The server provides advice and information to users and posts to forums and chats.

[0909] Real-time Contest Module

[0910] Users enter a real-time contest and begin live streaming at a designated time.

[0911] The terminal transmits live video to the server in real time.

[0912] The server passes the received video data to the artificial intelligence system in real time.

[0913] The AI ​​system analyzes live video in real time and evaluates the user's performance, with the results being sent to the server each time.

[0914] The server updates the analysis results in real time and displays the contest scores instantly.

[0915] The server displays the scores in real time on the contest page and communicates them to all users.

[0916] As a specific example, when user A wants to have his / her training performance evaluated, the following procedure is carried out.

[0917] 1. User A logs in to the application and uploads a training video.

[0918] 2. The server sends the video data to an AI system, which analyzes the video and calculates a score. Specifically, the AI ​​detects the positions of key joints and calculates an evaluation score based on their movements.

[0919] 3. The server generates a rating score and feedback and provides it to User A.

[0920] Furthermore, when user B uses the community function to interact with other users, the following occurs.

[0921] 1. User B accesses the forum and posts a question or training result.

[0922] 2. The server saves the post in a database and notifies other users.

[0923] 3. Along with replies from other users, the AI ​​will also provide advice based on a prompt such as, "I'm having trouble with my training lately. Do you have any advice?"

[0924] This system allows many bodybuilding enthusiasts to train effectively, interact through the community, and compete against each other in real-time contests.

[0925] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0926] User Authentication

[0927] Step 1:

[0928] The user accesses the application from a terminal and enters their username and password.

[0929] Input: Username, Password

[0930] Output: Data input to the terminal

[0931] In this step, the user enters their authentication information.

[0932] Step 2:

[0933] The device sends the username and password to the server.

[0934] Input: Username, Password

[0935] Output: Authentication information to the server

[0936] The terminal sends the input information to the server in the appropriate format.

[0937] Step 3:

[0938] The server checks the received authentication information against a database.

[0939] Input: Credentials

[0940] Output: Authentication success or failure

[0941] It is matched against a user table in a database (e.g., MySQL), and if authentication is successful, a JWT is generated.

[0942] Step 4:

[0943] The server issues an authentication token (JWT) and sends it to the device.

[0944] Input: Authentication success or failure

[0945] Output: Authentication token

[0946] If authentication is successful, a JWT is generated and sent back to the device.

[0947] Step 5:

[0948] The device receives the authentication token and starts the user's session.

[0949] Input: Authentication token

[0950] Output: Authenticated session

[0951] Session management begins, allowing the user to operate within the application.

[0952] AI Judgment System

[0953] Step 1:

[0954] The user takes a training video and uploads it to the system.

[0955] Input: Training footage

[0956] Output: Video data to the device

[0957] The user records their own training and prepares to send it to the system.

[0958] Step 2:

[0959] The terminal transmits the uploaded video data to the server.

[0960] Input: Training video data

[0961] Output: Video data to the server

[0962] The video data is transmitted from the terminal to the server.

[0963] Step 3:

[0964] The server passes the video data to an artificial intelligence system.

[0965] Input: Training video data

[0966] Output: Video data to an artificial intelligence system

[0967] The server sends the data to an AI model using TensorFlow or PyTorch.

[0968] Step 4:

[0969] An artificial intelligence system analyzes the video data and evaluates muscle development and form.

[0970] Input: Training video data

[0971] Output: Evaluation score, analysis results

[0972] The AI ​​system detects key joint points, analyzes movements and calculates an evaluation score.

[0973] Step 5:

[0974] The server obtains the evaluation results and prepares them as feedback.

[0975] Input: Evaluation score, analysis results

[0976] Output: trimmed feedback

[0977] The server uses the analysis results to generate feedback to provide to the user.

[0978] Step 6:

[0979] The server sends feedback and scores to the device and displays them to the user.

[0980] Input: Feedback, Rating Score

[0981] Output: Data displayed to the user

[0982] The feedback is formatted and displayed to the user.

[0983] Generative AI Support System

[0984] Step 1:

[0985] The user inputs health information and training goals from the device.

[0986] Input: Health information, training goals

[0987] Output: Data input to the terminal

[0988] Users enter their health information and training goals.

[0989] Step 2:

[0990] The terminal sends the input data to the server.

[0991] Input: Health information, training goals

[0992] Output: Input data to the server

[0993] This data is sent to the server.

[0994] Step 3:

[0995] The server passes the input data to the generation AI.

[0996] Input: Health information, training goals

[0997] Output: Data to the generative AI

[0998] The server passes the data to the generation AI (such as GPT-4).

[0999] Step 4:

[1000] Generative AI analyzes user data and generates the optimal plan.

[1001] Input: Health information, training goals

[1002] Output: Optimal training and nutrition plans

[1003] For example, a plan is generated based on the prompt statement, "My weight is 70 kg and my height is 175 cm. I want to gain 5 kg of muscle in one month."

[1004] Step 5:

[1005] The server takes the generated plan and formats it to return to the user.

[1006] Input: Best Plan

[1007] Output: Cleaned plan format

[1008] The generated plan is then trimmed and prepared in a format that can be presented to the user.

[1009] Step 6:

[1010] The server sends the personalized plan to the device and displays it to the user.

[1011] Input: Trimmed plan format

[1012] Output: Data displayed to the user

[1013] Users will be able to view the best plan on their device.

[1014] Online Community Platform

[1015] Step 1:

[1016] A user accesses a chat or forum and posts a message.

[1017] Input: message

[1018] Output: Posted data to the terminal

[1019] Users create and post messages within the community.

[1020] Step 2:

[1021] The terminal sends the user's message to the server.

[1022] Input: message

[1023] Output: Message data to the server

[1024] It is sent from the terminal to the server.

[1025] Step 3:

[1026] The server saves the message in a database and adds it to the associated thread.

[1027] Input: Message data

[1028] Output: Save information to database

[1029] Messages are stored in a database using PostgreSQL or similar.

[1030] Step 4:

[1031] The server notifies other users that a new message has been posted.

[1032] Input: New message information

[1033] Output: Notification information

[1034] Notify other users that you have posted something new.

[1035] Step 5:

[1036] Generative AI analyzes interaction data and provides appropriate advice and relevant information.

[1037] Input: AC data

[1038] Output: Advice, related information

[1039] Generative AI analyzes and generates appropriate advice and information.

[1040] Step 6:

[1041] The server provides advice and information to users and posts to forums and chats.

[1042] Input: Advice, Information

[1043] Output: Data provided to the user

[1044] The advice and information provided is displayed to the user.

[1045] Real-time Contest Module

[1046] Step 1:

[1047] A user enters a real-time contest and starts live streaming at a specified time.

[1048] Input: Entry information, live video

[1049] Output: Live video data from the device

[1050] A user participates in a contest and starts live streaming.

[1051] Step 2:

[1052] The terminal transmits live video to the server in real time.

[1053] Input: Live video data

[1054] Output: Live video data to server

[1055] The video is transmitted in real time.

[1056] Step 3:

[1057] The server passes the received video data to the artificial intelligence system in real time.

[1058] Input: Live video data

[1059] Output: Data to an artificial intelligence system

[1060] The server passes it to the AI ​​system.

[1061] Step 4:

[1062] An artificial intelligence system analyzes live footage and evaluates user performance.

[1063] Input: Live video data

[1064] Output: Evaluation score, analysis results

[1065] Real-time analysis is performed.

[1066] Step 5:

[1067] The server updates the analysis results in real time and displays the contest scores instantly.

[1068] Input: Evaluation score, analysis results

[1069] Output: Real-time score display data

[1070] The server will display the score instantly.

[1071] Step 6:

[1072] The server displays the scores in real time on the contest page and communicates them to all users.

[1073] Input: Real-time score display data

[1074] Output: Display score to user

[1075] Scores are displayed to all users in real time.

[1076] (Application example 1)

[1077] 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."

[1078] Conventional factory robot management systems have had the problem of making it difficult to grasp the robot's operating efficiency and areas for improvement in real time and provide personalized work plans.In addition, there were delays in sharing information and providing advice between operators, making it difficult to contribute to improving productivity throughout the factory.

[1079] 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.

[1080] In this invention, the server includes: means for accepting authentication information from a user and issuing an authentication token; artificial intelligence means for analyzing video data uploaded by the user and evaluating the robot's performance; means for generating operational feedback based on the AI's evaluation results and providing it to the user; means for providing a community function, analyzing interaction data, and providing appropriate advice and information; and means for analyzing video data in real time, evaluating the robot's performance, and providing the evaluation results to the user. This makes it possible to grasp the robot's operational efficiency and areas for improvement in real time and provide corresponding feedback. It also enables effective information sharing between operators and rapid advice provision, contributing to improved productivity throughout the factory.

[1081] "User" refers to a person who uses the system to operate and manage it.

[1082] "Authentication information" refers to information such as a username and password that a user uses to log in to a system.

[1083] An "authentication token" refers to temporary data that is issued after verifying user authentication and is used to maintain a session and control access.

[1084] "Video data" refers to video and still image data captured using cameras and sensors.

[1085] "Artificial intelligence" refers to computer programs that use machine learning and deep learning to analyze data and make evaluations and judgments.

[1086] "Operational feedback" refers to advice on improving work and efficiency provided to users based on the results of analysis by systems and artificial intelligence.

[1087] "Community functions" refer to functions such as chat and forums that allow users to share information and interact with each other.

[1088] "Interaction data" refers to data such as messages, posts, and comments exchanged by users through community functions.

[1089] "Advice" refers to recommendations or suggestions provided to users by a system or artificial intelligence.

[1090] "Real-time" refers to processing that occurs simultaneously with real time.

[1091] "Movement evaluation" refers to a system or artificial intelligence determining and evaluating the efficiency and accuracy of a specific movement based on video data.

[1092] The system for realizing this invention consists of five main components: user authentication, behavior evaluation using artificial intelligence, generative AI support system, community function, and real-time evaluation function.

[1093] The system includes a means for accepting authentication information from a user and issuing an authentication token. Specifically, the user accesses an application from a terminal and enters a username and password. The terminal sends this authentication information to a server, which then collates the received authentication information with a database to perform authentication. If authentication is successful, the server generates an authentication token and returns it to the terminal.

[1094] The system then includes an AI that analyzes the video data uploaded by the user and evaluates its performance. The user takes a video of the factory robot in operation and uploads it to the system. The terminal sends this video data to a server, which then passes it on to the AI. The AI ​​analyzes the video and evaluates the robot's operating efficiency and areas for improvement. The evaluation results are sent to the server and provided to the user as feedback.

[1095] The system also includes a generative AI support system. Users input operational information and goals from their devices, which then send the data to a server. The server passes the data to the generative AI, which then generates optimal work and maintenance plans. The generated plans are then provided to the user via the server.

[1096] The system also includes a community function. Users can access chat rooms and forums and post messages. The device sends the message to the server, which stores it in a database. At the same time, related users are notified that a new message has been posted. The generative AI analyzes the interaction data and provides appropriate advice and information.

[1097] Finally, the system includes a function for analyzing video data in real time and evaluating the user's actions. When a user uploads a video in real time, the server immediately passes the video data to an artificial intelligence system for evaluation. The evaluation results are displayed in real time and provided to the user.

[1098] The hardware used includes industrial PCs, communication infrastructure, AI-enabled cameras, and robot controllers, while the software used is Python, PyTorch / TensorFlow, Django / Flask, and PostgreSQL / MySQL.

[1099] As a concrete example, consider the case where a user uploads a video of a factory robot in operation and the AI ​​evaluates that operation. The evaluation results are fed back to the user, and the generation AI generates an optimal work plan. Examples of prompt sentences include "Please evaluate this video and provide feedback on the robot's work efficiency" and "Please analyze the robot's recent work log and generate the optimal work plan for the next time."

[1100] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1101] Step 1:

[1102] A user accesses an application from a terminal and enters a username and password. The entered authentication information is sent from the terminal to the server in the form of a user ID and password. The server compares the received authentication information with a database to confirm that the user is a legitimate user. If authentication is successful, the server generates an authentication token and returns this token to the terminal. This starts the user's session.

[1103] Input: User ID, Password

[1104] Output: Authentication token

[1105] Step 2:

[1106] The user takes a video of the factory robot in operation and uploads it to the system. The device sends this video data to the server. The server receives the video data and passes it on to the AI ​​system. The AI ​​system analyzes the video data and evaluates the robot's operating efficiency and areas for improvement. The evaluation results are sent back to the server as a series of numerical and text data.

[1107] Input: Motion video data

[1108] Output: Evaluation results (numerical data, text data)

[1109] Step 3:

[1110] The server generates operational feedback based on the evaluation results returned by the AI ​​system. Specifically, specific advice is generated based on the analysis results of the AI ​​model regarding areas for improving the robot's operational efficiency and the need for maintenance. The generated feedback is formatted in a way that is easy for the user to understand and sent to the terminal.

[1111] Input: Evaluation results (numeric data, text data)

[1112] Output: Operational feedback (text data)

[1113] Step 4:

[1114] The user inputs operational information and goals from a terminal. This input data is sent to the server as operational information (e.g., robot work logs) and goals (e.g., improving work efficiency). The server passes this data to the generative AI, and the generative AI model generates optimal work and maintenance plans. The generated plans are provided to the user via the server.

[1115] Input: Operational information, target

[1116] Output: Optimal work plan, maintenance plan (text data)

[1117] Step 5:

[1118] A user accesses a chat or forum and posts a message. The device sends the posted message to the server, which stores the message in a database and adds it to the relevant thread. It also notifies other users that a new message has been posted. The server uses generative AI to analyze the interaction data and provide appropriate advice and information. This advice and information is also posted to the forum or chat.

[1119] Input: Message (text data)

[1120] Output: Stored messages, notifications, and advice (text data)

[1121] Step 6:

[1122] Users upload live streaming of factory robot operations in real time. The terminal transmits the live video to a server in real time, and the server passes the received video data to an AI system in real time. The AI ​​system analyzes the live video in real time and evaluates the robot's operating efficiency and areas for improvement. The evaluation results are immediately sent back to the server, which displays them to the user in real time.

[1123] Input: Live video data

[1124] Output: Real-time evaluation results (numerical data, text data)

[1125] 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.

[1126] The embodiment of this invention is a system consisting of a user authentication, AI judgment system, generative AI support system, online community platform, real-time contest module, and emotion engine that recognizes user emotions. The program processing of this system will be explained below with each component and its specific operation.

[1127] User authentication

[1128] 1. User: First, the user accesses the application from a terminal and enters their username and password.

[1129] 2. Terminal: Sends username and password to server.

[1130] 3. Server: Checks the received credentials against a database.

[1131] 4. Server: If the user has valid credentials, it generates an authentication token and sends it back to the device, thereby starting the user session.

[1132] AI Judgment System

[1133] 1. User: Films training footage and uploads it through the platform.

[1134] 2. Terminal: Sends video data to the server.

[1135] 3. Server: Passes the video data to the AI ​​Judgment System.

[1136] 4. AI system: Analyzes video data and evaluates muscle development and form.

[1137] 5. Server: Obtains the analysis results and prepares them as feedback to be given to the user.

[1138] 6. Server: Sends feedback and scores to the device and displays them to the user.

[1139] Generative AI Support System

[1140] 1. User: Enter your health information and training goals.

[1141] 2. Terminal: Sends input data to the server.

[1142] 3. Server: Passes input data to the generation AI.

[1143] 4. Generative AI: Analyzes user data and generates optimal training and nutrition plans.

[1144] 5. Server: Prepares the generated plan and prepares it for delivery to the user.

[1145] 6. Server: Sends the personalized plan to the device and displays it to the user.

[1146] Online Community Platform

[1147] 1. Users: Post messages in forums and chats.

[1148] 2. Terminal: Sends the user's message to the server.

[1149] 3. Server: Stores received messages in a database and adds them to the associated thread.

[1150] 4. Server: Notifies other users of new messages.

[1151] 5. Generative AI: Analyzes interaction data and provides appropriate advice and information.

[1152] 6. Server: Displays advice and information provided by the generative AI to the user.

[1153] Real-time Contest Module

[1154] 1. User: Enter a real-time contest and start live streaming.

[1155] 2. Terminal: Sends live video to the server in real time.

[1156] 3. Server: Passes video data to the AI ​​Judgment System in real time.

[1157] 4. AI system: Analyzes live footage in real time and evaluates performance.

[1158] 5. Server: Updates the analysis results in real time and displays the contest scores instantly.

[1159] 6. Terminal: The scores will be displayed in real time on the contest page and communicated to all users.

[1160] Incorporating an emotion engine

[1161] 1. User: Provides video and audio data for sentiment analysis.

[1162] 2. Terminal: Sends the provided data to the server.

[1163] 3. Server: Passes video and audio data to the emotion engine.

[1164] 4. Emotion engine: Analyzes data and recognizes the user's emotional state.

[1165] 5. Server: Passes the recognized emotional state to the generative AI.

[1166] 6. Generative AI: Generate personalized training plans and feedback based on your emotional state.

[1167] 7. Server: Provides emotion-based feedback to users.

[1168] Specific examples

[1169] For example, when user A wants to have his / her training performance evaluated, the process is as follows:

[1170] 1. User A logs in to the application and uploads a training video.

[1171] 2. The server sends the video data to the AI ​​Judgment System, which then evaluates the muscles and checks the form.

[1172] 3. The server generates a rating score and feedback and provides it to User A.

[1173] Furthermore, when user B uses the community function to interact with other users, the following occurs.

[1174] 1. User B accesses the forum and starts chatting and posting.

[1175] 2. The server saves the post in a database and notifies other users.

[1176] 3. The generating AI analyzes the content of the interaction and provides useful advice to User B.

[1177] Furthermore, when using the emotion engine, the flow of how user C receives emotion-based feedback is as follows.

[1178] 1. User C uploads video and audio data for the emotion engine.

[1179] 2. The server passes the data to the emotion engine to recognize the emotional state.

[1180] 3. Based on the recognized emotional state, the generative AI generates appropriate feedback and provides it to user C.

[1181] In this way, each module works together to provide comprehensive support to the user.

[1182] The processing flow will be explained below.

[1183] User authentication process steps

[1184] Step 1:

[1185] User: Accesses the application from a terminal and enters a username and password.

[1186] Step 2:

[1187] Terminal: Sends the entered username and password to the server.

[1188] Step 3:

[1189] Server: Checks the received authentication information against a database.

[1190] Step 4:

[1191] Server: If the user is found to be valid, generate an authentication token.

[1192] Step 5:

[1193] Server: Returns the authentication token to the device and starts the user session.

[1194] AI Judgment System Processing Steps

[1195] Step 1:

[1196] User: Films training footage and uploads it through the application.

[1197] Step 2:

[1198] Terminal: Sends video data to the server.

[1199] Step 3:

[1200] Server: Passes the received video data to the AI ​​Judgment System.

[1201] Step 4:

[1202] AI system: Analyzes video data and evaluates muscle development and form.

[1203] Step 5:

[1204] AI system: Calculates a score as an analysis result and generates feedback.

[1205] Step 6:

[1206] Server: Sends the generated feedback and scores to the device.

[1207] Step 7:

[1208] Device: Shows feedback and score to the user.

[1209] Processing steps of the generative AI support system

[1210] Step 1:

[1211] User: Enter health information and training goals.

[1212] Step 2:

[1213] Terminal: Sends the entered data to the server.

[1214] Step 3:

[1215] Server: Passes the received data to the generation AI.

[1216] Step 4:

[1217] Generative AI: Analyzes user data and generates optimal training and nutrition plans.

[1218] Step 5:

[1219] Server: Formats the generated plan for delivery to the user.

[1220] Step 6:

[1221] Server: Sends the personalized plan to the device and displays it to the user.

[1222] Online community platform processing steps

[1223] Step 1:

[1224] Users: Post messages in forums and chats.

[1225] Step 2:

[1226] Terminal: Sends the user's messages to the server.

[1227] Step 3:

[1228] Server: Stores received messages in a database and adds them to the associated thread.

[1229] Step 4:

[1230] Server: Notifies other users of new messages.

[1231] Step 5:

[1232] Generative AI: Analyzes interaction data and provides appropriate advice and information.

[1233] Step 6:

[1234] Server: Displays advice and information provided by the generated AI to the user.

[1235] Processing steps of the real-time contest module

[1236] Step 1:

[1237] User: Enter a real-time contest and start live streaming.

[1238] Step 2:

[1239] Terminal: Sends live video to the server in real time.

[1240] Step 3:

[1241] Server: Passes video data to the AI ​​Judgment System in real time.

[1242] Step 4:

[1243] AI system: Analyzes live footage in real time and evaluates performance.

[1244] Step 5:

[1245] Server: Updates analysis results in real time and displays contest scores instantly.

[1246] Step 6:

[1247] Device: The scores will be displayed in real time on the contest page and communicated to all users.

[1248] Incorporating an emotion engine

[1249] Step 1:

[1250] User: Provides video and audio data for sentiment analysis.

[1251] Step 2:

[1252] Terminal: Sends the provided data to the server.

[1253] Step 3:

[1254] Server: Passes video and audio data to the emotion engine.

[1255] Step 4:

[1256] Emotion engine: Analyzes data and recognizes the user's emotional state.

[1257] Step 5:

[1258] Server: Passes the recognized emotional state to the generation AI.

[1259] Step 6:

[1260] Generative AI: Generates personalized training plans and feedback based on your emotional state.

[1261] Step 7:

[1262] Server: Provides emotion-based feedback to users.

[1263] Example 2

[1264] 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."

[1265] Conventional training support systems have had the problem of being unable to effectively evaluate users' training performance and form, and are unable to provide appropriate feedback. Furthermore, feedback based on the user's emotions and real-time performance evaluation are insufficient, making it difficult to maximize the effectiveness of training. Furthermore, there has been a lack of systems that allow users to effectively maintain motivation and share information through interactions with other users.

[1266] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting authentication information from a user and issuing an authentication token, artificial intelligence system means for analyzing video data uploaded by the user and evaluating muscles and checking training form, means for generating training feedback based on the evaluation results of the artificial intelligence system and providing it to the user, means for generating and providing feedback based on the user's emotions using an emotion engine that analyzes the user's emotional state, and real-time contest module means for analyzing the video data in real time and instantly displaying contest scores. This makes it possible to provide personalized feedback based on the user's training performance and emotional state, enabling real-time performance evaluation and effective information exchange with other users and maintaining motivation.

[1267] An "authentication token" is a unique identifier generated based on a user's authentication information, and is used to maintain a user session and ensure secure communication.

[1268] An "artificial intelligence system" is a system that uses machine learning and deep learning technologies to analyze data and automatically make judgments and evaluations that humans would normally make.

[1269] An "emotion engine" is a system that analyzes video and audio data to recognize a user's emotional state, and outputs the category and certainty of the emotional state.

[1270] The "Real-time Contest Module" is a system that analyzes live streaming footage from users in real time and instantly evaluates their performance and displays their scores.

[1271] "Health Information" refers to data related to personal health such as weight, age, height, and health condition provided by a user.

[1272] A "training goal" refers to a specific exercise or health goal set by a user, such as improving muscle strength, losing weight, or improving endurance.

[1273] "Feedback" refers to providing evaluations and advice on a user's behavior and performance in the form of text messages, graphs, etc.

[1274] "Personalized training plan" refers to an individually optimized training menu and nutrition plan generated based on an individual user's health information and training goals.

[1275] A "community function" is a function that provides an online platform such as a chat or forum for users to interact with other users and share information.

[1276] "Generative AI" refers to artificial intelligence technology that uses natural language processing and deep learning techniques to automatically generate appropriate training plans and advice based on input data from users.

[1277] This invention relates to a system consisting of a user authentication, AI judgment system, generative AI support system, online community platform, real-time contest module, and emotion engine that recognizes user emotions. Specific embodiments of the system are described below.

[1278] User authentication

[1279] The program for this system begins with user authentication. The user accesses the application using a device such as a smartphone or PC and enters their username and password. The device sends this information to the server using HTTPS communication. The server compares the received authentication information with a database (for example, MySQL), and if the information is correct, generates an authentication token (JWT: JSON Web Token). This authentication token is used to maintain the user session and is sent back to the device.

[1280] AI Judgment System

[1281] Next, the user moves on to the step of uploading the training video they have filmed. The user films the training video using a smartphone or other device and uploads the video data through the application. The device sends the video data to a server, which stores it in cloud storage (e.g., AWS S3). This video data is then passed from the server to an AI judgment system. The AI ​​judgment system analyzes the video data using deep learning models such as TensorFlow and evaluates the level of muscle development and training form. The evaluation results are sent back to the server, which uses them to generate training feedback and provide it to the user.

[1282] Generative AI Support System

[1283] Users can also input health information (age, weight, goals, etc.) and training goals within the application. This information is sent from the device to a server, which passes the input data to a generation AI. This generation AI analyzes the user data and generates optimal training and nutrition plans, using models such as GPT-4. The server then formats the generated plans, sends them to the device, and displays them to the user.

[1284] Online Community Platform

[1285] Furthermore, users can interact with other users through forums and chats. When a user posts a message, it is sent from the device to the server, where it is stored in a database. After being saved, other users are notified in real time. The generative AI analyzes this interaction data, generates appropriate advice and information, and provides it to users through the server.

[1286] Real-time Contest Module

[1287] Users can also enter real-time contests. Users start live streaming and send the video data from their device to the server in real time. The server passes the video data to the AI ​​Judgment System, where it is analyzed in real time. The analysis results are immediately sent back to the server, and the contest score is updated in real time. This updated score is displayed on the contest page from the device and communicated to all users.

[1288] Emotion Engine

[1289] The emotion engine analyzes the video and audio data provided by the user to recognize the user's emotional state. The user uploads video and audio data for emotion analysis and sends it to the server via their device. The server passes this data to the emotion engine, which analyzes the emotional state. The analysis results are passed to the generation AI, which generates a personalized training plan and feedback based on the results. This is also provided to the user via the server.

[1290] Examples of specific examples and prompts

[1291] For example, if User A wants to have their training performance evaluated, the process would be as follows:

[1292] 1. User A logs in to the application and uploads a training video.

[1293] 2. The server sends the video data to the AI ​​Judgment System, which then evaluates the muscles and checks the form.

[1294] 3. The server generates a rating score and feedback and provides it to User A.

[1295] Example prompt sentence:

[1296] "The user is 30 years old, weighs 70kg, and wants to improve their muscle mass. Please generate the optimal training plan for this user."

[1297] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1298] Step 1: User authentication

[1299] 1. Input: The user enters their username and password into the terminal.

[1300] 2. Operation: The device sends the username and password to the server using HTTPS communication.

[1301] 3. Data processing: The server queries the database for the received authentication information and verifies the username and password.

[1302] 4. Output: If there is a match, the server generates an authentication token (JWT) and sends it back to the device, which stores the received JWT.

[1303] Step 2: Upload your training footage

[1304] 1. Input: The user uses the device to select the training video data (MP4 format) they have taken and press the upload button.

[1305] 2. Operation: The device converts the video data into the appropriate format and sends it to the server.

[1306] 3. Data processing: The server receives the video data and stores it in cloud storage.

[1307] 4. Output: The server confirms that the video data has been saved and saves the path to the video data.

[1308] Step 3: Analysis by AI Judgment System

[1309] 1. Input: The server obtains the path to the stored video data and passes it to the AI ​​Judgment System.

[1310] 2. Operation: To analyze the video data, the AI ​​Judgment System loads a TensorFlow model and inputs the data.

[1311] 3. Data processing: Using deep learning models, we run algorithms to evaluate muscle development and training form.

[1312] 4. Output: Analysis results are generated and sent back to the server, including scores and feedback comments.

[1313] Step 4: Plan generation by generative AI support system

[1314] 1. Input: The user inputs health information (age, weight, etc.) and training goals into the device.

[1315] 2. Operation: The terminal sends the input data to the server, which generates a prompt and passes it to the generation AI.

[1316] 3. Data processing: The generative AI analyzes the data based on the prompts and generates optimal training and nutrition plans.

[1317] 4. Output: The generated plan is sent back to the server, which formats it appropriately (e.g. HTML or JSON) and sends it to the device for display.

[1318] Step 5: Online community features

[1319] 1. Input: A user types a message into a forum or chat and presses the post button.

[1320] 2. Operation: The device sends the post content to the server.

[1321] 3. Data processing: The server saves the message in a database, adds it to the associated thread, and notifies other users via WebSocket that a new post has been made.

[1322] 4. Output: The message is displayed in real time to other users viewing the forum or chat.

[1323] Step 6: Real-time contest

[1324] 1. Enter: A user starts a live stream and participates in a real-time contest.

[1325] 2. Operation: The device transmits live video to the server in real time.

[1326] 3. Data processing: The server passes the video data to the AI ​​Judgment System in real time for analysis.

[1327] 4. Output: The analysis results are displayed in real time, and the contest scores are updated instantly, allowing users to check the results in real time.

[1328] Step 7: Feedback from the Emotion Engine

[1329] 1. Input: User uploads video and audio data for sentiment analysis.

[1330] 2. Operation: The device compresses the data and sends it to the server, which then passes it to the emotion engine.

[1331] 3. Data processing: The emotion engine analyzes video and audio data and runs algorithms to recognize the user's emotional state.

[1332] 4. Output: The recognized emotional state is passed to the generation AI, which generates feedback based on the results and provides it to the user via the server.

[1333] (Application example 2)

[1334] 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."

[1335] In modern manufacturing, factory work efficiency, part quality control, real-time performance evaluation, and operator stress management are key issues. Conventional systems can only achieve these functions individually, so integrated and efficient solutions are required. Furthermore, there is a lack of means to share knowledge between engineers and operators and improve work efficiency, making it difficult to improve overall productivity.

[1336] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for accepting authentication information from a user and issuing an authentication token; an AI system for analyzing video data uploaded by the user and evaluating parts; means for generating and providing work feedback to the user based on the AI ​​system's evaluation results; a community function for users to interact with other users, analyzing interaction data using the generating AI and providing appropriate advice and information; means for analyzing the real-time performance of equipment operated by the user and displaying scores; and means for analyzing the user's facial expressions and voice data to recognize their emotional state and provide advice and feedback based on the results. This enables integrated management of work efficiency and part quality control, real-time performance evaluation, and operator stress management within the factory. Furthermore, productivity is improved through knowledge sharing among engineers and work efficiency contest functions.

[1337] "User" refers to an individual or corporation that uses the system.

[1338] "Authentication information" refers to information (such as a user name and password) used to identify a user and verify their legitimacy.

[1339] An "authentication token" is a unique string of characters issued after successful authentication to maintain the authenticated state of a user.

[1340] "Video data" refers to video information that a user captures using a device such as a camera and uploads to the system.

[1341] "Part" refers to a material or part of a product used in the manufacturing process.

[1342] An "AI system" refers to a system that uses artificial intelligence to analyze and evaluate video data.

[1343] "Work feedback" refers to improvements and advice provided based on the evaluation results of the AI ​​system.

[1344] "Community function" refers to a platform for users to share information and interact with each other.

[1345] "Generative AI" refers to artificial intelligence that automatically generates optimal plans and advice based on user information and data.

[1346] "Real-time performance" refers to the ability to evaluate the current operational efficiency and results of the equipment operated by the user over time.

[1347] "Facial expression data" refers to information obtained by capturing the user's facial expressions using a camera or other device.

[1348] "Voice data" refers to acoustic information obtained using the user's voice.

[1349] "Emotional state" refers to a user's current psychological and emotional state.

[1350] "Advice" refers to suggestions for improvement or guidance regarding the user's work or status.

[1351] "Feedback" refers to the evaluation results and comments provided to the user by the system.

[1352] MODE FOR CARRYING OUT THE INVENTION

[1353] This invention is a system that improves factory work efficiency and integrates parts quality control, real-time performance evaluation, and operator emotion management. The system consists of the following components:

[1354] 1. User Authentication

[1355] The server accepts authentication information from the user and issues an authentication token, which controls access to the system and allows only authorized users to perform operations.

[1356] 2. AI Judgment System

[1357] The server receives the video data uploaded by the user and passes it to the AI ​​system, which then analyzes the video data and performs an accurate evaluation of the parts, enabling it to identify defective products in real time and improve the quality of the production line.

[1358] 3. Real-time feedback

[1359] The server generates work feedback based on the AI ​​system's evaluation results and provides it to the user in real time, allowing the user to immediately understand areas for improvement and work more efficiently.

[1360] 4. Generative AI Support System

[1361] The server receives the operational information and work goals entered by the user and passes them to the generation AI. The generation AI analyzes the user data and generates personalized work plans and improvement plans. The server provides the generated plans to the user, helping to improve work efficiency.

[1362] 5. Online Community Platforms

[1363] The server provides a community function that allows users to interact with other users. Furthermore, it uses generative AI to analyze interaction data and provide appropriate advice and information. This enables knowledge sharing and problem solving between users quickly.

[1364] 6. Real-time Contest Module

[1365] The server analyzes the real-time performance of the devices operated by users and displays a score, allowing users to compete against each other in terms of work efficiency and quality.

[1366] 7. Emotion Engine

[1367] The server receives the user's facial expressions and voice data and passes it to the emotion engine. The emotion engine analyzes the data and recognizes the user's emotional state. The results are passed to the generative AI, which generates appropriate feedback and advice based on the user's emotional state. The server provides this to the user, supporting the operator's stress management.

[1368] Examples:

[1369] For example, when a factory operator checks the quality of a part, the process goes as follows: The user enters their authentication information to log in to the system and uploads video data of the part to the server. The server passes the video data to the AI ​​system, which then evaluates the part. Based on the results, the server generates an evaluation score and work feedback, which are provided to the user.

[1370] To interact with other engineers using the community function, users access the forum and post questions or questions. The server stores the posts in a database and notifies other users. The generative AI analyzes the content of the interactions and provides useful advice to the user.

[1371] Example prompt sentence:

[1372] Create a Python program to authenticate by entering a username and password. The following conditions must be met:

[1373] 1. User name and password are obtained via standard input.

[1374] 2. Passwords are hashed using SHA-256 hashing.

[1375] 3. The hashed password is checked against a dictionary database to verify it is correct.

[1376] 4. Displays the authentication success or failure result.

[1377] This enables work efficiency within the factory, part quality control, real-time performance evaluation, and operator stress management.

[1378] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1379] Step 1:

[1380] A user accesses an application and enters authentication information (user name and password) from a terminal. The terminal sends this authentication information to a server.

[1381] Input: Username, Password

[1382] Output: Authentication information sent to the server

[1383] How it works: The user enters authentication information using the keyboard and clicks the send button on the terminal. The terminal receives the authentication information and sends it to the server.

[1384] Step 2:

[1385] The server checks the received authentication information against existing user information in its database, and if authentication is successful, generates an authentication token and returns it to the terminal.

[1386] Input: Credentials

[1387] Output: Authentication token or authentication failure message

[1388] How it works: The server authenticates the user by comparing the received authentication information with the hashed password stored in its database. If authentication is successful, it generates an authentication token and returns it to the device.

[1389] Step 3:

[1390] After the user logs in to the system, the image data of the part is uploaded from the terminal to the server.

[1391] Input: Video data

[1392] Output: Video data sent to the server

[1393] Operation: The user takes video data and sends it to the server using the device's upload function.

[1394] Step 4:

[1395] The server passes the received video data to the AI ​​Judgment System, which analyzes the video data and evaluates the parts.

[1396] Input: Video data

[1397] Output: Part evaluation results (score, feedback)

[1398] How it works: The server passes the video data to the AI ​​Judgment System, which then analyzes the video and evaluates the condition of the parts. The evaluation results are generated in the form of a score and feedback.

[1399] Step 5:

[1400] The server generates work feedback based on the evaluation results received from the AI ​​judgment system and provides it to the user.

[1401] Input: Evaluation result

[1402] Output: Work feedback

[1403] How it works: The server formats the evaluation results received from the AI ​​system and provides them to the user in an easy-to-understand format.

[1404] Step 6:

[1405] The user inputs operational information and work goals from a terminal and sends them to the server, which then passes the information to the generation AI.

[1406] Input: Operational information, work goals

[1407] Output: Operational information and work goals sent to the server

[1408] How it works: The user enters the necessary information and sends it from their device to the server, which then passes it on to the generation AI.

[1409] Step 7:

[1410] The generation AI analyzes user data and generates personalized work plans and improvement plans, which are then sent back to the server.

[1411] Input: Operational information, work goals

[1412] Output: personalized work plan, improvement plan

[1413] How it works: The generative AI analyzes the input data, generates optimal work plans and improvement plans, and sends them back to the server.

[1414] Step 8:

[1415] The server provides the generated work plan to the user, who can then view and execute the work plan through their terminal.

[1416] Input: Personalized work plan, improvement plan

[1417] Output: Work plan and improvement plan provided to the user

[1418] How it works: The server formats the plan received from the generation AI and sends it to the user's device. The user receives it and applies it to their actual work.

[1419] Step 9:

[1420] Users can use the community function to post messages and questions to interact with other users, and the device sends that data to the server.

[1421] Input: message, question

[1422] Output: Community data sent to the server

[1423] How it works: A user uses the forum or chat feature to send messages or questions to other users. The device sends that data to the server.

[1424] Step 10:

[1425] The server stores community data, which is analyzed by the AI ​​generator, who then provides appropriate advice and information, which the server then displays to the user.

[1426] Input: Community Data

[1427] Output: Advice, Information

[1428] Operation: The generation AI analyzes the content of the interaction, generates useful advice for the user, and displays it to the user via the server.

[1429] Step 11:

[1430] The user sends real-time performance data of the equipment they operate from their device to the server, which then passes the data to the AI ​​Judgment System for performance analysis.

[1431] Input: Performance data

[1432] Output: Performance evaluation

[1433] How it works: Users send performance data of their operating equipment from their devices to the server, which then passes the data to the AI ​​system, where it is analyzed in real time.

[1434] Step 12:

[1435] The server displays the evaluated performance results in real time to the user, allowing comparison between users.

[1436] Input: Performance Rating

[1437] Output: Performance score displayed

[1438] How it works: The server updates the evaluation results received from the AI ​​system in real time and displays them to the user, allowing users to compete against each other on work efficiency and quality.

[1439] Step 13:

[1440] The user's facial expression and voice data are sent from the device to the server, which then passes the data to the emotion engine to analyze the user's emotional state.

[1441] Input: facial expression data, voice data

[1442] Output: Emotional state

[1443] How it works: The user uses a camera and microphone to input facial and voice data into the device, which is then sent to the server, which then passes the data to the emotion engine for analysis.

[1444] Step 14:

[1445] The emotion engine passes the emotional state based on the analysis results to the generative AI, which then generates optimal feedback and advice, which the server then provides to the user.

[1446] Input: Emotional state

[1447] Output: Emotion-based feedback and advice

[1448] How it works: The emotion engine analyzes facial expressions and voice data to recognize the user's emotional state. The generative AI then generates feedback and advice based on that information, which the server then provides to the user.

[1449] 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.

[1450] 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.

[1451] 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.

[1452] [Third embodiment]

[1453] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1454] 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.

[1455] 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).

[1456] 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.

[1457] 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.

[1458] 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).

[1459] 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.

[1460] 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.

[1461] 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.

[1462] 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.

[1463] 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.

[1464] 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."

[1465] The embodiment of this invention is a system consisting of a user authentication, AI judgment system, generative AI support system, online community platform, and real-time contest module. The main components and their specific operations will be described for the program processing of this system.

[1466] User authentication

[1467] 1. User: First, the user accesses the application from a terminal and enters their username and password.

[1468] 2. Terminal: Sends username and password to server.

[1469] 3. Server: Checks the received authentication information against a database and issues an authentication token if the user has valid credentials.

[1470] 4. Server: Sends an authentication token back to the device, starting the user's session.

[1471] AI Judgment System

[1472] 1. User: The user shoots training footage and uploads it to the system.

[1473] 2. Terminal: Sends the uploaded video data to the server.

[1474] 3. Server: Passes the video data to the AI ​​Judgment System.

[1475] 4. AI system: The AI ​​system analyzes video data and evaluates muscle development and form.

[1476] 5. Server: Obtains the evaluation results and prepares them as feedback to be given to the user.

[1477] 6. Server: Sends feedback and scores to the device and displays them to the user.

[1478] Generative AI Support System

[1479] 1. User: Enter health information and training goals on the device.

[1480] 2. Terminal: Sends input data to the server.

[1481] 3. Server: Passes input data to the generation AI.

[1482] 4. Generative AI: Generative AI analyzes user data and generates optimal training and nutrition plans.

[1483] 5. Server: Takes the generated plan and formats it to be returned to the user.

[1484] 6. Server: Sends the personalized plan to the device and displays it to the user.

[1485] Online Community Platform

[1486] 1. User: Accesses chats and forums and posts messages.

[1487] 2. Terminal: Sends the user's message to the server.

[1488] 3. Server: Stores the message in a database and adds it to the associated thread.

[1489] 4. Server: Notifies other participants that a new message has been posted.

[1490] 5. Generative AI: Analyzes interaction data and provides appropriate advice and relevant information to users.

[1491] 6. Server: Providing advice and information to users, posting in forums and chats.

[1492] Real-time Contest Module

[1493] 1. User: Enter the real-time contest and start live streaming at the specified time.

[1494] 2. Terminal: Sends live video to the server in real time.

[1495] 3. Server: Passes the received video data to the AI ​​Judgment System in real time.

[1496] 4. AI system: Analyzes live footage in real time and evaluates user performance.

[1497] 5. Server: Updates the analysis results in real time and displays the contest scores instantly.

[1498] 6. Server: Display the scores in real time on the contest page and communicate them to all users.

[1499] Specific examples

[1500] For example, when user A wants to have his / her training performance evaluated, the process is as follows.

[1501] 1. User A logs in to the application and uploads a training video.

[1502] 2. The server sends the video data to the AI ​​Judgment System, which analyzes the video and calculates the score.

[1503] 3. The server generates a rating score and feedback and provides it to User A.

[1504] Furthermore, when user B uses the community function to interact with other users, the following occurs.

[1505] 1. User B accesses the forum and posts a question or training result.

[1506] 2. The server saves the post in a database and notifies other users.

[1507] 3. Along with replies from other users, advice provided by the generating AI will also be displayed.

[1508] The present invention can be implemented in the above-described manner, and will become a very useful system for many bodybuilding enthusiasts.

[1509] The processing flow will be explained below.

[1510] User authentication process steps

[1511] Step 1:

[1512] User: Accesses the application from a terminal and enters a username and password.

[1513] Step 2:

[1514] Terminal: Sends the entered username and password to the server.

[1515] Step 3:

[1516] Server: Checks the received authentication information against a database.

[1517] Step 4:

[1518] Server: If the user is found to be valid, generate an authentication token.

[1519] Step 5:

[1520] Server: Returns the authentication token to the device and starts the user session.

[1521] AI Judgment System Processing Steps

[1522] Step 1:

[1523] User: Films training footage and uploads it through the application.

[1524] Step 2:

[1525] Terminal: Sends video data to the server.

[1526] Step 3:

[1527] Server: Passes the received video data to the AI ​​Judgment System.

[1528] Step 4:

[1529] AI system: Analyzes video data and evaluates muscle development and form.

[1530] Step 5:

[1531] AI system: Calculates a score as an analysis result and generates feedback.

[1532] Step 6:

[1533] Server: Sends the generated feedback and scores to the device.

[1534] Step 7:

[1535] Device: Shows feedback and score to the user.

[1536] Processing steps of the generative AI support system

[1537] Step 1:

[1538] User: Enter health information and training goals.

[1539] Step 2:

[1540] Terminal: Sends the entered data to the server.

[1541] Step 3:

[1542] Server: Passes the received data to the generation AI.

[1543] Step 4:

[1544] Generative AI: Analyzes user data and generates optimal training and nutrition plans.

[1545] Step 5:

[1546] Server: Formats the generated plan for delivery to the user.

[1547] Step 6:

[1548] Server: Sends the personalized plan to the device and displays it to the user.

[1549] Online community platform processing steps

[1550] Step 1:

[1551] Users: Post messages in forums and chats.

[1552] Step 2:

[1553] Terminal: Sends the user's messages to the server.

[1554] Step 3:

[1555] Server: Stores received messages in a database and adds them to the associated thread.

[1556] Step 4:

[1557] Server: Notifies other users of new messages.

[1558] Step 5:

[1559] Generative AI: Analyzes interaction data and provides appropriate advice and information.

[1560] Step 6:

[1561] Server: Displays advice and information provided by the generated AI to the user.

[1562] Processing steps of the real-time contest module

[1563] Step 1:

[1564] User: Enter a real-time contest and start live streaming.

[1565] Step 2:

[1566] Terminal: Sends live video to the server in real time.

[1567] Step 3:

[1568] Server: Passes the received video data to the AI ​​Judgment System in real time.

[1569] Step 4:

[1570] AI system: Analyzes live footage in real time and evaluates performance.

[1571] Step 5:

[1572] Server: Updates analysis results in real time and displays contest scores instantly.

[1573] Step 6:

[1574] Device: The scores will be displayed in real time on the contest page and communicated to all users.

[1575] Example 1

[1576] 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."

[1577] Conventional training support systems are limited to functions such as user authentication, video data analysis, and training feedback generation, and lack functions such as providing personalized training and nutrition plans, analyzing user performance in real time, and providing interaction and advice between users. Furthermore, there are no integrated systems that combine these functions, which creates the issue of low user convenience when using multi-functional systems.

[1578] 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.

[1579] In this invention, the server includes means for receiving authentication information from a user and issuing an authentication token, an artificial intelligence system for analyzing video data uploaded by the user and evaluating the muscles, and means for generating training feedback based on the evaluation results of the artificial intelligence system and providing it to the user, thereby enabling user authentication, analysis of video data, and generation of training feedback.

[1580] In addition, in this invention, the server includes a generating artificial intelligence means for generating optimal training plans and nutrition plans based on user data, a means for analyzing and evaluating live streaming performed by the user in real time, and a generating artificial intelligence means for providing a community function for users to interact with other users and analyzing the interaction data to provide advice and information. This makes it possible to comprehensively realize multiple functions such as generating and providing personalized training plans and nutrition plans, analyzing performance in real time, and interacting with users and providing advice.

[1581] "User authentication" is the process of verifying whether a user is a legitimate user based on the authentication information provided by the user.

[1582] An "authentication token" is a digital certificate issued after successful user authentication and used to securely manage a user's session.

[1583] "Video data" refers to video and image data that users shoot and upload.

[1584] The "artificial intelligence system" analyzes video data and other data, mimicking human judgment to evaluate muscle development and form.

[1585] "Training feedback" refers to advice and evaluations about training provided to users based on the results of analysis by the artificial intelligence system.

[1586] "Generative AI" is AI that generates personalized training and nutrition plans based on user data.

[1587] "Live streaming" is a method for users to broadcast video in real time and share the process with other users or systems in real time.

[1588] "Real-time analysis" refers to the process of instantly analyzing data received during live streaming and providing results in real time.

[1589] The "community function" is a system that provides functions such as chat and forums for users to interact with each other.

[1590] "Advice and information" refers to content provided by artificial intelligence or other users to help users solve problems or improve performance.

[1591] MODE FOR CARRYING OUT THE INVENTION

[1592] This invention is a system that includes user authentication, analysis using artificial intelligence, generative AI support, online community functionality, and a real-time contest module. Specific embodiments for implementing this invention will be described below.

[1593] User Authentication

[1594] The user accesses the application from a terminal and enters their username and password.

[1595] The terminal sends the user name and password to the server.

[1596] The server checks the received credentials against a database (e.g. MySQL) by comparing the username in a user table with the hashed password.

[1597] If authentication is successful, the server generates a JSON Web Token (JWT) and returns it to the device.

[1598] Once the authentication token is received, the user's session begins.

[1599] AI Judgment System

[1600] Users film their training videos and upload them to the system.

[1601] The terminal transmits the uploaded video data to the server.

[1602] The server passes the video data to an artificial intelligence system, which uses AI models based on TensorFlow and PyTorch.

[1603] The AI ​​system analyzes the video data to assess muscle development and form, for example by detecting the positions of key joints in each frame of the video and calculating an evaluation score based on their movements.

[1604] The server takes the evaluation results and formats them to provide feedback to the user, which may take the form of textual advice or a score.

[1605] The server sends the feedback and score to the device and displays it to the user.

[1606] Generative AI Support System

[1607] Users input their health information and training goals through the terminal.

[1608] The terminal transmits the input data to the server.

[1609] The server passes the input data to the generative AI, which uses GPT-4 as its generative AI model.

[1610] The generative AI analyzes user data and generates personalized training and nutrition plans based on prompts, such as "I weigh 70 kg and am 175 cm tall. I want to gain 5 kg of muscle in one month."

[1611] The server takes the generated plan and formats it for delivery to the user.

[1612] The server sends the personalized plan to the terminal and displays it to the user.

[1613] Online Community Platform

[1614] Users access chats and forums and post messages.

[1615] The terminal sends the user's message to the server.

[1616] The server stores the message in a database (e.g. PostgreSQL) and adds it to the associated thread.

[1617] The server notifies other users that a new message has been posted.

[1618] Generative AI analyzes interaction data and provides appropriate advice and related information to users. It analyzes past posts and prompts to generate appropriate answers.

[1619] The server provides advice and information to users and posts to forums and chats.

[1620] Real-time Contest Module

[1621] Users enter a real-time contest and begin live streaming at a designated time.

[1622] The terminal transmits live video to the server in real time.

[1623] The server passes the received video data to the artificial intelligence system in real time.

[1624] The AI ​​system analyzes live video in real time and evaluates the user's performance, with the results being sent to the server each time.

[1625] The server updates the analysis results in real time and displays the contest scores instantly.

[1626] The server displays the scores in real time on the contest page and communicates them to all users.

[1627] As a specific example, when user A wants to have his / her training performance evaluated, the following procedure is carried out.

[1628] 1. User A logs in to the application and uploads a training video.

[1629] 2. The server sends the video data to an AI system, which analyzes the video and calculates a score. Specifically, the AI ​​detects the positions of key joints and calculates an evaluation score based on their movements.

[1630] 3. The server generates a rating score and feedback and provides it to User A.

[1631] Furthermore, when user B uses the community function to interact with other users, the following occurs.

[1632] 1. User B accesses the forum and posts a question or training result.

[1633] 2. The server saves the post in a database and notifies other users.

[1634] 3. Along with replies from other users, the AI ​​will also provide advice based on a prompt such as, "I'm having trouble with my training lately. Do you have any advice?"

[1635] This system allows many bodybuilding enthusiasts to train effectively, interact through the community, and compete against each other in real-time contests.

[1636] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1637] User Authentication

[1638] Step 1:

[1639] The user accesses the application from a terminal and enters their username and password.

[1640] Input: Username, Password

[1641] Output: Data input to the terminal

[1642] In this step, the user enters their authentication information.

[1643] Step 2:

[1644] The device sends the username and password to the server.

[1645] Input: Username, Password

[1646] Output: Authentication information to the server

[1647] The terminal sends the input information to the server in the appropriate format.

[1648] Step 3:

[1649] The server checks the received authentication information against a database.

[1650] Input: Credentials

[1651] Output: Authentication success or failure

[1652] It is matched against a user table in a database (e.g., MySQL), and if authentication is successful, a JWT is generated.

[1653] Step 4:

[1654] The server issues an authentication token (JWT) and sends it to the device.

[1655] Input: Authentication success or failure

[1656] Output: Authentication token

[1657] If authentication is successful, a JWT is generated and sent back to the device.

[1658] Step 5:

[1659] The device receives the authentication token and starts the user's session.

[1660] Input: Authentication token

[1661] Output: Authenticated session

[1662] Session management begins, allowing the user to operate within the application.

[1663] AI Judgment System

[1664] Step 1:

[1665] The user takes a training video and uploads it to the system.

[1666] Input: Training footage

[1667] Output: Video data to the device

[1668] The user records their own training and prepares to send it to the system.

[1669] Step 2:

[1670] The terminal transmits the uploaded video data to the server.

[1671] Input: Training video data

[1672] Output: Video data to the server

[1673] The video data is transmitted from the terminal to the server.

[1674] Step 3:

[1675] The server passes the video data to an artificial intelligence system.

[1676] Input: Training video data

[1677] Output: Video data to an artificial intelligence system

[1678] The server sends the data to an AI model using TensorFlow or PyTorch.

[1679] Step 4:

[1680] An artificial intelligence system analyzes the video data and evaluates muscle development and form.

[1681] Input: Training video data

[1682] Output: Evaluation score, analysis results

[1683] The AI ​​system detects key joint points, analyzes movements and calculates an evaluation score.

[1684] Step 5:

[1685] The server obtains the evaluation results and prepares them as feedback.

[1686] Input: Evaluation score, analysis results

[1687] Output: trimmed feedback

[1688] The server uses the analysis results to generate feedback to provide to the user.

[1689] Step 6:

[1690] The server sends feedback and scores to the device and displays them to the user.

[1691] Input: Feedback, Rating Score

[1692] Output: Data displayed to the user

[1693] The feedback is formatted and displayed to the user.

[1694] Generative AI Support System

[1695] Step 1:

[1696] The user inputs health information and training goals from the device.

[1697] Input: Health information, training goals

[1698] Output: Data input to the terminal

[1699] Users enter their health information and training goals.

[1700] Step 2:

[1701] The terminal sends the input data to the server.

[1702] Input: Health information, training goals

[1703] Output: Input data to the server

[1704] This data is sent to the server.

[1705] Step 3:

[1706] The server passes the input data to the generation AI.

[1707] Input: Health information, training goals

[1708] Output: Data to the generative AI

[1709] The server passes the data to the generation AI (such as GPT-4).

[1710] Step 4:

[1711] Generative AI analyzes user data and generates the optimal plan.

[1712] Input: Health information, training goals

[1713] Output: Optimal training and nutrition plans

[1714] For example, a plan is generated based on the prompt statement, "My weight is 70 kg and my height is 175 cm. I want to gain 5 kg of muscle in one month."

[1715] Step 5:

[1716] The server takes the generated plan and formats it to return to the user.

[1717] Input: Best Plan

[1718] Output: Cleaned plan format

[1719] The generated plan is then trimmed and prepared in a format that can be presented to the user.

[1720] Step 6:

[1721] The server sends the personalized plan to the device and displays it to the user.

[1722] Input: Trimmed plan format

[1723] Output: Data displayed to the user

[1724] Users will be able to view the best plan on their device.

[1725] Online Community Platform

[1726] Step 1:

[1727] A user accesses a chat or forum and posts a message.

[1728] Input: message

[1729] Output: Posted data to the terminal

[1730] Users create and post messages within the community.

[1731] Step 2:

[1732] The terminal sends the user's message to the server.

[1733] Input: message

[1734] Output: Message data to the server

[1735] It is sent from the terminal to the server.

[1736] Step 3:

[1737] The server saves the message in a database and adds it to the associated thread.

[1738] Input: Message data

[1739] Output: Save information to database

[1740] Messages are stored in a database using PostgreSQL or similar.

[1741] Step 4:

[1742] The server notifies other users that a new message has been posted.

[1743] Input: New message information

[1744] Output: Notification information

[1745] Notify other users that you have posted something new.

[1746] Step 5:

[1747] Generative AI analyzes interaction data and provides appropriate advice and relevant information.

[1748] Input: AC data

[1749] Output: Advice, related information

[1750] Generative AI analyzes and generates appropriate advice and information.

[1751] Step 6:

[1752] The server provides advice and information to users and posts to forums and chats.

[1753] Input: Advice, Information

[1754] Output: Data provided to the user

[1755] The advice and information provided is displayed to the user.

[1756] Real-time Contest Module

[1757] Step 1:

[1758] A user enters a real-time contest and starts live streaming at a specified time.

[1759] Input: Entry information, live video

[1760] Output: Live video data from the device

[1761] A user participates in a contest and starts live streaming.

[1762] Step 2:

[1763] The terminal transmits live video to the server in real time.

[1764] Input: Live video data

[1765] Output: Live video data to server

[1766] The video is transmitted in real time.

[1767] Step 3:

[1768] The server passes the received video data to the artificial intelligence system in real time.

[1769] Input: Live video data

[1770] Output: Data to an artificial intelligence system

[1771] The server passes it to the AI ​​system.

[1772] Step 4:

[1773] An artificial intelligence system analyzes live footage and evaluates user performance.

[1774] Input: Live video data

[1775] Output: Evaluation score, analysis results

[1776] Real-time analysis is performed.

[1777] Step 5:

[1778] The server updates the analysis results in real time and displays the contest scores instantly.

[1779] Input: Evaluation score, analysis results

[1780] Output: Real-time score display data

[1781] The server will display the score instantly.

[1782] Step 6:

[1783] The server displays the scores in real time on the contest page and communicates them to all users.

[1784] Input: Real-time score display data

[1785] Output: Display score to user

[1786] Scores are displayed to all users in real time.

[1787] (Application example 1)

[1788] 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."

[1789] Conventional factory robot management systems have had the problem of making it difficult to grasp the robot's operating efficiency and areas for improvement in real time and provide personalized work plans.In addition, there were delays in sharing information and providing advice between operators, making it difficult to contribute to improving productivity throughout the factory.

[1790] 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.

[1791] In this invention, the server includes: means for accepting authentication information from a user and issuing an authentication token; artificial intelligence means for analyzing video data uploaded by the user and evaluating the robot's performance; means for generating operational feedback based on the AI's evaluation results and providing it to the user; means for providing a community function, analyzing interaction data, and providing appropriate advice and information; and means for analyzing video data in real time, evaluating the robot's performance, and providing the evaluation results to the user. This makes it possible to grasp the robot's operational efficiency and areas for improvement in real time and provide corresponding feedback. It also enables effective information sharing between operators and rapid advice provision, contributing to improved productivity throughout the factory.

[1792] "User" refers to a person who uses the system to operate and manage it.

[1793] "Authentication information" refers to information such as a username and password that a user uses to log in to a system.

[1794] An "authentication token" refers to temporary data that is issued after verifying user authentication and is used to maintain a session and control access.

[1795] "Video data" refers to video and still image data captured using cameras and sensors.

[1796] "Artificial intelligence" refers to computer programs that use machine learning and deep learning to analyze data and make evaluations and judgments.

[1797] "Operational feedback" refers to advice on improving work and efficiency provided to users based on the results of analysis by systems and artificial intelligence.

[1798] "Community functions" refer to functions such as chat and forums that allow users to share information and interact with each other.

[1799] "Interaction data" refers to data such as messages, posts, and comments exchanged by users through community functions.

[1800] "Advice" refers to recommendations or suggestions provided to users by a system or artificial intelligence.

[1801] "Real-time" refers to processing that occurs simultaneously with real time.

[1802] "Movement evaluation" refers to a system or artificial intelligence determining and evaluating the efficiency and accuracy of a specific movement based on video data.

[1803] The system for realizing this invention consists of five main components: user authentication, behavior evaluation using artificial intelligence, generative AI support system, community function, and real-time evaluation function.

[1804] The system includes a means for accepting authentication information from a user and issuing an authentication token. Specifically, the user accesses an application from a terminal and enters a username and password. The terminal sends this authentication information to a server, which then collates the received authentication information with a database to perform authentication. If authentication is successful, the server generates an authentication token and returns it to the terminal.

[1805] The system then includes an AI that analyzes the video data uploaded by the user and evaluates its performance. The user takes a video of the factory robot in operation and uploads it to the system. The terminal sends this video data to a server, which then passes it on to the AI. The AI ​​analyzes the video and evaluates the robot's operating efficiency and areas for improvement. The evaluation results are sent to the server and provided to the user as feedback.

[1806] The system also includes a generative AI support system. Users input operational information and goals from their devices, which then send the data to a server. The server passes the data to the generative AI, which then generates optimal work and maintenance plans. The generated plans are then provided to the user via the server.

[1807] The system also includes a community function. Users can access chat rooms and forums and post messages. The device sends the message to the server, which stores it in a database. At the same time, related users are notified that a new message has been posted. The generative AI analyzes the interaction data and provides appropriate advice and information.

[1808] Finally, the system includes a function for analyzing video data in real time and evaluating the user's actions. When a user uploads a video in real time, the server immediately passes the video data to an artificial intelligence system for evaluation. The evaluation results are displayed in real time and provided to the user.

[1809] The hardware used includes industrial PCs, communication infrastructure, AI-enabled cameras, and robot controllers, while the software used is Python, PyTorch / TensorFlow, Django / Flask, and PostgreSQL / MySQL.

[1810] As a concrete example, consider the case where a user uploads a video of a factory robot in operation and the AI ​​evaluates that operation. The evaluation results are fed back to the user, and the generation AI generates an optimal work plan. Examples of prompt sentences include "Please evaluate this video and provide feedback on the robot's work efficiency" and "Please analyze the robot's recent work log and generate the optimal work plan for the next time."

[1811] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1812] Step 1:

[1813] A user accesses an application from a terminal and enters a username and password. The entered authentication information is sent from the terminal to the server in the form of a user ID and password. The server compares the received authentication information with a database to confirm that the user is a legitimate user. If authentication is successful, the server generates an authentication token and returns this token to the terminal. This starts the user's session.

[1814] Input: User ID, Password

[1815] Output: Authentication token

[1816] Step 2:

[1817] The user takes a video of the factory robot in operation and uploads it to the system. The device sends this video data to the server. The server receives the video data and passes it on to the AI ​​system. The AI ​​system analyzes the video data and evaluates the robot's operating efficiency and areas for improvement. The evaluation results are sent back to the server as a series of numerical and text data.

[1818] Input: Motion video data

[1819] Output: Evaluation results (numerical data, text data)

[1820] Step 3:

[1821] The server generates operational feedback based on the evaluation results returned by the AI ​​system. Specifically, specific advice is generated based on the analysis results of the AI ​​model regarding areas for improving the robot's operational efficiency and the need for maintenance. The generated feedback is formatted in a way that is easy for the user to understand and sent to the terminal.

[1822] Input: Evaluation results (numeric data, text data)

[1823] Output: Operational feedback (text data)

[1824] Step 4:

[1825] The user inputs operational information and goals from a terminal. This input data is sent to the server as operational information (e.g., robot work logs) and goals (e.g., improving work efficiency). The server passes this data to the generative AI, and the generative AI model generates optimal work and maintenance plans. The generated plans are provided to the user via the server.

[1826] Input: Operational information, target

[1827] Output: Optimal work plan, maintenance plan (text data)

[1828] Step 5:

[1829] A user accesses a chat or forum and posts a message. The device sends the posted message to the server, which stores the message in a database and adds it to the relevant thread. It also notifies other users that a new message has been posted. The server uses generative AI to analyze the interaction data and provide appropriate advice and information. This advice and information is also posted to the forum or chat.

[1830] Input: Message (text data)

[1831] Output: Stored messages, notifications, and advice (text data)

[1832] Step 6:

[1833] Users upload live streaming of factory robot operations in real time. The terminal transmits the live video to a server in real time, and the server passes the received video data to an AI system in real time. The AI ​​system analyzes the live video in real time and evaluates the robot's operating efficiency and areas for improvement. The evaluation results are immediately sent back to the server, which displays them to the user in real time.

[1834] Input: Live video data

[1835] Output: Real-time evaluation results (numerical data, text data)

[1836] 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.

[1837] The embodiment of this invention is a system consisting of a user authentication, AI judgment system, generative AI support system, online community platform, real-time contest module, and emotion engine that recognizes user emotions. The program processing of this system will be explained below with each component and its specific operation.

[1838] User authentication

[1839] 1. User: First, the user accesses the application from a terminal and enters their username and password.

[1840] 2. Terminal: Sends username and password to server.

[1841] 3. Server: Checks the received credentials against a database.

[1842] 4. Server: If the user has valid credentials, it generates an authentication token and sends it back to the device, thereby starting the user session.

[1843] AI Judgment System

[1844] 1. User: Films training footage and uploads it through the platform.

[1845] 2. Terminal: Sends video data to the server.

[1846] 3. Server: Passes the video data to the AI ​​Judgment System.

[1847] 4. AI system: Analyzes video data and evaluates muscle development and form.

[1848] 5. Server: Obtains the analysis results and prepares them as feedback to be given to the user.

[1849] 6. Server: Sends feedback and scores to the device and displays them to the user.

[1850] Generative AI Support System

[1851] 1. User: Enter your health information and training goals.

[1852] 2. Terminal: Sends input data to the server.

[1853] 3. Server: Passes input data to the generation AI.

[1854] 4. Generative AI: Analyzes user data and generates optimal training and nutrition plans.

[1855] 5. Server: Prepares the generated plan and prepares it for delivery to the user.

[1856] 6. Server: Sends the personalized plan to the device and displays it to the user.

[1857] Online Community Platform

[1858] 1. Users: Post messages in forums and chats.

[1859] 2. Terminal: Sends the user's message to the server.

[1860] 3. Server: Stores received messages in a database and adds them to the associated thread.

[1861] 4. Server: Notifies other users of new messages.

[1862] 5. Generative AI: Analyzes interaction data and provides appropriate advice and information.

[1863] 6. Server: Displays advice and information provided by the generative AI to the user.

[1864] Real-time Contest Module

[1865] 1. User: Enter a real-time contest and start live streaming.

[1866] 2. Terminal: Sends live video to the server in real time.

[1867] 3. Server: Passes video data to the AI ​​Judgment System in real time.

[1868] 4. AI system: Analyzes live footage in real time and evaluates performance.

[1869] 5. Server: Updates the analysis results in real time and displays the contest scores instantly.

[1870] 6. Terminal: The scores will be displayed in real time on the contest page and communicated to all users.

[1871] Incorporating an emotion engine

[1872] 1. User: Provides video and audio data for sentiment analysis.

[1873] 2. Terminal: Sends the provided data to the server.

[1874] 3. Server: Passes video and audio data to the emotion engine.

[1875] 4. Emotion engine: Analyzes data and recognizes the user's emotional state.

[1876] 5. Server: Passes the recognized emotional state to the generative AI.

[1877] 6. Generative AI: Generate personalized training plans and feedback based on your emotional state.

[1878] 7. Server: Provides emotion-based feedback to users.

[1879] Specific examples

[1880] For example, when user A wants to have his / her training performance evaluated, the process is as follows:

[1881] 1. User A logs in to the application and uploads a training video.

[1882] 2. The server sends the video data to the AI ​​Judgment System, which then evaluates the muscles and checks the form.

[1883] 3. The server generates a rating score and feedback and provides it to User A.

[1884] Furthermore, when user B uses the community function to interact with other users, the following occurs.

[1885] 1. User B accesses the forum and starts chatting and posting.

[1886] 2. The server saves the post in a database and notifies other users.

[1887] 3. The generating AI analyzes the content of the interaction and provides useful advice to User B.

[1888] Furthermore, when using the emotion engine, the flow of how user C receives emotion-based feedback is as follows.

[1889] 1. User C uploads video and audio data for the emotion engine.

[1890] 2. The server passes the data to the emotion engine to recognize the emotional state.

[1891] 3. Based on the recognized emotional state, the generative AI generates appropriate feedback and provides it to user C.

[1892] In this way, each module works together to provide comprehensive support to the user.

[1893] The processing flow will be explained below.

[1894] User authentication process steps

[1895] Step 1:

[1896] User: Accesses the application from a terminal and enters a username and password.

[1897] Step 2:

[1898] Terminal: Sends the entered username and password to the server.

[1899] Step 3:

[1900] Server: Checks the received authentication information against a database.

[1901] Step 4:

[1902] Server: If the user is found to be valid, generate an authentication token.

[1903] Step 5:

[1904] Server: Returns the authentication token to the device and starts the user session.

[1905] AI Judgment System Processing Steps

[1906] Step 1:

[1907] User: Films training footage and uploads it through the application.

[1908] Step 2:

[1909] Terminal: Sends video data to the server.

[1910] Step 3:

[1911] Server: Passes the received video data to the AI ​​Judgment System.

[1912] Step 4:

[1913] AI system: Analyzes video data and evaluates muscle development and form.

[1914] Step 5:

[1915] AI system: Calculates a score as an analysis result and generates feedback.

[1916] Step 6:

[1917] Server: Sends the generated feedback and scores to the device.

[1918] Step 7:

[1919] Device: Shows feedback and score to the user.

[1920] Processing steps of the generative AI support system

[1921] Step 1:

[1922] User: Enter health information and training goals.

[1923] Step 2:

[1924] Terminal: Sends the entered data to the server.

[1925] Step 3:

[1926] Server: Passes the received data to the generation AI.

[1927] Step 4:

[1928] Generative AI: Analyzes user data and generates optimal training and nutrition plans.

[1929] Step 5:

[1930] Server: Formats the generated plan for delivery to the user.

[1931] Step 6:

[1932] Server: Sends the personalized plan to the device and displays it to the user.

[1933] Online community platform processing steps

[1934] Step 1:

[1935] Users: Post messages in forums and chats.

[1936] Step 2:

[1937] Terminal: Sends the user's messages to the server.

[1938] Step 3:

[1939] Server: Stores received messages in a database and adds them to the associated thread.

[1940] Step 4:

[1941] Server: Notifies other users of new messages.

[1942] Step 5:

[1943] Generative AI: Analyzes interaction data and provides appropriate advice and information.

[1944] Step 6:

[1945] Server: Displays advice and information provided by the generated AI to the user.

[1946] Processing steps of the real-time contest module

[1947] Step 1:

[1948] User: Enter a real-time contest and start live streaming.

[1949] Step 2:

[1950] Terminal: Sends live video to the server in real time.

[1951] Step 3:

[1952] Server: Passes video data to the AI ​​Judgment System in real time.

[1953] Step 4:

[1954] AI system: Analyzes live footage in real time and evaluates performance.

[1955] Step 5:

[1956] Server: Updates analysis results in real time and displays contest scores instantly.

[1957] Step 6:

[1958] Device: The scores will be displayed in real time on the contest page and communicated to all users.

[1959] Incorporating an emotion engine

[1960] Step 1:

[1961] User: Provides video and audio data for sentiment analysis.

[1962] Step 2:

[1963] Terminal: Sends the provided data to the server.

[1964] Step 3:

[1965] Server: Passes video and audio data to the emotion engine.

[1966] Step 4:

[1967] Emotion engine: Analyzes data and recognizes the user's emotional state.

[1968] Step 5:

[1969] Server: Passes the recognized emotional state to the generation AI.

[1970] Step 6:

[1971] Generative AI: Generates personalized training plans and feedback based on your emotional state.

[1972] Step 7:

[1973] Server: Provides emotion-based feedback to users.

[1974] Example 2

[1975] 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."

[1976] Conventional training support systems have had the problem of being unable to effectively evaluate users' training performance and form, and are unable to provide appropriate feedback. Furthermore, feedback based on the user's emotions and real-time performance evaluation are insufficient, making it difficult to maximize the effectiveness of training. Furthermore, there has been a lack of systems that allow users to effectively maintain motivation and share information through interactions with other users.

[1977] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting authentication information from a user and issuing an authentication token, artificial intelligence system means for analyzing video data uploaded by the user and evaluating muscles and checking training form, means for generating training feedback based on the evaluation results of the artificial intelligence system and providing it to the user, means for generating and providing feedback based on the user's emotions using an emotion engine that analyzes the user's emotional state, and real-time contest module means for analyzing the video data in real time and instantly displaying contest scores. This makes it possible to provide personalized feedback based on the user's training performance and emotional state, enabling real-time performance evaluation and effective information exchange with other users and maintaining motivation.

[1978] An "authentication token" is a unique identifier generated based on a user's authentication information, and is used to maintain a user session and ensure secure communication.

[1979] An "artificial intelligence system" is a system that uses machine learning and deep learning technologies to analyze data and automatically make judgments and evaluations that humans would normally make.

[1980] An "emotion engine" is a system that analyzes video and audio data to recognize a user's emotional state, and outputs the category and certainty of the emotional state.

[1981] The "Real-time Contest Module" is a system that analyzes live streaming footage from users in real time and instantly evaluates their performance and displays their scores.

[1982] "Health Information" refers to data related to personal health such as weight, age, height, and health condition provided by a user.

[1983] A "training goal" refers to a specific exercise or health goal set by a user, such as improving muscle strength, losing weight, or improving endurance.

[1984] "Feedback" refers to providing evaluations and advice on a user's behavior and performance in the form of text messages, graphs, etc.

[1985] "Personalized training plan" refers to an individually optimized training menu and nutrition plan generated based on an individual user's health information and training goals.

[1986] A "community function" is a function that provides an online platform such as a chat or forum for users to interact with other users and share information.

[1987] "Generative AI" refers to artificial intelligence technology that uses natural language processing and deep learning techniques to automatically generate appropriate training plans and advice based on input data from users.

[1988] This invention relates to a system consisting of a user authentication, AI judgment system, generative AI support system, online community platform, real-time contest module, and emotion engine that recognizes user emotions. Specific embodiments of the system are described below.

[1989] User authentication

[1990] The program for this system begins with user authentication. The user accesses the application using a device such as a smartphone or PC and enters their username and password. The device sends this information to the server using HTTPS communication. The server compares the received authentication information with a database (for example, MySQL), and if the information is correct, generates an authentication token (JWT: JSON Web Token). This authentication token is used to maintain the user session and is sent back to the device.

[1991] AI Judgment System

[1992] Next, the user moves on to the step of uploading the training video they have filmed. The user films the training video using a smartphone or other device and uploads the video data through the application. The device sends the video data to a server, which stores it in cloud storage (e.g., AWS S3). This video data is then passed from the server to an AI judgment system. The AI ​​judgment system analyzes the video data using deep learning models such as TensorFlow and evaluates the level of muscle development and training form. The evaluation results are sent back to the server, which uses them to generate training feedback and provide it to the user.

[1993] Generative AI Support System

[1994] Users can also input health information (age, weight, goals, etc.) and training goals within the application. This information is sent from the device to a server, which passes the input data to a generation AI. This generation AI analyzes the user data and generates optimal training and nutrition plans, using models such as GPT-4. The server then formats the generated plans, sends them to the device, and displays them to the user.

[1995] Online Community Platform

[1996] Furthermore, users can interact with other users through forums and chats. When a user posts a message, it is sent from the device to the server, where it is stored in a database. After being saved, other users are notified in real time. The generative AI analyzes this interaction data, generates appropriate advice and information, and provides it to users through the server.

[1997] Real-time Contest Module

[1998] Users can also enter real-time contests. Users start live streaming and send the video data from their device to the server in real time. The server passes the video data to the AI ​​Judgment System, where it is analyzed in real time. The analysis results are immediately sent back to the server, and the contest score is updated in real time. This updated score is displayed on the contest page from the device and communicated to all users.

[1999] Emotion Engine

[2000] The emotion engine analyzes the video and audio data provided by the user to recognize the user's emotional state. The user uploads video and audio data for emotion analysis and sends it to the server via their device. The server passes this data to the emotion engine, which analyzes the emotional state. The analysis results are passed to the generation AI, which generates a personalized training plan and feedback based on the results. This is also provided to the user via the server.

[2001] Examples of specific examples and prompts

[2002] For example, if User A wants to have their training performance evaluated, the process would be as follows:

[2003] 1. User A logs in to the application and uploads a training video.

[2004] 2. The server sends the video data to the AI ​​Judgment System, which then evaluates the muscles and checks the form.

[2005] 3. The server generates a rating score and feedback and provides it to User A.

[2006] Example prompt sentence:

[2007] "The user is 30 years old, weighs 70kg, and wants to improve their muscle mass. Please generate the optimal training plan for this user."

[2008] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2009] Step 1: User authentication

[2010] 1. Input: The user enters their username and password into the terminal.

[2011] 2. Operation: The device sends the username and password to the server using HTTPS communication.

[2012] 3. Data processing: The server queries the database for the received authentication information and verifies the username and password.

[2013] 4. Output: If there is a match, the server generates an authentication token (JWT) and sends it back to the device, which stores the received JWT.

[2014] Step 2: Upload your training footage

[2015] 1. Input: The user uses the device to select the training video data (MP4 format) they have taken and press the upload button.

[2016] 2. Operation: The device converts the video data into the appropriate format and sends it to the server.

[2017] 3. Data processing: The server receives the video data and stores it in cloud storage.

[2018] 4. Output: The server confirms that the video data has been saved and saves the path to the video data.

[2019] Step 3: Analysis by AI Judgment System

[2020] 1. Input: The server obtains the path to the stored video data and passes it to the AI ​​Judgment System.

[2021] 2. Operation: To analyze the video data, the AI ​​Judgment System loads a TensorFlow model and inputs the data.

[2022] 3. Data processing: Using deep learning models, we run algorithms to evaluate muscle development and training form.

[2023] 4. Output: Analysis results are generated and sent back to the server, including scores and feedback comments.

[2024] Step 4: Plan generation by generative AI support system

[2025] 1. Input: The user inputs health information (age, weight, etc.) and training goals into the device.

[2026] 2. Operation: The terminal sends the input data to the server, which generates a prompt and passes it to the generation AI.

[2027] 3. Data processing: The generative AI analyzes the data based on the prompts and generates optimal training and nutrition plans.

[2028] 4. Output: The generated plan is sent back to the server, which formats it appropriately (e.g. HTML or JSON) and sends it to the device for display.

[2029] Step 5: Online community features

[2030] 1. Input: A user types a message into a forum or chat and presses the post button.

[2031] 2. Operation: The device sends the post content to the server.

[2032] 3. Data processing: The server saves the message in a database, adds it to the associated thread, and notifies other users via WebSocket that a new post has been made.

[2033] 4. Output: The message is displayed in real time to other users viewing the forum or chat.

[2034] Step 6: Real-time contest

[2035] 1. Enter: A user starts a live stream and participates in a real-time contest.

[2036] 2. Operation: The device transmits live video to the server in real time.

[2037] 3. Data processing: The server passes the video data to the AI ​​Judgment System in real time for analysis.

[2038] 4. Output: The analysis results are displayed in real time, and the contest scores are updated instantly, allowing users to check the results in real time.

[2039] Step 7: Feedback from the Emotion Engine

[2040] 1. Input: User uploads video and audio data for sentiment analysis.

[2041] 2. Operation: The device compresses the data and sends it to the server, which then passes it to the emotion engine.

[2042] 3. Data processing: The emotion engine analyzes video and audio data and runs algorithms to recognize the user's emotional state.

[2043] 4. Output: The recognized emotional state is passed to the generation AI, which generates feedback based on the results and provides it to the user via the server.

[2044] (Application example 2)

[2045] 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."

[2046] In modern manufacturing, factory work efficiency, part quality control, real-time performance evaluation, and operator stress management are key issues. Conventional systems can only achieve these functions individually, so integrated and efficient solutions are required. Furthermore, there is a lack of means to share knowledge between engineers and operators and improve work efficiency, making it difficult to improve overall productivity.

[2047] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for accepting authentication information from a user and issuing an authentication token; an AI system for analyzing video data uploaded by the user and evaluating parts; means for generating and providing work feedback to the user based on the AI ​​system's evaluation results; a community function for users to interact with other users, analyzing interaction data using the generating AI and providing appropriate advice and information; means for analyzing the real-time performance of equipment operated by the user and displaying scores; and means for analyzing the user's facial expressions and voice data to recognize their emotional state and provide advice and feedback based on the results. This enables integrated management of work efficiency and part quality control, real-time performance evaluation, and operator stress management within the factory. Furthermore, productivity is improved through knowledge sharing among engineers and work efficiency contest functions.

[2048] "User" refers to an individual or corporation that uses the system.

[2049] "Authentication information" refers to information (such as a user name and password) used to identify a user and verify their legitimacy.

[2050] An "authentication token" is a unique string of characters issued after successful authentication to maintain the authenticated state of a user.

[2051] "Video data" refers to video information that a user captures using a device such as a camera and uploads to the system.

[2052] "Part" refers to a material or part of a product used in the manufacturing process.

[2053] An "AI system" refers to a system that uses artificial intelligence to analyze and evaluate video data.

[2054] "Work feedback" refers to improvements and advice provided based on the evaluation results of the AI ​​system.

[2055] "Community function" refers to a platform for users to share information and interact with each other.

[2056] "Generative AI" refers to artificial intelligence that automatically generates optimal plans and advice based on user information and data.

[2057] "Real-time performance" refers to the ability to evaluate the current operational efficiency and results of the equipment operated by the user over time.

[2058] "Facial expression data" refers to information obtained by capturing the user's facial expressions using a camera or other device.

[2059] "Voice data" refers to acoustic information obtained using the user's voice.

[2060] "Emotional state" refers to a user's current psychological and emotional state.

[2061] "Advice" refers to suggestions for improvement or guidance regarding the user's work or status.

[2062] "Feedback" refers to the evaluation results and comments provided to the user by the system.

[2063] MODE FOR CARRYING OUT THE INVENTION

[2064] This invention is a system that improves factory work efficiency and integrates parts quality control, real-time performance evaluation, and operator emotion management. The system consists of the following components:

[2065] 1. User Authentication

[2066] The server accepts authentication information from the user and issues an authentication token, which controls access to the system and allows only authorized users to perform operations.

[2067] 2. AI Judgment System

[2068] The server receives the video data uploaded by the user and passes it to the AI ​​system, which then analyzes the video data and performs an accurate evaluation of the parts, enabling it to identify defective products in real time and improve the quality of the production line.

[2069] 3. Real-time feedback

[2070] The server generates work feedback based on the AI ​​system's evaluation results and provides it to the user in real time, allowing the user to immediately understand areas for improvement and work more efficiently.

[2071] 4. Generative AI Support System

[2072] The server receives the operational information and work goals entered by the user and passes them to the generation AI. The generation AI analyzes the user data and generates personalized work plans and improvement plans. The server provides the generated plans to the user, helping to improve work efficiency.

[2073] 5. Online Community Platforms

[2074] The server provides a community function that allows users to interact with other users. Furthermore, it uses generative AI to analyze interaction data and provide appropriate advice and information. This enables knowledge sharing and problem solving between users quickly.

[2075] 6. Real-time Contest Module

[2076] The server analyzes the real-time performance of the devices operated by users and displays a score, allowing users to compete against each other in terms of work efficiency and quality.

[2077] 7. Emotion Engine

[2078] The server receives the user's facial expressions and voice data and passes it to the emotion engine. The emotion engine analyzes the data and recognizes the user's emotional state. The results are passed to the generative AI, which generates appropriate feedback and advice based on the user's emotional state. The server provides this to the user, supporting the operator's stress management.

[2079] Examples:

[2080] For example, when a factory operator checks the quality of a part, the process goes as follows: The user enters their authentication information to log in to the system and uploads video data of the part to the server. The server passes the video data to the AI ​​system, which then evaluates the part. Based on the results, the server generates an evaluation score and work feedback, which are provided to the user.

[2081] To interact with other engineers using the community function, users access the forum and post questions or questions. The server stores the posts in a database and notifies other users. The generative AI analyzes the content of the interactions and provides useful advice to the user.

[2082] Example prompt sentence:

[2083] Create a Python program to authenticate by entering a username and password. The following conditions must be met:

[2084] 1. User name and password are obtained via standard input.

[2085] 2. Passwords are hashed using SHA-256 hashing.

[2086] 3. The hashed password is checked against a dictionary database to verify it is correct.

[2087] 4. Displays the authentication success or failure result.

[2088] This enables work efficiency within the factory, part quality control, real-time performance evaluation, and operator stress management.

[2089] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2090] Step 1:

[2091] A user accesses an application and enters authentication information (user name and password) from a terminal. The terminal sends this authentication information to a server.

[2092] Input: Username, Password

[2093] Output: Authentication information sent to the server

[2094] How it works: The user enters authentication information using the keyboard and clicks the send button on the terminal. The terminal receives the authentication information and sends it to the server.

[2095] Step 2:

[2096] The server checks the received authentication information against existing user information in its database, and if authentication is successful, generates an authentication token and returns it to the terminal.

[2097] Input: Credentials

[2098] Output: Authentication token or authentication failure message

[2099] How it works: The server authenticates the user by comparing the received authentication information with the hashed password stored in its database. If authentication is successful, it generates an authentication token and returns it to the device.

[2100] Step 3:

[2101] After the user logs in to the system, the image data of the part is uploaded from the terminal to the server.

[2102] Input: Video data

[2103] Output: Video data sent to the server

[2104] Operation: The user takes video data and sends it to the server using the device's upload function.

[2105] Step 4:

[2106] The server passes the received video data to the AI ​​Judgment System, which analyzes the video data and evaluates the parts.

[2107] Input: Video data

[2108] Output: Part evaluation results (score, feedback)

[2109] How it works: The server passes the video data to the AI ​​Judgment System, which then analyzes the video and evaluates the condition of the parts. The evaluation results are generated in the form of a score and feedback.

[2110] Step 5:

[2111] The server generates work feedback based on the evaluation results received from the AI ​​judgment system and provides it to the user.

[2112] Input: Evaluation result

[2113] Output: Work feedback

[2114] How it works: The server formats the evaluation results received from the AI ​​system and provides them to the user in an easy-to-understand format.

[2115] Step 6:

[2116] The user inputs operational information and work goals from a terminal and sends them to the server, which then passes the information to the generation AI.

[2117] Input: Operational information, work goals

[2118] Output: Operational information and work goals sent to the server

[2119] How it works: The user enters the necessary information and sends it from their device to the server, which then passes it on to the generation AI.

[2120] Step 7:

[2121] The generation AI analyzes user data and generates personalized work plans and improvement plans, which are then sent back to the server.

[2122] Input: Operational information, work goals

[2123] Output: personalized work plan, improvement plan

[2124] How it works: The generative AI analyzes the input data, generates optimal work plans and improvement plans, and sends them back to the server.

[2125] Step 8:

[2126] The server provides the generated work plan to the user, who can then view and execute the work plan through their terminal.

[2127] Input: Personalized work plan, improvement plan

[2128] Output: Work plan and improvement plan provided to the user

[2129] How it works: The server formats the plan received from the generation AI and sends it to the user's device. The user receives it and applies it to their actual work.

[2130] Step 9:

[2131] Users can use the community function to post messages and questions to interact with other users, and the device sends that data to the server.

[2132] Input: message, question

[2133] Output: Community data sent to the server

[2134] How it works: A user uses the forum or chat feature to send messages or questions to other users. The device sends that data to the server.

[2135] Step 10:

[2136] The server stores community data, which is analyzed by the AI ​​generator, who then provides appropriate advice and information, which the server then displays to the user.

[2137] Input: Community Data

[2138] Output: Advice, Information

[2139] Operation: The generation AI analyzes the content of the interaction, generates useful advice for the user, and displays it to the user via the server.

[2140] Step 11:

[2141] The user sends real-time performance data of the equipment they operate from their device to the server, which then passes the data to the AI ​​Judgment System for performance analysis.

[2142] Input: Performance data

[2143] Output: Performance evaluation

[2144] How it works: Users send performance data of their operating equipment from their devices to the server, which then passes the data to the AI ​​system, where it is analyzed in real time.

[2145] Step 12:

[2146] The server displays the evaluated performance results in real time to the user, allowing comparison between users.

[2147] Input: Performance Rating

[2148] Output: Performance score displayed

[2149] How it works: The server updates the evaluation results received from the AI ​​system in real time and displays them to the user, allowing users to compete against each other on work efficiency and quality.

[2150] Step 13:

[2151] The user's facial expression and voice data are sent from the device to the server, which then passes the data to the emotion engine to analyze the user's emotional state.

[2152] Input: facial expression data, voice data

[2153] Output: Emotional state

[2154] How it works: The user uses a camera and microphone to input facial and voice data into the device, which is then sent to the server, which then passes the data to the emotion engine for analysis.

[2155] Step 14:

[2156] The emotion engine passes the emotional state based on the analysis results to the generative AI, which then generates optimal feedback and advice, which the server then provides to the user.

[2157] Input: Emotional state

[2158] Output: Emotion-based feedback and advice

[2159] How it works: The emotion engine analyzes facial expressions and voice data to recognize the user's emotional state. The generative AI then generates feedback and advice based on that information, which the server then provides to the user.

[2160] 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.

[2161] 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.

[2162] 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.

[2163] [Fourth embodiment]

[2164] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2165] 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.

[2166] 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).

[2167] 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.

[2168] 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.

[2169] 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).

[2170] 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.

[2171] 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.

[2172] 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.

[2173] 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.

[2174] 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.

[2175] 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.

[2176] 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."

[2177] The embodiment of this invention is a system consisting of a user authentication, AI judgment system, generative AI support system, online community platform, and real-time contest module. The main components and their specific operations will be described for the program processing of this system.

[2178] User authentication

[2179] 1. User: First, the user accesses the application from a terminal and enters their username and password.

[2180] 2. Terminal: Sends username and password to server.

[2181] 3. Server: Checks the received authentication information against a database and issues an authentication token if the user has valid credentials.

[2182] 4. Server: Sends an authentication token back to the device, starting the user's session.

[2183] AI Judgment System

[2184] 1. User: The user shoots training footage and uploads it to the system.

[2185] 2. Terminal: Sends the uploaded video data to the server.

[2186] 3. Server: Passes the video data to the AI ​​Judgment System.

[2187] 4. AI system: The AI ​​system analyzes video data and evaluates muscle development and form.

[2188] 5. Server: Obtains the evaluation results and prepares them as feedback to be given to the user.

[2189] 6. Server: Sends feedback and scores to the device and displays them to the user.

[2190] Generative AI Support System

[2191] 1. User: Enter health information and training goals on the device.

[2192] 2. Terminal: Sends input data to the server.

[2193] 3. Server: Passes input data to the generation AI.

[2194] 4. Generative AI: Generative AI analyzes user data and generates optimal training and nutrition plans.

[2195] 5. Server: Takes the generated plan and formats it to be returned to the user.

[2196] 6. Server: Sends the personalized plan to the device and displays it to the user.

[2197] Online Community Platform

[2198] 1. User: Accesses chats and forums and posts messages.

[2199] 2. Terminal: Sends the user's message to the server.

[2200] 3. Server: Stores the message in a database and adds it to the associated thread.

[2201] 4. Server: Notifies other participants that a new message has been posted.

[2202] 5. Generative AI: Analyzes interaction data and provides appropriate advice and relevant information to users.

[2203] 6. Server: Providing advice and information to users, posting in forums and chats.

[2204] Real-time Contest Module

[2205] 1. User: Enter the real-time contest and start live streaming at the specified time.

[2206] 2. Terminal: Sends live video to the server in real time.

[2207] 3. Server: Passes the received video data to the AI ​​Judgment System in real time.

[2208] 4. AI system: Analyzes live footage in real time and evaluates user performance.

[2209] 5. Server: Updates the analysis results in real time and displays the contest scores instantly.

[2210] 6. Server: Display the scores in real time on the contest page and communicate them to all users.

[2211] Specific examples

[2212] For example, when user A wants to have his / her training performance evaluated, the process is as follows.

[2213] 1. User A logs in to the application and uploads a training video.

[2214] 2. The server sends the video data to the AI ​​Judgment System, which analyzes the video and calculates the score.

[2215] 3. The server generates a rating score and feedback and provides it to User A.

[2216] Furthermore, when user B uses the community function to interact with other users, the following occurs.

[2217] 1. User B accesses the forum and posts a question or training result.

[2218] 2. The server saves the post in a database and notifies other users.

[2219] 3. Along with replies from other users, advice provided by the generating AI will also be displayed.

[2220] The present invention can be implemented in the above-described manner, and will become a very useful system for many bodybuilding enthusiasts.

[2221] The processing flow will be explained below.

[2222] User authentication process steps

[2223] Step 1:

[2224] User: Accesses the application from a terminal and enters a username and password.

[2225] Step 2:

[2226] Terminal: Sends the entered username and password to the server.

[2227] Step 3:

[2228] Server: Checks the received authentication information against a database.

[2229] Step 4:

[2230] Server: If the user is found to be valid, generate an authentication token.

[2231] Step 5:

[2232] Server: Returns the authentication token to the device and starts the user session.

[2233] AI Judgment System Processing Steps

[2234] Step 1:

[2235] User: Films training footage and uploads it through the application.

[2236] Step 2:

[2237] Terminal: Sends video data to the server.

[2238] Step 3:

[2239] Server: Passes the received video data to the AI ​​Judgment System.

[2240] Step 4:

[2241] AI system: Analyzes video data and evaluates muscle development and form.

[2242] Step 5:

[2243] AI system: Calculates a score as an analysis result and generates feedback.

[2244] Step 6:

[2245] Server: Sends the generated feedback and scores to the device.

[2246] Step 7:

[2247] Device: Shows feedback and score to the user.

[2248] Processing steps of the generative AI support system

[2249] Step 1:

[2250] User: Enter health information and training goals.

[2251] Step 2:

[2252] Terminal: Sends the entered data to the server.

[2253] Step 3:

[2254] Server: Passes the received data to the generation AI.

[2255] Step 4:

[2256] Generative AI: Analyzes user data and generates optimal training and nutrition plans.

[2257] Step 5:

[2258] Server: Formats the generated plan for delivery to the user.

[2259] Step 6:

[2260] Server: Sends the personalized plan to the device and displays it to the user.

[2261] Online community platform processing steps

[2262] Step 1:

[2263] Users: Post messages in forums and chats.

[2264] Step 2:

[2265] Terminal: Sends the user's messages to the server.

[2266] Step 3:

[2267] Server: Stores received messages in a database and adds them to the associated thread.

[2268] Step 4:

[2269] Server: Notifies other users of new messages.

[2270] Step 5:

[2271] Generative AI: Analyzes interaction data and provides appropriate advice and information.

[2272] Step 6:

[2273] Server: Displays advice and information provided by the generated AI to the user.

[2274] Processing steps of the real-time contest module

[2275] Step 1:

[2276] User: Enter a real-time contest and start live streaming.

[2277] Step 2:

[2278] Terminal: Sends live video to the server in real time.

[2279] Step 3:

[2280] Server: Passes the received video data to the AI ​​Judgment System in real time.

[2281] Step 4:

[2282] AI system: Analyzes live footage in real time and evaluates performance.

[2283] Step 5:

[2284] Server: Updates analysis results in real time and displays contest scores instantly.

[2285] Step 6:

[2286] Device: The scores will be displayed in real time on the contest page and communicated to all users.

[2287] Example 1

[2288] 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."

[2289] Conventional training support systems are limited to functions such as user authentication, video data analysis, and training feedback generation, and lack functions such as providing personalized training and nutrition plans, analyzing user performance in real time, and providing interaction and advice between users. Furthermore, there are no integrated systems that combine these functions, which creates the issue of low user convenience when using multi-functional systems.

[2290] 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.

[2291] In this invention, the server includes means for receiving authentication information from a user and issuing an authentication token, an artificial intelligence system for analyzing video data uploaded by the user and evaluating the muscles, and means for generating training feedback based on the evaluation results of the artificial intelligence system and providing it to the user, thereby enabling user authentication, analysis of video data, and generation of training feedback.

[2292] In addition, in this invention, the server includes a generating artificial intelligence means for generating optimal training plans and nutrition plans based on user data, a means for analyzing and evaluating live streaming performed by the user in real time, and a generating artificial intelligence means for providing a community function for users to interact with other users and analyzing the interaction data to provide advice and information. This makes it possible to comprehensively realize multiple functions such as generating and providing personalized training plans and nutrition plans, analyzing performance in real time, and interacting with users and providing advice.

[2293] "User authentication" is the process of verifying whether a user is a legitimate user based on the authentication information provided by the user.

[2294] An "authentication token" is a digital certificate issued after successful user authentication and used to securely manage a user's session.

[2295] "Video data" refers to video and image data that users shoot and upload.

[2296] The "artificial intelligence system" analyzes video data and other data, mimicking human judgment to evaluate muscle development and form.

[2297] "Training feedback" refers to advice and evaluations about training provided to users based on the results of analysis by the artificial intelligence system.

[2298] "Generative AI" is AI that generates personalized training and nutrition plans based on user data.

[2299] "Live streaming" is a method for users to broadcast video in real time and share the process with other users or systems in real time.

[2300] "Real-time analysis" refers to the process of instantly analyzing data received during live streaming and providing results in real time.

[2301] The "community function" is a system that provides functions such as chat and forums for users to interact with each other.

[2302] "Advice and information" refers to content provided by artificial intelligence or other users to help users solve problems or improve performance.

[2303] MODE FOR CARRYING OUT THE INVENTION

[2304] This invention is a system that includes user authentication, analysis using artificial intelligence, generative AI support, online community functionality, and a real-time contest module. Specific embodiments for implementing this invention will be described below.

[2305] User Authentication

[2306] The user accesses the application from a terminal and enters their username and password.

[2307] The terminal sends the user name and password to the server.

[2308] The server checks the received credentials against a database (e.g. MySQL) by comparing the username in a user table with the hashed password.

[2309] If authentication is successful, the server generates a JSON Web Token (JWT) and returns it to the device.

[2310] Once the authentication token is received, the user's session begins.

[2311] AI Judgment System

[2312] Users film their training videos and upload them to the system.

[2313] The terminal transmits the uploaded video data to the server.

[2314] The server passes the video data to an artificial intelligence system, which uses AI models based on TensorFlow and PyTorch.

[2315] The AI ​​system analyzes the video data to assess muscle development and form, for example by detecting the positions of key joints in each frame of the video and calculating an evaluation score based on their movements.

[2316] The server takes the evaluation results and formats them to provide feedback to the user, which may take the form of textual advice or a score.

[2317] The server sends the feedback and score to the device and displays it to the user.

[2318] Generative AI Support System

[2319] Users input their health information and training goals through the terminal.

[2320] The terminal transmits the input data to the server.

[2321] The server passes the input data to the generative AI, which uses GPT-4 as its generative AI model.

[2322] The generative AI analyzes user data and generates personalized training and nutrition plans based on prompts, such as "I weigh 70 kg and am 175 cm tall. I want to gain 5 kg of muscle in one month."

[2323] The server takes the generated plan and formats it for delivery to the user.

[2324] The server sends the personalized plan to the terminal and displays it to the user.

[2325] Online Community Platform

[2326] Users access chats and forums and post messages.

[2327] The terminal sends the user's message to the server.

[2328] The server stores the message in a database (e.g. PostgreSQL) and adds it to the associated thread.

[2329] The server notifies other users that a new message has been posted.

[2330] Generative AI analyzes interaction data and provides appropriate advice and related information to users. It analyzes past posts and prompts to generate appropriate answers.

[2331] The server provides advice and information to users and posts to forums and chats.

[2332] Real-time Contest Module

[2333] Users enter a real-time contest and begin live streaming at a designated time.

[2334] The terminal transmits live video to the server in real time.

[2335] The server passes the received video data to the artificial intelligence system in real time.

[2336] The AI ​​system analyzes live video in real time and evaluates the user's performance, with the results being sent to the server each time.

[2337] The server updates the analysis results in real time and displays the contest scores instantly.

[2338] The server displays the scores in real time on the contest page and communicates them to all users.

[2339] As a specific example, when user A wants to have his / her training performance evaluated, the following procedure is carried out.

[2340] 1. User A logs in to the application and uploads a training video.

[2341] 2. The server sends the video data to an AI system, which analyzes the video and calculates a score. Specifically, the AI ​​detects the positions of key joints and calculates an evaluation score based on their movements.

[2342] 3. The server generates a rating score and feedback and provides it to User A.

[2343] Furthermore, when user B uses the community function to interact with other users, the following occurs.

[2344] 1. User B accesses the forum and posts a question or training result.

[2345] 2. The server saves the post in a database and notifies other users.

[2346] 3. Along with replies from other users, the AI ​​will also provide advice based on a prompt such as, "I'm having trouble with my training lately. Do you have any advice?"

[2347] This system allows many bodybuilding enthusiasts to train effectively, interact through the community, and compete against each other in real-time contests.

[2348] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2349] User Authentication

[2350] Step 1:

[2351] The user accesses the application from a terminal and enters their username and password.

[2352] Input: Username, Password

[2353] Output: Data input to the terminal

[2354] In this step, the user enters their authentication information.

[2355] Step 2:

[2356] The device sends the username and password to the server.

[2357] Input: Username, Password

[2358] Output: Authentication information to the server

[2359] The terminal sends the input information to the server in the appropriate format.

[2360] Step 3:

[2361] The server checks the received authentication information against a database.

[2362] Input: Credentials

[2363] Output: Authentication success or failure

[2364] It is matched against a user table in a database (e.g., MySQL), and if authentication is successful, a JWT is generated.

[2365] Step 4:

[2366] The server issues an authentication token (JWT) and sends it to the device.

[2367] Input: Authentication success or failure

[2368] Output: Authentication token

[2369] If authentication is successful, a JWT is generated and sent back to the device.

[2370] Step 5:

[2371] The device receives the authentication token and starts the user's session.

[2372] Input: Authentication token

[2373] Output: Authenticated session

[2374] Session management begins, allowing the user to operate within the application.

[2375] AI Judgment System

[2376] Step 1:

[2377] The user takes a training video and uploads it to the system.

[2378] Input: Training footage

[2379] Output: Video data to the device

[2380] The user records their own training and prepares to send it to the system.

[2381] Step 2:

[2382] The terminal transmits the uploaded video data to the server.

[2383] Input: Training video data

[2384] Output: Video data to the server

[2385] The video data is transmitted from the terminal to the server.

[2386] Step 3:

[2387] The server passes the video data to an artificial intelligence system.

[2388] Input: Training video data

[2389] Output: Video data to an artificial intelligence system

[2390] The server sends the data to an AI model using TensorFlow or PyTorch.

[2391] Step 4:

[2392] An artificial intelligence system analyzes the video data and evaluates muscle development and form.

[2393] Input: Training video data

[2394] Output: Evaluation score, analysis results

[2395] The AI ​​system detects key joint points, analyzes movements and calculates an evaluation score.

[2396] Step 5:

[2397] The server obtains the evaluation results and prepares them as feedback.

[2398] Input: Evaluation score, analysis results

[2399] Output: trimmed feedback

[2400] The server uses the analysis results to generate feedback to provide to the user.

[2401] Step 6:

[2402] The server sends feedback and scores to the device and displays them to the user.

[2403] Input: Feedback, Rating Score

[2404] Output: Data displayed to the user

[2405] The feedback is formatted and displayed to the user.

[2406] Generative AI Support System

[2407] Step 1:

[2408] The user inputs health information and training goals from the device.

[2409] Input: Health information, training goals

[2410] Output: Data input to the terminal

[2411] Users enter their health information and training goals.

[2412] Step 2:

[2413] The terminal sends the input data to the server.

[2414] Input: Health information, training goals

[2415] Output: Input data to the server

[2416] This data is sent to the server.

[2417] Step 3:

[2418] The server passes the input data to the generation AI.

[2419] Input: Health information, training goals

[2420] Output: Data to the generative AI

[2421] The server passes the data to the generation AI (such as GPT-4).

[2422] Step 4:

[2423] Generative AI analyzes user data and generates the optimal plan.

[2424] Input: Health information, training goals

[2425] Output: Optimal training and nutrition plans

[2426] For example, a plan is generated based on the prompt statement, "My weight is 70 kg and my height is 175 cm. I want to gain 5 kg of muscle in one month."

[2427] Step 5:

[2428] The server takes the generated plan and formats it to return to the user.

[2429] Input: Best Plan

[2430] Output: Cleaned plan format

[2431] The generated plan is then trimmed and prepared in a format that can be presented to the user.

[2432] Step 6:

[2433] The server sends the personalized plan to the device and displays it to the user.

[2434] Input: Trimmed plan format

[2435] Output: Data displayed to the user

[2436] Users will be able to view the best plan on their device.

[2437] Online Community Platform

[2438] Step 1:

[2439] A user accesses a chat or forum and posts a message.

[2440] Input: message

[2441] Output: Posted data to the terminal

[2442] Users create and post messages within the community.

[2443] Step 2:

[2444] The terminal sends the user's message to the server.

[2445] Input: message

[2446] Output: Message data to the server

[2447] It is sent from the terminal to the server.

[2448] Step 3:

[2449] The server saves the message in a database and adds it to the associated thread.

[2450] Input: Message data

[2451] Output: Save information to database

[2452] Messages are stored in a database using PostgreSQL or similar.

[2453] Step 4:

[2454] The server notifies other users that a new message has been posted.

[2455] Input: New message information

[2456] Output: Notification information

[2457] Notify other users that you have posted something new.

[2458] Step 5:

[2459] Generative AI analyzes interaction data and provides appropriate advice and relevant information.

[2460] Input: AC data

[2461] Output: Advice, related information

[2462] Generative AI analyzes and generates appropriate advice and information.

[2463] Step 6:

[2464] The server provides advice and information to users and posts to forums and chats.

[2465] Input: Advice, Information

[2466] Output: Data provided to the user

[2467] The advice and information provided is displayed to the user.

[2468] Real-time Contest Module

[2469] Step 1:

[2470] A user enters a real-time contest and starts live streaming at a specified time.

[2471] Input: Entry information, live video

[2472] Output: Live video data from the device

[2473] A user participates in a contest and starts live streaming.

[2474] Step 2:

[2475] The terminal transmits live video to the server in real time.

[2476] Input: Live video data

[2477] Output: Live video data to server

[2478] The video is transmitted in real time.

[2479] Step 3:

[2480] The server passes the received video data to the artificial intelligence system in real time.

[2481] Input: Live video data

[2482] Output: Data to an artificial intelligence system

[2483] The server passes it to the AI ​​system.

[2484] Step 4:

[2485] An artificial intelligence system analyzes live footage and evaluates user performance.

[2486] Input: Live video data

[2487] Output: Evaluation score, analysis results

[2488] Real-time analysis is performed.

[2489] Step 5:

[2490] The server updates the analysis results in real time and displays the contest scores instantly.

[2491] Input: Evaluation score, analysis results

[2492] Output: Real-time score display data

[2493] The server will display the score instantly.

[2494] Step 6:

[2495] The server displays the scores in real time on the contest page and communicates them to all users.

[2496] Input: Real-time score display data

[2497] Output: Display score to user

[2498] Scores are displayed to all users in real time.

[2499] (Application example 1)

[2500] 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."

[2501] Conventional factory robot management systems have had the problem of making it difficult to grasp the robot's operating efficiency and areas for improvement in real time and provide personalized work plans.In addition, there were delays in sharing information and providing advice between operators, making it difficult to contribute to improving productivity throughout the factory.

[2502] 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.

[2503] In this invention, the server includes: means for accepting authentication information from a user and issuing an authentication token; artificial intelligence means for analyzing video data uploaded by the user and evaluating the robot's performance; means for generating operational feedback based on the AI's evaluation results and providing it to the user; means for providing a community function, analyzing interaction data, and providing appropriate advice and information; and means for analyzing video data in real time, evaluating the robot's performance, and providing the evaluation results to the user. This makes it possible to grasp the robot's operational efficiency and areas for improvement in real time and provide corresponding feedback. It also enables effective information sharing between operators and rapid advice provision, contributing to improved productivity throughout the factory.

[2504] "User" refers to a person who uses the system to operate and manage it.

[2505] "Authentication information" refers to information such as a username and password that a user uses to log in to a system.

[2506] An "authentication token" refers to temporary data that is issued after verifying user authentication and is used to maintain a session and control access.

[2507] "Video data" refers to video and still image data captured using cameras and sensors.

[2508] "Artificial intelligence" refers to computer programs that use machine learning and deep learning to analyze data and make evaluations and judgments.

[2509] "Operational feedback" refers to advice on improving work and efficiency provided to users based on the results of analysis by systems and artificial intelligence.

[2510] "Community functions" refer to functions such as chat and forums that allow users to share information and interact with each other.

[2511] "Interaction data" refers to data such as messages, posts, and comments exchanged by users through community functions.

[2512] "Advice" refers to recommendations or suggestions provided to users by a system or artificial intelligence.

[2513] "Real-time" refers to processing that occurs simultaneously with real time.

[2514] "Movement evaluation" refers to a system or artificial intelligence determining and evaluating the efficiency and accuracy of a specific movement based on video data.

[2515] The system for realizing this invention consists of five main components: user authentication, behavior evaluation using artificial intelligence, generative AI support system, community function, and real-time evaluation function.

[2516] The system includes a means for accepting authentication information from a user and issuing an authentication token. Specifically, the user accesses an application from a terminal and enters a username and password. The terminal sends this authentication information to a server, which then collates the received authentication information with a database to perform authentication. If authentication is successful, the server generates an authentication token and returns it to the terminal.

[2517] The system then includes an AI that analyzes the video data uploaded by the user and evaluates its performance. The user takes a video of the factory robot in operation and uploads it to the system. The terminal sends this video data to a server, which then passes it on to the AI. The AI ​​analyzes the video and evaluates the robot's operating efficiency and areas for improvement. The evaluation results are sent to the server and provided to the user as feedback.

[2518] The system also includes a generative AI support system. Users input operational information and goals from their devices, which then send the data to a server. The server passes the data to the generative AI, which then generates optimal work and maintenance plans. The generated plans are then provided to the user via the server.

[2519] The system also includes a community function. Users can access chat rooms and forums and post messages. The device sends the message to the server, which stores it in a database. At the same time, related users are notified that a new message has been posted. The generative AI analyzes the interaction data and provides appropriate advice and information.

[2520] Finally, the system includes a function for analyzing video data in real time and evaluating the user's actions. When a user uploads a video in real time, the server immediately passes the video data to an artificial intelligence system for evaluation. The evaluation results are displayed in real time and provided to the user.

[2521] The hardware used includes industrial PCs, communication infrastructure, AI-enabled cameras, and robot controllers, while the software used is Python, PyTorch / TensorFlow, Django / Flask, and PostgreSQL / MySQL.

[2522] As a concrete example, consider the case where a user uploads a video of a factory robot in operation and the AI ​​evaluates that operation. The evaluation results are fed back to the user, and the generation AI generates an optimal work plan. Examples of prompt sentences include "Please evaluate this video and provide feedback on the robot's work efficiency" and "Please analyze the robot's recent work log and generate the optimal work plan for the next time."

[2523] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2524] Step 1:

[2525] A user accesses an application from a terminal and enters a username and password. The entered authentication information is sent from the terminal to the server in the form of a user ID and password. The server compares the received authentication information with a database to confirm that the user is a legitimate user. If authentication is successful, the server generates an authentication token and returns this token to the terminal. This starts the user's session.

[2526] Input: User ID, Password

[2527] Output: Authentication token

[2528] Step 2:

[2529] The user takes a video of the factory robot in operation and uploads it to the system. The device sends this video data to the server. The server receives the video data and passes it on to the AI ​​system. The AI ​​system analyzes the video data and evaluates the robot's operating efficiency and areas for improvement. The evaluation results are sent back to the server as a series of numerical and text data.

[2530] Input: Motion video data

[2531] Output: Evaluation results (numerical data, text data)

[2532] Step 3:

[2533] The server generates operational feedback based on the evaluation results returned by the AI ​​system. Specifically, specific advice is generated based on the analysis results of the AI ​​model regarding areas for improving the robot's operational efficiency and the need for maintenance. The generated feedback is formatted in a way that is easy for the user to understand and sent to the terminal.

[2534] Input: Evaluation results (numeric data, text data)

[2535] Output: Operational feedback (text data)

[2536] Step 4:

[2537] The user inputs operational information and goals from a terminal. This input data is sent to the server as operational information (e.g., robot work logs) and goals (e.g., improving work efficiency). The server passes this data to the generative AI, and the generative AI model generates optimal work and maintenance plans. The generated plans are provided to the user via the server.

[2538] Input: Operational information, target

[2539] Output: Optimal work plan, maintenance plan (text data)

[2540] Step 5:

[2541] A user accesses a chat or forum and posts a message. The device sends the posted message to the server, which stores the message in a database and adds it to the relevant thread. It also notifies other users that a new message has been posted. The server uses generative AI to analyze the interaction data and provide appropriate advice and information. This advice and information is also posted to the forum or chat.

[2542] Input: Message (text data)

[2543] Output: Stored messages, notifications, and advice (text data)

[2544] Step 6:

[2545] Users upload live streaming of factory robot operations in real time. The terminal transmits the live video to a server in real time, and the server passes the received video data to an AI system in real time. The AI ​​system analyzes the live video in real time and evaluates the robot's operating efficiency and areas for improvement. The evaluation results are immediately sent back to the server, which displays them to the user in real time.

[2546] Input: Live video data

[2547] Output: Real-time evaluation results (numerical data, text data)

[2548] 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.

[2549] The embodiment of this invention is a system consisting of a user authentication, AI judgment system, generative AI support system, online community platform, real-time contest module, and emotion engine that recognizes user emotions. The program processing of this system will be explained below with each component and its specific operation.

[2550] User authentication

[2551] 1. User: First, the user accesses the application from a terminal and enters their username and password.

[2552] 2. Terminal: Sends username and password to server.

[2553] 3. Server: Checks the received credentials against a database.

[2554] 4. Server: If the user has valid credentials, it generates an authentication token and sends it back to the device, thereby starting the user session.

[2555] AI Judgment System

[2556] 1. User: Films training footage and uploads it through the platform.

[2557] 2. Terminal: Sends video data to the server.

[2558] 3. Server: Passes the video data to the AI ​​Judgment System.

[2559] 4. AI system: Analyzes video data and evaluates muscle development and form.

[2560] 5. Server: Obtains the analysis results and prepares them as feedback to be given to the user.

[2561] 6. Server: Sends feedback and scores to the device and displays them to the user.

[2562] Generative AI Support System

[2563] 1. User: Enter your health information and training goals.

[2564] 2. Terminal: Sends input data to the server.

[2565] 3. Server: Passes input data to the generation AI.

[2566] 4. Generative AI: Analyzes user data and generates optimal training and nutrition plans.

[2567] 5. Server: Prepares the generated plan and prepares it for delivery to the user.

[2568] 6. Server: Sends the personalized plan to the device and displays it to the user.

[2569] Online Community Platform

[2570] 1. Users: Post messages in forums and chats.

[2571] 2. Terminal: Sends the user's message to the server.

[2572] 3. Server: Stores received messages in a database and adds them to the associated thread.

[2573] 4. Server: Notifies other users of new messages.

[2574] 5. Generative AI: Analyzes interaction data and provides appropriate advice and information.

[2575] 6. Server: Displays advice and information provided by the generative AI to the user.

[2576] Real-time Contest Module

[2577] 1. User: Enter a real-time contest and start live streaming.

[2578] 2. Terminal: Sends live video to the server in real time.

[2579] 3. Server: Passes video data to the AI ​​Judgment System in real time.

[2580] 4. AI system: Analyzes live footage in real time and evaluates performance.

[2581] 5. Server: Updates the analysis results in real time and displays the contest scores instantly.

[2582] 6. Terminal: The scores will be displayed in real time on the contest page and communicated to all users.

[2583] Incorporating an emotion engine

[2584] 1. User: Provides video and audio data for sentiment analysis.

[2585] 2. Terminal: Sends the provided data to the server.

[2586] 3. Server: Passes video and audio data to the emotion engine.

[2587] 4. Emotion engine: Analyzes data and recognizes the user's emotional state.

[2588] 5. Server: Passes the recognized emotional state to the generative AI.

[2589] 6. Generative AI: Generate personalized training plans and feedback based on your emotional state.

[2590] 7. Server: Provides emotion-based feedback to users.

[2591] Specific examples

[2592] For example, when user A wants to have his / her training performance evaluated, the process is as follows:

[2593] 1. User A logs in to the application and uploads a training video.

[2594] 2. The server sends the video data to the AI ​​Judgment System, which then evaluates the muscles and checks the form.

[2595] 3. The server generates a rating score and feedback and provides it to User A.

[2596] Furthermore, when user B uses the community function to interact with other users, the following occurs.

[2597] 1. User B accesses the forum and starts chatting and posting.

[2598] 2. The server saves the post in a database and notifies other users.

[2599] 3. The generating AI analyzes the content of the interaction and provides useful advice to User B.

[2600] Furthermore, when using the emotion engine, the flow of how user C receives emotion-based feedback is as follows.

[2601] 1. User C uploads video and audio data for the emotion engine.

[2602] 2. The server passes the data to the emotion engine to recognize the emotional state.

[2603] 3. Based on the recognized emotional state, the generative AI generates appropriate feedback and provides it to user C.

[2604] In this way, each module works together to provide comprehensive support to the user.

[2605] The processing flow will be explained below.

[2606] User authentication process steps

[2607] Step 1:

[2608] User: Accesses the application from a terminal and enters a username and password.

[2609] Step 2:

[2610] Terminal: Sends the entered username and password to the server.

[2611] Step 3:

[2612] Server: Checks the received authentication information against a database.

[2613] Step 4:

[2614] Server: If the user is found to be valid, generate an authentication token.

[2615] Step 5:

[2616] Server: Returns the authentication token to the device and starts the user session.

[2617] AI Judgment System Processing Steps

[2618] Step 1:

[2619] User: Films training footage and uploads it through the application.

[2620] Step 2:

[2621] Terminal: Sends video data to the server.

[2622] Step 3:

[2623] Server: Passes the received video data to the AI ​​Judgment System.

[2624] Step 4:

[2625] AI system: Analyzes video data and evaluates muscle development and form.

[2626] Step 5:

[2627] AI system: Calculates a score as an analysis result and generates feedback.

[2628] Step 6:

[2629] Server: Sends the generated feedback and scores to the device.

[2630] Step 7:

[2631] Device: Shows feedback and score to the user.

[2632] Processing steps of the generative AI support system

[2633] Step 1:

[2634] User: Enter health information and training goals.

[2635] Step 2:

[2636] Terminal: Sends the entered data to the server.

[2637] Step 3:

[2638] Server: Passes the received data to the generation AI.

[2639] Step 4:

[2640] Generative AI: Analyzes user data and generates optimal training and nutrition plans.

[2641] Step 5:

[2642] Server: Formats the generated plan for delivery to the user.

[2643] Step 6:

[2644] Server: Sends the personalized plan to the device and displays it to the user.

[2645] Online community platform processing steps

[2646] Step 1:

[2647] Users: Post messages in forums and chats.

[2648] Step 2:

[2649] Terminal: Sends the user's messages to the server.

[2650] Step 3:

[2651] Server: Stores received messages in a database and adds them to the associated thread.

[2652] Step 4:

[2653] Server: Notifies other users of new messages.

[2654] Step 5:

[2655] Generative AI: Analyzes interaction data and provides appropriate advice and information.

[2656] Step 6:

[2657] Server: Displays advice and information provided by the generated AI to the user.

[2658] Processing steps of the real-time contest module

[2659] Step 1:

[2660] User: Enter a real-time contest and start live streaming.

[2661] Step 2:

[2662] Terminal: Sends live video to the server in real time.

[2663] Step 3:

[2664] Server: Passes video data to the AI ​​Judgment System in real time.

[2665] Step 4:

[2666] AI system: Analyzes live footage in real time and evaluates performance.

[2667] Step 5:

[2668] Server: Updates analysis results in real time and displays contest scores instantly.

[2669] Step 6:

[2670] Device: The scores will be displayed in real time on the contest page and communicated to all users.

[2671] Incorporating an emotion engine

[2672] Step 1:

[2673] User: Provides video and audio data for sentiment analysis.

[2674] Step 2:

[2675] Terminal: Sends the provided data to the server.

[2676] Step 3:

[2677] Server: Passes video and audio data to the emotion engine.

[2678] Step 4:

[2679] Emotion engine: Analyzes data and recognizes the user's emotional state.

[2680] Step 5:

[2681] Server: Passes the recognized emotional state to the generation AI.

[2682] Step 6:

[2683] Generative AI: Generates personalized training plans and feedback based on your emotional state.

[2684] Step 7:

[2685] Server: Provides emotion-based feedback to users.

[2686] Example 2

[2687] 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."

[2688] Conventional training support systems have had the problem of being unable to effectively evaluate users' training performance and form, and are unable to provide appropriate feedback. Furthermore, feedback based on the user's emotions and real-time performance evaluation are insufficient, making it difficult to maximize the effectiveness of training. Furthermore, there has been a lack of systems that allow users to effectively maintain motivation and share information through interactions with other users.

[2689] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accepting authentication information from a user and issuing an authentication token, artificial intelligence system means for analyzing video data uploaded by the user and evaluating muscles and checking training form, means for generating training feedback based on the evaluation results of the artificial intelligence system and providing it to the user, means for generating and providing feedback based on the user's emotions using an emotion engine that analyzes the user's emotional state, and real-time contest module means for analyzing the video data in real time and instantly displaying contest scores. This makes it possible to provide personalized feedback based on the user's training performance and emotional state, enabling real-time performance evaluation and effective information exchange with other users and maintaining motivation.

[2690] An "authentication token" is a unique identifier generated based on a user's authentication information, and is used to maintain a user session and ensure secure communication.

[2691] An "artificial intelligence system" is a system that uses machine learning and deep learning technologies to analyze data and automatically make judgments and evaluations that humans would normally make.

[2692] An "emotion engine" is a system that analyzes video and audio data to recognize a user's emotional state, and outputs the category and certainty of the emotional state.

[2693] The "Real-time Contest Module" is a system that analyzes live streaming footage from users in real time and instantly evaluates their performance and displays their scores.

[2694] "Health Information" refers to data related to personal health such as weight, age, height, and health condition provided by a user.

[2695] A "training goal" refers to a specific exercise or health goal set by a user, such as improving muscle strength, losing weight, or improving endurance.

[2696] "Feedback" refers to providing evaluations and advice on a user's behavior and performance in the form of text messages, graphs, etc.

[2697] "Personalized training plan" refers to an individually optimized training menu and nutrition plan generated based on an individual user's health information and training goals.

[2698] A "community function" is a function that provides an online platform such as a chat or forum for users to interact with other users and share information.

[2699] "Generative AI" refers to artificial intelligence technology that uses natural language processing and deep learning techniques to automatically generate appropriate training plans and advice based on input data from users.

[2700] This invention relates to a system consisting of a user authentication, AI judgment system, generative AI support system, online community platform, real-time contest module, and emotion engine that recognizes user emotions. Specific embodiments of the system are described below.

[2701] User authentication

[2702] The program for this system begins with user authentication. The user accesses the application using a device such as a smartphone or PC and enters their username and password. The device sends this information to the server using HTTPS communication. The server compares the received authentication information with a database (for example, MySQL), and if the information is correct, generates an authentication token (JWT: JSON Web Token). This authentication token is used to maintain the user session and is sent back to the device.

[2703] AI Judgment System

[2704] Next, the user moves on to the step of uploading the training video they have filmed. The user films the training video using a smartphone or other device and uploads the video data through the application. The device sends the video data to a server, which stores it in cloud storage (e.g., AWS S3). This video data is then passed from the server to an AI judgment system. The AI ​​judgment system analyzes the video data using deep learning models such as TensorFlow and evaluates the level of muscle development and training form. The evaluation results are sent back to the server, which uses them to generate training feedback and provide it to the user.

[2705] Generative AI Support System

[2706] Users can also input health information (age, weight, goals, etc.) and training goals within the application. This information is sent from the device to a server, which passes the input data to a generation AI. This generation AI analyzes the user data and generates optimal training and nutrition plans, using models such as GPT-4. The server then formats the generated plans, sends them to the device, and displays them to the user.

[2707] Online Community Platform

[2708] Furthermore, users can interact with other users through forums and chats. When a user posts a message, it is sent from the device to the server, where it is stored in a database. After being saved, other users are notified in real time. The generative AI analyzes this interaction data, generates appropriate advice and information, and provides it to users through the server.

[2709] Real-time Contest Module

[2710] Users can also enter real-time contests. Users start live streaming and send the video data from their device to the server in real time. The server passes the video data to the AI ​​Judgment System, where it is analyzed in real time. The analysis results are immediately sent back to the server, and the contest score is updated in real time. This updated score is displayed on the contest page from the device and communicated to all users.

[2711] Emotion Engine

[2712] The emotion engine analyzes the video and audio data provided by the user to recognize the user's emotional state. The user uploads video and audio data for emotion analysis and sends it to the server via their device. The server passes this data to the emotion engine, which analyzes the emotional state. The analysis results are passed to the generation AI, which generates a personalized training plan and feedback based on the results. This is also provided to the user via the server.

[2713] Examples of specific examples and prompts

[2714] For example, if User A wants to have their training performance evaluated, the process would be as follows:

[2715] 1. User A logs in to the application and uploads a training video.

[2716] 2. The server sends the video data to the AI ​​Judgment System, which then evaluates the muscles and checks the form.

[2717] 3. The server generates a rating score and feedback and provides it to User A.

[2718] Example prompt sentence:

[2719] "The user is 30 years old, weighs 70kg, and wants to improve their muscle mass. Please generate the optimal training plan for this user."

[2720] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2721] Step 1: User authentication

[2722] 1. Input: The user enters their username and password into the terminal.

[2723] 2. Operation: The device sends the username and password to the server using HTTPS communication.

[2724] 3. Data processing: The server queries the database for the received authentication information and verifies the username and password.

[2725] 4. Output: If there is a match, the server generates an authentication token (JWT) and sends it back to the device, which stores the received JWT.

[2726] Step 2: Upload your training footage

[2727] 1. Input: The user uses the device to select the training video data (MP4 format) they have taken and press the upload button.

[2728] 2. Operation: The device converts the video data into the appropriate format and sends it to the server.

[2729] 3. Data processing: The server receives the video data and stores it in cloud storage.

[2730] 4. Output: The server confirms that the video data has been saved and saves the path to the video data.

[2731] Step 3: Analysis by AI Judgment System

[2732] 1. Input: The server obtains the path to the stored video data and passes it to the AI ​​Judgment System.

[2733] 2. Operation: To analyze the video data, the AI ​​Judgment System loads a TensorFlow model and inputs the data.

[2734] 3. Data processing: Using deep learning models, we run algorithms to evaluate muscle development and training form.

[2735] 4. Output: Analysis results are generated and sent back to the server, including scores and feedback comments.

[2736] Step 4: Plan generation by generative AI support system

[2737] 1. Input: The user inputs health information (age, weight, etc.) and training goals into the device.

[2738] 2. Operation: The terminal sends the input data to the server, which generates a prompt and passes it to the generation AI.

[2739] 3. Data processing: The generative AI analyzes the data based on the prompts and generates optimal training and nutrition plans.

[2740] 4. Output: The generated plan is sent back to the server, which formats it appropriately (e.g. HTML or JSON) and sends it to the device for display.

[2741] Step 5: Online community features

[2742] 1. Input: A user types a message into a forum or chat and presses the post button.

[2743] 2. Operation: The device sends the post content to the server.

[2744] 3. Data processing: The server saves the message in a database, adds it to the associated thread, and notifies other users via WebSocket that a new post has been made.

[2745] 4. Output: The message is displayed in real time to other users viewing the forum or chat.

[2746] Step 6: Real-time contest

[2747] 1. Enter: A user starts a live stream and participates in a real-time contest.

[2748] 2. Operation: The device transmits live video to the server in real time.

[2749] 3. Data processing: The server passes the video data to the AI ​​Judgment System in real time for analysis.

[2750] 4. Output: The analysis results are displayed in real time, and the contest scores are updated instantly, allowing users to check the results in real time.

[2751] Step 7: Feedback from the Emotion Engine

[2752] 1. Input: User uploads video and audio data for sentiment analysis.

[2753] 2. Operation: The device compresses the data and sends it to the server, which then passes it to the emotion engine.

[2754] 3. Data processing: The emotion engine analyzes video and audio data and runs algorithms to recognize the user's emotional state.

[2755] 4. Output: The recognized emotional state is passed to the generation AI, which generates feedback based on the results and provides it to the user via the server.

[2756] (Application example 2)

[2757] 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."

[2758] In modern manufacturing, factory work efficiency, part quality control, real-time performance evaluation, and operator stress management are key issues. Co...

Claims

1. means for accepting authentication information from a user and issuing an authentication token; An AI system means for analyzing video data uploaded by users and evaluating muscles; A means for generating training feedback based on the evaluation results of the AI ​​system and providing it to the user; A system including:

2. 10. The system of claim 1, further comprising means for using generative AI to provide personalized training and nutrition plans based on health information and training goals input by the user.

3. The system of claim 1, further comprising means for providing a community function for users to interact with other users, and for analyzing interaction data using a generating AI to provide appropriate advice and information.

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