System

Abstract

A system is provided.SOLUTION: A user registration and login unit, configured for a user to register and log in an account; a body type photographing unit, configured to photograph a body type of the user by using a camera to obtain image data; and analyze the obtained image data to characterize a current body type of the user; A body shape analysis and generation unit for generating an ideal body shape, a training plan generation unit for creating a training plan based on the ideal body shape and providing the training plan to a user, a training data collection unit for collecting training data in real time and transmitting the training data to a central server, a user data synchronization unit for storing data in a user's account and synchronizing with an application based on the collected data, and a dietary management and mental care unit for generating and providing appropriate dietary management and mental care advice based on the user's body shape data.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] While many people today are interested in fitness, hiring a personal trainer is expensive and difficult to schedule and book. Furthermore, gyms lacking trainers often lack the ability to provide proper instruction, limiting the effectiveness of training. Furthermore, lack of support for dietary management and mental health care while users are away from the gym makes it difficult to maintain a sustainable fitness routine. For these reasons, there is a demand for a system that allows users to train efficiently and easily and establishes a consistent support system. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including a user registration and login means for users to register an account and log in, a body shape photography means for photographing the user's body shape using a camera and acquiring image data, a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape, a training plan generation means for creating a training plan based on the ideal body shape and providing it to the user, a training data collection means for collecting training data in real time and sending it to a central server, a user data synchronization means for saving the collected data in the user's account based on the collected data and synchronizing it with an application, and a diet management and mental care means for generating and providing appropriate diet management and mental care advice based on the user's body shape data. The present invention provides users with an efficient and easy training environment, increasing the sustainability of their training and strengthening the support system for diet management and mental care.

[0006] "User Registration and Login Facility" means the facility through which a User creates an account and logs in to access the Fitness System.

[0007] The "body shape photographing means" is a function for photographing the user's body shape using a camera and acquiring the image data.

[0008] The "body shape analysis and generation means" is a function that analyzes the acquired image data, characterizes the user's current body shape, and generates an ideal body shape.

[0009] The "training plan generation means" is a function that creates a training plan suited to the user based on the ideal body type and provides it to the user.

[0010] The "training data collection means" is a function that collects data on the training performed by the user in real time and transmits it to a central server.

[0011] The "user data synchronization means" is a function that links the collected training data to the user's account, saves it, and synchronizes it with the application.

[0012] The "dietary management and mental care means" is a function that generates and provides appropriate dietary management and mental care advice based on the user's body type data.

[0013] "Mental care tools" is a function that generates and notifies encouraging messages and motivational content based on the user's body shape data.

[0014] "Lecture means" is a function that uses video and audio to instruct users on the correct training form and important points to note based on their ideal body type. [Brief explanation of the drawings]

[0015] [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 illustrating 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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] This invention is a system that allows users to train efficiently and easily and maintain sustainable fitness habits. The system uses AI-enabled monitors and cameras installed in fitness clubs and gyms, and also works in conjunction with a mobile application.

[0037] Feature Overview

[0038] User Registration / Login

[0039] Users create an account through the mobile application and log in, which involves entering their name, email address, and password, and the server stores this information in a database for authentication.

[0040] Body shape analysis and generation

[0041] A camera captures the user's body shape as they stand in front of the gym monitor, and sends the image data to a server. The server then uses an AI model to analyze the user's current body shape based on the acquired image data. It then generates an ideal body shape and displays it to the user.

[0042] Training plan generation

[0043] Based on the generated ideal body shape, the AI ​​trainer creates a daily training plan and provides it to the user. The exercise names, repetitions, and number of sets are displayed on the monitor, and video and audio instructions are provided on the correct form and important points to note.

[0044] Training data collection and synchronization

[0045] Data on the user's workout (e.g., duration, repetition, heart rate, etc.) is collected in real time and sent to a server, where it is stored and linked to the user's account and synchronized with the mobile application.

[0046] Dietary management and mental care

[0047] The server generates appropriate dietary advice and mental health messages based on the user's body shape data. These advice and messages are periodically provided to the user via a mobile application to support the user's fitness habits.

[0048] Specific Examples

[0049] A user's daily routine

[0050] 1. The user stands in front of the gym monitor and logs in using the account registered on the mobile app.

[0051] 2. The device uses a camera to take a picture of the user's body shape and sends the data to the server.

[0052] 3. The server analyzes the image data to identify the user's current body shape and generate an ideal body shape.

[0053] 4. The device displays the ideal body type and the training plan for the day, and the user follows this plan to train.

[0054] 5. The device records the training progress in real time and sends it to the server.

[0055] 6. The server stores the received data in the user's profile and synchronizes it with the mobile app.

[0056] 7. Users receive dietary advice and mental health messages through the mobile app.

[0057] This allows users to consistently train and manage their health whether they're at the gym or at home.

[0058] The processing flow will be explained below.

[0059] Step 1: The user stands in front of the gym monitor and logs in using the account registered on the mobile app.

[0060] Step 2: The device captures a picture of the user's body shape with its camera and acquires image data.

[0061] Step 3: The device sends the acquired image data to the server.

[0062] Step 4: The server receives the image data and inputs it into the AI ​​model.

[0063] Step 5: The server uses the AI ​​model to analyze the image data and extract the user's current body shape characteristics.

[0064] Step 6: The server simulates the ideal body shape based on the extracted features and generates images and data of the ideal body shape.

[0065] Step 7: The server sends the generated ideal body shape data back to the device.

[0066] Step 8: The device displays the ideal body shape data received from the server on the monitor.

[0067] Step 9: The device will then use the AI ​​trainer to create a training plan for the day based on your ideal body shape data.

[0068] Step 10: The device displays the training details (exercise name, number of repetitions, number of sets) to the user in a lecture format.

[0069] Step 11: The device will guide you through the training using video and audio instructions on proper form and important points to note.

[0070] Step 12: The user performs training under the guidance of the AI ​​trainer.

[0071] Step 13: The device collects real-time data during your workout (exercises performed, repetitions, time, heart rate, etc.).

[0072] Step 14: The terminal transmits the collected training data to the server.

[0073] Step 15: The server associates the received training data with the user's account and stores it in a database.

[0074] Step 16: The server generates the user's achievement level and the next training plan based on the stored data.

[0075] Step 17: The server returns the generated data to the terminal.

[0076] Step 18: The terminal displays the training results to the user.

[0077] Step 19: The server generates appropriate dietary advice based on the user's body type data.

[0078] Step 20: The server generates an appropriate mental health care message.

[0079] Step 21: The server sends the generated dietary management and mental care advice to the mobile application.

[0080] Step 22: The mobile application notifies the user of dietary advice.

[0081] Step 23: The mobile application notifies the user of the mental care message.

[0082] Example 1

[0083] 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."

[0084] Conventional fitness systems have faced challenges in helping users maintain effective and sustainable training plans. Specifically, they struggle to properly analyze a user's body type and provide an ideal personalized training plan, or to collect and analyze training data in real time to support consistent fitness habits. Furthermore, they lacked appropriate methods for providing individualized advice on dietary management and mental health, resulting in insufficient support for users to maintain their long-term health.

[0085] 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.

[0086] In this invention, the server includes a user registration and login means for a user to register an account and log in, a body shape photographing means for photographing the user's body shape using a camera and acquiring image data, and a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape, thereby enabling analysis and generation of an ideal body shape based on the user's individual body shape data.

[0087] "User Registration and Login Means" means the function by which a User registers and logs in to an account to access the System.

[0088] The "body shape photographing means" is a function that photographs the user's body shape using a camera and acquires image data.

[0089] The "body shape analysis and generation means" is a function that analyzes the acquired image data, characterizes the user's current body shape, and generates an ideal body shape.

[0090] The "training plan generation means" is a function that creates a training plan based on an ideal body type and provides it to the user.

[0091] The "training data collection means" is a function that collects training data in real time and transmits it to a central server.

[0092] The "user data synchronization means" is a function that saves data in the user's account based on the collected data and synchronizes it with the application.

[0093] The "dietary management and mental care means" is a function that generates and provides appropriate dietary management and mental care advice based on the user's body type data.

[0094] The "motion analysis means" is a function that collects and analyzes the user's motion and vital data to monitor fitness progress.

[0095] The "mental care notification means" is a function that notifies the user of encouraging messages and content that will increase motivation.

[0096] The "personal fitness advice means" is a function that analyzes the training data performed by the user and provides personalized advice to support continuous fitness habits.

[0097] This invention is a system that allows users to train efficiently and easily and maintain sustainable fitness habits. The system uses AI-equipped monitors and cameras installed in fitness clubs and gyms, and also works in conjunction with a mobile application. The system is configured as follows:

[0098] User registration and login methods

[0099] A user creates an account and logs in using a mobile application. This involves entering their name, email address, and password. The device sends this information to the server, which stores it in a database for authentication. For example, when a user enters their name, email address, and password and presses the "Register" button, the device encrypts the information they entered and sends it to the server.

[0100] Body photography methods

[0101] When a user arrives at the gym and stands in front of the AI-equipped monitor, the device (AI-equipped monitor) uses its camera to take a photo of the user's body shape and sends the image data to the server. The device then automatically recognizes the user using its camera, takes multiple images, and sends them to the server.

[0102] Body shape analysis and generation method

[0103] The server analyzes the user's current body shape using an AI model (such as TensorFlow or PyTorch) based on the acquired image data. The server then generates an ideal body shape based on the analysis results and sends that data to the device. For example, the server applies face recognition and body shape recognition algorithms to the AI ​​model and sends the analysis results in JSON format to the device.

[0104] Training plan generation method

[0105] The server uses an AI trainer (e.g., OpenAI's GPT-4) to create a training plan for the day based on the user's ideal body shape. The device displays the training plan to the user, prompting them for the exercise name, number of repetitions, and number of sets. For example, a training plan may be generated with a prompt such as, "Based on the user's current body shape, please create a one-day training plan for a man in his 30s who is looking to lose weight."

[0106] Training Data Collection Methods

[0107] When a user exercises, the device records the user's training data (e.g., time, number of repetitions, heart rate) in real time and sends it to the server. For example, the device sends the data to the server in batches at the end of the session or at regular intervals.

[0108] User data synchronization means

[0109] The server associates the received data with the user's account, stores it, and synchronizes it with the mobile app. For example, the server saves the data in a database and sends a push notification to the mobile app to notify it of synchronization completion.

[0110] Dietary management and mental health care measures

[0111] The server generates appropriate dietary advice and mental health messages based on the user's body shape and training data. The server then sends these to a mobile application, where the user can receive them. For example, the user can check the notifications received in the mobile app and incorporate them into their daily lives.

[0112] Motion analysis means

[0113] The device collects and analyzes the user's movement and vital data to monitor fitness progress. For example, the monitor's camera and sensors continuously record the user's movement and vital data and send it to a server.

[0114] Mental care notification method

[0115] The server then sends encouraging messages and motivational content to the user, generating personalized messages based on a prompt such as, "Please create a three-day training menu per week for a woman in her twenties who wants to build muscle."

[0116] Personalized Fitness Advice Tool

[0117] The server analyzes the user's training data and provides personalized advice to support ongoing fitness habits. For example, based on the collected training data, the AI ​​model generates improvements and new goals for the next training session.

[0118] These tools allow users to consistently train and manage their health whether they are at a fitness club or gym or at home.

[0119] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0120] Step 1:

[0121] A user launches a mobile application and enters their name, email address, and password to create an account.

[0122] Input: Name, Email Address, Password

[0123] Output: Submit registration information

[0124] How it works: After filling out the registration form on the mobile app and pressing the "Register" button, the device encrypts the information entered and sends an HTTP POST request to the server.

[0125] Step 2:

[0126] The server stores the received information in a database and manages the user authentication information.

[0127] Input: Encrypted user information

[0128] Output: Credentials stored in the database

[0129] What happens: The server parses the incoming data and creates a new user record in the database. The user logs in using their saved credentials.

[0130] Step 3:

[0131] The user arrives at the gym and stands in front of the AI-equipped monitor.

[0132] Input: User location and camera visual data

[0133] Output: Camera image acquisition

[0134] How it works: The user stands still in front of the monitor and poses as instructed by the camera. The device automatically recognizes the user, captures an image, and sends it to the server.

[0135] Step 4:

[0136] The server uses an AI model to analyze the user's current body type based on the acquired image data.

[0137] Input: Image data

[0138] Output: Analysis data of current body type and ideal body type data

[0139] How it works: The server applies face recognition and body recognition algorithms to an AI model (e.g., TensorFlow or PyTorch) to analyze the current body shape, then generates an ideal body shape and sends it to the device.

[0140] Step 5:

[0141] The terminal displays the generated ideal body type to the user.

[0142] Input: Ideal body type data

[0143] Output: Ideal body image displayed on the monitor

[0144] How it works: The monitor displays an image of your ideal body shape on the user's screen for them to review.

[0145] Step 6:

[0146] The server uses an AI trainer to create a training plan for the day based on the user's ideal body type.

[0147] Input: Ideal body type data

[0148] Output: Training plan data

[0149] How it works: On the server, the AI ​​model generates a training plan based on a prompt. For example, it generates a training plan based on the prompt, "Based on my current body shape, please create a one-day training plan for a man in his 30s who is trying to lose weight."

[0150] Step 7:

[0151] The terminal displays the training plan to the user and prompts the user for the exercise name, number of repetitions, and number of sets.

[0152] Input: Training plan data

[0153] Output: Training plan displayed on the monitor

[0154] How it works: The training plan details are displayed in list format and the monitor plays audio and video guides.

[0155] Step 8:

[0156] The user performs the training.

[0157] Input: User movement and vital data

[0158] Output: A record of training progress

[0159] How it works: The user performs exercises according to the instructions on the monitor. The device records the user's movements and vital data in real time.

[0160] Step 9:

[0161] The terminal transmits the collected data to the server.

[0162] Input: Training data

[0163] Output: Training data sent to the server

[0164] How it works: At the end of a session or at regular intervals, the device sends a batch of data to the server.

[0165] Step 10:

[0166] The server stores the received data, associates it with the user's account, and synchronizes it with the mobile app.

[0167] Input: Training data

[0168] Output: Data stored in the account and sync notifications to the app

[0169] How it works: The server saves the data to the database and sends a push notification to the mobile app when the synchronization is complete.

[0170] Step 11:

[0171] The server generates appropriate dietary advice and mental care messages based on the user's body type and training data.

[0172] Input: Body type data and training data

[0173] Output: Dietary advice and mental health care messages

[0174] How it works: The AI ​​model on the server analyzes the data and generates personalized advice, which is then sent to the mobile app and received by the user.

[0175] Step 12:

[0176] The device collects and analyzes the user's movement and vital signs to monitor their fitness progress.

[0177] Input: User motion and vital data

[0178] Output: Parsed progress data

[0179] Operation: The monitor's camera and sensors continuously record the user's movements and vital data and transmit them to a server.

[0180] Step 13:

[0181] The server notifies the user of encouraging messages and motivational content.

[0182] Input: User's body type and training data

[0183] Output: Mental care message

[0184] What it does: Notifies the user of server-generated messages to keep them motivated.

[0185] Step 14:

[0186] The server analyzes the user's training data and provides personalized advice to support ongoing fitness habits.

[0187] Input: Training data

[0188] Output: Personalized advice

[0189] How it works: Based on the collected training data, the AI ​​model generates and provides users with improvements and new goals for their next training session.

[0190] (Application example 1)

[0191] 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."

[0192] Conventional fitness systems have been insufficient in supporting users to train efficiently and sustainably, particularly due to the difficulty of providing real-time feedback and motion analysis. Furthermore, there is a need for systems that can properly analyze workers' postures and movements in workplaces such as factories, and provide efficient, fatigue-reducing work methods. Given this background, there is a need for systems that comprehensively support fitness training and improving the work environment.

[0193] 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.

[0194] In this invention, the server includes a user registration and login means for a user to register an account and log in, a body shape photographing means for photographing the user's body shape using a camera and acquiring image data, a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape, a training plan generation means for creating a training plan based on the ideal body shape and providing it to the user, a training data collection means for collecting training data in real time and sending it to a central server, a user data synchronization means for saving the collected data in the user's account based on the data and synchronizing it with an application, a dietary management and mental care means for generating and providing appropriate dietary management and mental care advice based on the user's body shape data, a posture analysis and movement plan generation means for analyzing the posture and movement of a worker in real time and generating an efficient work method, and a real-time feedback means for feeding the generated movement plan back to the worker in real time. This enables users to train efficiently and sustainably, and enables workers to adopt efficient and less fatigued work methods in real time.

[0195] "User registration and login means" refers to the function that allows users to register and log in to their accounts.

[0196] The "body shape photographing means" is a function that photographs the user's body shape using a camera and acquires image data.

[0197] The "body shape analysis and generation means" is a function that analyzes the acquired image data, characterizes the user's current body shape, and generates an ideal body shape.

[0198] The "training plan generation means" is a function that creates a training plan based on an ideal body type and provides it to the user.

[0199] The "training data collection means" is a function that collects training data in real time and transmits it to a central server.

[0200] The "user data synchronization means" is a function that saves data in the user's account based on the collected data and synchronizes it with the application.

[0201] The "dietary management and mental care means" is a function that generates and provides appropriate dietary management and mental care advice based on the user's body type data.

[0202] The "posture analysis and motion plan generation means" is a function that analyzes the posture and motion of a worker in real time and generates an efficient work method.

[0203] The "real-time feedback means" is a function that provides feedback of the generated operation plan to the worker in real time.

[0204] System configuration

[0205] This invention is a system that enables users to maintain fitness habits efficiently and sustainably. This system can also be used as an example of a factory robot. The main components of the system include a user registration and login means, a body shape photography means, a body shape analysis and generation means, a training plan generation means, a training data collection means, a user data synchronization means, a diet management and mental care means, a posture analysis and movement plan generation means, and a real-time feedback means.

[0206] Hardware and Software

[0207] Hardware: cameras, monitors, servers, factory robots

[0208] Software: TensorFlow, OpenCV, cloud storage services (Azure, AWS)

[0209] Example

[0210] User Registration and Login

[0211] Users can register an account using the interface of their mobile device or factory robot and log in. To log in, they need to enter their name, email address, password, etc., and this information is stored on the server.

[0212] Body photography and analysis

[0213] When a user stands in front of the camera, it captures a photo of the user's body shape and captures image data. This data is sent to a server, where it uses a TensorFlow model to analyze the user's body shape and generate a current and ideal body shape.

[0214] Training plan generation

[0215] The server then creates a training plan based on the generated ideal body shape and provides it to the user. The training plan includes exercise names, repetitions, and number of sets, as well as video and audio instruction on correct form and important points to note.

[0216] Collecting training data

[0217] As users train, data (e.g., duration, repetition, heart rate, etc.) is collected in real time and sent to a server, which stores this data in the user's account and synchronizes it with the mobile application.

[0218] Dietary management and mental care

[0219] The server generates and provides appropriate dietary and mental health advice based on the user's body shape data. These advice and messages are periodically provided to the user via the mobile application.

[0220] Posture analysis and motion plan generation

[0221] The factory robot monitors the worker's movements in real time and captures their posture with a camera. The data obtained is sent to a server where it is analyzed using an AI model. The server then generates an efficient work method and provides the plan to the worker.

[0222] Real-time feedback

[0223] The generated motion plan is displayed in real time on the robot's interface, and the worker can use this feedback to adjust their posture and perform the task efficiently and with less fatigue.

[0224] Specific examples

[0225] A concrete example is a scenario where real-time feedback is provided to a worker saying, "Stand up straight." An example prompt for this application is:

[0226] Example prompt sentence:

[0227] The system captures the user working in front of the camera and analyzes their posture in real time. Based on the analysis results, it instructs the worker to straighten their back. This feedback aims to improve work efficiency and reduce fatigue.

[0228] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0229] Step 1:

[0230] A user registers an account using the interface of a mobile device or factory robot and logs in. The inputs include name, email address, and password, which are stored on the server. The server authenticates the input data and stores the user information in a database.

[0231] Step 2:

[0232] The device's camera captures the user's body shape and captures the image data. This image data is sent to the server in real time. The server receives and stores this data.

[0233] Step 3:

[0234] The server analyzes the user's body shape using the TensorFlow model based on the acquired image data. Specifically, it detects posture, body parts, etc. from the image data and identifies the current body shape. As a result of the analysis, it outputs the characteristics of the user's current body shape.

[0235] Step 4:

[0236] The server generates an ideal body shape based on the analysis results. Using a generative AI model, it calculates the difference from the current body shape and outputs the target body shape. This generated ideal body shape data becomes the basis for creating a training plan.

[0237] Step 5:

[0238] The server creates a training plan based on the ideal body type data. The plan, including exercise names, repetitions, and number of sets, is automatically generated by the AI ​​model. This plan is provided to the user and displayed on the monitor.

[0239] Step 6:

[0240] During training, the device's sensors collect the user's training data (e.g., time, repetitions, heart rate) in real time. This collected data is immediately sent to the server, where it is stored and linked to the user's account.

[0241] Step 7:

[0242] The server stores the collected training data in the user's account and synchronizes it with the mobile application, allowing users to view their training data from any device.

[0243] Step 8:

[0244] The server generates appropriate dietary and mental health advice based on the user's physical and training data, and these advice and messages are periodically provided to the user via a mobile application.

[0245] Step 9:

[0246] The factory robot monitors the worker's movements in real time and captures their posture with a camera. The acquired image data is sent to a server, which analyzes this data and generates efficient work methods.

[0247] Step 10:

[0248] The generated motion plan is displayed in real time on the robot's interface, and the worker can use this feedback to correct their posture and perform the task in an efficient and less tiring manner, thereby improving worker efficiency and reducing fatigue.

[0249] 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.

[0250] This invention is a system that allows users to train efficiently and easily and maintain sustainable fitness habits, and has the function of recognizing the user's emotions and adjusting training plans and mental care. This system works by using AI-equipped monitors and cameras installed in fitness clubs and gyms, as well as an emotion engine, and also works in conjunction with a mobile application.

[0251] Feature Overview

[0252] User Registration / Login

[0253] Users create an account through the mobile application and log in, which involves entering their name, email address, and password, and the server stores this information in a database for authentication.

[0254] Body shape analysis and generation

[0255] A camera captures the user's body shape as they stand in front of the gym monitor, and sends the image data to a server. The server then uses an AI model to analyze the user's current body shape based on the acquired image data. It then generates an ideal body shape and displays it to the user.

[0256] Training plan generation

[0257] Based on the generated ideal body shape, the AI ​​trainer creates a daily training plan and provides it to the user. The exercise names, repetitions, and number of sets are displayed on the monitor, and video and audio instructions are provided on the correct form and important points to note.

[0258] Emotion Engine and Analytics

[0259] The emotion engine analyzes the user's facial expressions and movements to recognize their emotions in real time. The device then sends the analysis results to a server, which uses this information to adjust training plans and mental care.

[0260] Training data collection and synchronization

[0261] Data on the user's workout (e.g., duration, repetition, heart rate, etc.) is collected in real time and sent to a server, where it is stored and linked to the user's account and synchronized with the mobile application.

[0262] Dietary management and mental care

[0263] The server generates appropriate dietary advice and mental health messages based on the user's body shape data. These advice and messages are periodically provided to the user via a mobile application to support the user's fitness habits.

[0264] Specific Examples

[0265] A user's daily routine

[0266] 1. The user stands in front of the gym monitor and logs in using the account registered on the mobile app.

[0267] 2. The device uses a camera to take a picture of the user's body shape and sends the data to the server.

[0268] 3. The server analyzes the image data to identify the user's current body shape and generate an ideal body shape.

[0269] 4. The device displays the ideal body shape and training plan for the day, and the user follows this plan to train.

[0270] 5. The emotion engine monitors the user's facial expressions and movements during training and recognizes emotions in real time.

[0271] 6. The device sends the emotion engine analysis results to the server.

[0272] 7. The server adjusts the training plan and mental care content based on the emotional data and provides feedback to the user.

[0273] 8. The device will display training instructions and encouraging messages based on your emotions.

[0274] 9. The device collects data during training and sends it to the server.

[0275] 10. The server stores this data in the user's profile and synchronizes it with the mobile app.

[0276] 11. Users receive dietary advice and mental health messages through a mobile app.

[0277] This allows users to consistently train and manage their health whether they are at the gym or at home.By using an emotion engine, flexible training support and mental care are realized that adapt to the user's situation and emotions.

[0278] The processing flow will be explained below.

[0279] Step 1: The user stands in front of the gym monitor and logs in using the account registered on the mobile app.

[0280] Step 2: The device captures a picture of the user's body shape with its camera and acquires image data.

[0281] Step 3: The device sends the acquired image data to the server.

[0282] Step 4: The server receives the image data and inputs it into the AI ​​model.

[0283] Step 5: The server uses the AI ​​model to analyze the image data and extract the user's current body shape characteristics.

[0284] Step 6: The server simulates the ideal body shape based on the extracted features and generates images and data of the ideal body shape.

[0285] Step 7: The server sends the generated ideal body shape data back to the device.

[0286] Step 8: The device displays the ideal body shape data received from the server on the monitor.

[0287] Step 9: The device will then use the AI ​​trainer to create a training plan for the day based on your ideal body shape data.

[0288] Step 10: The device displays the training details (exercise name, number of repetitions, number of sets) to the user in a lecture format.

[0289] Step 11: The device will guide you through the training using video and audio instructions on proper form and important points to note.

[0290] Step 12: The emotion engine analyzes the user's facial expressions and movements and recognizes emotions in real time.

[0291] Step 13: The device sends the emotion engine analysis results to the server.

[0292] Step 14: The server adjusts the training plan and mental care content based on the emotion analysis results.

[0293] Step 15: The device displays the adjusted training instructions and mental care content.

[0294] Step 16: The user performs training according to the AI ​​trainer's guidance and emotional feedback.

[0295] Step 17: The device collects real-time data during your workout (exercises performed, repetitions, time, heart rate, etc.).

[0296] Step 18: The terminal transmits the collected training data to the server.

[0297] Step 19: The server associates the received training data with the user's account and stores it in a database.

[0298] Step 20: The server generates the user's achievement level and the next training plan based on the stored data.

[0299] Step 21: The server returns the generated data to the terminal.

[0300] Step 22: The terminal displays the training results to the user.

[0301] Step 23: The server generates appropriate dietary advice based on the user's body type data.

[0302] Step 24: The server generates an appropriate mental health care message.

[0303] Step 25: The server sends the generated dietary management and mental care advice to the mobile application.

[0304] Step 26: The mobile application notifies the user of dietary advice.

[0305] Step 27: The mobile application notifies the user of the mental care message.

[0306] Example 2

[0307] 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."

[0308] Modern fitness facilities require systems that enable users to train efficiently and maintain sustainable fitness habits. However, conventional systems have difficulty accurately analyzing individual users' physical shapes and emotions and providing training plans and mental care based on those analyses. In particular, there is a lack of systems that can monitor users' facial expressions and movements in real time and provide support according to their emotions.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0310] In this invention, the server includes a user registration and login means for a user to register an account and log in, a body shape photographing means for photographing the user's body shape using a camera and acquiring image data, a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape, a training plan generation means for creating a training plan based on the ideal body shape and providing it to the user, an emotion analysis means for analyzing the user's facial expressions and movements in real time and recognizing emotions, a training data collection means for collecting training data in real time and transmitting it to a central server, a user data synchronization means for saving the collected data in the user's account and synchronizing it with an application, and a diet management and mental care means for generating and providing appropriate diet management and mental care advice based on the user's body shape data. This not only provides users with optimal training plans tailored to their individual body shapes and emotions, but also enables them to maintain flexible and sustainable fitness habits through the analysis of emotion data in real time.

[0311] "User Registration and Login Method" means the authentication method by which a User creates an account and accesses the System.

[0312] The "body shape photographing means" is a means for photographing the user's body shape using a camera and acquiring image data thereof.

[0313] The "body shape analysis and generation means" is a means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape.

[0314] The "training plan generation means" is a means for creating a training plan based on an ideal body type and providing it to the user.

[0315] The "emotion analysis means" is a means for analyzing the user's facial expressions and movements in real time and recognizing their emotions.

[0316] The "training data collection means" is a means for collecting data during training in real time and transmitting it to a central server.

[0317] The "user data synchronization means" is a means for saving data in a user's account based on collected data and synchronizing it with an application.

[0318] The "dietary management and mental care means" is a means for generating and providing appropriate dietary management and mental care advice based on the user's body type data.

[0319] The present invention provides a system that allows users to efficiently and easily train and maintain sustainable fitness habits. The system has the function of recognizing the user's emotions and adjusting the training plan and mental care. Specific examples are described in detail below.

[0320] User Registration / Login

[0321] A user first opens the mobile application and creates an account. They enter their username, email address, and password and submit them to the server. The server receives the data, stores it in a database, and sends a confirmation email to the user. The user clicks on a link in the confirmation email to verify their email address, allowing them to log in to the system.

[0322] Examples:

[0323] Enter your username, email address, and password on the registration screen of the mobile application and tap the "Register" button.

[0324] Example prompt: "To create a new account, please enter your name, email address, and password."

[0325] Body shape analysis and generation

[0326] The user stands in front of a monitor in the gym and takes a picture of their body with a camera. The device sends the image data to a server. The server analyzes the image data using an AI model (e.g., OpenPose or DensePose) to identify the user's current body shape. It then generates an ideal body shape and sends it along with the analysis results to the device. The device then displays the analysis results and the ideal body shape to the user.

[0327] Examples:

[0328] When the user stands in front of the monitor, the camera automatically captures their body shape and the results are displayed within seconds.

[0329] Example prompt: "Stand in front of the monitor at the gym and take a picture of your body shape."

[0330] Training plan generation

[0331] The server uses an AI trainer to generate a training plan based on the analyzed ideal body type, while the device displays the exercise name, number of repetitions, and number of sets on the monitor and provides instructions on correct form and important points to note via video and audio.

[0332] Examples:

[0333] Users can watch videos showing them the correct way to perform the exercises.

[0334] Example prompt: "Based on your ideal body type, here's today's workout plan."

[0335] Emotion analysis means

[0336] The emotion engine analyzes the user's facial expressions and movements in real time to recognize their emotions. The device then sends the emotion engine's analysis results to the server. The server adjusts the training plan and mental care content based on the emotion data and provides feedback to the user. The device then displays the feedback and provides training and mental care messages according to the emotion.

[0337] Examples:

[0338] If the emotion engine detects that the user is tired, it will adjust the training plan and display encouraging messages.

[0339] Sample prompt: "Analyze the user's facial expressions and provide advice based on their emotions."

[0340] Training data collection and synchronization

[0341] The device collects data (e.g., time, repetition, heart rate, etc.) in real time as the user trains and sends it to the server, which associates this data with the user's account, stores it, and syncs it with the mobile app.

[0342] Examples:

[0343] During training, your heart rate and number of repetitions are recorded and stored on a server.

[0344] Sample prompt: "Collect data during training and send it to the server in real time."

[0345] Dietary management and mental care

[0346] The server generates appropriate dietary advice and mental care messages based on the user's body data and periodically provides them to the user via a mobile app.

[0347] Examples:

[0348] The mobile app will send you dietary advice tailored to your situation.

[0349] Sample prompt: "Generate appropriate dietary advice based on the user's body type data."

[0350] This system not only provides users with optimal training plans tailored to their individual body types and emotions, but also enables them to maintain flexible and sustainable fitness habits through real-time analysis of emotional data.

[0351] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0352] Step 1:

[0353] A user opens a mobile application and creates an account. They enter their username, email address, and password and submit them to the server. The server receives the input data, stores the user information, including a hashed password, in a database, and sends a confirmation email to the user. The user clicks on a link in the confirmation email to verify their email address, allowing them to log in to the system.

[0354] Input: Username, Email Address, Password

[0355] Processing: Receives input data, hashes password, then stores it in the database and sends a confirmation email

[0356] Output: Confirmation email, user information stored in database

[0357] Specific actions: Enter your username, email address, and password on the mobile application's registration screen and tap the "Register" button.

[0358] Step 2:

[0359] The user stands in front of a monitor in the gym and takes a picture of their body shape with a camera. The device sends the image data to a server. The server analyzes the image data using an AI model (e.g., OpenPose or DensePose) to identify the user's current body shape. It then generates an ideal body shape and sends it along with the analysis results to the device. The device then displays the analysis results and the ideal body shape to the user.

[0360] Input: User's body image

[0361] Processing: Analyze image data to identify current body shape and generate ideal body shape

[0362] Output: Analysis results, ideal body shape image

[0363] How it works: When the user stands in front of the monitor, the camera automatically takes a photo of their body shape and displays the results within a few seconds.

[0364] Step 3:

[0365] The server uses an AI trainer to generate a training plan based on the analyzed ideal body type. The device displays the exercise name, number of repetitions, and number of sets on the monitor, and provides instructions on correct form and important points to note via video and audio.

[0366] Input: Ideal body type data

[0367] Processing: Generate training plan (determine exercise name, repetitions, number of sets)

[0368] Output: Training plan

[0369] Specific actions: Users watch videos to learn the correct way to perform the exercises.

[0370] Step 4:

[0371] The emotion engine analyzes the user's facial expressions and movements in real time to recognize their emotions. The device then sends the emotion engine's analysis results to a server. The server adjusts the training plan and mental care content based on the emotion data and provides feedback to the user. The device then displays the feedback and provides training and mental care messages based on the emotion.

[0372] Input: User's facial expression and movement data

[0373] Processing: Analysis of facial expressions and movement data, emotion recognition

[0374] Output: Sentiment analysis results, training plan adjustments, mental care messages

[0375] What it does: If the emotion engine recognizes that the user is tired, it will adjust their training plan or mental care messages.

[0376] Step 5:

[0377] The device collects data (e.g., time, number of repetitions, heart rate, etc.) in real time as the user exercises and sends it to the server, which then associates this data with the user's account, stores it, and syncs it with the mobile app.

[0378] Input: Training data (time, repetitions, heart rate, etc.)

[0379] Processing: Data collection, transmission to server, data storage

[0380] Output: Saved training data, synced app data

[0381] Specific operation: Heart rate and number of repetitions recorded during training are stored on the server and can be viewed on the mobile app.

[0382] Step 6:

[0383] The server generates appropriate dietary advice and mental care messages based on the user's body data and provides them to the user periodically via a mobile app.

[0384] Input: User's body data

[0385] Processing: Generating dietary management advice and mental care messages

[0386] Output: Generated advice and messages

[0387] Specific behavior: The mobile app sends users appropriate dietary advice and mental health care messages.

[0388] These are the specific processing steps of the system, which enable users to maintain an efficient and sustainable fitness routine.

[0389] (Application example 2)

[0390] 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."

[0391] While conventional fitness systems offer basic functions such as providing training plans based on the user's physique data and providing dietary advice, they lack the flexibility to take the user's emotional state into account, making it difficult to maintain the user's motivation. In particular, in today's society, where continuity in fitness is important, there is a demand for systems that provide appropriate psychological support to users. Therefore, the purpose of this invention is to support users' sustainable training habits by customizing fitness plans based on the user's emotional state and providing mental care feedback.

[0392] The specific processing by the specific 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 a user registration and login means for a user to register an account and log in, a body shape photographing means for photographing the user's body shape using a camera and acquiring image data, a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape, a training plan generation means for creating a training plan based on the ideal body shape and providing it to the user, a training data collection means for collecting training data in real time and sending it to a central server, a user data synchronization means for saving the collected data in the user's account based on the collected data and synchronizing it with an application, a diet management and mental care means for generating and providing appropriate diet management and mental care advice based on the user's body shape data, an emotion analysis means for dynamically adjusting the training plan and mental care based on emotion analysis, and an emotion feedback means for analyzing the user's emotions in real time and providing feedback. This makes it possible to analyze the user's emotional state in real time and provide a fitness plan and feedback accordingly.

[0393] "User registration and login means" means a means that allows a user to create an account, register, and log in.

[0394] The "body shape photographing means" is a means for photographing the user's body shape using a camera and acquiring image data thereof.

[0395] The "body shape analysis and generation means" is a means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape.

[0396] The "training plan generation means" is a means for creating a training plan based on the generated ideal body type and providing it to the user.

[0397] "Training data collection means" refers to a means for collecting training data in real time and transmitting it to a central server.

[0398] The "user data synchronization means" is a means for saving data in a user's account based on collected data and synchronizing it with an application.

[0399] The "dietary management and mental care means" is a means for generating and providing appropriate dietary management advice and mental care advice based on the user's body type data.

[0400] The "emotion analysis means" is a means for analyzing the user's emotions and dynamically adjusting training plans and mental care in real time.

[0401] The "emotion feedback means" is a means for analyzing the user's emotions in real time and providing feedback based on the results.

[0402] This invention is a system that allows users to train efficiently and easily and maintain sustainable fitness habits, and has the function of recognizing the user's emotions and adjusting training plans and mental care. A specific implementation method is described below.

[0403] composition

[0404] server

[0405] The server includes the following means:

[0406] User registration and login methods

[0407] Body photography methods

[0408] Body shape analysis and generation method

[0409] Training plan generation method

[0410] Training Data Collection Methods

[0411] User data synchronization means

[0412] Dietary management and mental health care measures

[0413] Emotion analysis means

[0414] Emotional feedback measures

[0415] Hardware and software used

[0416] The specific hardware and software implemented on the server and terminal are as follows:

[0417] Hardware: camera sensors, monitors, smartphones

[0418] Software: Python, TensorFlow, OpenCV, Django, PostgreSQL

[0419] Processing flow

[0420] 1. User Registration and Login

[0421] Users create an account using a smartphone application, registering and logging in by entering their name, email address and password. Django is used to send data to the server and store the information in PostgreSQL.

[0422] 2. Body shape photography and analysis

[0423] When a user stands in front of the monitor in a physical store, a camera captures their body shape, and the acquired image data is analyzed using OpenCV. The analyzed data is then used by TensorFlow to generate their current and ideal body shapes, which are then sent to the server.

[0424] 3. Creation and provision of training plans

[0425] Based on the ideal body shape, the AI ​​model generates a training plan that is displayed on the monitor or smartphone app. The plan includes exercise names, repetitions, and number of sets, and includes video and audio instructions on correct form and important points to note.

[0426] 4. Emotional analysis and feedback

[0427] During training, a camera monitors the user's facial expressions and movements, and TensorFlow is used to analyze their emotions in real time. Based on the analysis results, the server dynamically adjusts the training plan and mental care content and provides appropriate feedback.

[0428] 5. Collect and sync training data

[0429] Training data (e.g., time, repetitions, heart rate, etc.) is collected in real time and sent to the server via Django and stored in a PostgreSQL database, which synchronizes the data with the user's application.

[0430] 6. Dietary management and mental health care

[0431] The server generates dietary management advice and mental care messages based on the user's body data and provides them via a smartphone app.

[0432] Specific examples

[0433] When a user visits the gym and stands in front of the monitor, they are automatically logged in, and the camera takes a picture of their body to analyze their current shape and generate an ideal body shape. A training plan is then customized and provided to the user. During training, the camera recognizes emotions in real time and provides appropriate feedback.

[0434] Prompt Sentence Examples

[0435] Generate a fitness plan for the user based on their current and ideal body shape data. Adjust the training plan and mental care content as needed, taking into account the user's emotional data.

[0436] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0437] Step 1:

[0438] A user creates an account and logs in using a smartphone application. The user enters their name, email address, and password into the application and performs registration. The input data (name, email address, password) is sent to the server and stored in a database (PostgreSQL) using Django. This process allows the user to obtain authentication information to access the system.

[0439] Step 2:

[0440] The user stands in front of a monitor in a physical store, and a camera captures the user's body shape. The terminal (monitor) acquires the image data captured by the camera and sends it to the server. The input data (image data) is analyzed using OpenCV, and the user's current body shape is identified. The server temporarily stores the results of this analysis (current body shape data) and uses it in subsequent processing.

[0441] Step 3:

[0442] The server uses TensorFlow to generate an ideal body shape based on the current body shape data analyzed by OpenCV. The generative AI model generates ideal body shape data based on the input data (current body shape data). The ideal body shape data is stored by the server and used in the next processing step.

[0443] Step 4:

[0444] The server uses an AI model to generate a training plan based on the generated ideal body data. Input data (ideal body data) is supplied to the generative AI model using prompt statements, and output data (training plan) is generated. The generated training plan is sent from the server to the device (monitor or smartphone app) and provided to the user.

[0445] Step 5:

[0446] The user starts training, and training data (time, repetitions, heart rate, etc.) is collected by the device. The collected data is sent to the server in real time. The server stores the training data in a database and makes it available for subsequent processing.

[0447] Step 6:

[0448] During training, a camera monitors the user's facial expressions and movements, analyzing their emotions in real time. The input data used for emotion analysis (user's facial expressions and movements) is processed using TensorFlow, and the output data (emotional state) is sent to a server. Based on this emotional state data, the training plan and mental care content are dynamically adjusted.

[0449] Step 7:

[0450] The server generates feedback (encouraging messages, adjustments to instruction, etc.) based on the results of emotion analysis and sends it to the device (monitor or smartphone app). This allows the user to receive appropriate feedback in real time. Feedback messages are generated based on the input data (emotional state) and provided to the user.

[0451] Step 8:

[0452] After the training is completed, the server synchronizes and stores all collected training data in the user's account. The user data stored in the database (PostgreSQL) is synchronized with the user's smartphone app and used as reference data for future training.

[0453] Step 9:

[0454] The server generates dietary advice and mental care messages based on the user's body shape data and provides them to the user periodically via a smartphone app. Based on the input data (body shape data), the generative AI model generates advice and provides feedback to the user.

[0455] These steps provide users with an efficient and consistent fitness experience, promoting sustainable training habits.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] [Second embodiment]

[0460] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0461] 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.

[0462] 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).

[0463] 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.

[0464] 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.

[0465] 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).

[0466] 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.

[0467] 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.

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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."

[0472] This invention is a system that allows users to train efficiently and easily and maintain sustainable fitness habits. The system uses AI-enabled monitors and cameras installed in fitness clubs and gyms, and also works in conjunction with a mobile application.

[0473] Feature Overview

[0474] User Registration / Login

[0475] Users create an account through the mobile application and log in, which involves entering their name, email address, and password, and the server stores this information in a database for authentication.

[0476] Body shape analysis and generation

[0477] A camera captures the user's body shape as they stand in front of the gym monitor, and sends the image data to a server. The server then uses an AI model to analyze the user's current body shape based on the acquired image data. It then generates an ideal body shape and displays it to the user.

[0478] Training plan generation

[0479] Based on the generated ideal body shape, the AI ​​trainer creates a daily training plan and provides it to the user. The exercise names, repetitions, and number of sets are displayed on the monitor, and video and audio instructions are provided on the correct form and important points to note.

[0480] Training data collection and synchronization

[0481] Data on the user's workout (e.g., duration, repetition, heart rate, etc.) is collected in real time and sent to a server, where it is stored and linked to the user's account and synchronized with the mobile application.

[0482] Dietary management and mental care

[0483] The server generates appropriate dietary advice and mental health messages based on the user's body shape data. These advice and messages are periodically provided to the user via a mobile application to support the user's fitness habits.

[0484] Specific Examples

[0485] A user's daily routine

[0486] 1. The user stands in front of the gym monitor and logs in using the account registered on the mobile app.

[0487] 2. The device uses a camera to take a picture of the user's body shape and sends the data to the server.

[0488] 3. The server analyzes the image data to identify the user's current body shape and generate an ideal body shape.

[0489] 4. The device displays the ideal body type and the training plan for the day, and the user follows this plan to train.

[0490] 5. The device records the training progress in real time and sends it to the server.

[0491] 6. The server stores the received data in the user's profile and synchronizes it with the mobile app.

[0492] 7. Users receive dietary advice and mental health messages through the mobile app.

[0493] This allows users to consistently train and manage their health whether they're at the gym or at home.

[0494] The processing flow will be explained below.

[0495] Step 1: The user stands in front of the gym monitor and logs in using the account registered on the mobile app.

[0496] Step 2: The device captures a picture of the user's body shape with its camera and acquires image data.

[0497] Step 3: The device sends the acquired image data to the server.

[0498] Step 4: The server receives the image data and inputs it into the AI ​​model.

[0499] Step 5: The server uses the AI ​​model to analyze the image data and extract the user's current body shape characteristics.

[0500] Step 6: The server simulates the ideal body shape based on the extracted features and generates images and data of the ideal body shape.

[0501] Step 7: The server sends the generated ideal body shape data back to the device.

[0502] Step 8: The device displays the ideal body shape data received from the server on the monitor.

[0503] Step 9: The device will then use the AI ​​trainer to create a training plan for the day based on your ideal body shape data.

[0504] Step 10: The device displays the training details (exercise name, number of repetitions, number of sets) to the user in a lecture format.

[0505] Step 11: The device will guide you through the training using video and audio instructions on proper form and important points to note.

[0506] Step 12: The user performs training under the guidance of the AI ​​trainer.

[0507] Step 13: The device collects real-time data during your workout (exercises performed, repetitions, time, heart rate, etc.).

[0508] Step 14: The terminal transmits the collected training data to the server.

[0509] Step 15: The server associates the received training data with the user's account and stores it in a database.

[0510] Step 16: The server generates the user's achievement level and the next training plan based on the stored data.

[0511] Step 17: The server returns the generated data to the terminal.

[0512] Step 18: The terminal displays the training results to the user.

[0513] Step 19: The server generates appropriate dietary advice based on the user's body type data.

[0514] Step 20: The server generates an appropriate mental health care message.

[0515] Step 21: The server sends the generated dietary management and mental care advice to the mobile application.

[0516] Step 22: The mobile application notifies the user of dietary advice.

[0517] Step 23: The mobile application notifies the user of the mental care message.

[0518] Example 1

[0519] 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."

[0520] Conventional fitness systems have faced challenges in helping users maintain effective and sustainable training plans. Specifically, they struggle to properly analyze a user's body type and provide an ideal personalized training plan, or to collect and analyze training data in real time to support consistent fitness habits. Furthermore, they lacked appropriate methods for providing individualized advice on dietary management and mental health, resulting in insufficient support for users to maintain their long-term health.

[0521] 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.

[0522] In this invention, the server includes a user registration and login means for a user to register an account and log in, a body shape photographing means for photographing the user's body shape using a camera and acquiring image data, and a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape, thereby enabling analysis and generation of an ideal body shape based on the user's individual body shape data.

[0523] "User Registration and Login Means" means the function by which a User registers and logs in to an account to access the System.

[0524] The "body shape photographing means" is a function that photographs the user's body shape using a camera and acquires image data.

[0525] The "body shape analysis and generation means" is a function that analyzes the acquired image data, characterizes the user's current body shape, and generates an ideal body shape.

[0526] The "training plan generation means" is a function that creates a training plan based on an ideal body type and provides it to the user.

[0527] The "training data collection means" is a function that collects training data in real time and transmits it to a central server.

[0528] The "user data synchronization means" is a function that saves data in the user's account based on the collected data and synchronizes it with the application.

[0529] The "dietary management and mental care means" is a function that generates and provides appropriate dietary management and mental care advice based on the user's body type data.

[0530] The "motion analysis means" is a function that collects and analyzes the user's motion and vital data to monitor fitness progress.

[0531] The "mental care notification means" is a function that notifies the user of encouraging messages and content that will increase motivation.

[0532] The "personal fitness advice means" is a function that analyzes the training data performed by the user and provides personalized advice to support continuous fitness habits.

[0533] This invention is a system that allows users to train efficiently and easily and maintain sustainable fitness habits. The system uses AI-equipped monitors and cameras installed in fitness clubs and gyms, and also works in conjunction with a mobile application. The system is configured as follows:

[0534] User registration and login methods

[0535] A user creates an account and logs in using a mobile application. This involves entering their name, email address, and password. The device sends this information to the server, which stores it in a database for authentication. For example, when a user enters their name, email address, and password and presses the "Register" button, the device encrypts the information they entered and sends it to the server.

[0536] Body photography methods

[0537] When a user arrives at the gym and stands in front of the AI-equipped monitor, the device (AI-equipped monitor) uses its camera to take a photo of the user's body shape and sends the image data to the server. The device then automatically recognizes the user using its camera, takes multiple images, and sends them to the server.

[0538] Body shape analysis and generation method

[0539] The server analyzes the user's current body shape using an AI model (such as TensorFlow or PyTorch) based on the acquired image data. The server then generates an ideal body shape based on the analysis results and sends that data to the device. For example, the server applies face recognition and body shape recognition algorithms to the AI ​​model and sends the analysis results in JSON format to the device.

[0540] Training plan generation method

[0541] The server uses an AI trainer (e.g., OpenAI's GPT-4) to create a training plan for the day based on the user's ideal body shape. The device displays the training plan to the user, prompting them for the exercise name, number of repetitions, and number of sets. For example, a training plan may be generated with a prompt such as, "Based on the user's current body shape, please create a one-day training plan for a man in his 30s who is looking to lose weight."

[0542] Training Data Collection Methods

[0543] When a user exercises, the device records the user's training data (e.g., time, number of repetitions, heart rate) in real time and sends it to the server. For example, the device sends the data to the server in batches at the end of the session or at regular intervals.

[0544] User data synchronization means

[0545] The server associates the received data with the user's account, stores it, and synchronizes it with the mobile app. For example, the server saves the data in a database and sends a push notification to the mobile app to notify it of synchronization completion.

[0546] Dietary management and mental health care measures

[0547] The server generates appropriate dietary advice and mental health messages based on the user's body shape and training data. The server then sends these to a mobile application, where the user can receive them. For example, the user can check the notifications received in the mobile app and incorporate them into their daily lives.

[0548] Motion analysis means

[0549] The device collects and analyzes the user's movement and vital data to monitor fitness progress. For example, the monitor's camera and sensors continuously record the user's movement and vital data and send it to a server.

[0550] Mental care notification method

[0551] The server then sends encouraging messages and motivational content to the user, generating personalized messages based on a prompt such as, "Please create a three-day training menu per week for a woman in her twenties who wants to build muscle."

[0552] Personalized Fitness Advice Tool

[0553] The server analyzes the user's training data and provides personalized advice to support ongoing fitness habits. For example, based on the collected training data, the AI ​​model generates improvements and new goals for the next training session.

[0554] These tools allow users to consistently train and manage their health whether they are at a fitness club or gym or at home.

[0555] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0556] Step 1:

[0557] A user launches a mobile application and enters their name, email address, and password to create an account.

[0558] Input: Name, Email Address, Password

[0559] Output: Submit registration information

[0560] How it works: After filling out the registration form on the mobile app and pressing the "Register" button, the device encrypts the information entered and sends an HTTP POST request to the server.

[0561] Step 2:

[0562] The server stores the received information in a database and manages the user authentication information.

[0563] Input: Encrypted user information

[0564] Output: Credentials stored in the database

[0565] What happens: The server parses the incoming data and creates a new user record in the database. The user logs in using their saved credentials.

[0566] Step 3:

[0567] The user arrives at the gym and stands in front of the AI-equipped monitor.

[0568] Input: User location and camera visual data

[0569] Output: Camera image acquisition

[0570] How it works: The user stands still in front of the monitor and poses as instructed by the camera. The device automatically recognizes the user, captures an image, and sends it to the server.

[0571] Step 4:

[0572] The server uses an AI model to analyze the user's current body type based on the acquired image data.

[0573] Input: Image data

[0574] Output: Analysis data of current body type and ideal body type data

[0575] How it works: The server applies face recognition and body recognition algorithms to an AI model (e.g., TensorFlow or PyTorch) to analyze the current body shape, then generates an ideal body shape and sends it to the device.

[0576] Step 5:

[0577] The terminal displays the generated ideal body type to the user.

[0578] Input: Ideal body type data

[0579] Output: Ideal body image displayed on the monitor

[0580] How it works: The monitor displays an image of your ideal body shape on the user's screen for them to review.

[0581] Step 6:

[0582] The server uses an AI trainer to create a training plan for the day based on the user's ideal body type.

[0583] Input: Ideal body type data

[0584] Output: Training plan data

[0585] How it works: On the server, the AI ​​model generates a training plan based on a prompt. For example, it generates a training plan based on the prompt, "Based on my current body shape, please create a one-day training plan for a man in his 30s who is trying to lose weight."

[0586] Step 7:

[0587] The terminal displays the training plan to the user and prompts the user for the exercise name, number of repetitions, and number of sets.

[0588] Input: Training plan data

[0589] Output: Training plan displayed on the monitor

[0590] How it works: The training plan details are displayed in list format and the monitor plays audio and video guides.

[0591] Step 8:

[0592] The user performs the training.

[0593] Input: User movement and vital data

[0594] Output: A record of training progress

[0595] How it works: The user performs exercises according to the instructions on the monitor. The device records the user's movements and vital data in real time.

[0596] Step 9:

[0597] The terminal transmits the collected data to the server.

[0598] Input: Training data

[0599] Output: Training data sent to the server

[0600] How it works: At the end of a session or at regular intervals, the device sends a batch of data to the server.

[0601] Step 10:

[0602] The server stores the received data, associates it with the user's account, and synchronizes it with the mobile app.

[0603] Input: Training data

[0604] Output: Data stored in the account and sync notifications to the app

[0605] How it works: The server saves the data to the database and sends a push notification to the mobile app when the synchronization is complete.

[0606] Step 11:

[0607] The server generates appropriate dietary advice and mental care messages based on the user's body type and training data.

[0608] Input: Body type data and training data

[0609] Output: Dietary advice and mental health care messages

[0610] How it works: The AI ​​model on the server analyzes the data and generates personalized advice, which is then sent to the mobile app and received by the user.

[0611] Step 12:

[0612] The device collects and analyzes the user's movement and vital signs to monitor their fitness progress.

[0613] Input: User motion and vital data

[0614] Output: Parsed progress data

[0615] Operation: The monitor's camera and sensors continuously record the user's movements and vital data and transmit them to a server.

[0616] Step 13:

[0617] The server notifies the user of encouraging messages and motivational content.

[0618] Input: User's body type and training data

[0619] Output: Mental care message

[0620] What it does: Notifies the user of server-generated messages to keep them motivated.

[0621] Step 14:

[0622] The server analyzes the user's training data and provides personalized advice to support ongoing fitness habits.

[0623] Input: Training data

[0624] Output: Personalized advice

[0625] How it works: Based on the collected training data, the AI ​​model generates and provides users with improvements and new goals for their next training session.

[0626] (Application example 1)

[0627] 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."

[0628] Conventional fitness systems have been insufficient in supporting users to train efficiently and sustainably, particularly due to the difficulty of providing real-time feedback and motion analysis. Furthermore, there is a need for systems that can properly analyze workers' postures and movements in workplaces such as factories, and provide efficient, fatigue-reducing work methods. Given this background, there is a need for systems that comprehensively support fitness training and improving the work environment.

[0629] 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.

[0630] In this invention, the server includes a user registration and login means for a user to register an account and log in, a body shape photographing means for photographing the user's body shape using a camera and acquiring image data, a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape, a training plan generation means for creating a training plan based on the ideal body shape and providing it to the user, a training data collection means for collecting training data in real time and sending it to a central server, a user data synchronization means for saving the collected data in the user's account based on the data and synchronizing it with an application, a dietary management and mental care means for generating and providing appropriate dietary management and mental care advice based on the user's body shape data, a posture analysis and movement plan generation means for analyzing the posture and movement of a worker in real time and generating an efficient work method, and a real-time feedback means for feeding the generated movement plan back to the worker in real time. This enables users to train efficiently and sustainably, and enables workers to adopt efficient and less fatigued work methods in real time.

[0631] "User registration and login means" refers to the function that allows users to register and log in to their accounts.

[0632] The "body shape photographing means" is a function that photographs the user's body shape using a camera and acquires image data.

[0633] The "body shape analysis and generation means" is a function that analyzes the acquired image data, characterizes the user's current body shape, and generates an ideal body shape.

[0634] The "training plan generation means" is a function that creates a training plan based on an ideal body type and provides it to the user.

[0635] The "training data collection means" is a function that collects training data in real time and transmits it to a central server.

[0636] The "user data synchronization means" is a function that saves data in the user's account based on the collected data and synchronizes it with the application.

[0637] The "dietary management and mental care means" is a function that generates and provides appropriate dietary management and mental care advice based on the user's body type data.

[0638] The "posture analysis and motion plan generation means" is a function that analyzes the posture and motion of a worker in real time and generates an efficient work method.

[0639] The "real-time feedback means" is a function that provides feedback of the generated operation plan to the worker in real time.

[0640] System configuration

[0641] This invention is a system that enables users to maintain fitness habits efficiently and sustainably. This system can also be used as an example of a factory robot. The main components of the system include a user registration and login means, a body shape photography means, a body shape analysis and generation means, a training plan generation means, a training data collection means, a user data synchronization means, a diet management and mental care means, a posture analysis and movement plan generation means, and a real-time feedback means.

[0642] Hardware and Software

[0643] Hardware: cameras, monitors, servers, factory robots

[0644] Software: TensorFlow, OpenCV, cloud storage services (Azure, AWS)

[0645] Example

[0646] User Registration and Login

[0647] Users can register an account using the interface of their mobile device or factory robot and log in. To log in, they need to enter their name, email address, password, etc., and this information is stored on the server.

[0648] Body photography and analysis

[0649] When a user stands in front of the camera, it captures a photo of the user's body shape and captures image data. This data is sent to a server, where it uses a TensorFlow model to analyze the user's body shape and generate a current and ideal body shape.

[0650] Training plan generation

[0651] The server then creates a training plan based on the generated ideal body shape and provides it to the user. The training plan includes exercise names, repetitions, and number of sets, as well as video and audio instruction on correct form and important points to note.

[0652] Collecting training data

[0653] As users train, data (e.g., duration, repetition, heart rate, etc.) is collected in real time and sent to a server, which stores this data in the user's account and synchronizes it with the mobile application.

[0654] Dietary management and mental care

[0655] The server generates and provides appropriate dietary and mental health advice based on the user's body shape data. These advice and messages are periodically provided to the user via the mobile application.

[0656] Posture analysis and motion plan generation

[0657] The factory robot monitors the worker's movements in real time and captures their posture with a camera. The data obtained is sent to a server where it is analyzed using an AI model. The server then generates an efficient work method and provides the plan to the worker.

[0658] Real-time feedback

[0659] The generated motion plan is displayed in real time on the robot's interface, and the worker can use this feedback to adjust their posture and perform the task efficiently and with less fatigue.

[0660] Specific examples

[0661] A concrete example is a scenario where real-time feedback is provided to a worker saying, "Stand up straight." An example prompt for this application is:

[0662] Example prompt sentence:

[0663] The system captures the user working in front of the camera and analyzes their posture in real time. Based on the analysis results, it instructs the worker to straighten their back. This feedback aims to improve work efficiency and reduce fatigue.

[0664] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0665] Step 1:

[0666] A user registers an account using the interface of a mobile device or factory robot and logs in. The inputs include name, email address, and password, which are stored on the server. The server authenticates the input data and stores the user information in a database.

[0667] Step 2:

[0668] The device's camera captures the user's body shape and captures the image data. This image data is sent to the server in real time. The server receives and stores this data.

[0669] Step 3:

[0670] The server analyzes the user's body shape using the TensorFlow model based on the acquired image data. Specifically, it detects posture, body parts, etc. from the image data and identifies the current body shape. As a result of the analysis, it outputs the characteristics of the user's current body shape.

[0671] Step 4:

[0672] The server generates an ideal body shape based on the analysis results. Using a generative AI model, it calculates the difference from the current body shape and outputs the target body shape. This generated ideal body shape data becomes the basis for creating a training plan.

[0673] Step 5:

[0674] The server creates a training plan based on the ideal body type data. The plan, including exercise names, repetitions, and number of sets, is automatically generated by the AI ​​model. This plan is provided to the user and displayed on the monitor.

[0675] Step 6:

[0676] During training, the device's sensors collect the user's training data (e.g., time, repetitions, heart rate) in real time. This collected data is immediately sent to the server, where it is stored and linked to the user's account.

[0677] Step 7:

[0678] The server stores the collected training data in the user's account and synchronizes it with the mobile application, allowing users to view their training data from any device.

[0679] Step 8:

[0680] The server generates appropriate dietary and mental health advice based on the user's physical and training data, and these advice and messages are periodically provided to the user via a mobile application.

[0681] Step 9:

[0682] The factory robot monitors the worker's movements in real time and captures their posture with a camera. The acquired image data is sent to a server, which analyzes this data and generates efficient work methods.

[0683] Step 10:

[0684] The generated motion plan is displayed in real time on the robot's interface, and the worker can use this feedback to correct their posture and perform the task in an efficient and less tiring manner, thereby improving worker efficiency and reducing fatigue.

[0685] 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.

[0686] This invention is a system that allows users to train efficiently and easily and maintain sustainable fitness habits, and has the function of recognizing the user's emotions and adjusting training plans and mental care. This system works by using AI-equipped monitors and cameras installed in fitness clubs and gyms, as well as an emotion engine, and also works in conjunction with a mobile application.

[0687] Feature Overview

[0688] User Registration / Login

[0689] Users create an account through the mobile application and log in, which involves entering their name, email address, and password, and the server stores this information in a database for authentication.

[0690] Body shape analysis and generation

[0691] A camera captures the user's body shape as they stand in front of the gym monitor, and sends the image data to a server. The server then uses an AI model to analyze the user's current body shape based on the acquired image data. It then generates an ideal body shape and displays it to the user.

[0692] Training plan generation

[0693] Based on the generated ideal body shape, the AI ​​trainer creates a daily training plan and provides it to the user. The exercise names, repetitions, and number of sets are displayed on the monitor, and video and audio instructions are provided on the correct form and important points to note.

[0694] Emotion Engine and Analytics

[0695] The emotion engine analyzes the user's facial expressions and movements to recognize their emotions in real time. The device then sends the analysis results to a server, which uses this information to adjust training plans and mental care.

[0696] Training data collection and synchronization

[0697] Data on the user's workout (e.g., duration, repetition, heart rate, etc.) is collected in real time and sent to a server, where it is stored and linked to the user's account and synchronized with the mobile application.

[0698] Dietary management and mental care

[0699] The server generates appropriate dietary advice and mental health messages based on the user's body shape data. These advice and messages are periodically provided to the user via a mobile application to support the user's fitness habits.

[0700] Specific Examples

[0701] A user's daily routine

[0702] 1. The user stands in front of the gym monitor and logs in using the account registered on the mobile app.

[0703] 2. The device uses a camera to take a picture of the user's body shape and sends the data to the server.

[0704] 3. The server analyzes the image data to identify the user's current body shape and generate an ideal body shape.

[0705] 4. The device displays the ideal body shape and training plan for the day, and the user follows this plan to train.

[0706] 5. The emotion engine monitors the user's facial expressions and movements during training and recognizes emotions in real time.

[0707] 6. The device sends the emotion engine analysis results to the server.

[0708] 7. The server adjusts the training plan and mental care content based on the emotional data and provides feedback to the user.

[0709] 8. The device will display training instructions and encouraging messages based on your emotions.

[0710] 9. The device collects data during training and sends it to the server.

[0711] 10. The server stores this data in the user's profile and synchronizes it with the mobile app.

[0712] 11. Users receive dietary advice and mental health messages through a mobile app.

[0713] This allows users to consistently train and manage their health whether they are at the gym or at home.By using an emotion engine, flexible training support and mental care are realized that adapt to the user's situation and emotions.

[0714] The processing flow will be explained below.

[0715] Step 1: The user stands in front of the gym monitor and logs in using the account registered on the mobile app.

[0716] Step 2: The device captures a picture of the user's body shape with its camera and acquires image data.

[0717] Step 3: The device sends the acquired image data to the server.

[0718] Step 4: The server receives the image data and inputs it into the AI ​​model.

[0719] Step 5: The server uses the AI ​​model to analyze the image data and extract the user's current body shape characteristics.

[0720] Step 6: The server simulates the ideal body shape based on the extracted features and generates images and data of the ideal body shape.

[0721] Step 7: The server sends the generated ideal body shape data back to the device.

[0722] Step 8: The device displays the ideal body shape data received from the server on the monitor.

[0723] Step 9: The device will then use the AI ​​trainer to create a training plan for the day based on your ideal body shape data.

[0724] Step 10: The device displays the training details (exercise name, number of repetitions, number of sets) to the user in a lecture format.

[0725] Step 11: The device will guide you through the training using video and audio instructions on proper form and important points to note.

[0726] Step 12: The emotion engine analyzes the user's facial expressions and movements and recognizes emotions in real time.

[0727] Step 13: The device sends the emotion engine analysis results to the server.

[0728] Step 14: The server adjusts the training plan and mental care content based on the emotion analysis results.

[0729] Step 15: The device displays the adjusted training instructions and mental care content.

[0730] Step 16: The user performs training according to the AI ​​trainer's guidance and emotional feedback.

[0731] Step 17: The device collects real-time data during your workout (exercises performed, repetitions, time, heart rate, etc.).

[0732] Step 18: The terminal transmits the collected training data to the server.

[0733] Step 19: The server associates the received training data with the user's account and stores it in a database.

[0734] Step 20: The server generates the user's achievement level and the next training plan based on the stored data.

[0735] Step 21: The server returns the generated data to the terminal.

[0736] Step 22: The terminal displays the training results to the user.

[0737] Step 23: The server generates appropriate dietary advice based on the user's body type data.

[0738] Step 24: The server generates an appropriate mental health care message.

[0739] Step 25: The server sends the generated dietary management and mental care advice to the mobile application.

[0740] Step 26: The mobile application notifies the user of dietary advice.

[0741] Step 27: The mobile application notifies the user of the mental care message.

[0742] Example 2

[0743] 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."

[0744] Modern fitness facilities require systems that enable users to train efficiently and maintain sustainable fitness habits. However, conventional systems have difficulty accurately analyzing individual users' physical shapes and emotions and providing training plans and mental care based on those analyses. In particular, there is a lack of systems that can monitor users' facial expressions and movements in real time and provide support according to their emotions.

[0745] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0746] In this invention, the server includes a user registration and login means for a user to register an account and log in, a body shape photographing means for photographing the user's body shape using a camera and acquiring image data, a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape, a training plan generation means for creating a training plan based on the ideal body shape and providing it to the user, an emotion analysis means for analyzing the user's facial expressions and movements in real time and recognizing emotions, a training data collection means for collecting training data in real time and transmitting it to a central server, a user data synchronization means for saving the collected data in the user's account and synchronizing it with an application, and a diet management and mental care means for generating and providing appropriate diet management and mental care advice based on the user's body shape data. This not only provides users with optimal training plans tailored to their individual body shapes and emotions, but also enables them to maintain flexible and sustainable fitness habits through the analysis of emotion data in real time.

[0747] "User Registration and Login Method" means the authentication method by which a User creates an account and accesses the System.

[0748] The "body shape photographing means" is a means for photographing the user's body shape using a camera and acquiring image data thereof.

[0749] The "body shape analysis and generation means" is a means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape.

[0750] The "training plan generation means" is a means for creating a training plan based on an ideal body type and providing it to the user.

[0751] The "emotion analysis means" is a means for analyzing the user's facial expressions and movements in real time and recognizing their emotions.

[0752] The "training data collection means" is a means for collecting data during training in real time and transmitting it to a central server.

[0753] The "user data synchronization means" is a means for saving data in a user's account based on collected data and synchronizing it with an application.

[0754] The "dietary management and mental care means" is a means for generating and providing appropriate dietary management and mental care advice based on the user's body type data.

[0755] The present invention provides a system that allows users to efficiently and easily train and maintain sustainable fitness habits. The system has the function of recognizing the user's emotions and adjusting the training plan and mental care. Specific examples are described in detail below.

[0756] User Registration / Login

[0757] A user first opens the mobile application and creates an account. They enter their username, email address, and password and submit them to the server. The server receives the data, stores it in a database, and sends a confirmation email to the user. The user clicks on a link in the confirmation email to verify their email address, allowing them to log in to the system.

[0758] Examples:

[0759] Enter your username, email address, and password on the registration screen of the mobile application and tap the "Register" button.

[0760] Example prompt: "To create a new account, please enter your name, email address, and password."

[0761] Body shape analysis and generation

[0762] The user stands in front of a monitor in the gym and takes a picture of their body with a camera. The device sends the image data to a server. The server analyzes the image data using an AI model (e.g., OpenPose or DensePose) to identify the user's current body shape. It then generates an ideal body shape and sends it along with the analysis results to the device. The device then displays the analysis results and the ideal body shape to the user.

[0763] Examples:

[0764] When the user stands in front of the monitor, the camera automatically captures their body shape and the results are displayed within seconds.

[0765] Example prompt: "Stand in front of the monitor at the gym and take a picture of your body shape."

[0766] Training plan generation

[0767] The server uses an AI trainer to generate a training plan based on the analyzed ideal body type, while the device displays the exercise name, number of repetitions, and number of sets on the monitor and provides instructions on correct form and important points to note via video and audio.

[0768] Examples:

[0769] Users can watch videos showing them the correct way to perform the exercises.

[0770] Example prompt: "Based on your ideal body type, here's today's workout plan."

[0771] Emotion analysis means

[0772] The emotion engine analyzes the user's facial expressions and movements in real time to recognize their emotions. The device then sends the emotion engine's analysis results to the server. The server adjusts the training plan and mental care content based on the emotion data and provides feedback to the user. The device then displays the feedback and provides training and mental care messages according to the emotion.

[0773] Examples:

[0774] If the emotion engine detects that the user is tired, it will adjust the training plan and display encouraging messages.

[0775] Sample prompt: "Analyze the user's facial expressions and provide advice based on their emotions."

[0776] Training data collection and synchronization

[0777] The device collects data (e.g., time, repetition, heart rate, etc.) in real time as the user trains and sends it to the server, which associates this data with the user's account, stores it, and syncs it with the mobile app.

[0778] Examples:

[0779] During training, your heart rate and number of repetitions are recorded and stored on a server.

[0780] Sample prompt: "Collect data during training and send it to the server in real time."

[0781] Dietary management and mental care

[0782] The server generates appropriate dietary advice and mental care messages based on the user's body data and periodically provides them to the user via a mobile app.

[0783] Examples:

[0784] The mobile app will send you dietary advice tailored to your situation.

[0785] Sample prompt: "Generate appropriate dietary advice based on the user's body type data."

[0786] This system not only provides users with optimal training plans tailored to their individual body types and emotions, but also enables them to maintain flexible and sustainable fitness habits through real-time analysis of emotional data.

[0787] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0788] Step 1:

[0789] A user opens a mobile application and creates an account. They enter their username, email address, and password and submit them to the server. The server receives the input data, stores the user information, including a hashed password, in a database, and sends a confirmation email to the user. The user clicks on a link in the confirmation email to verify their email address, allowing them to log in to the system.

[0790] Input: Username, Email Address, Password

[0791] Processing: Receives input data, hashes password, then stores it in the database and sends a confirmation email

[0792] Output: Confirmation email, user information stored in database

[0793] Specific actions: Enter your username, email address, and password on the mobile application's registration screen and tap the "Register" button.

[0794] Step 2:

[0795] The user stands in front of a monitor in the gym and takes a picture of their body shape with a camera. The device sends the image data to a server. The server analyzes the image data using an AI model (e.g., OpenPose or DensePose) to identify the user's current body shape. It then generates an ideal body shape and sends it along with the analysis results to the device. The device then displays the analysis results and the ideal body shape to the user.

[0796] Input: User's body image

[0797] Processing: Analyze image data to identify current body shape and generate ideal body shape

[0798] Output: Analysis results, ideal body shape image

[0799] How it works: When the user stands in front of the monitor, the camera automatically takes a photo of their body shape and displays the results within a few seconds.

[0800] Step 3:

[0801] The server uses an AI trainer to generate a training plan based on the analyzed ideal body type. The device displays the exercise name, number of repetitions, and number of sets on the monitor, and provides instructions on correct form and important points to note via video and audio.

[0802] Input: Ideal body type data

[0803] Processing: Generate training plan (determine exercise name, repetitions, number of sets)

[0804] Output: Training plan

[0805] Specific actions: Users watch videos to learn the correct way to perform the exercises.

[0806] Step 4:

[0807] The emotion engine analyzes the user's facial expressions and movements in real time to recognize their emotions. The device then sends the emotion engine's analysis results to a server. The server adjusts the training plan and mental care content based on the emotion data and provides feedback to the user. The device then displays the feedback and provides training and mental care messages based on the emotion.

[0808] Input: User's facial expression and movement data

[0809] Processing: Analysis of facial expressions and movement data, emotion recognition

[0810] Output: Sentiment analysis results, training plan adjustments, mental care messages

[0811] What it does: If the emotion engine recognizes that the user is tired, it will adjust their training plan or mental care messages.

[0812] Step 5:

[0813] The device collects data (e.g., time, number of repetitions, heart rate, etc.) in real time as the user exercises and sends it to the server, which then associates this data with the user's account, stores it, and syncs it with the mobile app.

[0814] Input: Training data (time, repetitions, heart rate, etc.)

[0815] Processing: Data collection, transmission to server, data storage

[0816] Output: Saved training data, synced app data

[0817] Specific operation: Heart rate and number of repetitions recorded during training are stored on the server and can be viewed on the mobile app.

[0818] Step 6:

[0819] The server generates appropriate dietary advice and mental care messages based on the user's body data and provides them to the user periodically via a mobile app.

[0820] Input: User's body data

[0821] Processing: Generating dietary management advice and mental care messages

[0822] Output: Generated advice and messages

[0823] Specific behavior: The mobile app sends users appropriate dietary advice and mental health care messages.

[0824] These are the specific processing steps of the system, which enable users to maintain an efficient and sustainable fitness routine.

[0825] (Application example 2)

[0826] 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."

[0827] While conventional fitness systems offer basic functions such as providing training plans based on the user's physique data and providing dietary advice, they lack the flexibility to take the user's emotional state into account, making it difficult to maintain the user's motivation. In particular, in today's society, where continuity in fitness is important, there is a demand for systems that provide appropriate psychological support to users. Therefore, the purpose of this invention is to support users' sustainable training habits by customizing fitness plans based on the user's emotional state and providing mental care feedback.

[0828] The specific processing by the specific 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 a user registration and login means for a user to register an account and log in, a body shape photographing means for photographing the user's body shape using a camera and acquiring image data, a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape, a training plan generation means for creating a training plan based on the ideal body shape and providing it to the user, a training data collection means for collecting training data in real time and sending it to a central server, a user data synchronization means for saving the collected data in the user's account based on the collected data and synchronizing it with an application, a diet management and mental care means for generating and providing appropriate diet management and mental care advice based on the user's body shape data, an emotion analysis means for dynamically adjusting the training plan and mental care based on emotion analysis, and an emotion feedback means for analyzing the user's emotions in real time and providing feedback. This makes it possible to analyze the user's emotional state in real time and provide a fitness plan and feedback accordingly.

[0829] "User registration and login means" means a means that allows a user to create an account, register, and log in.

[0830] The "body shape photographing means" is a means for photographing the user's body shape using a camera and acquiring image data thereof.

[0831] The "body shape analysis and generation means" is a means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape.

[0832] The "training plan generation means" is a means for creating a training plan based on the generated ideal body type and providing it to the user.

[0833] "Training data collection means" refers to a means for collecting training data in real time and transmitting it to a central server.

[0834] The "user data synchronization means" is a means for saving data in a user's account based on collected data and synchronizing it with an application.

[0835] The "dietary management and mental care means" is a means for generating and providing appropriate dietary management advice and mental care advice based on the user's body type data.

[0836] The "emotion analysis means" is a means for analyzing the user's emotions and dynamically adjusting training plans and mental care in real time.

[0837] The "emotion feedback means" is a means for analyzing the user's emotions in real time and providing feedback based on the results.

[0838] This invention is a system that allows users to train efficiently and easily and maintain sustainable fitness habits, and has the function of recognizing the user's emotions and adjusting training plans and mental care. A specific implementation method is described below.

[0839] composition

[0840] server

[0841] The server includes the following means:

[0842] User registration and login methods

[0843] Body photography methods

[0844] Body shape analysis and generation method

[0845] Training plan generation method

[0846] Training Data Collection Methods

[0847] User data synchronization means

[0848] Dietary management and mental health care measures

[0849] Emotion analysis means

[0850] Emotional feedback measures

[0851] Hardware and software used

[0852] The specific hardware and software implemented on the server and terminal are as follows:

[0853] Hardware: camera sensors, monitors, smartphones

[0854] Software: Python, TensorFlow, OpenCV, Django, PostgreSQL

[0855] Processing flow

[0856] 1. User Registration and Login

[0857] Users create an account using a smartphone application, registering and logging in by entering their name, email address and password. Django is used to send data to the server and store the information in PostgreSQL.

[0858] 2. Body shape photography and analysis

[0859] When a user stands in front of the monitor in a physical store, a camera captures their body shape, and the acquired image data is analyzed using OpenCV. The analyzed data is then used by TensorFlow to generate their current and ideal body shapes, which are then sent to the server.

[0860] 3. Creation and provision of training plans

[0861] Based on the ideal body shape, the AI ​​model generates a training plan that is displayed on the monitor or smartphone app. The plan includes exercise names, repetitions, and number of sets, and includes video and audio instructions on correct form and important points to note.

[0862] 4. Emotional analysis and feedback

[0863] During training, a camera monitors the user's facial expressions and movements, and TensorFlow is used to analyze their emotions in real time. Based on the analysis results, the server dynamically adjusts the training plan and mental care content and provides appropriate feedback.

[0864] 5. Collect and sync training data

[0865] Training data (e.g., time, repetitions, heart rate, etc.) is collected in real time and sent to the server via Django and stored in a PostgreSQL database, which synchronizes the data with the user's application.

[0866] 6. Dietary management and mental health care

[0867] The server generates dietary management advice and mental care messages based on the user's body data and provides them via a smartphone app.

[0868] Specific examples

[0869] When a user visits the gym and stands in front of the monitor, they are automatically logged in, and the camera takes a picture of their body to analyze their current shape and generate an ideal body shape. A training plan is then customized and provided to the user. During training, the camera recognizes emotions in real time and provides appropriate feedback.

[0870] Prompt Sentence Examples

[0871] Generate a fitness plan for the user based on their current and ideal body shape data. Adjust the training plan and mental care content as needed, taking into account the user's emotional data.

[0872] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0873] Step 1:

[0874] A user creates an account and logs in using a smartphone application. The user enters their name, email address, and password into the application and performs registration. The input data (name, email address, password) is sent to the server and stored in a database (PostgreSQL) using Django. This process allows the user to obtain authentication information to access the system.

[0875] Step 2:

[0876] The user stands in front of a monitor in a physical store, and a camera captures the user's body shape. The terminal (monitor) acquires the image data captured by the camera and sends it to the server. The input data (image data) is analyzed using OpenCV, and the user's current body shape is identified. The server temporarily stores the results of this analysis (current body shape data) and uses it in subsequent processing.

[0877] Step 3:

[0878] The server uses TensorFlow to generate an ideal body shape based on the current body shape data analyzed by OpenCV. The generative AI model generates ideal body shape data based on the input data (current body shape data). The ideal body shape data is stored by the server and used in the next processing step.

[0879] Step 4:

[0880] The server uses an AI model to generate a training plan based on the generated ideal body data. Input data (ideal body data) is supplied to the generative AI model using prompt statements, and output data (training plan) is generated. The generated training plan is sent from the server to the device (monitor or smartphone app) and provided to the user.

[0881] Step 5:

[0882] The user starts training, and training data (time, repetitions, heart rate, etc.) is collected by the device. The collected data is sent to the server in real time. The server stores the training data in a database and makes it available for subsequent processing.

[0883] Step 6:

[0884] During training, a camera monitors the user's facial expressions and movements, analyzing their emotions in real time. The input data used for emotion analysis (user's facial expressions and movements) is processed using TensorFlow, and the output data (emotional state) is sent to a server. Based on this emotional state data, the training plan and mental care content are dynamically adjusted.

[0885] Step 7:

[0886] The server generates feedback (encouraging messages, adjustments to instruction, etc.) based on the results of emotion analysis and sends it to the device (monitor or smartphone app). This allows the user to receive appropriate feedback in real time. Feedback messages are generated based on the input data (emotional state) and provided to the user.

[0887] Step 8:

[0888] After the training is completed, the server synchronizes and stores all collected training data in the user's account. The user data stored in the database (PostgreSQL) is synchronized with the user's smartphone app and used as reference data for future training.

[0889] Step 9:

[0890] The server generates dietary advice and mental care messages based on the user's body shape data and provides them to the user periodically via a smartphone app. Based on the input data (body shape data), the generative AI model generates advice and provides feedback to the user.

[0891] These steps provide users with an efficient and consistent fitness experience, promoting sustainable training habits.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] [Third embodiment]

[0896] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0897] 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.

[0898] 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).

[0899] 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.

[0900] 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.

[0901] 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).

[0902] 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.

[0903] 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.

[0904] 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.

[0905] 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.

[0906] 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.

[0907] 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."

[0908] This invention is a system that allows users to train efficiently and easily and maintain sustainable fitness habits. The system uses AI-enabled monitors and cameras installed in fitness clubs and gyms, and also works in conjunction with a mobile application.

[0909] Feature Overview

[0910] User Registration / Login

[0911] Users create an account through the mobile application and log in, which involves entering their name, email address, and password, and the server stores this information in a database for authentication.

[0912] Body shape analysis and generation

[0913] A camera captures the user's body shape as they stand in front of the gym monitor, and sends the image data to a server. The server then uses an AI model to analyze the user's current body shape based on the acquired image data. It then generates an ideal body shape and displays it to the user.

[0914] Training plan generation

[0915] Based on the generated ideal body shape, the AI ​​trainer creates a daily training plan and provides it to the user. The exercise names, repetitions, and number of sets are displayed on the monitor, and video and audio instructions are provided on the correct form and important points to note.

[0916] Training data collection and synchronization

[0917] Data on the user's workout (e.g., duration, repetition, heart rate, etc.) is collected in real time and sent to a server, where it is stored and linked to the user's account and synchronized with the mobile application.

[0918] Dietary management and mental care

[0919] The server generates appropriate dietary advice and mental health messages based on the user's body shape data. These advice and messages are periodically provided to the user via a mobile application to support the user's fitness habits.

[0920] Specific Examples

[0921] A user's daily routine

[0922] 1. The user stands in front of the gym monitor and logs in using the account registered on the mobile app.

[0923] 2. The device uses a camera to take a picture of the user's body shape and sends the data to the server.

[0924] 3. The server analyzes the image data to identify the user's current body shape and generate an ideal body shape.

[0925] 4. The device displays the ideal body type and the training plan for the day, and the user follows this plan to train.

[0926] 5. The device records the training progress in real time and sends it to the server.

[0927] 6. The server stores the received data in the user's profile and synchronizes it with the mobile app.

[0928] 7. Users receive dietary advice and mental health messages through the mobile app.

[0929] This allows users to consistently train and manage their health whether they're at the gym or at home.

[0930] The processing flow will be explained below.

[0931] Step 1: The user stands in front of the gym monitor and logs in using the account registered on the mobile app.

[0932] Step 2: The device captures a picture of the user's body shape with its camera and acquires image data.

[0933] Step 3: The device sends the acquired image data to the server.

[0934] Step 4: The server receives the image data and inputs it into the AI ​​model.

[0935] Step 5: The server uses the AI ​​model to analyze the image data and extract the user's current body shape characteristics.

[0936] Step 6: The server simulates the ideal body shape based on the extracted features and generates images and data of the ideal body shape.

[0937] Step 7: The server sends the generated ideal body shape data back to the device.

[0938] Step 8: The device displays the ideal body shape data received from the server on the monitor.

[0939] Step 9: The device will then use the AI ​​trainer to create a training plan for the day based on your ideal body shape data.

[0940] Step 10: The device displays the training details (exercise name, number of repetitions, number of sets) to the user in a lecture format.

[0941] Step 11: The device will guide you through the training using video and audio instructions on proper form and important points to note.

[0942] Step 12: The user performs training under the guidance of the AI ​​trainer.

[0943] Step 13: The device collects real-time data during your workout (exercises performed, repetitions, time, heart rate, etc.).

[0944] Step 14: The terminal transmits the collected training data to the server.

[0945] Step 15: The server associates the received training data with the user's account and stores it in a database.

[0946] Step 16: The server generates the user's achievement level and the next training plan based on the stored data.

[0947] Step 17: The server returns the generated data to the terminal.

[0948] Step 18: The terminal displays the training results to the user.

[0949] Step 19: The server generates appropriate dietary advice based on the user's body type data.

[0950] Step 20: The server generates an appropriate mental health care message.

[0951] Step 21: The server sends the generated dietary management and mental care advice to the mobile application.

[0952] Step 22: The mobile application notifies the user of dietary advice.

[0953] Step 23: The mobile application notifies the user of the mental care message.

[0954] Example 1

[0955] 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."

[0956] Conventional fitness systems have faced challenges in helping users maintain effective and sustainable training plans. Specifically, they struggle to properly analyze a user's body type and provide an ideal personalized training plan, or to collect and analyze training data in real time to support consistent fitness habits. Furthermore, they lacked appropriate methods for providing individualized advice on dietary management and mental health, resulting in insufficient support for users to maintain their long-term health.

[0957] 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.

[0958] In this invention, the server includes a user registration and login means for a user to register an account and log in, a body shape photographing means for photographing the user's body shape using a camera and acquiring image data, and a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape, thereby enabling analysis and generation of an ideal body shape based on the user's individual body shape data.

[0959] "User Registration and Login Means" means the function by which a User registers and logs in to an account to access the System.

[0960] The "body shape photographing means" is a function that photographs the user's body shape using a camera and acquires image data.

[0961] The "body shape analysis and generation means" is a function that analyzes the acquired image data, characterizes the user's current body shape, and generates an ideal body shape.

[0962] The "training plan generation means" is a function that creates a training plan based on an ideal body type and provides it to the user.

[0963] The "training data collection means" is a function that collects training data in real time and transmits it to a central server.

[0964] The "user data synchronization means" is a function that saves data in the user's account based on the collected data and synchronizes it with the application.

[0965] The "dietary management and mental care means" is a function that generates and provides appropriate dietary management and mental care advice based on the user's body type data.

[0966] The "motion analysis means" is a function that collects and analyzes the user's motion and vital data to monitor fitness progress.

[0967] The "mental care notification means" is a function that notifies the user of encouraging messages and content that will increase motivation.

[0968] The "personal fitness advice means" is a function that analyzes the training data performed by the user and provides personalized advice to support continuous fitness habits.

[0969] This invention is a system that allows users to train efficiently and easily and maintain sustainable fitness habits. The system uses AI-equipped monitors and cameras installed in fitness clubs and gyms, and also works in conjunction with a mobile application. The system is configured as follows:

[0970] User registration and login methods

[0971] A user creates an account and logs in using a mobile application. This involves entering their name, email address, and password. The device sends this information to the server, which stores it in a database for authentication. For example, when a user enters their name, email address, and password and presses the "Register" button, the device encrypts the information they entered and sends it to the server.

[0972] Body photography methods

[0973] When a user arrives at the gym and stands in front of the AI-equipped monitor, the device (AI-equipped monitor) uses its camera to take a photo of the user's body shape and sends the image data to the server. The device then automatically recognizes the user using its camera, takes multiple images, and sends them to the server.

[0974] Body shape analysis and generation method

[0975] The server analyzes the user's current body shape using an AI model (such as TensorFlow or PyTorch) based on the acquired image data. The server then generates an ideal body shape based on the analysis results and sends that data to the device. For example, the server applies face recognition and body shape recognition algorithms to the AI ​​model and sends the analysis results in JSON format to the device.

[0976] Training plan generation method

[0977] The server uses an AI trainer (e.g., OpenAI's GPT-4) to create a training plan for the day based on the user's ideal body shape. The device displays the training plan to the user, prompting them for the exercise name, number of repetitions, and number of sets. For example, a training plan may be generated with a prompt such as, "Based on the user's current body shape, please create a one-day training plan for a man in his 30s who is looking to lose weight."

[0978] Training Data Collection Methods

[0979] When a user exercises, the device records the user's training data (e.g., time, number of repetitions, heart rate) in real time and sends it to the server. For example, the device sends the data to the server in batches at the end of the session or at regular intervals.

[0980] User data synchronization means

[0981] The server associates the received data with the user's account, stores it, and synchronizes it with the mobile app. For example, the server saves the data in a database and sends a push notification to the mobile app to notify it of synchronization completion.

[0982] Dietary management and mental health care measures

[0983] The server generates appropriate dietary advice and mental health messages based on the user's body shape and training data. The server then sends these to a mobile application, where the user can receive them. For example, the user can check the notifications received in the mobile app and incorporate them into their daily lives.

[0984] Motion analysis means

[0985] The device collects and analyzes the user's movement and vital data to monitor fitness progress. For example, the monitor's camera and sensors continuously record the user's movement and vital data and send it to a server.

[0986] Mental care notification method

[0987] The server then sends encouraging messages and motivational content to the user, generating personalized messages based on a prompt such as, "Please create a three-day training menu per week for a woman in her twenties who wants to build muscle."

[0988] Personalized Fitness Advice Tool

[0989] The server analyzes the user's training data and provides personalized advice to support ongoing fitness habits. For example, based on the collected training data, the AI ​​model generates improvements and new goals for the next training session.

[0990] These tools allow users to consistently train and manage their health whether they are at a fitness club or gym or at home.

[0991] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0992] Step 1:

[0993] A user launches a mobile application and enters their name, email address, and password to create an account.

[0994] Input: Name, Email Address, Password

[0995] Output: Submit registration information

[0996] How it works: After filling out the registration form on the mobile app and pressing the "Register" button, the device encrypts the information entered and sends an HTTP POST request to the server.

[0997] Step 2:

[0998] The server stores the received information in a database and manages the user authentication information.

[0999] Input: Encrypted user information

[1000] Output: Credentials stored in the database

[1001] What happens: The server parses the incoming data and creates a new user record in the database. The user logs in using their saved credentials.

[1002] Step 3:

[1003] The user arrives at the gym and stands in front of the AI-equipped monitor.

[1004] Input: User location and camera visual data

[1005] Output: Camera image acquisition

[1006] How it works: The user stands still in front of the monitor and poses as instructed by the camera. The device automatically recognizes the user, captures an image, and sends it to the server.

[1007] Step 4:

[1008] The server uses an AI model to analyze the user's current body type based on the acquired image data.

[1009] Input: Image data

[1010] Output: Analysis data of current body type and ideal body type data

[1011] How it works: The server applies face recognition and body recognition algorithms to an AI model (e.g., TensorFlow or PyTorch) to analyze the current body shape, then generates an ideal body shape and sends it to the device.

[1012] Step 5:

[1013] The terminal displays the generated ideal body type to the user.

[1014] Input: Ideal body type data

[1015] Output: Ideal body image displayed on the monitor

[1016] How it works: The monitor displays an image of your ideal body shape on the user's screen for them to review.

[1017] Step 6:

[1018] The server uses an AI trainer to create a training plan for the day based on the user's ideal body type.

[1019] Input: Ideal body type data

[1020] Output: Training plan data

[1021] How it works: On the server, the AI ​​model generates a training plan based on a prompt. For example, it generates a training plan based on the prompt, "Based on my current body shape, please create a one-day training plan for a man in his 30s who is trying to lose weight."

[1022] Step 7:

[1023] The terminal displays the training plan to the user and prompts the user for the exercise name, number of repetitions, and number of sets.

[1024] Input: Training plan data

[1025] Output: Training plan displayed on the monitor

[1026] How it works: The training plan details are displayed in list format and the monitor plays audio and video guides.

[1027] Step 8:

[1028] The user performs the training.

[1029] Input: User movement and vital data

[1030] Output: A record of training progress

[1031] How it works: The user performs exercises according to the instructions on the monitor. The device records the user's movements and vital data in real time.

[1032] Step 9:

[1033] The terminal transmits the collected data to the server.

[1034] Input: Training data

[1035] Output: Training data sent to the server

[1036] How it works: At the end of a session or at regular intervals, the device sends a batch of data to the server.

[1037] Step 10:

[1038] The server stores the received data, associates it with the user's account, and synchronizes it with the mobile app.

[1039] Input: Training data

[1040] Output: Data stored in the account and sync notifications to the app

[1041] How it works: The server saves the data to the database and sends a push notification to the mobile app when the synchronization is complete.

[1042] Step 11:

[1043] The server generates appropriate dietary advice and mental care messages based on the user's body type and training data.

[1044] Input: Body type data and training data

[1045] Output: Dietary advice and mental health care messages

[1046] How it works: The AI ​​model on the server analyzes the data and generates personalized advice, which is then sent to the mobile app and received by the user.

[1047] Step 12:

[1048] The device collects and analyzes the user's movement and vital signs to monitor their fitness progress.

[1049] Input: User motion and vital data

[1050] Output: Parsed progress data

[1051] Operation: The monitor's camera and sensors continuously record the user's movements and vital data and transmit them to a server.

[1052] Step 13:

[1053] The server notifies the user of encouraging messages and motivational content.

[1054] Input: User's body type and training data

[1055] Output: Mental care message

[1056] What it does: Notifies the user of server-generated messages to keep them motivated.

[1057] Step 14:

[1058] The server analyzes the user's training data and provides personalized advice to support ongoing fitness habits.

[1059] Input: Training data

[1060] Output: Personalized advice

[1061] How it works: Based on the collected training data, the AI ​​model generates and provides users with improvements and new goals for their next training session.

[1062] (Application example 1)

[1063] 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."

[1064] Conventional fitness systems have been insufficient in supporting users to train efficiently and sustainably, particularly due to the difficulty of providing real-time feedback and motion analysis. Furthermore, there is a need for systems that can properly analyze workers' postures and movements in workplaces such as factories, and provide efficient, fatigue-reducing work methods. Given this background, there is a need for systems that comprehensively support fitness training and improving the work environment.

[1065] 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.

[1066] In this invention, the server includes a user registration and login means for a user to register an account and log in, a body shape photographing means for photographing the user's body shape using a camera and acquiring image data, a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape, a training plan generation means for creating a training plan based on the ideal body shape and providing it to the user, a training data collection means for collecting training data in real time and sending it to a central server, a user data synchronization means for saving the collected data in the user's account based on the data and synchronizing it with an application, a dietary management and mental care means for generating and providing appropriate dietary management and mental care advice based on the user's body shape data, a posture analysis and movement plan generation means for analyzing the posture and movement of a worker in real time and generating an efficient work method, and a real-time feedback means for feeding the generated movement plan back to the worker in real time. This enables users to train efficiently and sustainably, and enables workers to adopt efficient and less fatigued work methods in real time.

[1067] "User registration and login means" refers to the function that allows users to register and log in to their accounts.

[1068] The "body shape photographing means" is a function that photographs the user's body shape using a camera and acquires image data.

[1069] The "body shape analysis and generation means" is a function that analyzes the acquired image data, characterizes the user's current body shape, and generates an ideal body shape.

[1070] The "training plan generation means" is a function that creates a training plan based on an ideal body type and provides it to the user.

[1071] The "training data collection means" is a function that collects training data in real time and transmits it to a central server.

[1072] The "user data synchronization means" is a function that saves data in the user's account based on the collected data and synchronizes it with the application.

[1073] The "dietary management and mental care means" is a function that generates and provides appropriate dietary management and mental care advice based on the user's body type data.

[1074] The "posture analysis and motion plan generation means" is a function that analyzes the posture and motion of a worker in real time and generates an efficient work method.

[1075] The "real-time feedback means" is a function that provides feedback of the generated operation plan to the worker in real time.

[1076] System configuration

[1077] This invention is a system that enables users to maintain fitness habits efficiently and sustainably. This system can also be used as an example of a factory robot. The main components of the system include a user registration and login means, a body shape photography means, a body shape analysis and generation means, a training plan generation means, a training data collection means, a user data synchronization means, a diet management and mental care means, a posture analysis and movement plan generation means, and a real-time feedback means.

[1078] Hardware and Software

[1079] Hardware: cameras, monitors, servers, factory robots

[1080] Software: TensorFlow, OpenCV, cloud storage services (Azure, AWS)

[1081] Example

[1082] User Registration and Login

[1083] Users can register an account using the interface of their mobile device or factory robot and log in. To log in, they need to enter their name, email address, password, etc., and this information is stored on the server.

[1084] Body photography and analysis

[1085] When a user stands in front of the camera, it captures a photo of the user's body shape and captures image data. This data is sent to a server, where it uses a TensorFlow model to analyze the user's body shape and generate a current and ideal body shape.

[1086] Training plan generation

[1087] The server then creates a training plan based on the generated ideal body shape and provides it to the user. The training plan includes exercise names, repetitions, and number of sets, as well as video and audio instruction on correct form and important points to note.

[1088] Collecting training data

[1089] As users train, data (e.g., duration, repetition, heart rate, etc.) is collected in real time and sent to a server, which stores this data in the user's account and synchronizes it with the mobile application.

[1090] Dietary management and mental care

[1091] The server generates and provides appropriate dietary and mental health advice based on the user's body shape data. These advice and messages are periodically provided to the user via the mobile application.

[1092] Posture analysis and motion plan generation

[1093] The factory robot monitors the worker's movements in real time and captures their posture with a camera. The data obtained is sent to a server where it is analyzed using an AI model. The server then generates an efficient work method and provides the plan to the worker.

[1094] Real-time feedback

[1095] The generated motion plan is displayed in real time on the robot's interface, and the worker can use this feedback to adjust their posture and perform the task efficiently and with less fatigue.

[1096] Specific examples

[1097] A concrete example is a scenario where real-time feedback is provided to a worker saying, "Stand up straight." An example prompt for this application is:

[1098] Example prompt sentence:

[1099] The system captures the user working in front of the camera and analyzes their posture in real time. Based on the analysis results, it instructs the worker to straighten their back. This feedback aims to improve work efficiency and reduce fatigue.

[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 registers an account using the interface of a mobile device or factory robot and logs in. The inputs include name, email address, and password, which are stored on the server. The server authenticates the input data and stores the user information in a database.

[1103] Step 2:

[1104] The device's camera captures the user's body shape and captures the image data. This image data is sent to the server in real time. The server receives and stores this data.

[1105] Step 3:

[1106] The server analyzes the user's body shape using the TensorFlow model based on the acquired image data. Specifically, it detects posture, body parts, etc. from the image data and identifies the current body shape. As a result of the analysis, it outputs the characteristics of the user's current body shape.

[1107] Step 4:

[1108] The server generates an ideal body shape based on the analysis results. Using a generative AI model, it calculates the difference from the current body shape and outputs the target body shape. This generated ideal body shape data becomes the basis for creating a training plan.

[1109] Step 5:

[1110] The server creates a training plan based on the ideal body type data. The plan, including exercise names, repetitions, and number of sets, is automatically generated by the AI ​​model. This plan is provided to the user and displayed on the monitor.

[1111] Step 6:

[1112] During training, the device's sensors collect the user's training data (e.g., time, repetitions, heart rate) in real time. This collected data is immediately sent to the server, where it is stored and linked to the user's account.

[1113] Step 7:

[1114] The server stores the collected training data in the user's account and synchronizes it with the mobile application, allowing users to view their training data from any device.

[1115] Step 8:

[1116] The server generates appropriate dietary and mental health advice based on the user's physical and training data, and these advice and messages are periodically provided to the user via a mobile application.

[1117] Step 9:

[1118] The factory robot monitors the worker's movements in real time and captures their posture with a camera. The acquired image data is sent to a server, which analyzes this data and generates efficient work methods.

[1119] Step 10:

[1120] The generated motion plan is displayed in real time on the robot's interface, and the worker can use this feedback to correct their posture and perform the task in an efficient and less tiring manner, thereby improving worker efficiency and reducing fatigue.

[1121] 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.

[1122] This invention is a system that allows users to train efficiently and easily and maintain sustainable fitness habits, and has the function of recognizing the user's emotions and adjusting training plans and mental care. This system works by using AI-equipped monitors and cameras installed in fitness clubs and gyms, as well as an emotion engine, and also works in conjunction with a mobile application.

[1123] Feature Overview

[1124] User Registration / Login

[1125] Users create an account through the mobile application and log in, which involves entering their name, email address, and password, and the server stores this information in a database for authentication.

[1126] Body shape analysis and generation

[1127] A camera captures the user's body shape as they stand in front of the gym monitor, and sends the image data to a server. The server then uses an AI model to analyze the user's current body shape based on the acquired image data. It then generates an ideal body shape and displays it to the user.

[1128] Training plan generation

[1129] Based on the generated ideal body shape, the AI ​​trainer creates a daily training plan and provides it to the user. The exercise names, repetitions, and number of sets are displayed on the monitor, and video and audio instructions are provided on the correct form and important points to note.

[1130] Emotion Engine and Analytics

[1131] The emotion engine analyzes the user's facial expressions and movements to recognize their emotions in real time. The device then sends the analysis results to a server, which uses this information to adjust training plans and mental care.

[1132] Training data collection and synchronization

[1133] Data on the user's workout (e.g., duration, repetition, heart rate, etc.) is collected in real time and sent to a server, where it is stored and linked to the user's account and synchronized with the mobile application.

[1134] Dietary management and mental care

[1135] The server generates appropriate dietary advice and mental health messages based on the user's body shape data. These advice and messages are periodically provided to the user via a mobile application to support the user's fitness habits.

[1136] Specific Examples

[1137] A user's daily routine

[1138] 1. The user stands in front of the gym monitor and logs in using the account registered on the mobile app.

[1139] 2. The device uses a camera to take a picture of the user's body shape and sends the data to the server.

[1140] 3. The server analyzes the image data to identify the user's current body shape and generate an ideal body shape.

[1141] 4. The device displays the ideal body shape and training plan for the day, and the user follows this plan to train.

[1142] 5. The emotion engine monitors the user's facial expressions and movements during training and recognizes emotions in real time.

[1143] 6. The device sends the emotion engine analysis results to the server.

[1144] 7. The server adjusts the training plan and mental care content based on the emotional data and provides feedback to the user.

[1145] 8. The device will display training instructions and encouraging messages based on your emotions.

[1146] 9. The device collects data during training and sends it to the server.

[1147] 10. The server stores this data in the user's profile and synchronizes it with the mobile app.

[1148] 11. Users receive dietary advice and mental health messages through a mobile app.

[1149] This allows users to consistently train and manage their health whether they are at the gym or at home.By using an emotion engine, flexible training support and mental care are realized that adapt to the user's situation and emotions.

[1150] The processing flow will be explained below.

[1151] Step 1: The user stands in front of the gym monitor and logs in using the account registered on the mobile app.

[1152] Step 2: The device captures a picture of the user's body shape with its camera and acquires image data.

[1153] Step 3: The device sends the acquired image data to the server.

[1154] Step 4: The server receives the image data and inputs it into the AI ​​model.

[1155] Step 5: The server uses the AI ​​model to analyze the image data and extract the user's current body shape characteristics.

[1156] Step 6: The server simulates the ideal body shape based on the extracted features and generates images and data of the ideal body shape.

[1157] Step 7: The server sends the generated ideal body shape data back to the device.

[1158] Step 8: The device displays the ideal body shape data received from the server on the monitor.

[1159] Step 9: The device will then use the AI ​​trainer to create a training plan for the day based on your ideal body shape data.

[1160] Step 10: The device displays the training details (exercise name, number of repetitions, number of sets) to the user in a lecture format.

[1161] Step 11: The device will guide you through the training using video and audio instructions on proper form and important points to note.

[1162] Step 12: The emotion engine analyzes the user's facial expressions and movements and recognizes emotions in real time.

[1163] Step 13: The device sends the emotion engine analysis results to the server.

[1164] Step 14: The server adjusts the training plan and mental care content based on the emotion analysis results.

[1165] Step 15: The device displays the adjusted training instructions and mental care content.

[1166] Step 16: The user performs training according to the AI ​​trainer's guidance and emotional feedback.

[1167] Step 17: The device collects real-time data during your workout (exercises performed, repetitions, time, heart rate, etc.).

[1168] Step 18: The terminal transmits the collected training data to the server.

[1169] Step 19: The server associates the received training data with the user's account and stores it in a database.

[1170] Step 20: The server generates the user's achievement level and the next training plan based on the stored data.

[1171] Step 21: The server returns the generated data to the terminal.

[1172] Step 22: The terminal displays the training results to the user.

[1173] Step 23: The server generates appropriate dietary advice based on the user's body type data.

[1174] Step 24: The server generates an appropriate mental health care message.

[1175] Step 25: The server sends the generated dietary management and mental care advice to the mobile application.

[1176] Step 26: The mobile application notifies the user of dietary advice.

[1177] Step 27: The mobile application notifies the user of the mental care message.

[1178] Example 2

[1179] 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."

[1180] Modern fitness facilities require systems that enable users to train efficiently and maintain sustainable fitness habits. However, conventional systems have difficulty accurately analyzing individual users' physical shapes and emotions and providing training plans and mental care based on those analyses. In particular, there is a lack of systems that can monitor users' facial expressions and movements in real time and provide support according to their emotions.

[1181] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1182] In this invention, the server includes a user registration and login means for a user to register an account and log in, a body shape photographing means for photographing the user's body shape using a camera and acquiring image data, a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape, a training plan generation means for creating a training plan based on the ideal body shape and providing it to the user, an emotion analysis means for analyzing the user's facial expressions and movements in real time and recognizing emotions, a training data collection means for collecting training data in real time and transmitting it to a central server, a user data synchronization means for saving the collected data in the user's account and synchronizing it with an application, and a diet management and mental care means for generating and providing appropriate diet management and mental care advice based on the user's body shape data. This not only provides users with optimal training plans tailored to their individual body shapes and emotions, but also enables them to maintain flexible and sustainable fitness habits through the analysis of emotion data in real time.

[1183] "User Registration and Login Method" means the authentication method by which a User creates an account and accesses the System.

[1184] The "body shape photographing means" is a means for photographing the user's body shape using a camera and acquiring image data thereof.

[1185] The "body shape analysis and generation means" is a means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape.

[1186] The "training plan generation means" is a means for creating a training plan based on an ideal body type and providing it to the user.

[1187] The "emotion analysis means" is a means for analyzing the user's facial expressions and movements in real time and recognizing their emotions.

[1188] The "training data collection means" is a means for collecting data during training in real time and transmitting it to a central server.

[1189] The "user data synchronization means" is a means for saving data in a user's account based on collected data and synchronizing it with an application.

[1190] The "dietary management and mental care means" is a means for generating and providing appropriate dietary management and mental care advice based on the user's body type data.

[1191] The present invention provides a system that allows users to efficiently and easily train and maintain sustainable fitness habits. The system has the function of recognizing the user's emotions and adjusting the training plan and mental care. Specific examples are described in detail below.

[1192] User Registration / Login

[1193] A user first opens the mobile application and creates an account. They enter their username, email address, and password and submit them to the server. The server receives the data, stores it in a database, and sends a confirmation email to the user. The user clicks on a link in the confirmation email to verify their email address, allowing them to log in to the system.

[1194] Examples:

[1195] Enter your username, email address, and password on the registration screen of the mobile application and tap the "Register" button.

[1196] Example prompt: "To create a new account, please enter your name, email address, and password."

[1197] Body shape analysis and generation

[1198] The user stands in front of a monitor in the gym and takes a picture of their body with a camera. The device sends the image data to a server. The server analyzes the image data using an AI model (e.g., OpenPose or DensePose) to identify the user's current body shape. It then generates an ideal body shape and sends it along with the analysis results to the device. The device then displays the analysis results and the ideal body shape to the user.

[1199] Examples:

[1200] When the user stands in front of the monitor, the camera automatically captures their body shape and the results are displayed within seconds.

[1201] Example prompt: "Stand in front of the monitor at the gym and take a picture of your body shape."

[1202] Training plan generation

[1203] The server uses an AI trainer to generate a training plan based on the analyzed ideal body type, while the device displays the exercise name, number of repetitions, and number of sets on the monitor and provides instructions on correct form and important points to note via video and audio.

[1204] Examples:

[1205] Users can watch videos showing them the correct way to perform the exercises.

[1206] Example prompt: "Based on your ideal body type, here's today's workout plan."

[1207] Emotion analysis means

[1208] The emotion engine analyzes the user's facial expressions and movements in real time to recognize their emotions. The device then sends the emotion engine's analysis results to the server. The server adjusts the training plan and mental care content based on the emotion data and provides feedback to the user. The device then displays the feedback and provides training and mental care messages according to the emotion.

[1209] Examples:

[1210] If the emotion engine detects that the user is tired, it will adjust the training plan and display encouraging messages.

[1211] Sample prompt: "Analyze the user's facial expressions and provide advice based on their emotions."

[1212] Training data collection and synchronization

[1213] The device collects data (e.g., time, repetition, heart rate, etc.) in real time as the user trains and sends it to the server, which associates this data with the user's account, stores it, and syncs it with the mobile app.

[1214] Examples:

[1215] During training, your heart rate and number of repetitions are recorded and stored on a server.

[1216] Sample prompt: "Collect data during training and send it to the server in real time."

[1217] Dietary management and mental care

[1218] The server generates appropriate dietary advice and mental care messages based on the user's body data and periodically provides them to the user via a mobile app.

[1219] Examples:

[1220] The mobile app will send you dietary advice tailored to your situation.

[1221] Sample prompt: "Generate appropriate dietary advice based on the user's body type data."

[1222] This system not only provides users with optimal training plans tailored to their individual body types and emotions, but also enables them to maintain flexible and sustainable fitness habits through real-time analysis of emotional data.

[1223] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1224] Step 1:

[1225] A user opens a mobile application and creates an account. They enter their username, email address, and password and submit them to the server. The server receives the input data, stores the user information, including a hashed password, in a database, and sends a confirmation email to the user. The user clicks on a link in the confirmation email to verify their email address, allowing them to log in to the system.

[1226] Input: Username, Email Address, Password

[1227] Processing: Receives input data, hashes password, then stores it in the database and sends a confirmation email

[1228] Output: Confirmation email, user information stored in database

[1229] Specific actions: Enter your username, email address, and password on the mobile application's registration screen and tap the "Register" button.

[1230] Step 2:

[1231] The user stands in front of a monitor in the gym and takes a picture of their body shape with a camera. The device sends the image data to a server. The server analyzes the image data using an AI model (e.g., OpenPose or DensePose) to identify the user's current body shape. It then generates an ideal body shape and sends it along with the analysis results to the device. The device then displays the analysis results and the ideal body shape to the user.

[1232] Input: User's body image

[1233] Processing: Analyze image data to identify current body shape and generate ideal body shape

[1234] Output: Analysis results, ideal body shape image

[1235] How it works: When the user stands in front of the monitor, the camera automatically takes a photo of their body shape and displays the results within a few seconds.

[1236] Step 3:

[1237] The server uses an AI trainer to generate a training plan based on the analyzed ideal body type. The device displays the exercise name, number of repetitions, and number of sets on the monitor, and provides instructions on correct form and important points to note via video and audio.

[1238] Input: Ideal body type data

[1239] Processing: Generate training plan (determine exercise name, repetitions, number of sets)

[1240] Output: Training plan

[1241] Specific actions: Users watch videos to learn the correct way to perform the exercises.

[1242] Step 4:

[1243] The emotion engine analyzes the user's facial expressions and movements in real time to recognize their emotions. The device then sends the emotion engine's analysis results to a server. The server adjusts the training plan and mental care content based on the emotion data and provides feedback to the user. The device then displays the feedback and provides training and mental care messages based on the emotion.

[1244] Input: User's facial expression and movement data

[1245] Processing: Analysis of facial expressions and movement data, emotion recognition

[1246] Output: Sentiment analysis results, training plan adjustments, mental care messages

[1247] What it does: If the emotion engine recognizes that the user is tired, it will adjust their training plan or mental care messages.

[1248] Step 5:

[1249] The device collects data (e.g., time, number of repetitions, heart rate, etc.) in real time as the user exercises and sends it to the server, which then associates this data with the user's account, stores it, and syncs it with the mobile app.

[1250] Input: Training data (time, repetitions, heart rate, etc.)

[1251] Processing: Data collection, transmission to server, data storage

[1252] Output: Saved training data, synced app data

[1253] Specific operation: Heart rate and number of repetitions recorded during training are stored on the server and can be viewed on the mobile app.

[1254] Step 6:

[1255] The server generates appropriate dietary advice and mental care messages based on the user's body data and provides them to the user periodically via a mobile app.

[1256] Input: User's body data

[1257] Processing: Generating dietary management advice and mental care messages

[1258] Output: Generated advice and messages

[1259] Specific behavior: The mobile app sends users appropriate dietary advice and mental health care messages.

[1260] These are the specific processing steps of the system, which enable users to maintain an efficient and sustainable fitness routine.

[1261] (Application example 2)

[1262] 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."

[1263] While conventional fitness systems offer basic functions such as providing training plans based on the user's physique data and providing dietary advice, they lack the flexibility to take the user's emotional state into account, making it difficult to maintain the user's motivation. In particular, in today's society, where continuity in fitness is important, there is a demand for systems that provide appropriate psychological support to users. Therefore, the purpose of this invention is to support users' sustainable training habits by customizing fitness plans based on the user's emotional state and providing mental care feedback.

[1264] The specific processing by the specific 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 a user registration and login means for a user to register an account and log in, a body shape photographing means for photographing the user's body shape using a camera and acquiring image data, a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape, a training plan generation means for creating a training plan based on the ideal body shape and providing it to the user, a training data collection means for collecting training data in real time and sending it to a central server, a user data synchronization means for saving the collected data in the user's account based on the collected data and synchronizing it with an application, a diet management and mental care means for generating and providing appropriate diet management and mental care advice based on the user's body shape data, an emotion analysis means for dynamically adjusting the training plan and mental care based on emotion analysis, and an emotion feedback means for analyzing the user's emotions in real time and providing feedback. This makes it possible to analyze the user's emotional state in real time and provide a fitness plan and feedback accordingly.

[1265] "User registration and login means" means a means that allows a user to create an account, register, and log in.

[1266] The "body shape photographing means" is a means for photographing the user's body shape using a camera and acquiring image data thereof.

[1267] The "body shape analysis and generation means" is a means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape.

[1268] The "training plan generation means" is a means for creating a training plan based on the generated ideal body type and providing it to the user.

[1269] "Training data collection means" refers to a means for collecting training data in real time and transmitting it to a central server.

[1270] The "user data synchronization means" is a means for saving data in a user's account based on collected data and synchronizing it with an application.

[1271] The "dietary management and mental care means" is a means for generating and providing appropriate dietary management advice and mental care advice based on the user's body type data.

[1272] The "emotion analysis means" is a means for analyzing the user's emotions and dynamically adjusting training plans and mental care in real time.

[1273] The "emotion feedback means" is a means for analyzing the user's emotions in real time and providing feedback based on the results.

[1274] This invention is a system that allows users to train efficiently and easily and maintain sustainable fitness habits, and has the function of recognizing the user's emotions and adjusting training plans and mental care. A specific implementation method is described below.

[1275] composition

[1276] server

[1277] The server includes the following means:

[1278] User registration and login methods

[1279] Body photography methods

[1280] Body shape analysis and generation method

[1281] Training plan generation method

[1282] Training Data Collection Methods

[1283] User data synchronization means

[1284] Dietary management and mental health care measures

[1285] Emotion analysis means

[1286] Emotional feedback measures

[1287] Hardware and software used

[1288] The specific hardware and software implemented on the server and terminal are as follows:

[1289] Hardware: camera sensors, monitors, smartphones

[1290] Software: Python, TensorFlow, OpenCV, Django, PostgreSQL

[1291] Processing flow

[1292] 1. User Registration and Login

[1293] Users create an account using a smartphone application, registering and logging in by entering their name, email address and password. Django is used to send data to the server and store the information in PostgreSQL.

[1294] 2. Body shape photography and analysis

[1295] When a user stands in front of the monitor in a physical store, a camera captures their body shape, and the acquired image data is analyzed using OpenCV. The analyzed data is then used by TensorFlow to generate their current and ideal body shapes, which are then sent to the server.

[1296] 3. Creation and provision of training plans

[1297] Based on the ideal body shape, the AI ​​model generates a training plan that is displayed on the monitor or smartphone app. The plan includes exercise names, repetitions, and number of sets, and includes video and audio instructions on correct form and important points to note.

[1298] 4. Emotional analysis and feedback

[1299] During training, a camera monitors the user's facial expressions and movements, and TensorFlow is used to analyze their emotions in real time. Based on the analysis results, the server dynamically adjusts the training plan and mental care content and provides appropriate feedback.

[1300] 5. Collect and sync training data

[1301] Training data (e.g., time, repetitions, heart rate, etc.) is collected in real time and sent to the server via Django and stored in a PostgreSQL database, which synchronizes the data with the user's application.

[1302] 6. Dietary management and mental health care

[1303] The server generates dietary management advice and mental care messages based on the user's body data and provides them via a smartphone app.

[1304] Specific examples

[1305] When a user visits the gym and stands in front of the monitor, they are automatically logged in, and the camera takes a picture of their body to analyze their current shape and generate an ideal body shape. A training plan is then customized and provided to the user. During training, the camera recognizes emotions in real time and provides appropriate feedback.

[1306] Prompt Sentence Examples

[1307] Generate a fitness plan for the user based on their current and ideal body shape data. Adjust the training plan and mental care content as needed, taking into account the user's emotional data.

[1308] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1309] Step 1:

[1310] A user creates an account and logs in using a smartphone application. The user enters their name, email address, and password into the application and performs registration. The input data (name, email address, password) is sent to the server and stored in a database (PostgreSQL) using Django. This process allows the user to obtain authentication information to access the system.

[1311] Step 2:

[1312] The user stands in front of a monitor in a physical store, and a camera captures the user's body shape. The terminal (monitor) acquires the image data captured by the camera and sends it to the server. The input data (image data) is analyzed using OpenCV, and the user's current body shape is identified. The server temporarily stores the results of this analysis (current body shape data) and uses it in subsequent processing.

[1313] Step 3:

[1314] The server uses TensorFlow to generate an ideal body shape based on the current body shape data analyzed by OpenCV. The generative AI model generates ideal body shape data based on the input data (current body shape data). The ideal body shape data is stored by the server and used in the next processing step.

[1315] Step 4:

[1316] The server uses an AI model to generate a training plan based on the generated ideal body data. Input data (ideal body data) is supplied to the generative AI model using prompt statements, and output data (training plan) is generated. The generated training plan is sent from the server to the device (monitor or smartphone app) and provided to the user.

[1317] Step 5:

[1318] The user starts training, and training data (time, repetitions, heart rate, etc.) is collected by the device. The collected data is sent to the server in real time. The server stores the training data in a database and makes it available for subsequent processing.

[1319] Step 6:

[1320] During training, a camera monitors the user's facial expressions and movements, analyzing their emotions in real time. The input data used for emotion analysis (user's facial expressions and movements) is processed using TensorFlow, and the output data (emotional state) is sent to a server. Based on this emotional state data, the training plan and mental care content are dynamically adjusted.

[1321] Step 7:

[1322] The server generates feedback (encouraging messages, adjustments to instruction, etc.) based on the results of emotion analysis and sends it to the device (monitor or smartphone app). This allows the user to receive appropriate feedback in real time. Feedback messages are generated based on the input data (emotional state) and provided to the user.

[1323] Step 8:

[1324] After the training is completed, the server synchronizes and stores all collected training data in the user's account. The user data stored in the database (PostgreSQL) is synchronized with the user's smartphone app and used as reference data for future training.

[1325] Step 9:

[1326] The server generates dietary advice and mental care messages based on the user's body shape data and provides them to the user periodically via a smartphone app. Based on the input data (body shape data), the generative AI model generates advice and provides feedback to the user.

[1327] These steps provide users with an efficient and consistent fitness experience, promoting sustainable training habits.

[1328] 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.

[1329] 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.

[1330] 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.

[1331] [Fourth embodiment]

[1332] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1333] 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.

[1334] 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).

[1335] 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.

[1336] 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.

[1337] 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).

[1338] 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.

[1339] 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.

[1340] 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.

[1341] 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.

[1342] 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.

[1343] 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.

[1344] 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."

[1345] This invention is a system that allows users to train efficiently and easily and maintain sustainable fitness habits. The system uses AI-enabled monitors and cameras installed in fitness clubs and gyms, and also works in conjunction with a mobile application.

[1346] Feature Overview

[1347] User Registration / Login

[1348] Users create an account through the mobile application and log in, which involves entering their name, email address, and password, and the server stores this information in a database for authentication.

[1349] Body shape analysis and generation

[1350] A camera captures the user's body shape as they stand in front of the gym monitor, and sends the image data to a server. The server then uses an AI model to analyze the user's current body shape based on the acquired image data. It then generates an ideal body shape and displays it to the user.

[1351] Training plan generation

[1352] Based on the generated ideal body shape, the AI ​​trainer creates a daily training plan and provides it to the user. The exercise names, repetitions, and number of sets are displayed on the monitor, and video and audio instructions are provided on the correct form and important points to note.

[1353] Training data collection and synchronization

[1354] Data on the user's workout (e.g., duration, repetition, heart rate, etc.) is collected in real time and sent to a server, where it is stored and linked to the user's account and synchronized with the mobile application.

[1355] Dietary management and mental care

[1356] The server generates appropriate dietary advice and mental health messages based on the user's body shape data. These advice and messages are periodically provided to the user via a mobile application to support the user's fitness habits.

[1357] Specific Examples

[1358] A user's daily routine

[1359] 1. The user stands in front of the gym monitor and logs in using the account registered on the mobile app.

[1360] 2. The device uses a camera to take a picture of the user's body shape and sends the data to the server.

[1361] 3. The server analyzes the image data to identify the user's current body shape and generate an ideal body shape.

[1362] 4. The device displays the ideal body type and the training plan for the day, and the user follows this plan to train.

[1363] 5. The device records the training progress in real time and sends it to the server.

[1364] 6. The server stores the received data in the user's profile and synchronizes it with the mobile app.

[1365] 7. Users receive dietary advice and mental health messages through the mobile app.

[1366] This allows users to consistently train and manage their health whether they're at the gym or at home.

[1367] The processing flow will be explained below.

[1368] Step 1: The user stands in front of the gym monitor and logs in using the account registered on the mobile app.

[1369] Step 2: The device captures a picture of the user's body shape with its camera and acquires image data.

[1370] Step 3: The device sends the acquired image data to the server.

[1371] Step 4: The server receives the image data and inputs it into the AI ​​model.

[1372] Step 5: The server uses the AI ​​model to analyze the image data and extract the user's current body shape characteristics.

[1373] Step 6: The server simulates the ideal body shape based on the extracted features and generates images and data of the ideal body shape.

[1374] Step 7: The server sends the generated ideal body shape data back to the device.

[1375] Step 8: The device displays the ideal body shape data received from the server on the monitor.

[1376] Step 9: The device will then use the AI ​​trainer to create a training plan for the day based on your ideal body shape data.

[1377] Step 10: The device displays the training details (exercise name, number of repetitions, number of sets) to the user in a lecture format.

[1378] Step 11: The device will guide you through the training using video and audio instructions on proper form and important points to note.

[1379] Step 12: The user performs training under the guidance of the AI ​​trainer.

[1380] Step 13: The device collects real-time data during your workout (exercises performed, repetitions, time, heart rate, etc.).

[1381] Step 14: The terminal transmits the collected training data to the server.

[1382] Step 15: The server associates the received training data with the user's account and stores it in a database.

[1383] Step 16: The server generates the user's achievement level and the next training plan based on the stored data.

[1384] Step 17: The server returns the generated data to the terminal.

[1385] Step 18: The terminal displays the training results to the user.

[1386] Step 19: The server generates appropriate dietary advice based on the user's body type data.

[1387] Step 20: The server generates an appropriate mental health care message.

[1388] Step 21: The server sends the generated dietary management and mental care advice to the mobile application.

[1389] Step 22: The mobile application notifies the user of dietary advice.

[1390] Step 23: The mobile application notifies the user of the mental care message.

[1391] Example 1

[1392] 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."

[1393] Conventional fitness systems have faced challenges in helping users maintain effective and sustainable training plans. Specifically, they struggle to properly analyze a user's body type and provide an ideal personalized training plan, or to collect and analyze training data in real time to support consistent fitness habits. Furthermore, they lacked appropriate methods for providing individualized advice on dietary management and mental health, resulting in insufficient support for users to maintain their long-term health.

[1394] 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.

[1395] In this invention, the server includes a user registration and login means for a user to register an account and log in, a body shape photographing means for photographing the user's body shape using a camera and acquiring image data, and a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape, thereby enabling analysis and generation of an ideal body shape based on the user's individual body shape data.

[1396] "User Registration and Login Means" means the function by which a User registers and logs in to an account to access the System.

[1397] The "body shape photographing means" is a function that photographs the user's body shape using a camera and acquires image data.

[1398] The "body shape analysis and generation means" is a function that analyzes the acquired image data, characterizes the user's current body shape, and generates an ideal body shape.

[1399] The "training plan generation means" is a function that creates a training plan based on an ideal body type and provides it to the user.

[1400] The "training data collection means" is a function that collects training data in real time and transmits it to a central server.

[1401] The "user data synchronization means" is a function that saves data in the user's account based on the collected data and synchronizes it with the application.

[1402] The "dietary management and mental care means" is a function that generates and provides appropriate dietary management and mental care advice based on the user's body type data.

[1403] The "motion analysis means" is a function that collects and analyzes the user's motion and vital data to monitor fitness progress.

[1404] The "mental care notification means" is a function that notifies the user of encouraging messages and content that will increase motivation.

[1405] The "personal fitness advice means" is a function that analyzes the training data performed by the user and provides personalized advice to support continuous fitness habits.

[1406] This invention is a system that allows users to train efficiently and easily and maintain sustainable fitness habits. The system uses AI-equipped monitors and cameras installed in fitness clubs and gyms, and also works in conjunction with a mobile application. The system is configured as follows:

[1407] User registration and login methods

[1408] A user creates an account and logs in using a mobile application. This involves entering their name, email address, and password. The device sends this information to the server, which stores it in a database for authentication. For example, when a user enters their name, email address, and password and presses the "Register" button, the device encrypts the information they entered and sends it to the server.

[1409] Body photography methods

[1410] When a user arrives at the gym and stands in front of the AI-equipped monitor, the device (AI-equipped monitor) uses its camera to take a photo of the user's body shape and sends the image data to the server. The device then automatically recognizes the user using its camera, takes multiple images, and sends them to the server.

[1411] Body shape analysis and generation method

[1412] The server analyzes the user's current body shape using an AI model (such as TensorFlow or PyTorch) based on the acquired image data. The server then generates an ideal body shape based on the analysis results and sends that data to the device. For example, the server applies face recognition and body shape recognition algorithms to the AI ​​model and sends the analysis results in JSON format to the device.

[1413] Training plan generation method

[1414] The server uses an AI trainer (e.g., OpenAI's GPT-4) to create a training plan for the day based on the user's ideal body shape. The device displays the training plan to the user, prompting them for the exercise name, number of repetitions, and number of sets. For example, a training plan may be generated with a prompt such as, "Based on the user's current body shape, please create a one-day training plan for a man in his 30s who is looking to lose weight."

[1415] Training Data Collection Methods

[1416] When a user exercises, the device records the user's training data (e.g., time, number of repetitions, heart rate) in real time and sends it to the server. For example, the device sends the data to the server in batches at the end of the session or at regular intervals.

[1417] User data synchronization means

[1418] The server associates the received data with the user's account, stores it, and synchronizes it with the mobile app. For example, the server saves the data in a database and sends a push notification to the mobile app to notify it of synchronization completion.

[1419] Dietary management and mental health care measures

[1420] The server generates appropriate dietary advice and mental health messages based on the user's body shape and training data. The server then sends these to a mobile application, where the user can receive them. For example, the user can check the notifications received in the mobile app and incorporate them into their daily lives.

[1421] Motion analysis means

[1422] The device collects and analyzes the user's movement and vital data to monitor fitness progress. For example, the monitor's camera and sensors continuously record the user's movement and vital data and send it to a server.

[1423] Mental care notification method

[1424] The server then sends encouraging messages and motivational content to the user, generating personalized messages based on a prompt such as, "Please create a three-day training menu per week for a woman in her twenties who wants to build muscle."

[1425] Personalized Fitness Advice Tool

[1426] The server analyzes the user's training data and provides personalized advice to support ongoing fitness habits. For example, based on the collected training data, the AI ​​model generates improvements and new goals for the next training session.

[1427] These tools allow users to consistently train and manage their health whether they are at a fitness club or gym or at home.

[1428] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1429] Step 1:

[1430] A user launches a mobile application and enters their name, email address, and password to create an account.

[1431] Input: Name, Email Address, Password

[1432] Output: Submit registration information

[1433] How it works: After filling out the registration form on the mobile app and pressing the "Register" button, the device encrypts the information entered and sends an HTTP POST request to the server.

[1434] Step 2:

[1435] The server stores the received information in a database and manages the user authentication information.

[1436] Input: Encrypted user information

[1437] Output: Credentials stored in the database

[1438] What happens: The server parses the incoming data and creates a new user record in the database. The user logs in using their saved credentials.

[1439] Step 3:

[1440] The user arrives at the gym and stands in front of the AI-equipped monitor.

[1441] Input: User location and camera visual data

[1442] Output: Camera image acquisition

[1443] How it works: The user stands still in front of the monitor and poses as instructed by the camera. The device automatically recognizes the user, captures an image, and sends it to the server.

[1444] Step 4:

[1445] The server uses an AI model to analyze the user's current body type based on the acquired image data.

[1446] Input: Image data

[1447] Output: Analysis data of current body type and ideal body type data

[1448] How it works: The server applies face recognition and body recognition algorithms to an AI model (e.g., TensorFlow or PyTorch) to analyze the current body shape, then generates an ideal body shape and sends it to the device.

[1449] Step 5:

[1450] The terminal displays the generated ideal body type to the user.

[1451] Input: Ideal body type data

[1452] Output: Ideal body image displayed on the monitor

[1453] How it works: The monitor displays an image of your ideal body shape on the user's screen for them to review.

[1454] Step 6:

[1455] The server uses an AI trainer to create a training plan for the day based on the user's ideal body type.

[1456] Input: Ideal body type data

[1457] Output: Training plan data

[1458] How it works: On the server, the AI ​​model generates a training plan based on a prompt. For example, it generates a training plan based on the prompt, "Based on my current body shape, please create a one-day training plan for a man in his 30s who is trying to lose weight."

[1459] Step 7:

[1460] The terminal displays the training plan to the user and prompts the user for the exercise name, number of repetitions, and number of sets.

[1461] Input: Training plan data

[1462] Output: Training plan displayed on the monitor

[1463] How it works: The training plan details are displayed in list format and the monitor plays audio and video guides.

[1464] Step 8:

[1465] The user performs the training.

[1466] Input: User movement and vital data

[1467] Output: A record of training progress

[1468] How it works: The user performs exercises according to the instructions on the monitor. The device records the user's movements and vital data in real time.

[1469] Step 9:

[1470] The terminal transmits the collected data to the server.

[1471] Input: Training data

[1472] Output: Training data sent to the server

[1473] How it works: At the end of a session or at regular intervals, the device sends a batch of data to the server.

[1474] Step 10:

[1475] The server stores the received data, associates it with the user's account, and synchronizes it with the mobile app.

[1476] Input: Training data

[1477] Output: Data stored in the account and sync notifications to the app

[1478] How it works: The server saves the data to the database and sends a push notification to the mobile app when the synchronization is complete.

[1479] Step 11:

[1480] The server generates appropriate dietary advice and mental care messages based on the user's body type and training data.

[1481] Input: Body type data and training data

[1482] Output: Dietary advice and mental health care messages

[1483] How it works: The AI ​​model on the server analyzes the data and generates personalized advice, which is then sent to the mobile app and received by the user.

[1484] Step 12:

[1485] The device collects and analyzes the user's movement and vital signs to monitor their fitness progress.

[1486] Input: User motion and vital data

[1487] Output: Parsed progress data

[1488] Operation: The monitor's camera and sensors continuously record the user's movements and vital data and transmit them to a server.

[1489] Step 13:

[1490] The server notifies the user of encouraging messages and motivational content.

[1491] Input: User's body type and training data

[1492] Output: Mental care message

[1493] What it does: Notifies the user of server-generated messages to keep them motivated.

[1494] Step 14:

[1495] The server analyzes the user's training data and provides personalized advice to support ongoing fitness habits.

[1496] Input: Training data

[1497] Output: Personalized advice

[1498] How it works: Based on the collected training data, the AI ​​model generates and provides users with improvements and new goals for their next training session.

[1499] (Application example 1)

[1500] 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."

[1501] Conventional fitness systems have been insufficient in supporting users to train efficiently and sustainably, particularly due to the difficulty of providing real-time feedback and motion analysis. Furthermore, there is a need for systems that can properly analyze workers' postures and movements in workplaces such as factories, and provide efficient, fatigue-reducing work methods. Given this background, there is a need for systems that comprehensively support fitness training and improving the work environment.

[1502] 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.

[1503] In this invention, the server includes a user registration and login means for a user to register an account and log in, a body shape photographing means for photographing the user's body shape using a camera and acquiring image data, a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape, a training plan generation means for creating a training plan based on the ideal body shape and providing it to the user, a training data collection means for collecting training data in real time and sending it to a central server, a user data synchronization means for saving the collected data in the user's account based on the data and synchronizing it with an application, a dietary management and mental care means for generating and providing appropriate dietary management and mental care advice based on the user's body shape data, a posture analysis and movement plan generation means for analyzing the posture and movement of a worker in real time and generating an efficient work method, and a real-time feedback means for feeding the generated movement plan back to the worker in real time. This enables users to train efficiently and sustainably, and enables workers to adopt efficient and less fatigued work methods in real time.

[1504] "User registration and login means" refers to the function that allows users to register and log in to their accounts.

[1505] The "body shape photographing means" is a function that photographs the user's body shape using a camera and acquires image data.

[1506] The "body shape analysis and generation means" is a function that analyzes the acquired image data, characterizes the user's current body shape, and generates an ideal body shape.

[1507] The "training plan generation means" is a function that creates a training plan based on an ideal body type and provides it to the user.

[1508] The "training data collection means" is a function that collects training data in real time and transmits it to a central server.

[1509] The "user data synchronization means" is a function that saves data in the user's account based on the collected data and synchronizes it with the application.

[1510] The "dietary management and mental care means" is a function that generates and provides appropriate dietary management and mental care advice based on the user's body type data.

[1511] The "posture analysis and motion plan generation means" is a function that analyzes the posture and motion of a worker in real time and generates an efficient work method.

[1512] The "real-time feedback means" is a function that provides feedback of the generated operation plan to the worker in real time.

[1513] System configuration

[1514] This invention is a system that enables users to maintain fitness habits efficiently and sustainably. This system can also be used as an example of a factory robot. The main components of the system include a user registration and login means, a body shape photography means, a body shape analysis and generation means, a training plan generation means, a training data collection means, a user data synchronization means, a diet management and mental care means, a posture analysis and movement plan generation means, and a real-time feedback means.

[1515] Hardware and Software

[1516] Hardware: cameras, monitors, servers, factory robots

[1517] Software: TensorFlow, OpenCV, cloud storage services (Azure, AWS)

[1518] Example

[1519] User Registration and Login

[1520] Users can register an account using the interface of their mobile device or factory robot and log in. To log in, they need to enter their name, email address, password, etc., and this information is stored on the server.

[1521] Body photography and analysis

[1522] When a user stands in front of the camera, it captures a photo of the user's body shape and captures image data. This data is sent to a server, where it uses a TensorFlow model to analyze the user's body shape and generate a current and ideal body shape.

[1523] Training plan generation

[1524] The server then creates a training plan based on the generated ideal body shape and provides it to the user. The training plan includes exercise names, repetitions, and number of sets, as well as video and audio instruction on correct form and important points to note.

[1525] Collecting training data

[1526] As users train, data (e.g., duration, repetition, heart rate, etc.) is collected in real time and sent to a server, which stores this data in the user's account and synchronizes it with the mobile application.

[1527] Dietary management and mental care

[1528] The server generates and provides appropriate dietary and mental health advice based on the user's body shape data. These advice and messages are periodically provided to the user via the mobile application.

[1529] Posture analysis and motion plan generation

[1530] The factory robot monitors the worker's movements in real time and captures their posture with a camera. The data obtained is sent to a server where it is analyzed using an AI model. The server then generates an efficient work method and provides the plan to the worker.

[1531] Real-time feedback

[1532] The generated motion plan is displayed in real time on the robot's interface, and the worker can use this feedback to adjust their posture and perform the task efficiently and with less fatigue.

[1533] Specific examples

[1534] A concrete example is a scenario where real-time feedback is provided to a worker saying, "Stand up straight." An example prompt for this application is:

[1535] Example prompt sentence:

[1536] The system captures the user working in front of the camera and analyzes their posture in real time. Based on the analysis results, it instructs the worker to straighten their back. This feedback aims to improve work efficiency and reduce fatigue.

[1537] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1538] Step 1:

[1539] A user registers an account using the interface of a mobile device or factory robot and logs in. The inputs include name, email address, and password, which are stored on the server. The server authenticates the input data and stores the user information in a database.

[1540] Step 2:

[1541] The device's camera captures the user's body shape and captures the image data. This image data is sent to the server in real time. The server receives and stores this data.

[1542] Step 3:

[1543] The server analyzes the user's body shape using the TensorFlow model based on the acquired image data. Specifically, it detects posture, body parts, etc. from the image data and identifies the current body shape. As a result of the analysis, it outputs the characteristics of the user's current body shape.

[1544] Step 4:

[1545] The server generates an ideal body shape based on the analysis results. Using a generative AI model, it calculates the difference from the current body shape and outputs the target body shape. This generated ideal body shape data becomes the basis for creating a training plan.

[1546] Step 5:

[1547] The server creates a training plan based on the ideal body type data. The plan, including exercise names, repetitions, and number of sets, is automatically generated by the AI ​​model. This plan is provided to the user and displayed on the monitor.

[1548] Step 6:

[1549] During training, the device's sensors collect the user's training data (e.g., time, repetitions, heart rate) in real time. This collected data is immediately sent to the server, where it is stored and linked to the user's account.

[1550] Step 7:

[1551] The server stores the collected training data in the user's account and synchronizes it with the mobile application, allowing users to view their training data from any device.

[1552] Step 8:

[1553] The server generates appropriate dietary and mental health advice based on the user's physical and training data, and these advice and messages are periodically provided to the user via a mobile application.

[1554] Step 9:

[1555] The factory robot monitors the worker's movements in real time and captures their posture with a camera. The acquired image data is sent to a server, which analyzes this data and generates efficient work methods.

[1556] Step 10:

[1557] The generated motion plan is displayed in real time on the robot's interface, and the worker can use this feedback to correct their posture and perform the task in an efficient and less tiring manner, thereby improving worker efficiency and reducing fatigue.

[1558] 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.

[1559] This invention is a system that allows users to train efficiently and easily and maintain sustainable fitness habits, and has the function of recognizing the user's emotions and adjusting training plans and mental care. This system works by using AI-equipped monitors and cameras installed in fitness clubs and gyms, as well as an emotion engine, and also works in conjunction with a mobile application.

[1560] Feature Overview

[1561] User Registration / Login

[1562] Users create an account through the mobile application and log in, which involves entering their name, email address, and password, and the server stores this information in a database for authentication.

[1563] Body shape analysis and generation

[1564] A camera captures the user's body shape as they stand in front of the gym monitor, and sends the image data to a server. The server then uses an AI model to analyze the user's current body shape based on the acquired image data. It then generates an ideal body shape and displays it to the user.

[1565] Training plan generation

[1566] Based on the generated ideal body shape, the AI ​​trainer creates a daily training plan and provides it to the user. The exercise names, repetitions, and number of sets are displayed on the monitor, and video and audio instructions are provided on the correct form and important points to note.

[1567] Emotion Engine and Analytics

[1568] The emotion engine analyzes the user's facial expressions and movements to recognize their emotions in real time. The device then sends the analysis results to a server, which uses this information to adjust training plans and mental care.

[1569] Training data collection and synchronization

[1570] Data on the user's workout (e.g., duration, repetition, heart rate, etc.) is collected in real time and sent to a server, where it is stored and linked to the user's account and synchronized with the mobile application.

[1571] Dietary management and mental care

[1572] The server generates appropriate dietary advice and mental health messages based on the user's body shape data. These advice and messages are periodically provided to the user via a mobile application to support the user's fitness habits.

[1573] Specific Examples

[1574] A user's daily routine

[1575] 1. The user stands in front of the gym monitor and logs in using the account registered on the mobile app.

[1576] 2. The device uses a camera to take a picture of the user's body shape and sends the data to the server.

[1577] 3. The server analyzes the image data to identify the user's current body shape and generate an ideal body shape.

[1578] 4. The device displays the ideal body shape and training plan for the day, and the user follows this plan to train.

[1579] 5. The emotion engine monitors the user's facial expressions and movements during training and recognizes emotions in real time.

[1580] 6. The device sends the emotion engine analysis results to the server.

[1581] 7. The server adjusts the training plan and mental care content based on the emotional data and provides feedback to the user.

[1582] 8. The device will display training instructions and encouraging messages based on your emotions.

[1583] 9. The device collects data during training and sends it to the server.

[1584] 10. The server stores this data in the user's profile and synchronizes it with the mobile app.

[1585] 11. Users receive dietary advice and mental health messages through a mobile app.

[1586] This allows users to consistently train and manage their health whether they are at the gym or at home.By using an emotion engine, flexible training support and mental care are realized that adapt to the user's situation and emotions.

[1587] The processing flow will be explained below.

[1588] Step 1: The user stands in front of the gym monitor and logs in using the account registered on the mobile app.

[1589] Step 2: The device captures a picture of the user's body shape with its camera and acquires image data.

[1590] Step 3: The device sends the acquired image data to the server.

[1591] Step 4: The server receives the image data and inputs it into the AI ​​model.

[1592] Step 5: The server uses the AI ​​model to analyze the image data and extract the user's current body shape characteristics.

[1593] Step 6: The server simulates the ideal body shape based on the extracted features and generates images and data of the ideal body shape.

[1594] Step 7: The server sends the generated ideal body shape data back to the device.

[1595] Step 8: The device displays the ideal body shape data received from the server on the monitor.

[1596] Step 9: The device will then use the AI ​​trainer to create a training plan for the day based on your ideal body shape data.

[1597] Step 10: The device displays the training details (exercise name, number of repetitions, number of sets) to the user in a lecture format.

[1598] Step 11: The device will guide you through the training using video and audio instructions on proper form and important points to note.

[1599] Step 12: The emotion engine analyzes the user's facial expressions and movements and recognizes emotions in real time.

[1600] Step 13: The device sends the emotion engine analysis results to the server.

[1601] Step 14: The server adjusts the training plan and mental care content based on the emotion analysis results.

[1602] Step 15: The device displays the adjusted training instructions and mental care content.

[1603] Step 16: The user performs training according to the AI ​​trainer's guidance and emotional feedback.

[1604] Step 17: The device collects real-time data during your workout (exercises performed, repetitions, time, heart rate, etc.).

[1605] Step 18: The terminal transmits the collected training data to the server.

[1606] Step 19: The server associates the received training data with the user's account and stores it in a database.

[1607] Step 20: The server generates the user's achievement level and the next training plan based on the stored data.

[1608] Step 21: The server returns the generated data to the terminal.

[1609] Step 22: The terminal displays the training results to the user.

[1610] Step 23: The server generates appropriate dietary advice based on the user's body type data.

[1611] Step 24: The server generates an appropriate mental health care message.

[1612] Step 25: The server sends the generated dietary management and mental care advice to the mobile application.

[1613] Step 26: The mobile application notifies the user of dietary advice.

[1614] Step 27: The mobile application notifies the user of the mental care message.

[1615] Example 2

[1616] 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."

[1617] Modern fitness facilities require systems that enable users to train efficiently and maintain sustainable fitness habits. However, conventional systems have difficulty accurately analyzing individual users' physical shapes and emotions and providing training plans and mental care based on those analyses. In particular, there is a lack of systems that can monitor users' facial expressions and movements in real time and provide support according to their emotions.

[1618] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1619] In this invention, the server includes a user registration and login means for a user to register an account and log in, a body shape photographing means for photographing the user's body shape using a camera and acquiring image data, a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape, a training plan generation means for creating a training plan based on the ideal body shape and providing it to the user, an emotion analysis means for analyzing the user's facial expressions and movements in real time and recognizing emotions, a training data collection means for collecting training data in real time and transmitting it to a central server, a user data synchronization means for saving the collected data in the user's account and synchronizing it with an application, and a diet management and mental care means for generating and providing appropriate diet management and mental care advice based on the user's body shape data. This not only provides users with optimal training plans tailored to their individual body shapes and emotions, but also enables them to maintain flexible and sustainable fitness habits through the analysis of emotion data in real time.

[1620] "User Registration and Login Method" means the authentication method by which a User creates an account and accesses the System.

[1621] The "body shape photographing means" is a means for photographing the user's body shape using a camera and acquiring image data thereof.

[1622] The "body shape analysis and generation means" is a means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape.

[1623] The "training plan generation means" is a means for creating a training plan based on an ideal body type and providing it to the user.

[1624] The "emotion analysis means" is a means for analyzing the user's facial expressions and movements in real time and recognizing their emotions.

[1625] The "training data collection means" is a means for collecting data during training in real time and transmitting it to a central server.

[1626] The "user data synchronization means" is a means for saving data in a user's account based on collected data and synchronizing it with an application.

[1627] The "dietary management and mental care means" is a means for generating and providing appropriate dietary management and mental care advice based on the user's body type data.

[1628] The present invention provides a system that allows users to efficiently and easily train and maintain sustainable fitness habits. The system has the function of recognizing the user's emotions and adjusting the training plan and mental care. Specific examples are described in detail below.

[1629] User Registration / Login

[1630] A user first opens the mobile application and creates an account. They enter their username, email address, and password and submit them to the server. The server receives the data, stores it in a database, and sends a confirmation email to the user. The user clicks on a link in the confirmation email to verify their email address, allowing them to log in to the system.

[1631] Examples:

[1632] Enter your username, email address, and password on the registration screen of the mobile application and tap the "Register" button.

[1633] Example prompt: "To create a new account, please enter your name, email address, and password."

[1634] Body shape analysis and generation

[1635] The user stands in front of a monitor in the gym and takes a picture of their body with a camera. The device sends the image data to a server. The server analyzes the image data using an AI model (e.g., OpenPose or DensePose) to identify the user's current body shape. It then generates an ideal body shape and sends it along with the analysis results to the device. The device then displays the analysis results and the ideal body shape to the user.

[1636] Examples:

[1637] When the user stands in front of the monitor, the camera automatically captures their body shape and the results are displayed within seconds.

[1638] Example prompt: "Stand in front of the monitor at the gym and take a picture of your body shape."

[1639] Training plan generation

[1640] The server uses an AI trainer to generate a training plan based on the analyzed ideal body type, while the device displays the exercise name, number of repetitions, and number of sets on the monitor and provides instructions on correct form and important points to note via video and audio.

[1641] Examples:

[1642] Users can watch videos showing them the correct way to perform the exercises.

[1643] Example prompt: "Based on your ideal body type, here's today's workout plan."

[1644] Emotion analysis means

[1645] The emotion engine analyzes the user's facial expressions and movements in real time to recognize their emotions. The device then sends the emotion engine's analysis results to the server. The server adjusts the training plan and mental care content based on the emotion data and provides feedback to the user. The device then displays the feedback and provides training and mental care messages according to the emotion.

[1646] Examples:

[1647] If the emotion engine detects that the user is tired, it will adjust the training plan and display encouraging messages.

[1648] Sample prompt: "Analyze the user's facial expressions and provide advice based on their emotions."

[1649] Training data collection and synchronization

[1650] The device collects data (e.g., time, repetition, heart rate, etc.) in real time as the user trains and sends it to the server, which associates this data with the user's account, stores it, and syncs it with the mobile app.

[1651] Examples:

[1652] During training, your heart rate and number of repetitions are recorded and stored on a server.

[1653] Sample prompt: "Collect data during training and send it to the server in real time."

[1654] Dietary management and mental care

[1655] The server generates appropriate dietary advice and mental care messages based on the user's body data and periodically provides them to the user via a mobile app.

[1656] Examples:

[1657] The mobile app will send you dietary advice tailored to your situation.

[1658] Sample prompt: "Generate appropriate dietary advice based on the user's body type data."

[1659] This system not only provides users with optimal training plans tailored to their individual body types and emotions, but also enables them to maintain flexible and sustainable fitness habits through real-time analysis of emotional data.

[1660] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1661] Step 1:

[1662] A user opens a mobile application and creates an account. They enter their username, email address, and password and submit them to the server. The server receives the input data, stores the user information, including a hashed password, in a database, and sends a confirmation email to the user. The user clicks on a link in the confirmation email to verify their email address, allowing them to log in to the system.

[1663] Input: Username, Email Address, Password

[1664] Processing: Receives input data, hashes password, then stores it in the database and sends a confirmation email

[1665] Output: Confirmation email, user information stored in database

[1666] Specific actions: Enter your username, email address, and password on the mobile application's registration screen and tap the "Register" button.

[1667] Step 2:

[1668] The user stands in front of a monitor in the gym and takes a picture of their body shape with a camera. The device sends the image data to a server. The server analyzes the image data using an AI model (e.g., OpenPose or DensePose) to identify the user's current body shape. It then generates an ideal body shape and sends it along with the analysis results to the device. The device then displays the analysis results and the ideal body shape to the user.

[1669] Input: User's body image

[1670] Processing: Analyze image data to identify current body shape and generate ideal body shape

[1671] Output: Analysis results, ideal body shape image

[1672] How it works: When the user stands in front of the monitor, the camera automatically takes a photo of their body shape and displays the results within a few seconds.

[1673] Step 3:

[1674] The server uses an AI trainer to generate a training plan based on the analyzed ideal body type. The device displays the exercise name, number of repetitions, and number of sets on the monitor, and provides instructions on correct form and important points to note via video and audio.

[1675] Input: Ideal body type data

[1676] Processing: Generate training plan (determine exercise name, repetitions, number of sets)

[1677] Output: Training plan

[1678] Specific actions: Users watch videos to learn the correct way to perform the exercises.

[1679] Step 4:

[1680] The emotion engine analyzes the user's facial expressions and movements in real time to recognize their emotions. The device then sends the emotion engine's analysis results to a server. The server adjusts the training plan and mental care content based on the emotion data and provides feedback to the user. The device then displays the feedback and provides training and mental care messages based on the emotion.

[1681] Input: User's facial expression and movement data

[1682] Processing: Analysis of facial expressions and movement data, emotion recognition

[1683] Output: Sentiment analysis results, training plan adjustments, mental care messages

[1684] What it does: If the emotion engine recognizes that the user is tired, it will adjust their training plan or mental care messages.

[1685] Step 5:

[1686] The device collects data (e.g., time, number of repetitions, heart rate, etc.) in real time as the user exercises and sends it to the server, which then associates this data with the user's account, stores it, and syncs it with the mobile app.

[1687] Input: Training data (time, repetitions, heart rate, etc.)

[1688] Processing: Data collection, transmission to server, data storage

[1689] Output: Saved training data, synced app data

[1690] Specific operation: Heart rate and number of repetitions recorded during training are stored on the server and can be viewed on the mobile app.

[1691] Step 6:

[1692] The server generates appropriate dietary advice and mental care messages based on the user's body data and provides them to the user periodically via a mobile app.

[1693] Input: User's body data

[1694] Processing: Generating dietary management advice and mental care messages

[1695] Output: Generated advice and messages

[1696] Specific behavior: The mobile app sends users appropriate dietary advice and mental health care messages.

[1697] These are the specific processing steps of the system, which enable users to maintain an efficient and sustainable fitness routine.

[1698] (Application example 2)

[1699] 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."

[1700] While conventional fitness systems offer basic functions such as providing training plans based on the user's physique data and providing dietary advice, they lack the flexibility to take the user's emotional state into account, making it difficult to maintain the user's motivation. In particular, in today's society, where continuity in fitness is important, there is a demand for systems that provide appropriate psychological support to users. Therefore, the purpose of this invention is to support users' sustainable training habits by customizing fitness plans based on the user's emotional state and providing mental care feedback.

[1701] The specific processing by the specific 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 a user registration and login means for a user to register an account and log in, a body shape photographing means for photographing the user's body shape using a camera and acquiring image data, a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape, a training plan generation means for creating a training plan based on the ideal body shape and providing it to the user, a training data collection means for collecting training data in real time and sending it to a central server, a user data synchronization means for saving the collected data in the user's account based on the collected data and synchronizing it with an application, a diet management and mental care means for generating and providing appropriate diet management and mental care advice based on the user's body shape data, an emotion analysis means for dynamically adjusting the training plan and mental care based on emotion analysis, and an emotion feedback means for analyzing the user's emotions in real time and providing feedback. This makes it possible to analyze the user's emotional state in real time and provide a fitness plan and feedback accordingly.

[1702] "User registration and login means" means a means that allows a user to create an account, register, and log in.

[1703] The "body shape photographing means" is a means for photographing the user's body shape using a camera and acquiring image data thereof.

[1704] The "body shape analysis and generation means" is a means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape.

[1705] The "training plan generation means" is a means for creating a training plan based on the generated ideal body type and providing it to the user.

[1706] "Training data collection means" refers to a means for collecting training data in real time and transmitting it to a central server.

[1707] The "user data synchronization means" is a means for saving data in a user's account based on collected data and synchronizing it with an application.

[1708] The "dietary management and mental care means" is a means for generating and providing appropriate dietary management advice and mental care advice based on the user's body type data.

[1709] The "emotion analysis means" is a means for analyzing the user's emotions and dynamically adjusting training plans and mental care in real time.

[1710] The "emotion feedback means" is a means for analyzing the user's emotions in real time and providing feedback based on the results.

[1711] This invention is a system that allows users to train efficiently and easily and maintain sustainable fitness habits, and has the function of recognizing the user's emotions and adjusting training plans and mental care. A specific implementation method is described below.

[1712] composition

[1713] server

[1714] The server includes the following means:

[1715] User registration and login methods

[1716] Body photography methods

[1717] Body shape analysis and generation method

[1718] Training plan generation method

[1719] Training Data Collection Methods

[1720] User data synchronization means

[1721] Dietary management and mental health care measures

[1722] Emotion analysis means

[1723] Emotional feedback measures

[1724] Hardware and software used

[1725] The specific hardware and software implemented on the server and terminal are as follows:

[1726] Hardware: camera sensors, monitors, smartphones

[1727] Software: Python, TensorFlow, OpenCV, Django, PostgreSQL

[1728] Processing flow

[1729] 1. User Registration and Login

[1730] Users create an account using a smartphone application, registering and logging in by entering their name, email address and password. Django is used to send data to the server and store the information in PostgreSQL.

[1731] 2. Body shape photography and analysis

[1732] When a user stands in front of the monitor in a physical store, a camera captures their body shape, and the acquired image data is analyzed using OpenCV. The analyzed data is then used by TensorFlow to generate their current and ideal body shapes, which are then sent to the server.

[1733] 3. Creation and provision of training plans

[1734] Based on the ideal body shape, the AI ​​model generates a training plan that is displayed on the monitor or smartphone app. The plan includes exercise names, repetitions, and number of sets, and includes video and audio instructions on correct form and important points to note.

[1735] 4. Emotional analysis and feedback

[1736] During training, a camera monitors the user's facial expressions and movements, and TensorFlow is used to analyze their emotions in real time. Based on the analysis results, the server dynamically adjusts the training plan and mental care content and provides appropriate feedback.

[1737] 5. Collect and sync training data

[1738] Training data (e.g., time, repetitions, heart rate, etc.) is collected in real time and sent to the server via Django and stored in a PostgreSQL database, which synchronizes the data with the user's application.

[1739] 6. Dietary management and mental health care

[1740] The server generates dietary management advice and mental care messages based on the user's body data and provides them via a smartphone app.

[1741] Specific examples

[1742] When a user visits the gym and stands in front of the monitor, they are automatically logged in, and the camera takes a picture of their body to analyze their current shape and generate an ideal body shape. A training plan is then customized and provided to the user. During training, the camera recognizes emotions in real time and provides appropriate feedback.

[1743] Prompt Sentence Examples

[1744] Generate a fitness plan for the user based on their current and ideal body shape data. Adjust the training plan and mental care content as needed, taking into account the user's emotional data.

[1745] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1746] Step 1:

[1747] A user creates an account and logs in using a smartphone application. The user enters their name, email address, and password into the application and performs registration. The input data (name, email address, password) is sent to the server and stored in a database (PostgreSQL) using Django. This process allows the user to obtain authentication information to access the system.

[1748] Step 2:

[1749] The user stands in front of a monitor in a physical store, and a camera captures the user's body shape. The terminal (monitor) acquires the image data captured by the camera and sends it to the server. The input data (image data) is analyzed using OpenCV, and the user's current body shape is identified. The server temporarily stores the results of this analysis (current body shape data) and uses it in subsequent processing.

[1750] Step 3:

[1751] The server uses TensorFlow to generate an ideal body shape based on the current body shape data analyzed by OpenCV. The generative AI model generates ideal body shape data based on the input data (current body shape data). The ideal body shape data is stored by the server and used in the next processing step.

[1752] Step 4:

[1753] The server uses an AI model to generate a training plan based on the generated ideal body data. Input data (ideal body data) is supplied to the generative AI model using prompt statements, and output data (training plan) is generated. The generated training plan is sent from the server to the device (monitor or smartphone app) and provided to the user.

[1754] Step 5:

[1755] The user starts training, and training data (time, repetitions, heart rate, etc.) is collected by the device. The collected data is sent to the server in real time. The server stores the training data in a database and makes it available for subsequent processing.

[1756] Step 6:

[1757] During training, a camera monitors the user's facial expressions and movements, analyzing their emotions in real time. The input data used for emotion analysis (user's facial expressions and movements) is processed using TensorFlow, and the output data (emotional state) is sent to a server. Based on this emotional state data, the training plan and mental care content are dynamically adjusted.

[1758] Step 7:

[1759] The server generates feedback (encouraging messages, adjustments to instruction, etc.) based on the results of emotion analysis and sends it to the device (monitor or smartphone app). This allows the user to receive appropriate feedback in real time. Feedback messages are generated based on the input data (emotional state) and provided to the user.

[1760] Step 8:

[1761] After the training is completed, the server synchronizes and stores all collected training data in the user's account. The user data stored in the database (PostgreSQL) is synchronized with the user's smartphone app and used as reference data for future training.

[1762] Step 9:

[1763] The server generates dietary advice and mental care messages based on the user's body shape data and provides them to the user periodically via a smartphone app. Based on the input data (body shape data), the generative AI model generates advice and provides feedback to the user.

[1764] These steps provide users with an efficient and consistent fitness experience, promoting sustainable training habits.

[1765] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1766] 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.

[1767] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1768] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1769] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1770] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1771] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1772] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1773] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1774] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1775] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1776] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1777] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1778] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1779] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1780] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1781] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1782] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1783] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1784] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1785] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1786] The following is further disclosed regarding the above embodiment.

[1787] (Claim 1)

[1788] User registration and login means for users to register and log in to their accounts;

[1789] a body shape photographing means for photographing a body shape of a user using a camera and acquiring image data;

[1790] a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape;

[1791] a training plan generation means for creating a training plan based on an ideal body type and providing the plan to a user;

[1792] training data collection means for collecting training data in real time and transmitting the training data to a central server;

[1793] a user data synchronization means for storing data in a user's account based on the collected data and synchronizing the data with the application;

[1794] a dietary management and mental care means for generating and providing appropriate dietary management and mental care advice based on the user's body shape data;

[1795] A system including:

[1796] (Claim 2)

[1797] 2. The system according to claim 1, further comprising a mental care means for generating and notifying encouraging messages and motivational content based on the user's body shape data.

[1798] (Claim 3)

[1799] 2. The system according to claim 1, further comprising a lecture means for instructing the user on the correct training form and important points through video and audio based on the ideal body type.

[1800] "Example 1"

[1801] (Claim 1)

[1802] User registration and login means for users to register and log in to their accounts;

[1803] a body shape photographing means for photographing a body shape of a user using a camera and acquiring image data;

[1804] a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape;

[1805] a training plan generation means for creating a training plan based on an ideal body type and providing the plan to a user;

[1806] training data collection means for collecting training data in real time and transmitting the training data to a central server;

[1807] a user data synchronization means for storing data in a user's account based on the collected data and synchronizing the data with the application;

[1808] a dietary management and mental care means for generating and providing appropriate dietary management and mental care advice based on the user's body shape data;

[1809] a motion analysis means for collecting and analyzing the user's motion and vital data to monitor fitness progress;

[1810] a mental care notification means for notifying the user of encouraging messages and motivational content;

[1811] a personal fitness advice means for analyzing the training data performed by the user and providing personalized advice to support continuous fitness habits;

[1812] A system including:

[1813] (Claim 2)

[1814] 2. The system according to claim 1, further comprising a mental care means for generating and notifying encouraging messages and motivational content based on the user's body shape data.

[1815] (Claim 3)

[1816] 2. The system according to claim 1, further comprising a lecture means for instructing the user on the correct training form and important points through video and audio based on the ideal body type.

[1817] "Application Example 1"

[1818] (Claim 1)

[1819] User registration and login means for users to register and log in to their accounts;

[1820] a body shape photographing means for photographing a body shape of a user using a camera and acquiring image data;

[1821] a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape;

[1822] a training plan generation means for creating a training plan based on an ideal body type and providing the plan to a user;

[1823] training data collection means for collecting training data in real time and transmitting the training data to a central server;

[1824] a user data synchronization means for storing data in a user's account based on the collected data and synchronizing the data with the application;

[1825] a dietary management and mental care means for generating and providing appropriate dietary management and mental care advice based on the user's body shape data;

[1826] a posture analysis and motion plan generation means for analyzing the posture and motion of a worker in real time and generating an efficient work method;

[1827] a real-time feedback means for feeding back the generated motion plan to a worker in real time;

[1828] A system including:

[1829] (Claim 2)

[1830] 2. The system according to claim 1, further comprising a mental care means for generating and notifying encouraging messages and motivational content based on the user's body shape data.

[1831] (Claim 3)

[1832] 2. The system according to claim 1, further comprising a lecture means for instructing the user on the correct training form and important points through video and audio based on the ideal body type.

[1833] "Example 2: Combining Emotion Engines"

[1834] (Claim 1)

[1835] User registration and login means for users to register and log in to their accounts;

[1836] a body shape photographing means for photographing a body shape of a user using a camera and acquiring image data;

[1837] a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape;

[1838] a training plan generation means for creating a training plan based on an ideal body type and providing the plan to a user;

[1839] An emotion analysis means for analyzing the user's facial expressions and movements in real time and recognizing their emotions;

[1840] training data collection means for collecting training data in real time and transmitting the training data to a central server;

[1841] a user data synchronization means for storing data in a user's account based on the collected data and synchronizing the data with the application;

[1842] a dietary management and mental care means for generating and providing appropriate dietary management and mental care advice based on the user's body shape data;

[1843] A system including:

[1844] (Claim 2)

[1845] 2. The system according to claim 1, further comprising a mental care means for generating and notifying encouraging messages and motivational content based on the user's body shape data and the results of emotion analysis.

[1846] (Claim 3)

[1847] 2. The system according to claim 1, further comprising a lecture means for instructing the user on the correct training form and important points through video and audio based on the ideal body type.

[1848] "Application example 2 when combining emotion engines"

[1849] (Claim 1)

[1850] User registration and login means for users to register and log in to their accounts;

[1851] a body shape photographing means for photographing a body shape of a user using a camera and acquiring image data;

[1852] a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape;

[1853] a training plan generation means for creating a training plan based on an ideal body type and providing the plan to a user;

[1854] training data collection means for collecting training data in real time and transmitting the training data to a central server;

[1855] a user data synchronization means for storing data in a user's account based on the collected data and synchronizing the data with the application;

[1856] a dietary management and mental care means for generating and providing appropriate dietary management and mental care advice based on the user's body shape data;

[1857] An emotion analysis means for dynamically adjusting training plans and mental care based on emotion analysis;

[1858] emotion feedback means for analyzing the user's emotion in real time and providing feedback;

[1859] A system including:

[1860] (Claim 2)

[1861] 2. The system according to claim 1, further comprising a mental care means for generating and notifying encouraging messages and motivational content based on the user's body shape data.

[1862] (Claim 3)

[1863] 2. The system according to claim 1, further comprising a lecture means for instructing the user on the correct training form and important points through video and audio based on the ideal body type. [Explanation of symbols]

[1864] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a user registration and login means for users to register and log in to their accounts; a body shape photographing means for photographing a body shape of a user using a camera and acquiring image data; a body shape analysis and generation means for analyzing the acquired image data to characterize the user's current body shape and generate an ideal body shape; a training plan generation means for creating a training plan based on an ideal body type and providing the plan to a user; training data collection means for collecting training data in real time and transmitting the training data to a central server; a user data synchronization means for storing data in a user's account based on the collected data and synchronizing the data with the application; a dietary management and mental care means for generating and providing appropriate dietary management and mental care advice based on the user's body shape data; A system including:

2. The system according to claim 1 , further comprising a mental care unit that generates and notifies encouraging messages and content that enhances motivation based on the user's body shape data.

3. 2. The system according to claim 1, further comprising a lecture means for instructing the user on the correct training form and important points by video and audio based on the ideal body type.

Images