System

Abstract

A system is provided.SOLUTION: A system, comprising: means for collecting physical information of a person using electronic devices; means for generating a training plan based on the collected information using a generated AI; means for monitoring the person's movements and providing feedback using motion sensors and real-time assessment devices; and means for analyzing the collected information and feedback and updating the training plan using the generated AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Currently, sports and fitness training lacks appropriate coaching methods tailored to individual physical and movement characteristics. As a result, training methods often rely on standardized training methods, which increases the risk of injury due to inappropriate training and makes it difficult to achieve effective training results. The present invention aims to solve these problems by providing a system that analyzes a user's physical information from multiple angles and provides individually optimized training plans and real-time feedback based on the results. [Means for solving the problem]

[0005] The present invention is a system that includes: (1) a means for collecting physical information of a user using an electronic device; (2) a means for generating a training plan based on the collected data using a generative AI; (3) a means for monitoring the user's movements and providing feedback using a motion sensor and a real-time evaluation device; and (4) a means for analyzing the collected data and feedback and updating the training plan using a generative AI.

[0006] Specifically, physical information such as the user's muscle mass, skeletal structure, and balance ability is collected using a body composition scale and a motion characteristic measurement device, and the data is sent to a server. The server then analyzes the data using a machine learning algorithm and uses generative AI to create an individually optimized training plan. Furthermore, real-time evaluation devices such as motion sensors and MR / AR glasses monitor the user's movements during training and provide appropriate feedback via voice. After training, feedback from the user is collected, and the server analyzes the data and uses generative AI to update the training plan, maximizing effectiveness while minimizing the risk of injury.

[0007] An "electronic device" is a device for collecting physical information about a user, and specifically includes a body composition monitor, a motion characteristic measuring device, a camera, and the like.

[0008] "Generative AI" refers to artificial intelligence techniques used to generate optimal training plans based on collected data.

[0009] A "motion sensor" is a sensor for monitoring a user's movements in real time, and specifically, is a device that includes an acceleration sensor and a gyro sensor.

[0010] A "real-time evaluation device" is a device that evaluates a user's movements during training in real time and provides feedback, and specifically includes devices such as MR / AR glasses.

[0011] A "training plan" is a plan that includes a training schedule and specific exercise instructions that are generated based on the user's physical information and goals.

[0012] "Feedback" means information provided to a user during or after a workout, including instructions on proper exercise form and advice regarding the user's progress.

[0013] "Machine learning algorithms" are a set of computational models and methods used to analyze data and assess a user's physical condition and training effectiveness.

[0014] A "server" is a computer system for storing and analyzing data, and is a device that performs processing including generating and updating training plans. [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 showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[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] The present invention is a system that collects a user's physical and movement information, generates an individually optimized training plan based on this data, and provides feedback in real time, thereby maximizing the user's training efficiency and minimizing the risk of injury.

[0037] System Overview

[0038] The system includes the following components:

[0039] 1. Electronic Devices

[0040] 2. Server

[0041] 3. Generation AI

[0042] 4. Motion Sensors and Real-Time Assessment Devices

[0043] 5. Training Plan

[0044] 6. Feedback

[0045] Program processing flow

[0046] 1. Data Collection

[0047] Terminal (electronic device): Collects the user's physical information (body fat percentage, muscle mass, bone density, etc.) and measures movement characteristics (flexibility, balance ability, etc.) using motion sensors and cameras.

[0048] 2. Data transmission and storage

[0049] Terminal: Sends collected data to the server, which receives the data and stores it in a database.

[0050] 3. Data Analysis

[0051] Server: Analyzes the stored data using a machine learning algorithm to evaluate the user's physical condition. Based on the results of this analysis, a generative AI is used to generate an individually optimized training plan.

[0052] 4. Plan Generation

[0053] Server: Based on the analysis results, a generative AI model is used to create and save a training plan tailored to the user's goals and physical condition.

[0054] 5. Real-time feedback

[0055] User: Starts training and puts on MR glasses or other real-time assessment device.

[0056] Device: Monitors the user's movements in real time during training and evaluates whether they are appropriate for the training plan.

[0057] Server: Analyzes the monitored data and provides appropriate feedback to the user via voice.

[0058] 6. Feedback Rating and Updates

[0059] Users: Provide feedback after training.

[0060] Terminal: Sends feedback data to the server.

[0061] Server: Analyzes feedback data and updates training plans using generative AI.

[0062] Specific examples

[0063] collection

[0064] When the user steps on the body composition scale, the device measures a body fat percentage of 25%, muscle mass of 60kg, and bone density of 1.2g / cm³. The user performs stretching and balance exercises, and the device collects movement characteristic data.

[0065] Send / Save

[0066] The collected data (physical and movement information) is sent from the device to a server, which stores the data in a database.

[0067] analysis

[0068] The server analyzes the data and uses machine learning algorithms to determine if muscle mass needs to be increased. A generative AI model then creates a training plan to target muscle gain.

[0069] Training plan generation

[0070] The server creates a training plan including specific exercises (e.g., weightlifting, stretching, balance training, etc.) based on the user's goals and current status, and stores it in a database.

[0071] Real-time feedback

[0072] The user puts on the MR glasses and starts training. The device (motion sensor and MR glasses) monitors the user's movements in real time.

[0073] The server detects improper behavior or forms and provides appropriate corrective instructions to the user via voice.

[0074] Feedback Rating and Updates

[0075] After completing the training, the user inputs feedback (e.g., fatigue, muscle pain, etc.).

[0076] The device sends feedback data to a server, which analyzes the data and uses generative AI to update the training plan.

[0077] This system allows users to efficiently perform training that is best suited to their physical condition, maximizing results while minimizing the risk of injury.

[0078] The processing flow will be explained below.

[0079] Step 1:

[0080] The user enters their name, age, sex, height, weight, fitness level, and training goals into the device.

[0081] Step 2:

[0082] The device (body composition scale) measures the user's muscle mass, fat mass, and bone density and collects the data.

[0083] Step 3:

[0084] The device (motion sensor and camera) measures the user's movement characteristics such as flexibility, balance ability, and range of motion, and collects the data.

[0085] Step 4:

[0086] The terminal transmits the data obtained in steps 2 and 3 to the server.

[0087] Step 5:

[0088] The server stores the received data in a database.

[0089] Step 6:

[0090] The server preprocesses the stored data and prepares it for input to machine learning algorithms, for example by denoising and normalizing the data.

[0091] Step 7:

[0092] The server analyzes the data using machine learning algorithms to assess the user's physical condition (e.g., muscle mass, flexibility, and balance ability).

[0093] Step 8:

[0094] Based on the analysis results, the server uses AI to generate an optimal training plan, including specific exercises and training schedules.

[0095] Step 9:

[0096] The training plan generated by the server is stored in a database and notified to the device.

[0097] Step 10:

[0098] The user puts on MR glasses or other real-time evaluation devices and begins training.

[0099] Step 11:

[0100] The device (MR / AR glasses, motion sensor) monitors the user's movements during training in real time and evaluates whether the movements are appropriate for the training plan.

[0101] Step 12:

[0102] The server analyzes the monitored data, and if the form is inappropriate, provides the user with voice feedback and instructions for correction via the terminal.

[0103] Step 13:

[0104] The user finishes the training and inputs post-training feedback (e.g., fatigue, muscle pain, satisfaction) into the terminal.

[0105] Step 14:

[0106] The terminal transmits the feedback data to the server.

[0107] Step 15:

[0108] The server analyzes the feedback data and uses generative AI to update and optimize the training plan.

[0109] Step 16:

[0110] The server saves the updated training plan in a database and notifies the device.

[0111] Example 1

[0112] 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."

[0113] Conventional training systems struggle to provide optimal training plans based on each user's individual physical condition and movements. Furthermore, they lack the ability to provide real-time feedback, making it difficult to maximize training effectiveness and minimize injury risk. Furthermore, they lack the ability to update plans based on post-training feedback.

[0114] 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.

[0115] In this invention, the server includes means for collecting physical information of a user using an electronic device, means for generating a training plan based on the collected data using a generation AI, means for monitoring the user's movements using a motion sensor and a real-time evaluation device and providing feedback, means for analyzing the collected data and feedback and updating the training plan using a generation AI, means for collecting user movement information using a camera, means for evaluating the physical condition using a machine learning algorithm, and means for storing the training plan in a database. This makes it possible to provide an optimal training plan based on the user's individual physical condition and movements, maximizing training effects and minimizing the risk of injury.

[0116] 1. "Electronic device" refers to a device used to collect a user's physical information, including body composition monitors and motion characteristic measuring devices.

[0117] 2. "Generative AI" refers to artificial intelligence technology used to generate training plans based on collected data.

[0118] 3. "Motion sensor" means a sensor device that detects user movements and collects and transmits that information.

[0119] 4. "Real-time evaluation device" means a device that evaluates a user's movements in real time during training and provides feedback.

[0120] 5. “Machine learning algorithms” are algorithms used to analyze data and learn patterns and trends.

[0121] 6. "Database" means the data storage system for storing and managing collected data and generated training plans.

[0122] 7. "User's physical information" refers to data that represents the user's physical condition, such as body fat percentage, muscle mass, and bone density.

[0123] 8. "Training Plan" refers to a specific exercise schedule and content developed based on the user's goals and physical condition.

[0124] 9. "Feedback" means advice or corrective instructions provided during or after training.

[0125] This invention provides a system that collects a user's physical information and movement information, generates an individually optimized training plan based on this data, and provides feedback in real time, thereby maximizing the user's training efficiency and minimizing the risk of injury. This system includes an electronic device, a server, a generating AI, a motion sensor and real-time evaluation device, a training plan, and feedback.

[0126] First, the electronic device serves as a terminal and collects the user's physical information. Specifically, a body composition scale is used to obtain data such as body fat percentage, muscle mass, and bone density. Motion sensors and cameras are also used to collect movement information such as the user's flexibility and balance ability.

[0127] The device then transmits the collected data to a server, which receives it and stores it in a database that keeps a record of all collected physical and movement information.

[0128] The server analyzes the stored data and uses machine learning algorithms to assess the user's physical condition. This process identifies the current state of the body and areas for improvement from the data. For example, a Python machine learning library (e.g., scikit-learn) can be used.

[0129] Once the user's physical condition is evaluated, the server uses a generative AI model to create an individually optimized training plan. Specific exercise content and schedules are generated by entering a prompt into the generative AI model. For example, the prompt could be, "I'd like you to generate a training plan to increase muscle mass."

[0130] The generated training plan is stored in a database by the server and contains detailed exercises based on the user's goals and current condition. For example, this training plan might include "3 x 60-minute workouts per week, targeting different muscle groups each time."

[0131] When the user starts training, they put on MR glasses and other real-time evaluation devices. The motion sensors and MR glasses on the device monitor the user's movements in real time during training. This information is sent to a server, which detects improper movements or form and provides appropriate corrective instructions via voice. For example, the server may provide voice feedback such as "Please correct your posture."

[0132] After completing a workout, the user inputs feedback such as fatigue and muscle pain into the device. The device then sends this feedback data to a server, which analyzes it and updates the training plan using a generative AI model. This update creates a new plan that reflects the feedback data and is optimized for the user.

[0133] Prompt Sentence Examples

[0134] "Write code that uses collected data about a user (e.g., body fat percentage 25%, muscle mass 60kg, bone density 1.2g / cm³) to generate a training plan using a generative AI model to target muscle growth for the user. Also, add the ability to detect improper form during training in real time and provide audio feedback."

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] Step 1:

[0137] Data collection

[0138] When a user steps onto the body composition scale, the device measures their physical information, such as their body fat percentage, muscle mass, and bone density. In addition, when the user performs stretching or balance exercises, the device's motion sensors and camera collect movement information.

[0139] Input: User's physical condition data (body fat percentage, muscle mass, bone density, etc.), movement information (flexibility, balance ability, etc.)

[0140] Output: Collected physical and movement information

[0141] Step 2:

[0142] Data transmission and storage

[0143] The device sends the collected data to the server, which stores the received data in a database.

[0144] Input: User's physical and movement information sent from the device

[0145] Output: Physical and movement information stored in a database

[0146] Step 3:

[0147] Data analysis

[0148] The server uses machine learning algorithms to analyze the data stored in the database, thereby assessing the user's physical condition and identifying areas for improvement.

[0149] Input: User's physical and movement information stored in a database

[0150] Output: User's physical condition assessment results and necessary improvements

[0151] Step 4:

[0152] Plan Generation

[0153] Based on the analysis results, the server uses a generative AI model to generate an individually optimized training plan and stores it in a database.

[0154] Input: User's physical condition assessment results and necessary improvements

[0155] Output: Generated training plan

[0156] Step 5:

[0157] Training start and real-time feedback

[0158] The user puts on the MR glasses and starts training. During the training, the device (motion sensor and MR glasses) monitors the user's movements in real time and sends the information to the server. The server detects improper movements or form and provides audio instructions for correction.

[0159] Input: User behavior information collected in real time

[0160] Output: Real-time audio feedback

[0161] Step 6:

[0162] Feedback rating and training plan updates

[0163] After completing a workout, the user inputs feedback such as fatigue and muscle pain. The device sends this feedback data to a server, which then uses generative AI to analyze the data and update the training plan.

[0164] Input: User-entered feedback data

[0165] Output: Updated training plan

[0166] (Application example 1)

[0167] 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."

[0168] Delivery workers are often overburdened in their daily work, which can increase the risk of fatigue and injury. Furthermore, delivery efficiency can decline due to a lack of knowledge of efficient routes and work methods. To solve these problems, a system is needed that collects physical and movement information from delivery workers in real time and provides appropriate feedback.

[0169] 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.

[0170] In this invention, the server includes means for collecting physical information of the user using an electronic device, means for generating a training plan based on the collected data using a generation AI, means for monitoring the user's movements using a motion sensor and a real-time evaluation device and providing feedback, means for analyzing the collected data and feedback and updating the training plan using a generation AI, and means for monitoring the physical information and movement information of the delivery person and maximizing delivery efficiency and improving safety based on the information, thereby reducing the burden on the delivery person and enabling them to perform their work efficiently and safely.

[0171] "Electronic Device" is a technological device used to collect physical and behavioral information about a delivery person.

[0172] "Generative AI" is artificial intelligence that generates optimal training and delivery plans based on collected data.

[0173] A "motion sensor" is a sensor that detects the movement characteristics of delivery personnel and evaluates them in real time.

[0174] A "real-time evaluation device" is a device that instantly analyzes collected data and provides immediate feedback to delivery personnel.

[0175] "Feedback" refers to instructions and advice provided to delivery personnel based on collected and analyzed data.

[0176] A "body composition scale" is a device that measures physical information such as a delivery person's body fat percentage, muscle mass, and bone density.

[0177] A "motion characteristic measuring device" is an instrument used to evaluate the motion characteristics of delivery personnel, such as their flexibility and balance ability.

[0178] A "smartwatch" is a wearable device that records a delivery person's physical information, such as heart rate, number of steps, and energy consumption, in real time.

[0179] "Smart glasses" are wearable devices that collect information on the delivery person's posture and movements and provide visual feedback.

[0180] The present invention provides a system that collects physical and motion information of delivery personnel and provides real-time feedback to maximize efficiency and improve safety during delivery operations. To this end, the system includes the following components:

[0181] 1. Electronic Devices (Terminals)

[0182] The user (delivery worker) wears a smartwatch or smart glasses, which collect various physical and movement information in real time, such as heart rate, number of steps, energy consumption, driving posture, walking posture, and how the package is carried.

[0183] 2. Data transmission and storage (server)

[0184] The data collected by the device is sent to a cloud server via wireless communication, and the server receives the data and stores it in a database.

[0185] 3. Data analysis (server)

[0186] The server analyzes the stored data using machine learning algorithms to assess the delivery person's current physical condition and movement patterns, using random forests or other suitable machine learning models.

[0187] 4. Feedback and Advice (Device)

[0188] Based on the analysis results, the server generates appropriate feedback and advice, such as when to take a break, suggesting an efficient route, teaching correct posture, etc. This feedback is conveyed to the user through audio output and visual presentations.

[0189] 5. Update training plan based on feedback (server)

[0190] Feedback data provided by the user (e.g., fatigue, muscle pain, distance traveled, etc.) is also sent to the server, and the training plan is updated using the generative AI.

[0191] Hardware and software used

[0192] Smartwatches and smart glasses: Used as data collection devices.

[0193] Cloud server: Stores and analyzes data.

[0194] Machine learning algorithms: using libraries such as TensorFlow and Scikit-learn.

[0195] Audio output and visual feedback devices: Provide feedback to the delivery person.

[0196] Specific examples

[0197] The user puts on the smartwatch and smart glasses and begins making deliveries, which collects data on heart rate, steps, energy expenditure, and posture.

[0198] The collected data is sent from the terminal via wireless communication to a server and stored there.

[0199] The server analyzes the data and generates an efficiency score for the delivery person using generative AI (e.g., a random forest model), and generates appropriate feedback, such as "You're tired. Please take a break."

[0200] Based on the analysis results, the device provides feedback to the delivery person via voice or visual feedback.

[0201] The user inputs the effects of training and feedback, and the feedback data is sent back to the server, where the training plan is updated using the generative AI.

[0202] Prompt Sentence Examples

[0203] "Design an algorithm to monitor the fatigue level and efficiency of delivery personnel and provide optimal feedback in real time. Data to be used includes heart rate, steps, energy consumption, and posture. It should also suggest break times and efficient routes."

[0204] This reduces the burden on delivery personnel and enables them to carry out their work efficiently and safely.

[0205] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0206] Step 1:

[0207] Data collection

[0208] Users wear smartwatches or smart glasses, which collect physical and movement information such as heart rate, number of steps, energy consumption, and posture information in real time.

[0209] The inputs are biometric and motion data obtained from smartwatches and smart glasses.

[0210] The output is an information packet containing these data.

[0211] Specifically, the sensors periodically collect data and temporarily store it in the memory of the smart glasses or smartwatch.

[0212] Step 2:

[0213] Data transmission and storage

[0214] The data collected by the device is sent to a server via wireless communication (e.g., Wi-Fi or Bluetooth), which receives the data and stores it in a cloud database.

[0215] The input is biometric data and motion data sent from the terminal.

[0216] The output is data stored in a cloud database.

[0217] Specifically, the device periodically divides data into packets and transmits them to the server via wireless communication. The server then automatically stores the received data in a database.

[0218] Step 3:

[0219] Data analysis

[0220] The server analyzes the data stored in the cloud database using machine learning algorithms (e.g., random forests), which evaluate the delivery person's current physical condition and movement patterns.

[0221] The inputs are stored biometric and motion data.

[0222] The output is an evaluation score for the delivery person's fatigue level and efficiency.

[0223] Specifically, the server standardizes the data, then analyzes it using machine learning models, generating evaluation results that are stored in a log file.

[0224] Step 4:

[0225] Providing feedback and advice

[0226] The server generates appropriate feedback and advice based on the analysis results, which is provided to the user through audio output or visual display.

[0227] The input is the evaluation score of the analysis result.

[0228] The output is feedback or advice provided to the user.

[0229] Specifically, the server generates an appropriate feedback message based on the evaluation score and sends it to an audio output device or a visual display device.

[0230] Step 5:

[0231] Get feedback and update your training plan

[0232] The user performs training and performs actions based on the feedback, and then provides feedback on the results to the input device. This feedback data (e.g., fatigue, muscle pain, distance traveled, etc.) is sent from the device to the server. The server analyzes this data and updates the training plan using generative AI.

[0233] The input is the feedback data entered by the user.

[0234] The output is an updated training plan.

[0235] Specifically, the device sends feedback data to the server, which analyzes the data, updates the training plan, and stores the results in a cloud database.

[0236] 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.

[0237] This system maximizes training efficiency and minimizes the risk of injury by collecting a user's physical and movement information, generating an individually optimized training plan based on this data, and providing real-time feedback. Furthermore, it combines an emotion engine that recognizes the user's emotions and optimizes training according to their emotional state.

[0238] System Overview

[0239] The system includes the following components:

[0240] 1. Electronic Devices

[0241] 2. Server

[0242] 3. Generation AI

[0243] 4. Motion Sensors and Real-Time Assessment Devices

[0244] 5. Emotion Engine

[0245] 6. Training Plan

[0246] 7. Feedback

[0247] Program processing flow

[0248] 1. Data Collection

[0249] Terminal (electronic device): Collects the user's physical information (body fat percentage, muscle mass, bone density, etc.) and measures movement characteristics (flexibility, balance ability, etc.) using motion sensors and cameras.

[0250] Terminal (emotion engine): Analyzes the user's facial expressions and voice patterns to recognize their emotional state.

[0251] 2. Data transmission and storage

[0252] Terminal: Sends collected data (physical information, movement information, and emotional information) to the server, which receives the data and stores it in a database.

[0253] 3. Data Analysis

[0254] Server: Preprocesses the stored data and prepares it for input to machine learning algorithms, for example by denoising and normalizing the data.

[0255] Server: Analyzes data using machine learning algorithms to assess the user's physical and emotional state.

[0256] 4. Plan Generation

[0257] Server: Based on the analysis results and emotional state, the server uses generative AI to generate an optimal training plan, including specific exercises and training schedules.

[0258] 5. Real-time feedback

[0259] User: Starts training and puts on MR glasses or other real-time assessment device.

[0260] Device: Monitors the user's movements during training in real time and evaluates whether the movements are appropriate for the training plan.

[0261] Server: Analyzes the monitored data, and if the form is inappropriate, provides the user with voice feedback and instructions for correction via the terminal.

[0262] 6. Feedback Rating and Updates

[0263] User: After completing the training, the user inputs post-training feedback (e.g., fatigue, muscle pain, satisfaction) into the terminal.

[0264] Terminal: Sends feedback data to the server.

[0265] Server: Analyzes feedback data and emotional state, and uses generative AI to update and optimize training plans.

[0266] Specific examples

[0267] collection

[0268] When the user steps on the body composition scale, the device measures a body fat percentage of 25%, muscle mass of 60kg, and bone density of 1.2g / cm³. The user performs stretching and balance exercises, and the device collects movement characteristic data.

[0269] The emotion engine collects the user's facial and voice patterns to recognize their emotional state (e.g., joy, fatigue, stress, etc.).

[0270] Send / Save

[0271] The collected data (physical information, movement information, and emotional information) is sent from the device to a server, which stores the data in a database.

[0272] analysis

[0273] The server analyzes the data and uses machine learning algorithms to assess the user's physical and emotional state. For example, it may determine that muscle mass needs to be increased and that stress levels are high during training. A generative AI model then creates a training plan that targets muscle gain and reduces stress.

[0274] Training plan generation

[0275] The server creates a training plan including specific exercises (e.g., weightlifting, relaxation exercises, mindfulness practice, etc.) based on the user's goals and current situation, and stores it in a database.

[0276] Real-time feedback

[0277] The user puts on the MR glasses and starts training. The device (motion sensor and MR glasses) monitors the user's movements in real time.

[0278] The server detects improper movements and forms and provides appropriate corrective instructions to the user via voice. It also monitors the user's emotional state and suggests training breaks and relaxation exercises if the user is feeling stressed.

[0279] Feedback Rating and Updates

[0280] After completing the training, the user inputs feedback (e.g., fatigue, muscle pain, emotional state).

[0281] The device sends the feedback data to a server, which then analyzes it and uses generative AI to update the training plan—for example, if muscle soreness is reported, the plan will be updated to adjust the load for the next workout.

[0282] This system allows users to efficiently train in a way that is optimal not only for their physical condition but also for their emotional state, maximizing results while minimizing the risk of injury.

[0283] The processing flow will be explained below.

[0284] Step 1:

[0285] The user enters their name, age, sex, height, weight, fitness level, and training goals into the device.

[0286] Step 2:

[0287] The device (body composition monitor) measures the user's muscle mass, body fat percentage, and bone density and collects the data.

[0288] Step 3:

[0289] The device (motion sensor and camera) measures the user's movement characteristics such as flexibility, balance ability, and range of motion, and collects the data.

[0290] Step 4:

[0291] The device (emotion engine) analyzes the user's facial expressions and voice patterns to recognize their emotional state. For example, a smile can be interpreted as "joy," while a stiff expression can be interpreted as "stress."

[0292] Step 5:

[0293] The terminal transmits the data obtained in steps 2, 3, and 4 to the server.

[0294] Step 6:

[0295] The server stores the received data in a database.

[0296] Step 7:

[0297] The server preprocesses the stored data and prepares it for input to machine learning algorithms, for example by denoising and normalizing the data.

[0298] Step 8:

[0299] The server analyzes the data using machine learning algorithms to assess the user's physical condition (muscle mass, body fat percentage, flexibility, etc.) and emotional state.

[0300] Step 9:

[0301] The server uses generative AI to generate an optimal training plan based on the analysis results and the user's emotional state. The plan includes specific exercises, a training schedule, and exercises tailored to the user's emotional state (e.g., adding relaxation exercises if stress levels are high).

[0302] Step 10:

[0303] The training plan generated by the server is stored in a database and notified to the device.

[0304] Step 11:

[0305] The user puts on MR glasses or other real-time evaluation devices and begins training.

[0306] Step 12:

[0307] The device (MR / AR glasses, motion sensor) monitors the user's movements during training in real time and evaluates whether the movements are appropriate for the training plan. The emotion engine also operates to continuously monitor the user's emotional state during training.

[0308] Step 13:

[0309] The server analyzes the monitored data and, if the user's form is improper or their emotional state fluctuates (e.g., stress increases), it provides the user with voice feedback and instructions for correction via the device. For example, it may say, "Your form is off. Please keep your body upright" or "Try breathing to relax."

[0310] Step 14:

[0311] The user finishes the training and inputs post-training feedback (e.g., fatigue, muscle pain, emotional state) into the terminal.

[0312] Step 15:

[0313] The terminal transmits the feedback data to the server.

[0314] Step 16:

[0315] The server analyzes the feedback data and emotional state and uses generative AI to update and optimize the training plan. For example, if muscle soreness is reported, the plan will be updated to adjust the load for the next workout. Or, if stress levels are reaching their peak, relaxation exercises will be added.

[0316] Step 17:

[0317] The server saves the updated training plan in a database and notifies the device.

[0318] Example 2

[0319] 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."

[0320] Conventional training systems generate training plans based solely on the user's physical information, making it difficult to provide an effective plan optimized for each individual user. Furthermore, because they do not take into account the user's movement characteristics or emotional state, there is a high risk of injury during training and it can lead to a loss of motivation. Furthermore, there are insufficient means of providing real-time feedback, making it difficult to make immediate adjustments to maximize training effectiveness.

[0321] 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.

[0322] In this invention, the server includes: means for collecting a user's physical information using an electronic device; means for generating a training plan based on the collected data using a generation AI; means for monitoring the user's movements using a motion sensor and a real-time evaluation device to measure movement characteristics and providing feedback; means for analyzing the user's facial expressions and voice patterns using an emotion engine to recognize their emotional state; and means for analyzing the collected data, feedback, and emotional state and updating the training plan using the generation AI. This allows for the generation and provision of an individually optimized training plan in real time that comprehensively takes into account the user's physical information, movement characteristics, and emotional state. This maximizes training efficiency, minimizes the risk of injury, and increases the user's motivation.

[0323] An "electronic device" is a device for collecting physical information about a user, and includes a body composition monitor and a motion characteristic measuring device.

[0324] "Generative AI" refers to artificial intelligence technology that automatically generates training plans based on collected data.

[0325] A "motion sensor" is a sensor for measuring the movement characteristics of a user and capturing specific data about the movement.

[0326] A "real-time evaluation device" is a device for evaluating a user's actions in real time and providing immediate feedback.

[0327] An "emotion engine" refers to a system that recognizes a user's emotional state by analyzing their facial expressions and voice patterns.

[0328] "Collected Data" collectively refers to physical information, movement information, and emotional information obtained through electronic devices, motion sensors, emotion engines, etc.

[0329] "Feedback" refers to advice and corrective instructions provided during and after training.

[0330] A "training plan" is an exercise or workout plan generated by the generative AI based on the user's physical information, movement characteristics, and emotional state.

[0331] "Analysis" refers to the process of evaluating and analyzing the user's physical and emotional state based on the collected data.

[0332] The present invention is a system that collects a user's physical and movement information, generates an individually optimized training plan based on this data, and provides real-time feedback. This maximizes the user's training efficiency and minimizes the risk of injury. The present invention also incorporates an emotion engine that recognizes the user's emotions, optimizing training according to their emotional state.

[0333] The system includes the following components:

[0334] 1. Electronic Devices

[0335] 2. Server

[0336] 3. Generation AI

[0337] 4. Motion Sensors and Real-Time Assessment Devices

[0338] 5. Emotion Engine

[0339] 6. Training Plan

[0340] 7. Feedback

[0341] Data collection

[0342] The device (electronic device) accurately measures the user's physical information, such as body fat percentage, muscle mass, and bone density. For example, when a user steps on a body composition scale, the device measures a body fat percentage of 25%, muscle mass of 60 kg, and bone density of 1.2 g / cm³. The device (motion sensor and camera) also records detailed movement characteristics (flexibility, balance ability, etc.) when the user performs stretching or balance exercises. It captures balance and flexibility data for each frame as the user performs specific movements. Furthermore, the device (emotion engine) analyzes the user's facial expressions and voice patterns to recognize their emotional state in real time. For example, it can detect a smile from the user's facial expression and recognize it as "happiness," or identify signs of fatigue from their voice patterns.

[0343] Data transmission and storage

[0344] The device sends the collected data (physical, movement, and emotional information) to a server in real time. For example, it sends a series of data in packets. The server immediately stores the received data in a database. After storage, the data integrity is checked to verify that there are no missing or abnormal values.

[0345] Data analysis

[0346] The server first preprocesses the data, for example by removing noise and normalizing the received data. It then runs algorithms to stabilize the data sample rate and remove outliers. The server then uses machine learning algorithms to analyze the user's physical and emotional state. Specifically, it compares the data with the user's previous data and analyzes fluctuations in physical state and emotional trends.

[0347] Training plan generation

[0348] The server uses generative AI to generate an optimal training plan based on the analysis results and the user's emotional state. For example, it combines exercises that increase the user's muscle mass while reducing stress. The plan includes specific exercises, number of sets, rest periods, etc. The generative AI model optimizes each element and outputs it.

[0349] Real-time feedback

[0350] When a user begins training, they put on MR glasses or other real-time evaluation devices, which constantly monitor their movements. The device analyzes the user's movements in real time. For example, if their squat form is improper, they will immediately receive audio feedback such as, "Push your hips back a little more." The server then reanalyzes the data, assessing whether their form is correct and whether emotional stress is present, and suggests resting instructions or relaxation exercises as needed.

[0351] Feedback Rating and Updates

[0352] After completing a workout, the user enters feedback such as fatigue, muscle soreness, and satisfaction into the device. For example, by manually entering the feedback into a feedback form. The device then sends this feedback data to the server, where it is immediately analyzed. The server then reanalyzes the feedback data and emotional state and uses a generative AI to update the next training plan. For example, if muscle soreness is reported, the load for the next workout will be adjusted appropriately.

[0353] Example prompt sentence:

[0354] "Measure your body fat percentage, muscle mass, and bone density, and do some stretching and balance exercises."

[0355] "Please tell us your current emotional state: joy, fatigue, stress"

[0356] "Please provide feedback after the training: fatigue, muscle soreness, satisfaction"

[0357] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0358] Step 1: Data collection

[0359] The terminal (electronic device) collects the user's physical information. Specifically, it measures body fat percentage, muscle mass, bone density, etc. This collected data becomes the input. For example, when a user steps on a body composition scale, the output is a body fat percentage of 25%, muscle mass of 60 kg, and bone density of 1.2 g / cm³. In addition, the terminal (motion sensor and camera) measures the user's movement characteristics (flexibility, balance ability, etc.) and obtains movement characteristic data. When the user performs stretching or balance exercises, movement data is captured for each frame and analyzed.

[0360] Step 2: Recognizing your emotional state

[0361] The device (emotion engine) analyzes the user's facial expressions and voice patterns in real time to recognize their emotional state. For example, if the user smiles, it recognizes this as "joy." Signs of fatigue are identified from the voice pattern and recorded in a database. This recognized data is input, and the emotional state is output.

[0362] Step 3: Send and save data

[0363] The device transmits the collected physical information, movement characteristic data, and emotional state data to a server. The server receives this data and stores it in a database. For example, the collected data may be sent in packets, which the server receives. This received data is the input, and the stored data is the output.

[0364] Step 4: Data Preprocessing

[0365] The server preprocesses the stored data. Specifically, it performs noise reduction and normalization on the received data. For example, it runs algorithms to remove outliers and normalize the data sample rate. This stored data is input as preprocessed data, and normalized data is output.

[0366] Step 5: Data analysis

[0367] The server uses machine learning algorithms to analyze the normalized data. The server analyzes the user's physical and emotional state. For example, it compares the data with previous data to identify fluctuations in physical state and emotional trends. The preprocessed data is used as input for the machine learning algorithm, which outputs the analysis results.

[0368] Step 6: Create a training plan

[0369] The server uses generative AI to generate an optimal training plan based on the analysis results and emotional state data. For example, it might suggest exercises that increase muscle mass while relieving stress. The analysis results and emotional state are input, and a training plan is output.

[0370] Step 7: Real-time feedback

[0371] The user puts on MR glasses or other real-time evaluation devices and begins training. The device monitors and analyzes the user's movements in real time. For example, if the user's form in a squat is incorrect, the device immediately provides audio feedback such as "pull your hips back a little more." This real-time data is the input, and the audio feedback is the output.

[0372] Step 8: Feedback evaluation and updates

[0373] After completing a workout, the user inputs feedback such as fatigue, muscle soreness, and satisfaction into the device. The device then sends this feedback data to the server, which immediately analyzes it. For example, if muscle soreness is reported, the load for the next workout can be adjusted. This feedback data is the input, and an updated training plan is the output.

[0374] (Application example 2)

[0375] 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."

[0376] Many conventional training systems and task optimization systems collect a user's physical and motion information to provide training and task plans, but often do not provide real-time feedback or update the plans to take the user's emotional state into account. This can lead to problems such as accumulated stress and fatigue in the user, preventing optimal performance or increasing the risk of injury. The present invention aims to solve these problems and support efficient and safe training and tasks by providing individually optimized plans tailored to the user's condition and real-time feedback.

[0377] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0378] In this invention, the server includes means for collecting physical information of the user using an electronic device, means for generating a training plan based on the collected data using a generation AI, means for monitoring the user's movements using a motion sensor and a real-time evaluation device and providing feedback, means for analyzing the collected data and feedback and updating the training plan using the generation AI, means for recognizing the worker's emotional state and generating an individually optimized work plan based on the analysis results, and means for evaluating the user's posture and movements during the work according to the generated work plan in real time and providing appropriate feedback. This enables the provision of an individually optimized plan based on the user's physical and emotional state and real-time feedback.

[0379] "Electronic device" refers to a device that collects physical information about the user, and is responsible for measuring the user's body composition and movement characteristics.

[0380] "Generative AI" is artificial intelligence that generates optimal training and work plans based on collected data. It uses machine learning algorithms to analyze the data and create personalized plans.

[0381] A "motion sensor" is a sensor that monitors the user's movements in real time and collects information about that movement. It can accurately measure the user's posture and movements.

[0382] A "real-time evaluation device" is a device that instantly evaluates a user's movements and provides appropriate feedback. It can analyze the user's movements in real time and provide necessary instructions.

[0383] "Emotional state" refers to the user's current emotional state, as recognized by analyzing the user's facial expressions and voice patterns. It is used to determine the user's stress, fatigue, excitement, etc.

[0384] "Training Plan" refers to an exercise plan generated based on the user's physical and movement information. It consists of individually optimized exercises and schedules.

[0385] The "work plan" is a work plan generated based on the user's physical and movement information to maximize work efficiency. It is optimized taking into account the user's health and emotional state.

[0386] "Feedback" refers to real-time instructions, such as movement or posture corrections, provided to users to help them train or perform tasks effectively and safely.

[0387] "Data analysis" is the process of analyzing collected physical, movement, and emotional information using machine learning algorithms to assess the user's condition.

[0388] "Update" is the process by which the generative AI reevaluates existing training and work plans based on collected data and feedback, and changes them to optimal plans.

[0389] This invention is a system for maximizing the training and work efficiency of factory workers. The system collects the user's physical information, movement information, and emotional state, and uses generative AI to generate optimal training and work plans based on this data and provides real-time feedback.

[0390] Hardware and software used

[0391] Hardware

[0392] Electronic devices: Examples include body composition monitors and performance measurement devices.

[0393] Motion sensor: A sensor for monitoring user movements in real time.

[0394] Real-time assessment devices: Examples include head-mounted displays (HMDs) and smart glasses. These devices assess the user's performance in real time and provide feedback.

[0395] Emotion engine: A device that analyzes the user's facial expressions and voice patterns to recognize their emotional state.

[0396] software

[0397] Generative AI: Specifically, there are MachineLearningModel and GenerateAIModel. These models are used to generate optimal plans for users based on collected data.

[0398] Data collection

[0399] When a user steps onto the body composition scale, an electronic device collects physical information such as heart rate, body fat percentage, and muscle mass. Next, as the user moves, a motion sensor collects movement characteristics. An emotion engine also collects the user's facial expressions and voice patterns to recognize their emotional state.

[0400] Data transmission and storage

[0401] The collected data is sent from the device to a server and stored in a database, which then performs preprocessing for analyzing the data.

[0402] Data analysis

[0403] The server preprocesses the stored physical, motion, and emotional information and analyzes the data using machine learning algorithms to assess the user's physical and emotional state.

[0404] Generate training and work plans

[0405] Based on the analysis results, the server uses generative AI to generate individually optimized training and work plans, including specific exercise and work schedules.

[0406] Real-time feedback

[0407] When a user begins training or performing a task, the real-time assessment device monitors the user's behavior and detects improper behavior or form. The server analyzes the data and provides appropriate feedback, such as a voice prompt to "correct your form."

[0408] Feedback Rating and Updates

[0409] After completing training or work, users input their feedback into the device. The collected feedback data is sent to the server, and the generation AI is used to update the next training plan or work plan.

[0410] Specific examples

[0411] Imagine a worker is working to carry heavy loads. Sensors collect information such as body fat percentage, heart rate, movement speed, and facial expressions, and the data is analyzed by a server. If the worker's emotional state is determined to be "feeling tired today" before starting work, the server generates instructions on safe lifting methods and the recommendation to take longer breaks. During work, motion sensors check the worker's lifting posture and provide feedback via the HMD, such as "Please bend your knees more when lifting." If the worker gives feedback after work such as "My lower back hurts a little," that data is also used for the next optimization.

[0412] Prompt Sentence Examples

[0413] User ID: worker_123, Physical information: {Heart rate: 85, Body fat percentage: 18%, Muscle mass: 70kg}, Movement information: {Movement speed: Medium, Flexibility: High}, Emotional state: Fatigue

[0414] Based on this data, generate an optimal work plan that maximizes work efficiency while reducing stress, and be sure to include appropriate breaks.

[0415] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0416] Step 1:

[0417] The server uses an electronic device to collect the user's physical information. Specifically, the user steps on a body composition scale to obtain data such as heart rate, body fat percentage, and muscle mass. This data is sent to the server as collected data. The input is physical information, and the output is the collected data.

[0418] Step 2:

[0419] The server uses a motion sensor and an emotion engine to collect the user's movement information and emotional state. The emotion engine analyzes the user's facial expressions and voice patterns to recognize the user's emotional state. Movement information includes movement speed, flexibility, balance, etc. The input is movement information and emotion information, and the output is the data.

[0420] Step 3:

[0421] The server stores the collected data in a database. The stored data includes physical information, movement information, and emotion information. The input is the collected data, and the output is storage in the database.

[0422] Step 4:

[0423] The server preprocesses the stored data, preparing it for input into the generative AI model. Preprocessing includes denoising and normalizing the data. This process transforms the data into a form suitable for analysis. The input is the stored data, and the output is the preprocessed data.

[0424] Step 5:

[0425] The server uses the preprocessed data to run machine learning algorithms to assess the user's physical and emotional states. Specifically, the analysis generates physical state assessment indicators and emotional state determinations. The input is the preprocessed data, and the output is the analysis results.

[0426] Step 6:

[0427] The server uses a generative AI model based on the analysis results to generate optimal training and work plans. The generative AI model uses prompts to suggest specific exercises and work schedules. This plan is stored in a database. The input is the analysis results and prompts, and the output is the generated plan.

[0428] Step 7:

[0429] The user wears the real-time evaluation device and begins training or work. The motion sensor monitors the user's movements and sends the information to the server. The input is real-time movement information, and the output is monitored movement information.

[0430] Step 8:

[0431] The server analyzes the monitored movement information in real time and detects improper movements or forms. The server provides feedback through the real-time evaluation device as needed. For example, it issues a voice command such as "Bend your knees more and lift." The input is the monitored movement information, and the output is the feedback.

[0432] Step 9:

[0433] The user inputs feedback after completing training or work. The feedback includes fatigue, muscle pain, emotional state, etc. This feedback is sent to the server via the terminal. The input is the feedback information, and the output is the information sent to the server.

[0434] Step 10:

[0435] The server analyzes the collected feedback data and emotional state and uses a generative AI model to update the training and work plans, which further optimizes the next training or work session. The input is the feedback data, and the output is the updated plan.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] [Second embodiment]

[0440] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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).

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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."

[0452] The present invention is a system that collects a user's physical and movement information, generates an individually optimized training plan based on this data, and provides feedback in real time, thereby maximizing the user's training efficiency and minimizing the risk of injury.

[0453] System Overview

[0454] The system includes the following components:

[0455] 1. Electronic Devices

[0456] 2. Server

[0457] 3. Generation AI

[0458] 4. Motion Sensors and Real-Time Assessment Devices

[0459] 5. Training Plan

[0460] 6. Feedback

[0461] Program processing flow

[0462] 1. Data Collection

[0463] Terminal (electronic device): Collects the user's physical information (body fat percentage, muscle mass, bone density, etc.) and measures movement characteristics (flexibility, balance ability, etc.) using motion sensors and cameras.

[0464] 2. Data transmission and storage

[0465] Terminal: Sends collected data to the server, which receives the data and stores it in a database.

[0466] 3. Data Analysis

[0467] Server: Analyzes the stored data using a machine learning algorithm to evaluate the user's physical condition. Based on the results of this analysis, a generative AI is used to generate an individually optimized training plan.

[0468] 4. Plan Generation

[0469] Server: Based on the analysis results, a generative AI model is used to create and save a training plan tailored to the user's goals and physical condition.

[0470] 5. Real-time feedback

[0471] User: Starts training and puts on MR glasses or other real-time assessment device.

[0472] Device: Monitors the user's movements in real time during training and evaluates whether they are appropriate for the training plan.

[0473] Server: Analyzes the monitored data and provides appropriate feedback to the user via voice.

[0474] 6. Feedback Rating and Updates

[0475] Users: Provide feedback after training.

[0476] Terminal: Sends feedback data to the server.

[0477] Server: Analyzes feedback data and updates training plans using generative AI.

[0478] Specific examples

[0479] collection

[0480] When the user steps on the body composition scale, the device measures a body fat percentage of 25%, muscle mass of 60kg, and bone density of 1.2g / cm³. The user performs stretching and balance exercises, and the device collects movement characteristic data.

[0481] Send / Save

[0482] The collected data (physical and movement information) is sent from the device to a server, which stores the data in a database.

[0483] analysis

[0484] The server analyzes the data and uses machine learning algorithms to determine if muscle mass needs to be increased. A generative AI model then creates a training plan to target muscle gain.

[0485] Training plan generation

[0486] The server creates a training plan including specific exercises (e.g., weightlifting, stretching, balance training, etc.) based on the user's goals and current status, and stores it in a database.

[0487] Real-time feedback

[0488] The user puts on the MR glasses and starts training. The device (motion sensor and MR glasses) monitors the user's movements in real time.

[0489] The server detects improper behavior or forms and provides appropriate corrective instructions to the user via voice.

[0490] Feedback Rating and Updates

[0491] After completing the training, the user inputs feedback (e.g., fatigue, muscle pain, etc.).

[0492] The device sends feedback data to a server, which analyzes the data and uses generative AI to update the training plan.

[0493] This system allows users to efficiently perform training that is best suited to their physical condition, maximizing results while minimizing the risk of injury.

[0494] The processing flow will be explained below.

[0495] Step 1:

[0496] The user enters their name, age, sex, height, weight, fitness level, and training goals into the device.

[0497] Step 2:

[0498] The device (body composition scale) measures the user's muscle mass, fat mass, and bone density and collects the data.

[0499] Step 3:

[0500] The device (motion sensor and camera) measures the user's movement characteristics such as flexibility, balance ability, and range of motion, and collects the data.

[0501] Step 4:

[0502] The terminal transmits the data obtained in steps 2 and 3 to the server.

[0503] Step 5:

[0504] The server stores the received data in a database.

[0505] Step 6:

[0506] The server preprocesses the stored data and prepares it for input to machine learning algorithms, for example by denoising and normalizing the data.

[0507] Step 7:

[0508] The server analyzes the data using machine learning algorithms to assess the user's physical condition (e.g., muscle mass, flexibility, and balance ability).

[0509] Step 8:

[0510] Based on the analysis results, the server uses AI to generate an optimal training plan, including specific exercises and training schedules.

[0511] Step 9:

[0512] The training plan generated by the server is stored in a database and notified to the device.

[0513] Step 10:

[0514] The user puts on MR glasses or other real-time evaluation devices and begins training.

[0515] Step 11:

[0516] The device (MR / AR glasses, motion sensor) monitors the user's movements during training in real time and evaluates whether the movements are appropriate for the training plan.

[0517] Step 12:

[0518] The server analyzes the monitored data, and if the form is inappropriate, provides the user with voice feedback and instructions for correction via the terminal.

[0519] Step 13:

[0520] The user finishes the training and inputs post-training feedback (e.g., fatigue, muscle pain, satisfaction) into the terminal.

[0521] Step 14:

[0522] The terminal transmits the feedback data to the server.

[0523] Step 15:

[0524] The server analyzes the feedback data and uses generative AI to update and optimize the training plan.

[0525] Step 16:

[0526] The server saves the updated training plan in a database and notifies the device.

[0527] Example 1

[0528] 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."

[0529] Conventional training systems struggle to provide optimal training plans based on each user's individual physical condition and movements. Furthermore, they lack the ability to provide real-time feedback, making it difficult to maximize training effectiveness and minimize injury risk. Furthermore, they lack the ability to update plans based on post-training feedback.

[0530] 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.

[0531] In this invention, the server includes means for collecting physical information of a user using an electronic device, means for generating a training plan based on the collected data using a generation AI, means for monitoring the user's movements using a motion sensor and a real-time evaluation device and providing feedback, means for analyzing the collected data and feedback and updating the training plan using a generation AI, means for collecting user movement information using a camera, means for evaluating the physical condition using a machine learning algorithm, and means for storing the training plan in a database. This makes it possible to provide an optimal training plan based on the user's individual physical condition and movements, maximizing training effects and minimizing the risk of injury.

[0532] 1. "Electronic device" refers to a device used to collect a user's physical information, including body composition monitors and motion characteristic measuring devices.

[0533] 2. "Generative AI" refers to artificial intelligence technology used to generate training plans based on collected data.

[0534] 3. "Motion sensor" means a sensor device that detects user movements and collects and transmits that information.

[0535] 4. "Real-time evaluation device" means a device that evaluates a user's movements in real time during training and provides feedback.

[0536] 5. “Machine learning algorithms” are algorithms used to analyze data and learn patterns and trends.

[0537] 6. "Database" means the data storage system for storing and managing collected data and generated training plans.

[0538] 7. "User's physical information" refers to data that represents the user's physical condition, such as body fat percentage, muscle mass, and bone density.

[0539] 8. "Training Plan" refers to a specific exercise schedule and content developed based on the user's goals and physical condition.

[0540] 9. "Feedback" means advice or corrective instructions provided during or after training.

[0541] This invention provides a system that collects a user's physical information and movement information, generates an individually optimized training plan based on this data, and provides feedback in real time, thereby maximizing the user's training efficiency and minimizing the risk of injury. This system includes an electronic device, a server, a generating AI, a motion sensor and real-time evaluation device, a training plan, and feedback.

[0542] First, the electronic device serves as a terminal and collects the user's physical information. Specifically, a body composition scale is used to obtain data such as body fat percentage, muscle mass, and bone density. Motion sensors and cameras are also used to collect movement information such as the user's flexibility and balance ability.

[0543] The device then transmits the collected data to a server, which receives it and stores it in a database that keeps a record of all collected physical and movement information.

[0544] The server analyzes the stored data and uses machine learning algorithms to assess the user's physical condition. This process identifies the current state of the body and areas for improvement from the data. For example, a Python machine learning library (e.g., scikit-learn) can be used.

[0545] Once the user's physical condition is evaluated, the server uses a generative AI model to create an individually optimized training plan. Specific exercise content and schedules are generated by entering a prompt into the generative AI model. For example, the prompt could be, "I'd like you to generate a training plan to increase muscle mass."

[0546] The generated training plan is stored in a database by the server and contains detailed exercises based on the user's goals and current condition. For example, this training plan might include "3 x 60-minute workouts per week, targeting different muscle groups each time."

[0547] When the user starts training, they put on MR glasses and other real-time evaluation devices. The motion sensors and MR glasses on the device monitor the user's movements in real time during training. This information is sent to a server, which detects improper movements or form and provides appropriate corrective instructions via voice. For example, the server may provide voice feedback such as "Please correct your posture."

[0548] After completing a workout, the user inputs feedback such as fatigue and muscle pain into the device. The device then sends this feedback data to a server, which analyzes it and updates the training plan using a generative AI model. This update creates a new plan that reflects the feedback data and is optimized for the user.

[0549] Prompt Sentence Examples

[0550] "Write code that uses collected data about a user (e.g., body fat percentage 25%, muscle mass 60kg, bone density 1.2g / cm³) to generate a training plan using a generative AI model to target muscle growth for the user. Also, add the ability to detect improper form during training in real time and provide audio feedback."

[0551] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0552] Step 1:

[0553] Data collection

[0554] When a user steps onto the body composition scale, the device measures their physical information, such as their body fat percentage, muscle mass, and bone density. In addition, when the user performs stretching or balance exercises, the device's motion sensors and camera collect movement information.

[0555] Input: User's physical condition data (body fat percentage, muscle mass, bone density, etc.), movement information (flexibility, balance ability, etc.)

[0556] Output: Collected physical and movement information

[0557] Step 2:

[0558] Data transmission and storage

[0559] The device sends the collected data to the server, which stores the received data in a database.

[0560] Input: User's physical and movement information sent from the device

[0561] Output: Physical and movement information stored in a database

[0562] Step 3:

[0563] Data analysis

[0564] The server uses machine learning algorithms to analyze the data stored in the database, thereby assessing the user's physical condition and identifying areas for improvement.

[0565] Input: User's physical and movement information stored in a database

[0566] Output: User's physical condition assessment results and necessary improvements

[0567] Step 4:

[0568] Plan Generation

[0569] Based on the analysis results, the server uses a generative AI model to generate an individually optimized training plan and stores it in a database.

[0570] Input: User's physical condition assessment results and necessary improvements

[0571] Output: Generated training plan

[0572] Step 5:

[0573] Training start and real-time feedback

[0574] The user puts on the MR glasses and starts training. During the training, the device (motion sensor and MR glasses) monitors the user's movements in real time and sends the information to the server. The server detects improper movements or form and provides audio instructions for correction.

[0575] Input: User behavior information collected in real time

[0576] Output: Real-time audio feedback

[0577] Step 6:

[0578] Feedback rating and training plan updates

[0579] After completing a workout, the user inputs feedback such as fatigue and muscle pain. The device sends this feedback data to a server, which then uses generative AI to analyze the data and update the training plan.

[0580] Input: User-entered feedback data

[0581] Output: Updated training plan

[0582] (Application example 1)

[0583] 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."

[0584] Delivery workers are often overburdened in their daily work, which can increase the risk of fatigue and injury. Furthermore, delivery efficiency can decline due to a lack of knowledge of efficient routes and work methods. To solve these problems, a system is needed that collects physical and movement information from delivery workers in real time and provides appropriate feedback.

[0585] 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.

[0586] In this invention, the server includes means for collecting physical information of the user using an electronic device, means for generating a training plan based on the collected data using a generation AI, means for monitoring the user's movements using a motion sensor and a real-time evaluation device and providing feedback, means for analyzing the collected data and feedback and updating the training plan using a generation AI, and means for monitoring the physical information and movement information of the delivery person and maximizing delivery efficiency and improving safety based on the information, thereby reducing the burden on the delivery person and enabling them to perform their work efficiently and safely.

[0587] "Electronic Device" is a technological device used to collect physical and behavioral information about a delivery person.

[0588] "Generative AI" is artificial intelligence that generates optimal training and delivery plans based on collected data.

[0589] A "motion sensor" is a sensor that detects the movement characteristics of delivery personnel and evaluates them in real time.

[0590] A "real-time evaluation device" is a device that instantly analyzes collected data and provides immediate feedback to delivery personnel.

[0591] "Feedback" refers to instructions and advice provided to delivery personnel based on collected and analyzed data.

[0592] A "body composition scale" is a device that measures physical information such as a delivery person's body fat percentage, muscle mass, and bone density.

[0593] A "motion characteristic measuring device" is an instrument used to evaluate the motion characteristics of delivery personnel, such as their flexibility and balance ability.

[0594] A "smartwatch" is a wearable device that records a delivery person's physical information, such as heart rate, number of steps, and energy consumption, in real time.

[0595] "Smart glasses" are wearable devices that collect information on the delivery person's posture and movements and provide visual feedback.

[0596] The present invention provides a system that collects physical and motion information of delivery personnel and provides real-time feedback to maximize efficiency and improve safety during delivery operations. To this end, the system includes the following components:

[0597] 1. Electronic Devices (Terminals)

[0598] The user (delivery worker) wears a smartwatch or smart glasses, which collect various physical and movement information in real time, such as heart rate, number of steps, energy consumption, driving posture, walking posture, and how the package is carried.

[0599] 2. Data transmission and storage (server)

[0600] The data collected by the device is sent to a cloud server via wireless communication, and the server receives the data and stores it in a database.

[0601] 3. Data analysis (server)

[0602] The server analyzes the stored data using machine learning algorithms to assess the delivery person's current physical condition and movement patterns, using random forests or other suitable machine learning models.

[0603] 4. Feedback and Advice (Device)

[0604] Based on the analysis results, the server generates appropriate feedback and advice, such as when to take a break, suggesting an efficient route, teaching correct posture, etc. This feedback is conveyed to the user through audio output and visual presentations.

[0605] 5. Update training plan based on feedback (server)

[0606] Feedback data provided by the user (e.g., fatigue, muscle pain, distance traveled, etc.) is also sent to the server, and the training plan is updated using the generative AI.

[0607] Hardware and software used

[0608] Smartwatches and smart glasses: Used as data collection devices.

[0609] Cloud server: Stores and analyzes data.

[0610] Machine learning algorithms: using libraries such as TensorFlow and Scikit-learn.

[0611] Audio output and visual feedback devices: Provide feedback to the delivery person.

[0612] Specific examples

[0613] The user puts on the smartwatch and smart glasses and begins making deliveries, which collects data on heart rate, steps, energy expenditure, and posture.

[0614] The collected data is sent from the terminal via wireless communication to a server and stored there.

[0615] The server analyzes the data and generates an efficiency score for the delivery person using generative AI (e.g., a random forest model), and generates appropriate feedback, such as "You're tired. Please take a break."

[0616] Based on the analysis results, the device provides feedback to the delivery person via voice or visual feedback.

[0617] The user inputs the effects of training and feedback, and the feedback data is sent back to the server, where the training plan is updated using the generative AI.

[0618] Prompt Sentence Examples

[0619] "Design an algorithm to monitor the fatigue level and efficiency of delivery personnel and provide optimal feedback in real time. Data to be used includes heart rate, steps, energy consumption, and posture. It should also suggest break times and efficient routes."

[0620] This reduces the burden on delivery personnel and enables them to carry out their work efficiently and safely.

[0621] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0622] Step 1:

[0623] Data collection

[0624] Users wear smartwatches or smart glasses, which collect physical and movement information such as heart rate, number of steps, energy consumption, and posture information in real time.

[0625] The inputs are biometric and motion data obtained from smartwatches and smart glasses.

[0626] The output is an information packet containing these data.

[0627] Specifically, the sensors periodically collect data and temporarily store it in the memory of the smart glasses or smartwatch.

[0628] Step 2:

[0629] Data transmission and storage

[0630] The data collected by the device is sent to a server via wireless communication (e.g., Wi-Fi or Bluetooth), which receives the data and stores it in a cloud database.

[0631] The input is biometric data and motion data sent from the terminal.

[0632] The output is data stored in a cloud database.

[0633] Specifically, the device periodically divides data into packets and transmits them to the server via wireless communication. The server then automatically stores the received data in a database.

[0634] Step 3:

[0635] Data analysis

[0636] The server analyzes the data stored in the cloud database using machine learning algorithms (e.g., random forests), which evaluate the delivery person's current physical condition and movement patterns.

[0637] The inputs are stored biometric and motion data.

[0638] The output is an evaluation score for the delivery person's fatigue level and efficiency.

[0639] Specifically, the server standardizes the data, then analyzes it using machine learning models, generating evaluation results that are stored in a log file.

[0640] Step 4:

[0641] Providing feedback and advice

[0642] The server generates appropriate feedback and advice based on the analysis results, which is provided to the user through audio output or visual display.

[0643] The input is the evaluation score of the analysis result.

[0644] The output is feedback or advice provided to the user.

[0645] Specifically, the server generates an appropriate feedback message based on the evaluation score and sends it to an audio output device or a visual display device.

[0646] Step 5:

[0647] Get feedback and update your training plan

[0648] The user performs training and performs actions based on the feedback, and then provides feedback on the results to the input device. This feedback data (e.g., fatigue, muscle pain, distance traveled, etc.) is sent from the device to the server. The server analyzes this data and updates the training plan using generative AI.

[0649] The input is the feedback data entered by the user.

[0650] The output is an updated training plan.

[0651] Specifically, the device sends feedback data to the server, which analyzes the data, updates the training plan, and stores the results in a cloud database.

[0652] 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.

[0653] This system maximizes training efficiency and minimizes the risk of injury by collecting a user's physical and movement information, generating an individually optimized training plan based on this data, and providing real-time feedback. Furthermore, it combines an emotion engine that recognizes the user's emotions and optimizes training according to their emotional state.

[0654] System Overview

[0655] The system includes the following components:

[0656] 1. Electronic Devices

[0657] 2. Server

[0658] 3. Generation AI

[0659] 4. Motion Sensors and Real-Time Assessment Devices

[0660] 5. Emotion Engine

[0661] 6. Training Plan

[0662] 7. Feedback

[0663] Program processing flow

[0664] 1. Data Collection

[0665] Terminal (electronic device): Collects the user's physical information (body fat percentage, muscle mass, bone density, etc.) and measures movement characteristics (flexibility, balance ability, etc.) using motion sensors and cameras.

[0666] Terminal (emotion engine): Analyzes the user's facial expressions and voice patterns to recognize their emotional state.

[0667] 2. Data transmission and storage

[0668] Terminal: Sends collected data (physical information, movement information, and emotional information) to the server, which receives the data and stores it in a database.

[0669] 3. Data Analysis

[0670] Server: Preprocesses the stored data and prepares it for input to machine learning algorithms, for example by denoising and normalizing the data.

[0671] Server: Analyzes data using machine learning algorithms to assess the user's physical and emotional state.

[0672] 4. Plan Generation

[0673] Server: Based on the analysis results and emotional state, the server uses generative AI to generate an optimal training plan, including specific exercises and training schedules.

[0674] 5. Real-time feedback

[0675] User: Starts training and puts on MR glasses or other real-time assessment device.

[0676] Device: Monitors the user's movements during training in real time and evaluates whether the movements are appropriate for the training plan.

[0677] Server: Analyzes the monitored data, and if the form is inappropriate, provides the user with voice feedback and instructions for correction via the terminal.

[0678] 6. Feedback Rating and Updates

[0679] User: After completing the training, the user inputs post-training feedback (e.g., fatigue, muscle pain, satisfaction) into the terminal.

[0680] Terminal: Sends feedback data to the server.

[0681] Server: Analyzes feedback data and emotional state, and uses generative AI to update and optimize training plans.

[0682] Specific examples

[0683] collection

[0684] When the user steps on the body composition scale, the device measures a body fat percentage of 25%, muscle mass of 60kg, and bone density of 1.2g / cm³. The user performs stretching and balance exercises, and the device collects movement characteristic data.

[0685] The emotion engine collects the user's facial and voice patterns to recognize their emotional state (e.g., joy, fatigue, stress, etc.).

[0686] Send / Save

[0687] The collected data (physical information, movement information, and emotional information) is sent from the device to a server, which stores the data in a database.

[0688] analysis

[0689] The server analyzes the data and uses machine learning algorithms to assess the user's physical and emotional state. For example, it may determine that muscle mass needs to be increased and that stress levels are high during training. A generative AI model then creates a training plan that targets muscle gain and reduces stress.

[0690] Training plan generation

[0691] The server creates a training plan including specific exercises (e.g., weightlifting, relaxation exercises, mindfulness practice, etc.) based on the user's goals and current situation, and stores it in a database.

[0692] Real-time feedback

[0693] The user puts on the MR glasses and starts training. The device (motion sensor and MR glasses) monitors the user's movements in real time.

[0694] The server detects improper movements and forms and provides appropriate corrective instructions to the user via voice. It also monitors the user's emotional state and suggests training breaks and relaxation exercises if the user is feeling stressed.

[0695] Feedback Rating and Updates

[0696] After completing the training, the user inputs feedback (e.g., fatigue, muscle pain, emotional state).

[0697] The device sends the feedback data to a server, which then analyzes it and uses generative AI to update the training plan—for example, if muscle soreness is reported, the plan will be updated to adjust the load for the next workout.

[0698] This system allows users to efficiently train in a way that is optimal not only for their physical condition but also for their emotional state, maximizing results while minimizing the risk of injury.

[0699] The processing flow will be explained below.

[0700] Step 1:

[0701] The user enters their name, age, sex, height, weight, fitness level, and training goals into the device.

[0702] Step 2:

[0703] The device (body composition monitor) measures the user's muscle mass, body fat percentage, and bone density and collects the data.

[0704] Step 3:

[0705] The device (motion sensor and camera) measures the user's movement characteristics such as flexibility, balance ability, and range of motion, and collects the data.

[0706] Step 4:

[0707] The device (emotion engine) analyzes the user's facial expressions and voice patterns to recognize their emotional state. For example, a smile can be interpreted as "joy," while a stiff expression can be interpreted as "stress."

[0708] Step 5:

[0709] The terminal transmits the data obtained in steps 2, 3, and 4 to the server.

[0710] Step 6:

[0711] The server stores the received data in a database.

[0712] Step 7:

[0713] The server preprocesses the stored data and prepares it for input to machine learning algorithms, for example by denoising and normalizing the data.

[0714] Step 8:

[0715] The server analyzes the data using machine learning algorithms to assess the user's physical condition (muscle mass, body fat percentage, flexibility, etc.) and emotional state.

[0716] Step 9:

[0717] The server uses generative AI to generate an optimal training plan based on the analysis results and the user's emotional state. The plan includes specific exercises, a training schedule, and exercises tailored to the user's emotional state (e.g., adding relaxation exercises if stress levels are high).

[0718] Step 10:

[0719] The training plan generated by the server is stored in a database and notified to the device.

[0720] Step 11:

[0721] The user puts on MR glasses or other real-time evaluation devices and begins training.

[0722] Step 12:

[0723] The device (MR / AR glasses, motion sensor) monitors the user's movements during training in real time and evaluates whether the movements are appropriate for the training plan. The emotion engine also operates to continuously monitor the user's emotional state during training.

[0724] Step 13:

[0725] The server analyzes the monitored data and, if the user's form is improper or their emotional state fluctuates (e.g., stress increases), it provides the user with voice feedback and instructions for correction via the device. For example, it may say, "Your form is off. Please keep your body upright" or "Try breathing to relax."

[0726] Step 14:

[0727] The user finishes the training and inputs post-training feedback (e.g., fatigue, muscle pain, emotional state) into the terminal.

[0728] Step 15:

[0729] The terminal transmits the feedback data to the server.

[0730] Step 16:

[0731] The server analyzes the feedback data and emotional state and uses generative AI to update and optimize the training plan. For example, if muscle soreness is reported, the plan will be updated to adjust the load for the next workout. Or, if stress levels are reaching their peak, relaxation exercises will be added.

[0732] Step 17:

[0733] The server saves the updated training plan in a database and notifies the device.

[0734] Example 2

[0735] 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."

[0736] Conventional training systems generate training plans based solely on the user's physical information, making it difficult to provide an effective plan optimized for each individual user. Furthermore, because they do not take into account the user's movement characteristics or emotional state, there is a high risk of injury during training and it can lead to a loss of motivation. Furthermore, there are insufficient means of providing real-time feedback, making it difficult to make immediate adjustments to maximize training effectiveness.

[0737] 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.

[0738] In this invention, the server includes: means for collecting a user's physical information using an electronic device; means for generating a training plan based on the collected data using a generation AI; means for monitoring the user's movements using a motion sensor and a real-time evaluation device to measure movement characteristics and providing feedback; means for analyzing the user's facial expressions and voice patterns using an emotion engine to recognize their emotional state; and means for analyzing the collected data, feedback, and emotional state and updating the training plan using the generation AI. This allows for the generation and provision of an individually optimized training plan in real time that comprehensively takes into account the user's physical information, movement characteristics, and emotional state. This maximizes training efficiency, minimizes the risk of injury, and increases the user's motivation.

[0739] An "electronic device" is a device for collecting physical information about a user, and includes a body composition monitor and a motion characteristic measuring device.

[0740] "Generative AI" refers to artificial intelligence technology that automatically generates training plans based on collected data.

[0741] A "motion sensor" is a sensor for measuring the movement characteristics of a user and capturing specific data about the movement.

[0742] A "real-time evaluation device" is a device for evaluating a user's actions in real time and providing immediate feedback.

[0743] An "emotion engine" refers to a system that recognizes a user's emotional state by analyzing their facial expressions and voice patterns.

[0744] "Collected Data" collectively refers to physical information, movement information, and emotional information obtained through electronic devices, motion sensors, emotion engines, etc.

[0745] "Feedback" refers to advice and corrective instructions provided during and after training.

[0746] A "training plan" is an exercise or workout plan generated by the generative AI based on the user's physical information, movement characteristics, and emotional state.

[0747] "Analysis" refers to the process of evaluating and analyzing the user's physical and emotional state based on the collected data.

[0748] The present invention is a system that collects a user's physical and movement information, generates an individually optimized training plan based on this data, and provides real-time feedback. This maximizes the user's training efficiency and minimizes the risk of injury. The present invention also incorporates an emotion engine that recognizes the user's emotions, optimizing training according to their emotional state.

[0749] The system includes the following components:

[0750] 1. Electronic Devices

[0751] 2. Server

[0752] 3. Generation AI

[0753] 4. Motion Sensors and Real-Time Assessment Devices

[0754] 5. Emotion Engine

[0755] 6. Training Plan

[0756] 7. Feedback

[0757] Data collection

[0758] The device (electronic device) accurately measures the user's physical information, such as body fat percentage, muscle mass, and bone density. For example, when a user steps on a body composition scale, the device measures a body fat percentage of 25%, muscle mass of 60 kg, and bone density of 1.2 g / cm³. The device (motion sensor and camera) also records detailed movement characteristics (flexibility, balance ability, etc.) when the user performs stretching or balance exercises. It captures balance and flexibility data for each frame as the user performs specific movements. Furthermore, the device (emotion engine) analyzes the user's facial expressions and voice patterns to recognize their emotional state in real time. For example, it can detect a smile from the user's facial expression and recognize it as "happiness," or identify signs of fatigue from their voice patterns.

[0759] Data transmission and storage

[0760] The device sends the collected data (physical, movement, and emotional information) to a server in real time. For example, it sends a series of data in packets. The server immediately stores the received data in a database. After storage, the data integrity is checked to verify that there are no missing or abnormal values.

[0761] Data analysis

[0762] The server first preprocesses the data, for example by removing noise and normalizing the received data. It then runs algorithms to stabilize the data sample rate and remove outliers. The server then uses machine learning algorithms to analyze the user's physical and emotional state. Specifically, it compares the data with the user's previous data and analyzes fluctuations in physical state and emotional trends.

[0763] Training plan generation

[0764] The server uses generative AI to generate an optimal training plan based on the analysis results and the user's emotional state. For example, it combines exercises that increase the user's muscle mass while reducing stress. The plan includes specific exercises, number of sets, rest periods, etc. The generative AI model optimizes each element and outputs it.

[0765] Real-time feedback

[0766] When a user begins training, they put on MR glasses or other real-time evaluation devices, which constantly monitor their movements. The device analyzes the user's movements in real time. For example, if their squat form is improper, they will immediately receive audio feedback such as, "Push your hips back a little more." The server then reanalyzes the data, assessing whether their form is correct and whether emotional stress is present, and suggests resting instructions or relaxation exercises as needed.

[0767] Feedback Rating and Updates

[0768] After completing a workout, the user enters feedback such as fatigue, muscle soreness, and satisfaction into the device. For example, by manually entering the feedback into a feedback form. The device then sends this feedback data to the server, where it is immediately analyzed. The server then reanalyzes the feedback data and emotional state and uses a generative AI to update the next training plan. For example, if muscle soreness is reported, the load for the next workout will be adjusted appropriately.

[0769] Example prompt sentence:

[0770] "Measure your body fat percentage, muscle mass, and bone density, and do some stretching and balance exercises."

[0771] "Please tell us your current emotional state: joy, fatigue, stress"

[0772] "Please provide feedback after the training: fatigue, muscle soreness, satisfaction"

[0773] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0774] Step 1: Data collection

[0775] The terminal (electronic device) collects the user's physical information. Specifically, it measures body fat percentage, muscle mass, bone density, etc. This collected data becomes the input. For example, when a user steps on a body composition scale, the output is a body fat percentage of 25%, muscle mass of 60 kg, and bone density of 1.2 g / cm³. In addition, the terminal (motion sensor and camera) measures the user's movement characteristics (flexibility, balance ability, etc.) and obtains movement characteristic data. When the user performs stretching or balance exercises, movement data is captured for each frame and analyzed.

[0776] Step 2: Recognizing your emotional state

[0777] The device (emotion engine) analyzes the user's facial expressions and voice patterns in real time to recognize their emotional state. For example, if the user smiles, it recognizes this as "joy." Signs of fatigue are identified from the voice pattern and recorded in a database. This recognized data is input, and the emotional state is output.

[0778] Step 3: Send and save data

[0779] The device transmits the collected physical information, movement characteristic data, and emotional state data to a server. The server receives this data and stores it in a database. For example, the collected data may be sent in packets, which the server receives. This received data is the input, and the stored data is the output.

[0780] Step 4: Data Preprocessing

[0781] The server preprocesses the stored data. Specifically, it performs noise reduction and normalization on the received data. For example, it runs algorithms to remove outliers and normalize the data sample rate. This stored data is input as preprocessed data, and normalized data is output.

[0782] Step 5: Data analysis

[0783] The server uses machine learning algorithms to analyze the normalized data. The server analyzes the user's physical and emotional state. For example, it compares the data with previous data to identify fluctuations in physical state and emotional trends. The preprocessed data is used as input for the machine learning algorithm, which outputs the analysis results.

[0784] Step 6: Create a training plan

[0785] The server uses generative AI to generate an optimal training plan based on the analysis results and emotional state data. For example, it might suggest exercises that increase muscle mass while relieving stress. The analysis results and emotional state are input, and a training plan is output.

[0786] Step 7: Real-time feedback

[0787] The user puts on MR glasses or other real-time evaluation devices and begins training. The device monitors and analyzes the user's movements in real time. For example, if the user's form in a squat is incorrect, the device immediately provides audio feedback such as "pull your hips back a little more." This real-time data is the input, and the audio feedback is the output.

[0788] Step 8: Feedback evaluation and updates

[0789] After completing a workout, the user inputs feedback such as fatigue, muscle soreness, and satisfaction into the device. The device then sends this feedback data to the server, which immediately analyzes it. For example, if muscle soreness is reported, the load for the next workout can be adjusted. This feedback data is the input, and an updated training plan is the output.

[0790] (Application example 2)

[0791] 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."

[0792] Many conventional training systems and task optimization systems collect a user's physical and motion information to provide training and task plans, but often do not provide real-time feedback or update the plans to take the user's emotional state into account. This can lead to problems such as accumulated stress and fatigue in the user, preventing optimal performance or increasing the risk of injury. The present invention aims to solve these problems and support efficient and safe training and tasks by providing individually optimized plans tailored to the user's condition and real-time feedback.

[0793] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0794] In this invention, the server includes means for collecting physical information of the user using an electronic device, means for generating a training plan based on the collected data using a generation AI, means for monitoring the user's movements using a motion sensor and a real-time evaluation device and providing feedback, means for analyzing the collected data and feedback and updating the training plan using the generation AI, means for recognizing the worker's emotional state and generating an individually optimized work plan based on the analysis results, and means for evaluating the user's posture and movements during the work according to the generated work plan in real time and providing appropriate feedback. This enables the provision of an individually optimized plan based on the user's physical and emotional state and real-time feedback.

[0795] "Electronic device" refers to a device that collects physical information about the user, and is responsible for measuring the user's body composition and movement characteristics.

[0796] "Generative AI" is artificial intelligence that generates optimal training and work plans based on collected data. It uses machine learning algorithms to analyze the data and create personalized plans.

[0797] A "motion sensor" is a sensor that monitors the user's movements in real time and collects information about that movement. It can accurately measure the user's posture and movements.

[0798] A "real-time evaluation device" is a device that instantly evaluates a user's movements and provides appropriate feedback. It can analyze the user's movements in real time and provide necessary instructions.

[0799] "Emotional state" refers to the user's current emotional state, as recognized by analyzing the user's facial expressions and voice patterns. It is used to determine the user's stress, fatigue, excitement, etc.

[0800] "Training Plan" refers to an exercise plan generated based on the user's physical and movement information. It consists of individually optimized exercises and schedules.

[0801] The "work plan" is a work plan generated based on the user's physical and movement information to maximize work efficiency. It is optimized taking into account the user's health and emotional state.

[0802] "Feedback" refers to real-time instructions, such as movement or posture corrections, provided to users to help them train or perform tasks effectively and safely.

[0803] "Data analysis" is the process of analyzing collected physical, movement, and emotional information using machine learning algorithms to assess the user's condition.

[0804] "Update" is the process by which the generative AI reevaluates existing training and work plans based on collected data and feedback, and changes them to optimal plans.

[0805] This invention is a system for maximizing the training and work efficiency of factory workers. The system collects the user's physical information, movement information, and emotional state, and uses generative AI to generate optimal training and work plans based on this data and provides real-time feedback.

[0806] Hardware and software used

[0807] Hardware

[0808] Electronic devices: Examples include body composition monitors and performance measurement devices.

[0809] Motion sensor: A sensor for monitoring user movements in real time.

[0810] Real-time assessment devices: Examples include head-mounted displays (HMDs) and smart glasses. These devices assess the user's performance in real time and provide feedback.

[0811] Emotion engine: A device that analyzes the user's facial expressions and voice patterns to recognize their emotional state.

[0812] software

[0813] Generative AI: Specifically, there are MachineLearningModel and GenerateAIModel. These models are used to generate optimal plans for users based on collected data.

[0814] Data collection

[0815] When a user steps onto the body composition scale, an electronic device collects physical information such as heart rate, body fat percentage, and muscle mass. Next, as the user moves, a motion sensor collects movement characteristics. An emotion engine also collects the user's facial expressions and voice patterns to recognize their emotional state.

[0816] Data transmission and storage

[0817] The collected data is sent from the device to a server and stored in a database, which then performs preprocessing for analyzing the data.

[0818] Data analysis

[0819] The server preprocesses the stored physical, motion, and emotional information and analyzes the data using machine learning algorithms to assess the user's physical and emotional state.

[0820] Generate training and work plans

[0821] Based on the analysis results, the server uses generative AI to generate individually optimized training and work plans, including specific exercise and work schedules.

[0822] Real-time feedback

[0823] When a user begins training or performing a task, the real-time assessment device monitors the user's behavior and detects improper behavior or form. The server analyzes the data and provides appropriate feedback, such as a voice prompt to "correct your form."

[0824] Feedback Rating and Updates

[0825] After completing training or work, users input their feedback into the device. The collected feedback data is sent to the server, and the generation AI is used to update the next training plan or work plan.

[0826] Specific examples

[0827] Imagine a worker is working to carry heavy loads. Sensors collect information such as body fat percentage, heart rate, movement speed, and facial expressions, and the data is analyzed by a server. If the worker's emotional state is determined to be "feeling tired today" before starting work, the server generates instructions on safe lifting methods and the recommendation to take longer breaks. During work, motion sensors check the worker's lifting posture and provide feedback via the HMD, such as "Please bend your knees more when lifting." If the worker gives feedback after work such as "My lower back hurts a little," that data is also used for the next optimization.

[0828] Prompt Sentence Examples

[0829] User ID: worker_123, Physical information: {Heart rate: 85, Body fat percentage: 18%, Muscle mass: 70kg}, Movement information: {Movement speed: Medium, Flexibility: High}, Emotional state: Fatigue

[0830] Based on this data, generate an optimal work plan that maximizes work efficiency while reducing stress, and be sure to include appropriate breaks.

[0831] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0832] Step 1:

[0833] The server uses an electronic device to collect the user's physical information. Specifically, the user steps on a body composition scale to obtain data such as heart rate, body fat percentage, and muscle mass. This data is sent to the server as collected data. The input is physical information, and the output is the collected data.

[0834] Step 2:

[0835] The server uses a motion sensor and an emotion engine to collect the user's movement information and emotional state. The emotion engine analyzes the user's facial expressions and voice patterns to recognize the user's emotional state. Movement information includes movement speed, flexibility, balance, etc. The input is movement information and emotion information, and the output is the data.

[0836] Step 3:

[0837] The server stores the collected data in a database. The stored data includes physical information, movement information, and emotion information. The input is the collected data, and the output is storage in the database.

[0838] Step 4:

[0839] The server preprocesses the stored data, preparing it for input into the generative AI model. Preprocessing includes denoising and normalizing the data. This process transforms the data into a form suitable for analysis. The input is the stored data, and the output is the preprocessed data.

[0840] Step 5:

[0841] The server uses the preprocessed data to run machine learning algorithms to assess the user's physical and emotional states. Specifically, the analysis generates physical state assessment indicators and emotional state determinations. The input is the preprocessed data, and the output is the analysis results.

[0842] Step 6:

[0843] The server uses a generative AI model based on the analysis results to generate optimal training and work plans. The generative AI model uses prompts to suggest specific exercises and work schedules. This plan is stored in a database. The input is the analysis results and prompts, and the output is the generated plan.

[0844] Step 7:

[0845] The user wears the real-time evaluation device and begins training or work. The motion sensor monitors the user's movements and sends the information to the server. The input is real-time movement information, and the output is monitored movement information.

[0846] Step 8:

[0847] The server analyzes the monitored movement information in real time and detects improper movements or forms. The server provides feedback through the real-time evaluation device as needed. For example, it issues a voice command such as "Bend your knees more and lift." The input is the monitored movement information, and the output is the feedback.

[0848] Step 9:

[0849] The user inputs feedback after completing training or work. The feedback includes fatigue, muscle pain, emotional state, etc. This feedback is sent to the server via the terminal. The input is the feedback information, and the output is the information sent to the server.

[0850] Step 10:

[0851] The server analyzes the collected feedback data and emotional state and uses a generative AI model to update the training and work plans, which further optimizes the next training or work session. The input is the feedback data, and the output is the updated plan.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] [Third embodiment]

[0856] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0857] 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.

[0858] 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).

[0859] 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.

[0860] 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.

[0861] 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).

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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."

[0868] The present invention is a system that collects a user's physical and movement information, generates an individually optimized training plan based on this data, and provides feedback in real time, thereby maximizing the user's training efficiency and minimizing the risk of injury.

[0869] System Overview

[0870] The system includes the following components:

[0871] 1. Electronic Devices

[0872] 2. Server

[0873] 3. Generation AI

[0874] 4. Motion Sensors and Real-Time Assessment Devices

[0875] 5. Training Plan

[0876] 6. Feedback

[0877] Program processing flow

[0878] 1. Data Collection

[0879] Terminal (electronic device): Collects the user's physical information (body fat percentage, muscle mass, bone density, etc.) and measures movement characteristics (flexibility, balance ability, etc.) using motion sensors and cameras.

[0880] 2. Data transmission and storage

[0881] Terminal: Sends collected data to the server, which receives the data and stores it in a database.

[0882] 3. Data Analysis

[0883] Server: Analyzes the stored data using a machine learning algorithm to evaluate the user's physical condition. Based on the results of this analysis, a generative AI is used to generate an individually optimized training plan.

[0884] 4. Plan Generation

[0885] Server: Based on the analysis results, a generative AI model is used to create and save a training plan tailored to the user's goals and physical condition.

[0886] 5. Real-time feedback

[0887] User: Starts training and puts on MR glasses or other real-time assessment device.

[0888] Device: Monitors the user's movements in real time during training and evaluates whether they are appropriate for the training plan.

[0889] Server: Analyzes the monitored data and provides appropriate feedback to the user via voice.

[0890] 6. Feedback Rating and Updates

[0891] Users: Provide feedback after training.

[0892] Terminal: Sends feedback data to the server.

[0893] Server: Analyzes feedback data and updates training plans using generative AI.

[0894] Specific examples

[0895] collection

[0896] When the user steps on the body composition scale, the device measures a body fat percentage of 25%, muscle mass of 60kg, and bone density of 1.2g / cm³. The user performs stretching and balance exercises, and the device collects movement characteristic data.

[0897] Send / Save

[0898] The collected data (physical and movement information) is sent from the device to a server, which stores the data in a database.

[0899] analysis

[0900] The server analyzes the data and uses machine learning algorithms to determine if muscle mass needs to be increased. A generative AI model then creates a training plan to target muscle gain.

[0901] Training plan generation

[0902] The server creates a training plan including specific exercises (e.g., weightlifting, stretching, balance training, etc.) based on the user's goals and current status, and stores it in a database.

[0903] Real-time feedback

[0904] The user puts on the MR glasses and starts training. The device (motion sensor and MR glasses) monitors the user's movements in real time.

[0905] The server detects improper behavior or forms and provides appropriate corrective instructions to the user via voice.

[0906] Feedback Rating and Updates

[0907] After completing the training, the user inputs feedback (e.g., fatigue, muscle pain, etc.).

[0908] The device sends feedback data to a server, which analyzes the data and uses generative AI to update the training plan.

[0909] This system allows users to efficiently perform training that is best suited to their physical condition, maximizing results while minimizing the risk of injury.

[0910] The processing flow will be explained below.

[0911] Step 1:

[0912] The user enters their name, age, sex, height, weight, fitness level, and training goals into the device.

[0913] Step 2:

[0914] The device (body composition scale) measures the user's muscle mass, fat mass, and bone density and collects the data.

[0915] Step 3:

[0916] The device (motion sensor and camera) measures the user's movement characteristics such as flexibility, balance ability, and range of motion, and collects the data.

[0917] Step 4:

[0918] The terminal transmits the data obtained in steps 2 and 3 to the server.

[0919] Step 5:

[0920] The server stores the received data in a database.

[0921] Step 6:

[0922] The server preprocesses the stored data and prepares it for input to machine learning algorithms, for example by denoising and normalizing the data.

[0923] Step 7:

[0924] The server analyzes the data using machine learning algorithms to assess the user's physical condition (e.g., muscle mass, flexibility, and balance ability).

[0925] Step 8:

[0926] Based on the analysis results, the server uses AI to generate an optimal training plan, including specific exercises and training schedules.

[0927] Step 9:

[0928] The training plan generated by the server is stored in a database and notified to the device.

[0929] Step 10:

[0930] The user puts on MR glasses or other real-time evaluation devices and begins training.

[0931] Step 11:

[0932] The device (MR / AR glasses, motion sensor) monitors the user's movements during training in real time and evaluates whether the movements are appropriate for the training plan.

[0933] Step 12:

[0934] The server analyzes the monitored data, and if the form is inappropriate, provides the user with voice feedback and instructions for correction via the terminal.

[0935] Step 13:

[0936] The user finishes the training and inputs post-training feedback (e.g., fatigue, muscle pain, satisfaction) into the terminal.

[0937] Step 14:

[0938] The terminal transmits the feedback data to the server.

[0939] Step 15:

[0940] The server analyzes the feedback data and uses generative AI to update and optimize the training plan.

[0941] Step 16:

[0942] The server saves the updated training plan in a database and notifies the device.

[0943] Example 1

[0944] 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."

[0945] Conventional training systems struggle to provide optimal training plans based on each user's individual physical condition and movements. Furthermore, they lack the ability to provide real-time feedback, making it difficult to maximize training effectiveness and minimize injury risk. Furthermore, they lack the ability to update plans based on post-training feedback.

[0946] 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.

[0947] In this invention, the server includes means for collecting physical information of a user using an electronic device, means for generating a training plan based on the collected data using a generation AI, means for monitoring the user's movements using a motion sensor and a real-time evaluation device and providing feedback, means for analyzing the collected data and feedback and updating the training plan using a generation AI, means for collecting user movement information using a camera, means for evaluating the physical condition using a machine learning algorithm, and means for storing the training plan in a database. This makes it possible to provide an optimal training plan based on the user's individual physical condition and movements, maximizing training effects and minimizing the risk of injury.

[0948] 1. "Electronic device" refers to a device used to collect a user's physical information, including body composition monitors and motion characteristic measuring devices.

[0949] 2. "Generative AI" refers to artificial intelligence technology used to generate training plans based on collected data.

[0950] 3. "Motion sensor" means a sensor device that detects user movements and collects and transmits that information.

[0951] 4. "Real-time evaluation device" means a device that evaluates a user's movements in real time during training and provides feedback.

[0952] 5. “Machine learning algorithms” are algorithms used to analyze data and learn patterns and trends.

[0953] 6. "Database" means the data storage system for storing and managing collected data and generated training plans.

[0954] 7. "User's physical information" refers to data that represents the user's physical condition, such as body fat percentage, muscle mass, and bone density.

[0955] 8. "Training Plan" refers to a specific exercise schedule and content developed based on the user's goals and physical condition.

[0956] 9. "Feedback" means advice or corrective instructions provided during or after training.

[0957] This invention provides a system that collects a user's physical information and movement information, generates an individually optimized training plan based on this data, and provides feedback in real time, thereby maximizing the user's training efficiency and minimizing the risk of injury. This system includes an electronic device, a server, a generating AI, a motion sensor and real-time evaluation device, a training plan, and feedback.

[0958] First, the electronic device serves as a terminal and collects the user's physical information. Specifically, a body composition scale is used to obtain data such as body fat percentage, muscle mass, and bone density. Motion sensors and cameras are also used to collect movement information such as the user's flexibility and balance ability.

[0959] The device then transmits the collected data to a server, which receives it and stores it in a database that keeps a record of all collected physical and movement information.

[0960] The server analyzes the stored data and uses machine learning algorithms to assess the user's physical condition. This process identifies the current state of the body and areas for improvement from the data. For example, a Python machine learning library (e.g., scikit-learn) can be used.

[0961] Once the user's physical condition is evaluated, the server uses a generative AI model to create an individually optimized training plan. Specific exercise content and schedules are generated by entering a prompt into the generative AI model. For example, the prompt could be, "I'd like you to generate a training plan to increase muscle mass."

[0962] The generated training plan is stored in a database by the server and contains detailed exercises based on the user's goals and current condition. For example, this training plan might include "3 x 60-minute workouts per week, targeting different muscle groups each time."

[0963] When the user starts training, they put on MR glasses and other real-time evaluation devices. The motion sensors and MR glasses on the device monitor the user's movements in real time during training. This information is sent to a server, which detects improper movements or form and provides appropriate corrective instructions via voice. For example, the server may provide voice feedback such as "Please correct your posture."

[0964] After completing a workout, the user inputs feedback such as fatigue and muscle pain into the device. The device then sends this feedback data to a server, which analyzes it and updates the training plan using a generative AI model. This update creates a new plan that reflects the feedback data and is optimized for the user.

[0965] Prompt Sentence Examples

[0966] "Write code that uses collected data about a user (e.g., body fat percentage 25%, muscle mass 60kg, bone density 1.2g / cm³) to generate a training plan using a generative AI model to target muscle growth for the user. Also, add the ability to detect improper form during training in real time and provide audio feedback."

[0967] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0968] Step 1:

[0969] Data collection

[0970] When a user steps onto the body composition scale, the device measures their physical information, such as their body fat percentage, muscle mass, and bone density. In addition, when the user performs stretching or balance exercises, the device's motion sensors and camera collect movement information.

[0971] Input: User's physical condition data (body fat percentage, muscle mass, bone density, etc.), movement information (flexibility, balance ability, etc.)

[0972] Output: Collected physical and movement information

[0973] Step 2:

[0974] Data transmission and storage

[0975] The device sends the collected data to the server, which stores the received data in a database.

[0976] Input: User's physical and movement information sent from the device

[0977] Output: Physical and movement information stored in a database

[0978] Step 3:

[0979] Data analysis

[0980] The server uses machine learning algorithms to analyze the data stored in the database, thereby assessing the user's physical condition and identifying areas for improvement.

[0981] Input: User's physical and movement information stored in a database

[0982] Output: User's physical condition assessment results and necessary improvements

[0983] Step 4:

[0984] Plan Generation

[0985] Based on the analysis results, the server uses a generative AI model to generate an individually optimized training plan and stores it in a database.

[0986] Input: User's physical condition assessment results and necessary improvements

[0987] Output: Generated training plan

[0988] Step 5:

[0989] Training start and real-time feedback

[0990] The user puts on the MR glasses and starts training. During the training, the device (motion sensor and MR glasses) monitors the user's movements in real time and sends the information to the server. The server detects improper movements or form and provides audio instructions for correction.

[0991] Input: User behavior information collected in real time

[0992] Output: Real-time audio feedback

[0993] Step 6:

[0994] Feedback rating and training plan updates

[0995] After completing a workout, the user inputs feedback such as fatigue and muscle pain. The device sends this feedback data to a server, which then uses generative AI to analyze the data and update the training plan.

[0996] Input: User-entered feedback data

[0997] Output: Updated training plan

[0998] (Application example 1)

[0999] 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."

[1000] Delivery workers are often overburdened in their daily work, which can increase the risk of fatigue and injury. Furthermore, delivery efficiency can decline due to a lack of knowledge of efficient routes and work methods. To solve these problems, a system is needed that collects physical and movement information from delivery workers in real time and provides appropriate feedback.

[1001] 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.

[1002] In this invention, the server includes means for collecting physical information of the user using an electronic device, means for generating a training plan based on the collected data using a generation AI, means for monitoring the user's movements using a motion sensor and a real-time evaluation device and providing feedback, means for analyzing the collected data and feedback and updating the training plan using a generation AI, and means for monitoring the physical information and movement information of the delivery person and maximizing delivery efficiency and improving safety based on the information, thereby reducing the burden on the delivery person and enabling them to perform their work efficiently and safely.

[1003] "Electronic Device" is a technological device used to collect physical and behavioral information about a delivery person.

[1004] "Generative AI" is artificial intelligence that generates optimal training and delivery plans based on collected data.

[1005] A "motion sensor" is a sensor that detects the movement characteristics of delivery personnel and evaluates them in real time.

[1006] A "real-time evaluation device" is a device that instantly analyzes collected data and provides immediate feedback to delivery personnel.

[1007] "Feedback" refers to instructions and advice provided to delivery personnel based on collected and analyzed data.

[1008] A "body composition scale" is a device that measures physical information such as a delivery person's body fat percentage, muscle mass, and bone density.

[1009] A "motion characteristic measuring device" is an instrument used to evaluate the motion characteristics of delivery personnel, such as their flexibility and balance ability.

[1010] A "smartwatch" is a wearable device that records a delivery person's physical information, such as heart rate, number of steps, and energy consumption, in real time.

[1011] "Smart glasses" are wearable devices that collect information on the delivery person's posture and movements and provide visual feedback.

[1012] The present invention provides a system that collects physical and motion information of delivery personnel and provides real-time feedback to maximize efficiency and improve safety during delivery operations. To this end, the system includes the following components:

[1013] 1. Electronic Devices (Terminals)

[1014] The user (delivery worker) wears a smartwatch or smart glasses, which collect various physical and movement information in real time, such as heart rate, number of steps, energy consumption, driving posture, walking posture, and how the package is carried.

[1015] 2. Data transmission and storage (server)

[1016] The data collected by the device is sent to a cloud server via wireless communication, and the server receives the data and stores it in a database.

[1017] 3. Data analysis (server)

[1018] The server analyzes the stored data using machine learning algorithms to assess the delivery person's current physical condition and movement patterns, using random forests or other suitable machine learning models.

[1019] 4. Feedback and Advice (Device)

[1020] Based on the analysis results, the server generates appropriate feedback and advice, such as when to take a break, suggesting an efficient route, teaching correct posture, etc. This feedback is conveyed to the user through audio output and visual presentations.

[1021] 5. Update training plan based on feedback (server)

[1022] Feedback data provided by the user (e.g., fatigue, muscle pain, distance traveled, etc.) is also sent to the server, and the training plan is updated using the generative AI.

[1023] Hardware and software used

[1024] Smartwatches and smart glasses: Used as data collection devices.

[1025] Cloud server: Stores and analyzes data.

[1026] Machine learning algorithms: using libraries such as TensorFlow and Scikit-learn.

[1027] Audio output and visual feedback devices: Provide feedback to the delivery person.

[1028] Specific examples

[1029] The user puts on the smartwatch and smart glasses and begins making deliveries, which collects data on heart rate, steps, energy expenditure, and posture.

[1030] The collected data is sent from the terminal via wireless communication to a server and stored there.

[1031] The server analyzes the data and generates an efficiency score for the delivery person using generative AI (e.g., a random forest model), and generates appropriate feedback, such as "You're tired. Please take a break."

[1032] Based on the analysis results, the device provides feedback to the delivery person via voice or visual feedback.

[1033] The user inputs the effects of training and feedback, and the feedback data is sent back to the server, where the training plan is updated using the generative AI.

[1034] Prompt Sentence Examples

[1035] "Design an algorithm to monitor the fatigue level and efficiency of delivery personnel and provide optimal feedback in real time. Data to be used includes heart rate, steps, energy consumption, and posture. It should also suggest break times and efficient routes."

[1036] This reduces the burden on delivery personnel and enables them to carry out their work efficiently and safely.

[1037] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1038] Step 1:

[1039] Data collection

[1040] Users wear smartwatches or smart glasses, which collect physical and movement information such as heart rate, number of steps, energy consumption, and posture information in real time.

[1041] The inputs are biometric and motion data obtained from smartwatches and smart glasses.

[1042] The output is an information packet containing these data.

[1043] Specifically, the sensors periodically collect data and temporarily store it in the memory of the smart glasses or smartwatch.

[1044] Step 2:

[1045] Data transmission and storage

[1046] The data collected by the device is sent to a server via wireless communication (e.g., Wi-Fi or Bluetooth), which receives the data and stores it in a cloud database.

[1047] The input is biometric data and motion data sent from the terminal.

[1048] The output is data stored in a cloud database.

[1049] Specifically, the device periodically divides data into packets and transmits them to the server via wireless communication. The server then automatically stores the received data in a database.

[1050] Step 3:

[1051] Data analysis

[1052] The server analyzes the data stored in the cloud database using machine learning algorithms (e.g., random forests), which evaluate the delivery person's current physical condition and movement patterns.

[1053] The inputs are stored biometric and motion data.

[1054] The output is an evaluation score for the delivery person's fatigue level and efficiency.

[1055] Specifically, the server standardizes the data, then analyzes it using machine learning models, generating evaluation results that are stored in a log file.

[1056] Step 4:

[1057] Providing feedback and advice

[1058] The server generates appropriate feedback and advice based on the analysis results, which is provided to the user through audio output or visual display.

[1059] The input is the evaluation score of the analysis result.

[1060] The output is feedback or advice provided to the user.

[1061] Specifically, the server generates an appropriate feedback message based on the evaluation score and sends it to an audio output device or a visual display device.

[1062] Step 5:

[1063] Get feedback and update your training plan

[1064] The user performs training and performs actions based on the feedback, and then provides feedback on the results to the input device. This feedback data (e.g., fatigue, muscle pain, distance traveled, etc.) is sent from the device to the server. The server analyzes this data and updates the training plan using generative AI.

[1065] The input is the feedback data entered by the user.

[1066] The output is an updated training plan.

[1067] Specifically, the device sends feedback data to the server, which analyzes the data, updates the training plan, and stores the results in a cloud database.

[1068] 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.

[1069] This system maximizes training efficiency and minimizes the risk of injury by collecting a user's physical and movement information, generating an individually optimized training plan based on this data, and providing real-time feedback. Furthermore, it combines an emotion engine that recognizes the user's emotions and optimizes training according to their emotional state.

[1070] System Overview

[1071] The system includes the following components:

[1072] 1. Electronic Devices

[1073] 2. Server

[1074] 3. Generation AI

[1075] 4. Motion Sensors and Real-Time Assessment Devices

[1076] 5. Emotion Engine

[1077] 6. Training Plan

[1078] 7. Feedback

[1079] Program processing flow

[1080] 1. Data Collection

[1081] Terminal (electronic device): Collects the user's physical information (body fat percentage, muscle mass, bone density, etc.) and measures movement characteristics (flexibility, balance ability, etc.) using motion sensors and cameras.

[1082] Terminal (emotion engine): Analyzes the user's facial expressions and voice patterns to recognize their emotional state.

[1083] 2. Data transmission and storage

[1084] Terminal: Sends collected data (physical information, movement information, and emotional information) to the server, which receives the data and stores it in a database.

[1085] 3. Data Analysis

[1086] Server: Preprocesses the stored data and prepares it for input to machine learning algorithms, for example by denoising and normalizing the data.

[1087] Server: Analyzes data using machine learning algorithms to assess the user's physical and emotional state.

[1088] 4. Plan Generation

[1089] Server: Based on the analysis results and emotional state, the server uses generative AI to generate an optimal training plan, including specific exercises and training schedules.

[1090] 5. Real-time feedback

[1091] User: Starts training and puts on MR glasses or other real-time assessment device.

[1092] Device: Monitors the user's movements during training in real time and evaluates whether the movements are appropriate for the training plan.

[1093] Server: Analyzes the monitored data, and if the form is inappropriate, provides the user with voice feedback and instructions for correction via the terminal.

[1094] 6. Feedback Rating and Updates

[1095] User: After completing the training, the user inputs post-training feedback (e.g., fatigue, muscle pain, satisfaction) into the terminal.

[1096] Terminal: Sends feedback data to the server.

[1097] Server: Analyzes feedback data and emotional state, and uses generative AI to update and optimize training plans.

[1098] Specific examples

[1099] collection

[1100] When the user steps on the body composition scale, the device measures a body fat percentage of 25%, muscle mass of 60kg, and bone density of 1.2g / cm³. The user performs stretching and balance exercises, and the device collects movement characteristic data.

[1101] The emotion engine collects the user's facial and voice patterns to recognize their emotional state (e.g., joy, fatigue, stress, etc.).

[1102] Send / Save

[1103] The collected data (physical information, movement information, and emotional information) is sent from the device to a server, which stores the data in a database.

[1104] analysis

[1105] The server analyzes the data and uses machine learning algorithms to assess the user's physical and emotional state. For example, it may determine that muscle mass needs to be increased and that stress levels are high during training. A generative AI model then creates a training plan that targets muscle gain and reduces stress.

[1106] Training plan generation

[1107] The server creates a training plan including specific exercises (e.g., weightlifting, relaxation exercises, mindfulness practice, etc.) based on the user's goals and current situation, and stores it in a database.

[1108] Real-time feedback

[1109] The user puts on the MR glasses and starts training. The device (motion sensor and MR glasses) monitors the user's movements in real time.

[1110] The server detects improper movements and forms and provides appropriate corrective instructions to the user via voice. It also monitors the user's emotional state and suggests training breaks and relaxation exercises if the user is feeling stressed.

[1111] Feedback Rating and Updates

[1112] After completing the training, the user inputs feedback (e.g., fatigue, muscle pain, emotional state).

[1113] The device sends the feedback data to a server, which then analyzes it and uses generative AI to update the training plan—for example, if muscle soreness is reported, the plan will be updated to adjust the load for the next workout.

[1114] This system allows users to efficiently train in a way that is optimal not only for their physical condition but also for their emotional state, maximizing results while minimizing the risk of injury.

[1115] The processing flow will be explained below.

[1116] Step 1:

[1117] The user enters their name, age, sex, height, weight, fitness level, and training goals into the device.

[1118] Step 2:

[1119] The device (body composition monitor) measures the user's muscle mass, body fat percentage, and bone density and collects the data.

[1120] Step 3:

[1121] The device (motion sensor and camera) measures the user's movement characteristics such as flexibility, balance ability, and range of motion, and collects the data.

[1122] Step 4:

[1123] The device (emotion engine) analyzes the user's facial expressions and voice patterns to recognize their emotional state. For example, a smile can be interpreted as "joy," while a stiff expression can be interpreted as "stress."

[1124] Step 5:

[1125] The terminal transmits the data obtained in steps 2, 3, and 4 to the server.

[1126] Step 6:

[1127] The server stores the received data in a database.

[1128] Step 7:

[1129] The server preprocesses the stored data and prepares it for input to machine learning algorithms, for example by denoising and normalizing the data.

[1130] Step 8:

[1131] The server analyzes the data using machine learning algorithms to assess the user's physical condition (muscle mass, body fat percentage, flexibility, etc.) and emotional state.

[1132] Step 9:

[1133] The server uses generative AI to generate an optimal training plan based on the analysis results and the user's emotional state. The plan includes specific exercises, a training schedule, and exercises tailored to the user's emotional state (e.g., adding relaxation exercises if stress levels are high).

[1134] Step 10:

[1135] The training plan generated by the server is stored in a database and notified to the device.

[1136] Step 11:

[1137] The user puts on MR glasses or other real-time evaluation devices and begins training.

[1138] Step 12:

[1139] The device (MR / AR glasses, motion sensor) monitors the user's movements during training in real time and evaluates whether the movements are appropriate for the training plan. The emotion engine also operates to continuously monitor the user's emotional state during training.

[1140] Step 13:

[1141] The server analyzes the monitored data and, if the user's form is improper or their emotional state fluctuates (e.g., stress increases), it provides the user with voice feedback and instructions for correction via the device. For example, it may say, "Your form is off. Please keep your body upright" or "Try breathing to relax."

[1142] Step 14:

[1143] The user finishes the training and inputs post-training feedback (e.g., fatigue, muscle pain, emotional state) into the terminal.

[1144] Step 15:

[1145] The terminal transmits the feedback data to the server.

[1146] Step 16:

[1147] The server analyzes the feedback data and emotional state and uses generative AI to update and optimize the training plan. For example, if muscle soreness is reported, the plan will be updated to adjust the load for the next workout. Or, if stress levels are reaching their peak, relaxation exercises will be added.

[1148] Step 17:

[1149] The server saves the updated training plan in a database and notifies the device.

[1150] Example 2

[1151] 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."

[1152] Conventional training systems generate training plans based solely on the user's physical information, making it difficult to provide an effective plan optimized for each individual user. Furthermore, because they do not take into account the user's movement characteristics or emotional state, there is a high risk of injury during training and it can lead to a loss of motivation. Furthermore, there are insufficient means of providing real-time feedback, making it difficult to make immediate adjustments to maximize training effectiveness.

[1153] 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.

[1154] In this invention, the server includes: means for collecting a user's physical information using an electronic device; means for generating a training plan based on the collected data using a generation AI; means for monitoring the user's movements using a motion sensor and a real-time evaluation device to measure movement characteristics and providing feedback; means for analyzing the user's facial expressions and voice patterns using an emotion engine to recognize their emotional state; and means for analyzing the collected data, feedback, and emotional state and updating the training plan using the generation AI. This allows for the generation and provision of an individually optimized training plan in real time that comprehensively takes into account the user's physical information, movement characteristics, and emotional state. This maximizes training efficiency, minimizes the risk of injury, and increases the user's motivation.

[1155] An "electronic device" is a device for collecting physical information about a user, and includes a body composition monitor and a motion characteristic measuring device.

[1156] "Generative AI" refers to artificial intelligence technology that automatically generates training plans based on collected data.

[1157] A "motion sensor" is a sensor for measuring the movement characteristics of a user and capturing specific data about the movement.

[1158] A "real-time evaluation device" is a device for evaluating a user's actions in real time and providing immediate feedback.

[1159] An "emotion engine" refers to a system that recognizes a user's emotional state by analyzing their facial expressions and voice patterns.

[1160] "Collected Data" collectively refers to physical information, movement information, and emotional information obtained through electronic devices, motion sensors, emotion engines, etc.

[1161] "Feedback" refers to advice and corrective instructions provided during and after training.

[1162] A "training plan" is an exercise or workout plan generated by the generative AI based on the user's physical information, movement characteristics, and emotional state.

[1163] "Analysis" refers to the process of evaluating and analyzing the user's physical and emotional state based on the collected data.

[1164] The present invention is a system that collects a user's physical and movement information, generates an individually optimized training plan based on this data, and provides real-time feedback. This maximizes the user's training efficiency and minimizes the risk of injury. The present invention also incorporates an emotion engine that recognizes the user's emotions, optimizing training according to their emotional state.

[1165] The system includes the following components:

[1166] 1. Electronic Devices

[1167] 2. Server

[1168] 3. Generation AI

[1169] 4. Motion Sensors and Real-Time Assessment Devices

[1170] 5. Emotion Engine

[1171] 6. Training Plan

[1172] 7. Feedback

[1173] Data collection

[1174] The device (electronic device) accurately measures the user's physical information, such as body fat percentage, muscle mass, and bone density. For example, when a user steps on a body composition scale, the device measures a body fat percentage of 25%, muscle mass of 60 kg, and bone density of 1.2 g / cm³. The device (motion sensor and camera) also records detailed movement characteristics (flexibility, balance ability, etc.) when the user performs stretching or balance exercises. It captures balance and flexibility data for each frame as the user performs specific movements. Furthermore, the device (emotion engine) analyzes the user's facial expressions and voice patterns to recognize their emotional state in real time. For example, it can detect a smile from the user's facial expression and recognize it as "happiness," or identify signs of fatigue from their voice patterns.

[1175] Data transmission and storage

[1176] The device sends the collected data (physical, movement, and emotional information) to a server in real time. For example, it sends a series of data in packets. The server immediately stores the received data in a database. After storage, the data integrity is checked to verify that there are no missing or abnormal values.

[1177] Data analysis

[1178] The server first preprocesses the data, for example by removing noise and normalizing the received data. It then runs algorithms to stabilize the data sample rate and remove outliers. The server then uses machine learning algorithms to analyze the user's physical and emotional state. Specifically, it compares the data with the user's previous data and analyzes fluctuations in physical state and emotional trends.

[1179] Training plan generation

[1180] The server uses generative AI to generate an optimal training plan based on the analysis results and the user's emotional state. For example, it combines exercises that increase the user's muscle mass while reducing stress. The plan includes specific exercises, number of sets, rest periods, etc. The generative AI model optimizes each element and outputs it.

[1181] Real-time feedback

[1182] When a user begins training, they put on MR glasses or other real-time evaluation devices, which constantly monitor their movements. The device analyzes the user's movements in real time. For example, if their squat form is improper, they will immediately receive audio feedback such as, "Push your hips back a little more." The server then reanalyzes the data, assessing whether their form is correct and whether emotional stress is present, and suggests resting instructions or relaxation exercises as needed.

[1183] Feedback Rating and Updates

[1184] After completing a workout, the user enters feedback such as fatigue, muscle soreness, and satisfaction into the device. For example, by manually entering the feedback into a feedback form. The device then sends this feedback data to the server, where it is immediately analyzed. The server then reanalyzes the feedback data and emotional state and uses a generative AI to update the next training plan. For example, if muscle soreness is reported, the load for the next workout will be adjusted appropriately.

[1185] Example prompt sentence:

[1186] "Measure your body fat percentage, muscle mass, and bone density, and do some stretching and balance exercises."

[1187] "Please tell us your current emotional state: joy, fatigue, stress"

[1188] "Please provide feedback after the training: fatigue, muscle soreness, satisfaction"

[1189] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1190] Step 1: Data collection

[1191] The terminal (electronic device) collects the user's physical information. Specifically, it measures body fat percentage, muscle mass, bone density, etc. This collected data becomes the input. For example, when a user steps on a body composition scale, the output is a body fat percentage of 25%, muscle mass of 60 kg, and bone density of 1.2 g / cm³. In addition, the terminal (motion sensor and camera) measures the user's movement characteristics (flexibility, balance ability, etc.) and obtains movement characteristic data. When the user performs stretching or balance exercises, movement data is captured for each frame and analyzed.

[1192] Step 2: Recognizing your emotional state

[1193] The device (emotion engine) analyzes the user's facial expressions and voice patterns in real time to recognize their emotional state. For example, if the user smiles, it recognizes this as "joy." Signs of fatigue are identified from the voice pattern and recorded in a database. This recognized data is input, and the emotional state is output.

[1194] Step 3: Send and save data

[1195] The device transmits the collected physical information, movement characteristic data, and emotional state data to a server. The server receives this data and stores it in a database. For example, the collected data may be sent in packets, which the server receives. This received data is the input, and the stored data is the output.

[1196] Step 4: Data Preprocessing

[1197] The server preprocesses the stored data. Specifically, it performs noise reduction and normalization on the received data. For example, it runs algorithms to remove outliers and normalize the data sample rate. This stored data is input as preprocessed data, and normalized data is output.

[1198] Step 5: Data analysis

[1199] The server uses machine learning algorithms to analyze the normalized data. The server analyzes the user's physical and emotional state. For example, it compares the data with previous data to identify fluctuations in physical state and emotional trends. The preprocessed data is used as input for the machine learning algorithm, which outputs the analysis results.

[1200] Step 6: Create a training plan

[1201] The server uses generative AI to generate an optimal training plan based on the analysis results and emotional state data. For example, it might suggest exercises that increase muscle mass while relieving stress. The analysis results and emotional state are input, and a training plan is output.

[1202] Step 7: Real-time feedback

[1203] The user puts on MR glasses or other real-time evaluation devices and begins training. The device monitors and analyzes the user's movements in real time. For example, if the user's form in a squat is incorrect, the device immediately provides audio feedback such as "pull your hips back a little more." This real-time data is the input, and the audio feedback is the output.

[1204] Step 8: Feedback evaluation and updates

[1205] After completing a workout, the user inputs feedback such as fatigue, muscle soreness, and satisfaction into the device. The device then sends this feedback data to the server, which immediately analyzes it. For example, if muscle soreness is reported, the load for the next workout can be adjusted. This feedback data is the input, and an updated training plan is the output.

[1206] (Application example 2)

[1207] 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."

[1208] Many conventional training systems and task optimization systems collect a user's physical and motion information to provide training and task plans, but often do not provide real-time feedback or update the plans to take the user's emotional state into account. This can lead to problems such as accumulated stress and fatigue in the user, preventing optimal performance or increasing the risk of injury. The present invention aims to solve these problems and support efficient and safe training and tasks by providing individually optimized plans tailored to the user's condition and real-time feedback.

[1209] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1210] In this invention, the server includes means for collecting physical information of the user using an electronic device, means for generating a training plan based on the collected data using a generation AI, means for monitoring the user's movements using a motion sensor and a real-time evaluation device and providing feedback, means for analyzing the collected data and feedback and updating the training plan using the generation AI, means for recognizing the worker's emotional state and generating an individually optimized work plan based on the analysis results, and means for evaluating the user's posture and movements during the work according to the generated work plan in real time and providing appropriate feedback. This enables the provision of an individually optimized plan based on the user's physical and emotional state and real-time feedback.

[1211] "Electronic device" refers to a device that collects physical information about the user, and is responsible for measuring the user's body composition and movement characteristics.

[1212] "Generative AI" is artificial intelligence that generates optimal training and work plans based on collected data. It uses machine learning algorithms to analyze the data and create personalized plans.

[1213] A "motion sensor" is a sensor that monitors the user's movements in real time and collects information about that movement. It can accurately measure the user's posture and movements.

[1214] A "real-time evaluation device" is a device that instantly evaluates a user's movements and provides appropriate feedback. It can analyze the user's movements in real time and provide necessary instructions.

[1215] "Emotional state" refers to the user's current emotional state, as recognized by analyzing the user's facial expressions and voice patterns. It is used to determine the user's stress, fatigue, excitement, etc.

[1216] "Training Plan" refers to an exercise plan generated based on the user's physical and movement information. It consists of individually optimized exercises and schedules.

[1217] The "work plan" is a work plan generated based on the user's physical and movement information to maximize work efficiency. It is optimized taking into account the user's health and emotional state.

[1218] "Feedback" refers to real-time instructions, such as movement or posture corrections, provided to users to help them train or perform tasks effectively and safely.

[1219] "Data analysis" is the process of analyzing collected physical, movement, and emotional information using machine learning algorithms to assess the user's condition.

[1220] "Update" is the process by which the generative AI reevaluates existing training and work plans based on collected data and feedback, and changes them to optimal plans.

[1221] This invention is a system for maximizing the training and work efficiency of factory workers. The system collects the user's physical information, movement information, and emotional state, and uses generative AI to generate optimal training and work plans based on this data and provides real-time feedback.

[1222] Hardware and software used

[1223] Hardware

[1224] Electronic devices: Examples include body composition monitors and performance measurement devices.

[1225] Motion sensor: A sensor for monitoring user movements in real time.

[1226] Real-time assessment devices: Examples include head-mounted displays (HMDs) and smart glasses. These devices assess the user's performance in real time and provide feedback.

[1227] Emotion engine: A device that analyzes the user's facial expressions and voice patterns to recognize their emotional state.

[1228] software

[1229] Generative AI: Specifically, there are MachineLearningModel and GenerateAIModel. These models are used to generate optimal plans for users based on collected data.

[1230] Data collection

[1231] When a user steps onto the body composition scale, an electronic device collects physical information such as heart rate, body fat percentage, and muscle mass. Next, as the user moves, a motion sensor collects movement characteristics. An emotion engine also collects the user's facial expressions and voice patterns to recognize their emotional state.

[1232] Data transmission and storage

[1233] The collected data is sent from the device to a server and stored in a database, which then performs preprocessing for analyzing the data.

[1234] Data analysis

[1235] The server preprocesses the stored physical, motion, and emotional information and analyzes the data using machine learning algorithms to assess the user's physical and emotional state.

[1236] Generate training and work plans

[1237] Based on the analysis results, the server uses generative AI to generate individually optimized training and work plans, including specific exercise and work schedules.

[1238] Real-time feedback

[1239] When a user begins training or performing a task, the real-time assessment device monitors the user's behavior and detects improper behavior or form. The server analyzes the data and provides appropriate feedback, such as a voice prompt to "correct your form."

[1240] Feedback Rating and Updates

[1241] After completing training or work, users input their feedback into the device. The collected feedback data is sent to the server, and the generation AI is used to update the next training plan or work plan.

[1242] Specific examples

[1243] Imagine a worker is working to carry heavy loads. Sensors collect information such as body fat percentage, heart rate, movement speed, and facial expressions, and the data is analyzed by a server. If the worker's emotional state is determined to be "feeling tired today" before starting work, the server generates instructions on safe lifting methods and the recommendation to take longer breaks. During work, motion sensors check the worker's lifting posture and provide feedback via the HMD, such as "Please bend your knees more when lifting." If the worker gives feedback after work such as "My lower back hurts a little," that data is also used for the next optimization.

[1244] Prompt Sentence Examples

[1245] User ID: worker_123, Physical information: {Heart rate: 85, Body fat percentage: 18%, Muscle mass: 70kg}, Movement information: {Movement speed: Medium, Flexibility: High}, Emotional state: Fatigue

[1246] Based on this data, generate an optimal work plan that maximizes work efficiency while reducing stress, and be sure to include appropriate breaks.

[1247] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1248] Step 1:

[1249] The server uses an electronic device to collect the user's physical information. Specifically, the user steps on a body composition scale to obtain data such as heart rate, body fat percentage, and muscle mass. This data is sent to the server as collected data. The input is physical information, and the output is the collected data.

[1250] Step 2:

[1251] The server uses a motion sensor and an emotion engine to collect the user's movement information and emotional state. The emotion engine analyzes the user's facial expressions and voice patterns to recognize the user's emotional state. Movement information includes movement speed, flexibility, balance, etc. The input is movement information and emotion information, and the output is the data.

[1252] Step 3:

[1253] The server stores the collected data in a database. The stored data includes physical information, movement information, and emotion information. The input is the collected data, and the output is storage in the database.

[1254] Step 4:

[1255] The server preprocesses the stored data, preparing it for input into the generative AI model. Preprocessing includes denoising and normalizing the data. This process transforms the data into a form suitable for analysis. The input is the stored data, and the output is the preprocessed data.

[1256] Step 5:

[1257] The server uses the preprocessed data to run machine learning algorithms to assess the user's physical and emotional states. Specifically, the analysis generates physical state assessment indicators and emotional state determinations. The input is the preprocessed data, and the output is the analysis results.

[1258] Step 6:

[1259] The server uses a generative AI model based on the analysis results to generate optimal training and work plans. The generative AI model uses prompts to suggest specific exercises and work schedules. This plan is stored in a database. The input is the analysis results and prompts, and the output is the generated plan.

[1260] Step 7:

[1261] The user wears the real-time evaluation device and begins training or work. The motion sensor monitors the user's movements and sends the information to the server. The input is real-time movement information, and the output is monitored movement information.

[1262] Step 8:

[1263] The server analyzes the monitored movement information in real time and detects improper movements or forms. The server provides feedback through the real-time evaluation device as needed. For example, it issues a voice command such as "Bend your knees more and lift." The input is the monitored movement information, and the output is the feedback.

[1264] Step 9:

[1265] The user inputs feedback after completing training or work. The feedback includes fatigue, muscle pain, emotional state, etc. This feedback is sent to the server via the terminal. The input is the feedback information, and the output is the information sent to the server.

[1266] Step 10:

[1267] The server analyzes the collected feedback data and emotional state and uses a generative AI model to update the training and work plans, which further optimizes the next training or work session. The input is the feedback data, and the output is the updated plan.

[1268] 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.

[1269] 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.

[1270] 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.

[1271] [Fourth embodiment]

[1272] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1273] 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.

[1274] 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).

[1275] 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.

[1276] 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.

[1277] 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).

[1278] 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.

[1279] 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.

[1280] 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.

[1281] 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.

[1282] 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.

[1283] 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.

[1284] 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."

[1285] The present invention is a system that collects a user's physical and movement information, generates an individually optimized training plan based on this data, and provides feedback in real time, thereby maximizing the user's training efficiency and minimizing the risk of injury.

[1286] System Overview

[1287] The system includes the following components:

[1288] 1. Electronic Devices

[1289] 2. Server

[1290] 3. Generation AI

[1291] 4. Motion Sensors and Real-Time Assessment Devices

[1292] 5. Training Plan

[1293] 6. Feedback

[1294] Program processing flow

[1295] 1. Data Collection

[1296] Terminal (electronic device): Collects the user's physical information (body fat percentage, muscle mass, bone density, etc.) and measures movement characteristics (flexibility, balance ability, etc.) using motion sensors and cameras.

[1297] 2. Data transmission and storage

[1298] Terminal: Sends collected data to the server, which receives the data and stores it in a database.

[1299] 3. Data Analysis

[1300] Server: Analyzes the stored data using a machine learning algorithm to evaluate the user's physical condition. Based on the results of this analysis, a generative AI is used to generate an individually optimized training plan.

[1301] 4. Plan Generation

[1302] Server: Based on the analysis results, a generative AI model is used to create and save a training plan tailored to the user's goals and physical condition.

[1303] 5. Real-time feedback

[1304] User: Starts training and puts on MR glasses or other real-time assessment device.

[1305] Device: Monitors the user's movements in real time during training and evaluates whether they are appropriate for the training plan.

[1306] Server: Analyzes the monitored data and provides appropriate feedback to the user via voice.

[1307] 6. Feedback Rating and Updates

[1308] Users: Provide feedback after training.

[1309] Terminal: Sends feedback data to the server.

[1310] Server: Analyzes feedback data and updates training plans using generative AI.

[1311] Specific examples

[1312] collection

[1313] When the user steps on the body composition scale, the device measures a body fat percentage of 25%, muscle mass of 60kg, and bone density of 1.2g / cm³. The user performs stretching and balance exercises, and the device collects movement characteristic data.

[1314] Send / Save

[1315] The collected data (physical and movement information) is sent from the device to a server, which stores the data in a database.

[1316] analysis

[1317] The server analyzes the data and uses machine learning algorithms to determine if muscle mass needs to be increased. A generative AI model then creates a training plan to target muscle gain.

[1318] Training plan generation

[1319] The server creates a training plan including specific exercises (e.g., weightlifting, stretching, balance training, etc.) based on the user's goals and current status, and stores it in a database.

[1320] Real-time feedback

[1321] The user puts on the MR glasses and starts training. The device (motion sensor and MR glasses) monitors the user's movements in real time.

[1322] The server detects improper behavior or forms and provides appropriate corrective instructions to the user via voice.

[1323] Feedback Rating and Updates

[1324] After completing the training, the user inputs feedback (e.g., fatigue, muscle pain, etc.).

[1325] The device sends feedback data to a server, which analyzes the data and uses generative AI to update the training plan.

[1326] This system allows users to efficiently perform training that is best suited to their physical condition, maximizing results while minimizing the risk of injury.

[1327] The processing flow will be explained below.

[1328] Step 1:

[1329] The user enters their name, age, sex, height, weight, fitness level, and training goals into the device.

[1330] Step 2:

[1331] The device (body composition scale) measures the user's muscle mass, fat mass, and bone density and collects the data.

[1332] Step 3:

[1333] The device (motion sensor and camera) measures the user's movement characteristics such as flexibility, balance ability, and range of motion, and collects the data.

[1334] Step 4:

[1335] The terminal transmits the data obtained in steps 2 and 3 to the server.

[1336] Step 5:

[1337] The server stores the received data in a database.

[1338] Step 6:

[1339] The server preprocesses the stored data and prepares it for input to machine learning algorithms, for example by denoising and normalizing the data.

[1340] Step 7:

[1341] The server analyzes the data using machine learning algorithms to assess the user's physical condition (e.g., muscle mass, flexibility, and balance ability).

[1342] Step 8:

[1343] Based on the analysis results, the server uses AI to generate an optimal training plan, including specific exercises and training schedules.

[1344] Step 9:

[1345] The training plan generated by the server is stored in a database and notified to the device.

[1346] Step 10:

[1347] The user puts on MR glasses or other real-time evaluation devices and begins training.

[1348] Step 11:

[1349] The device (MR / AR glasses, motion sensor) monitors the user's movements during training in real time and evaluates whether the movements are appropriate for the training plan.

[1350] Step 12:

[1351] The server analyzes the monitored data, and if the form is inappropriate, provides the user with voice feedback and instructions for correction via the terminal.

[1352] Step 13:

[1353] The user finishes the training and inputs post-training feedback (e.g., fatigue, muscle pain, satisfaction) into the terminal.

[1354] Step 14:

[1355] The terminal transmits the feedback data to the server.

[1356] Step 15:

[1357] The server analyzes the feedback data and uses generative AI to update and optimize the training plan.

[1358] Step 16:

[1359] The server saves the updated training plan in a database and notifies the device.

[1360] Example 1

[1361] 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."

[1362] Conventional training systems struggle to provide optimal training plans based on each user's individual physical condition and movements. Furthermore, they lack the ability to provide real-time feedback, making it difficult to maximize training effectiveness and minimize injury risk. Furthermore, they lack the ability to update plans based on post-training feedback.

[1363] 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.

[1364] In this invention, the server includes means for collecting physical information of a user using an electronic device, means for generating a training plan based on the collected data using a generation AI, means for monitoring the user's movements using a motion sensor and a real-time evaluation device and providing feedback, means for analyzing the collected data and feedback and updating the training plan using a generation AI, means for collecting user movement information using a camera, means for evaluating the physical condition using a machine learning algorithm, and means for storing the training plan in a database. This makes it possible to provide an optimal training plan based on the user's individual physical condition and movements, maximizing training effects and minimizing the risk of injury.

[1365] 1. "Electronic device" refers to a device used to collect a user's physical information, including body composition monitors and motion characteristic measuring devices.

[1366] 2. "Generative AI" refers to artificial intelligence technology used to generate training plans based on collected data.

[1367] 3. "Motion sensor" means a sensor device that detects user movements and collects and transmits that information.

[1368] 4. "Real-time evaluation device" means a device that evaluates a user's movements in real time during training and provides feedback.

[1369] 5. “Machine learning algorithms” are algorithms used to analyze data and learn patterns and trends.

[1370] 6. "Database" means the data storage system for storing and managing collected data and generated training plans.

[1371] 7. "User's physical information" refers to data that represents the user's physical condition, such as body fat percentage, muscle mass, and bone density.

[1372] 8. "Training Plan" refers to a specific exercise schedule and content developed based on the user's goals and physical condition.

[1373] 9. "Feedback" means advice or corrective instructions provided during or after training.

[1374] This invention provides a system that collects a user's physical information and movement information, generates an individually optimized training plan based on this data, and provides feedback in real time, thereby maximizing the user's training efficiency and minimizing the risk of injury. This system includes an electronic device, a server, a generating AI, a motion sensor and real-time evaluation device, a training plan, and feedback.

[1375] First, the electronic device serves as a terminal and collects the user's physical information. Specifically, a body composition scale is used to obtain data such as body fat percentage, muscle mass, and bone density. Motion sensors and cameras are also used to collect movement information such as the user's flexibility and balance ability.

[1376] The device then transmits the collected data to a server, which receives it and stores it in a database that keeps a record of all collected physical and movement information.

[1377] The server analyzes the stored data and uses machine learning algorithms to assess the user's physical condition. This process identifies the current state of the body and areas for improvement from the data. For example, a Python machine learning library (e.g., scikit-learn) can be used.

[1378] Once the user's physical condition is evaluated, the server uses a generative AI model to create an individually optimized training plan. Specific exercise content and schedules are generated by entering a prompt into the generative AI model. For example, the prompt could be, "I'd like you to generate a training plan to increase muscle mass."

[1379] The generated training plan is stored in a database by the server and contains detailed exercises based on the user's goals and current condition. For example, this training plan might include "3 x 60-minute workouts per week, targeting different muscle groups each time."

[1380] When the user starts training, they put on MR glasses and other real-time evaluation devices. The motion sensors and MR glasses on the device monitor the user's movements in real time during training. This information is sent to a server, which detects improper movements or form and provides appropriate corrective instructions via voice. For example, the server may provide voice feedback such as "Please correct your posture."

[1381] After completing a workout, the user inputs feedback such as fatigue and muscle pain into the device. The device then sends this feedback data to a server, which analyzes it and updates the training plan using a generative AI model. This update creates a new plan that reflects the feedback data and is optimized for the user.

[1382] Prompt Sentence Examples

[1383] "Write code that uses collected data about a user (e.g., body fat percentage 25%, muscle mass 60kg, bone density 1.2g / cm³) to generate a training plan using a generative AI model to target muscle growth for the user. Also, add the ability to detect improper form during training in real time and provide audio feedback."

[1384] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1385] Step 1:

[1386] Data collection

[1387] When a user steps onto the body composition scale, the device measures their physical information, such as their body fat percentage, muscle mass, and bone density. In addition, when the user performs stretching or balance exercises, the device's motion sensors and camera collect movement information.

[1388] Input: User's physical condition data (body fat percentage, muscle mass, bone density, etc.), movement information (flexibility, balance ability, etc.)

[1389] Output: Collected physical and movement information

[1390] Step 2:

[1391] Data transmission and storage

[1392] The device sends the collected data to the server, which stores the received data in a database.

[1393] Input: User's physical and movement information sent from the device

[1394] Output: Physical and movement information stored in a database

[1395] Step 3:

[1396] Data analysis

[1397] The server uses machine learning algorithms to analyze the data stored in the database, thereby assessing the user's physical condition and identifying areas for improvement.

[1398] Input: User's physical and movement information stored in a database

[1399] Output: User's physical condition assessment results and necessary improvements

[1400] Step 4:

[1401] Plan Generation

[1402] Based on the analysis results, the server uses a generative AI model to generate an individually optimized training plan and stores it in a database.

[1403] Input: User's physical condition assessment results and necessary improvements

[1404] Output: Generated training plan

[1405] Step 5:

[1406] Training start and real-time feedback

[1407] The user puts on the MR glasses and starts training. During the training, the device (motion sensor and MR glasses) monitors the user's movements in real time and sends the information to the server. The server detects improper movements or form and provides audio instructions for correction.

[1408] Input: User behavior information collected in real time

[1409] Output: Real-time audio feedback

[1410] Step 6:

[1411] Feedback rating and training plan updates

[1412] After completing a workout, the user inputs feedback such as fatigue and muscle pain. The device sends this feedback data to a server, which then uses generative AI to analyze the data and update the training plan.

[1413] Input: User-entered feedback data

[1414] Output: Updated training plan

[1415] (Application example 1)

[1416] 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."

[1417] Delivery workers are often overburdened in their daily work, which can increase the risk of fatigue and injury. Furthermore, delivery efficiency can decline due to a lack of knowledge of efficient routes and work methods. To solve these problems, a system is needed that collects physical and movement information from delivery workers in real time and provides appropriate feedback.

[1418] 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.

[1419] In this invention, the server includes means for collecting physical information of the user using an electronic device, means for generating a training plan based on the collected data using a generation AI, means for monitoring the user's movements using a motion sensor and a real-time evaluation device and providing feedback, means for analyzing the collected data and feedback and updating the training plan using a generation AI, and means for monitoring the physical information and movement information of the delivery person and maximizing delivery efficiency and improving safety based on the information, thereby reducing the burden on the delivery person and enabling them to perform their work efficiently and safely.

[1420] "Electronic Device" is a technological device used to collect physical and behavioral information about a delivery person.

[1421] "Generative AI" is artificial intelligence that generates optimal training and delivery plans based on collected data.

[1422] A "motion sensor" is a sensor that detects the movement characteristics of delivery personnel and evaluates them in real time.

[1423] A "real-time evaluation device" is a device that instantly analyzes collected data and provides immediate feedback to delivery personnel.

[1424] "Feedback" refers to instructions and advice provided to delivery personnel based on collected and analyzed data.

[1425] A "body composition scale" is a device that measures physical information such as a delivery person's body fat percentage, muscle mass, and bone density.

[1426] A "motion characteristic measuring device" is an instrument used to evaluate the motion characteristics of delivery personnel, such as their flexibility and balance ability.

[1427] A "smartwatch" is a wearable device that records a delivery person's physical information, such as heart rate, number of steps, and energy consumption, in real time.

[1428] "Smart glasses" are wearable devices that collect information on the delivery person's posture and movements and provide visual feedback.

[1429] The present invention provides a system that collects physical and motion information of delivery personnel and provides real-time feedback to maximize efficiency and improve safety during delivery operations. To this end, the system includes the following components:

[1430] 1. Electronic Devices (Terminals)

[1431] The user (delivery worker) wears a smartwatch or smart glasses, which collect various physical and movement information in real time, such as heart rate, number of steps, energy consumption, driving posture, walking posture, and how the package is carried.

[1432] 2. Data transmission and storage (server)

[1433] The data collected by the device is sent to a cloud server via wireless communication, and the server receives the data and stores it in a database.

[1434] 3. Data analysis (server)

[1435] The server analyzes the stored data using machine learning algorithms to assess the delivery person's current physical condition and movement patterns, using random forests or other suitable machine learning models.

[1436] 4. Feedback and Advice (Device)

[1437] Based on the analysis results, the server generates appropriate feedback and advice, such as when to take a break, suggesting an efficient route, teaching correct posture, etc. This feedback is conveyed to the user through audio output and visual presentations.

[1438] 5. Update training plan based on feedback (server)

[1439] Feedback data provided by the user (e.g., fatigue, muscle pain, distance traveled, etc.) is also sent to the server, and the training plan is updated using the generative AI.

[1440] Hardware and software used

[1441] Smartwatches and smart glasses: Used as data collection devices.

[1442] Cloud server: Stores and analyzes data.

[1443] Machine learning algorithms: using libraries such as TensorFlow and Scikit-learn.

[1444] Audio output and visual feedback devices: Provide feedback to the delivery person.

[1445] Specific examples

[1446] The user puts on the smartwatch and smart glasses and begins making deliveries, which collects data on heart rate, steps, energy expenditure, and posture.

[1447] The collected data is sent from the terminal via wireless communication to a server and stored there.

[1448] The server analyzes the data and generates an efficiency score for the delivery person using generative AI (e.g., a random forest model), and generates appropriate feedback, such as "You're tired. Please take a break."

[1449] Based on the analysis results, the device provides feedback to the delivery person via voice or visual feedback.

[1450] The user inputs the effects of training and feedback, and the feedback data is sent back to the server, where the training plan is updated using the generative AI.

[1451] Prompt Sentence Examples

[1452] "Design an algorithm to monitor the fatigue level and efficiency of delivery personnel and provide optimal feedback in real time. Data to be used includes heart rate, steps, energy consumption, and posture. It should also suggest break times and efficient routes."

[1453] This reduces the burden on delivery personnel and enables them to carry out their work efficiently and safely.

[1454] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1455] Step 1:

[1456] Data collection

[1457] Users wear smartwatches or smart glasses, which collect physical and movement information such as heart rate, number of steps, energy consumption, and posture information in real time.

[1458] The inputs are biometric and motion data obtained from smartwatches and smart glasses.

[1459] The output is an information packet containing these data.

[1460] Specifically, the sensors periodically collect data and temporarily store it in the memory of the smart glasses or smartwatch.

[1461] Step 2:

[1462] Data transmission and storage

[1463] The data collected by the device is sent to a server via wireless communication (e.g., Wi-Fi or Bluetooth), which receives the data and stores it in a cloud database.

[1464] The input is biometric data and motion data sent from the terminal.

[1465] The output is data stored in a cloud database.

[1466] Specifically, the device periodically divides data into packets and transmits them to the server via wireless communication. The server then automatically stores the received data in a database.

[1467] Step 3:

[1468] Data analysis

[1469] The server analyzes the data stored in the cloud database using machine learning algorithms (e.g., random forests), which evaluate the delivery person's current physical condition and movement patterns.

[1470] The inputs are stored biometric and motion data.

[1471] The output is an evaluation score for the delivery person's fatigue level and efficiency.

[1472] Specifically, the server standardizes the data, then analyzes it using machine learning models, generating evaluation results that are stored in a log file.

[1473] Step 4:

[1474] Providing feedback and advice

[1475] The server generates appropriate feedback and advice based on the analysis results, which is provided to the user through audio output or visual display.

[1476] The input is the evaluation score of the analysis result.

[1477] The output is feedback or advice provided to the user.

[1478] Specifically, the server generates an appropriate feedback message based on the evaluation score and sends it to an audio output device or a visual display device.

[1479] Step 5:

[1480] Get feedback and update your training plan

[1481] The user performs training and performs actions based on the feedback, and then provides feedback on the results to the input device. This feedback data (e.g., fatigue, muscle pain, distance traveled, etc.) is sent from the device to the server. The server analyzes this data and updates the training plan using generative AI.

[1482] The input is the feedback data entered by the user.

[1483] The output is an updated training plan.

[1484] Specifically, the device sends feedback data to the server, which analyzes the data, updates the training plan, and stores the results in a cloud database.

[1485] 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.

[1486] This system maximizes training efficiency and minimizes the risk of injury by collecting a user's physical and movement information, generating an individually optimized training plan based on this data, and providing real-time feedback. Furthermore, it combines an emotion engine that recognizes the user's emotions and optimizes training according to their emotional state.

[1487] System Overview

[1488] The system includes the following components:

[1489] 1. Electronic Devices

[1490] 2. Server

[1491] 3. Generation AI

[1492] 4. Motion Sensors and Real-Time Assessment Devices

[1493] 5. Emotion Engine

[1494] 6. Training Plan

[1495] 7. Feedback

[1496] Program processing flow

[1497] 1. Data Collection

[1498] Terminal (electronic device): Collects the user's physical information (body fat percentage, muscle mass, bone density, etc.) and measures movement characteristics (flexibility, balance ability, etc.) using motion sensors and cameras.

[1499] Terminal (emotion engine): Analyzes the user's facial expressions and voice patterns to recognize their emotional state.

[1500] 2. Data transmission and storage

[1501] Terminal: Sends collected data (physical information, movement information, and emotional information) to the server, which receives the data and stores it in a database.

[1502] 3. Data Analysis

[1503] Server: Preprocesses the stored data and prepares it for input to machine learning algorithms, for example by denoising and normalizing the data.

[1504] Server: Analyzes data using machine learning algorithms to assess the user's physical and emotional state.

[1505] 4. Plan Generation

[1506] Server: Based on the analysis results and emotional state, the server uses generative AI to generate an optimal training plan, including specific exercises and training schedules.

[1507] 5. Real-time feedback

[1508] User: Starts training and puts on MR glasses or other real-time assessment device.

[1509] Device: Monitors the user's movements during training in real time and evaluates whether the movements are appropriate for the training plan.

[1510] Server: Analyzes the monitored data, and if the form is inappropriate, provides the user with voice feedback and instructions for correction via the terminal.

[1511] 6. Feedback Rating and Updates

[1512] User: After completing the training, the user inputs post-training feedback (e.g., fatigue, muscle pain, satisfaction) into the terminal.

[1513] Terminal: Sends feedback data to the server.

[1514] Server: Analyzes feedback data and emotional state, and uses generative AI to update and optimize training plans.

[1515] Specific examples

[1516] collection

[1517] When the user steps on the body composition scale, the device measures a body fat percentage of 25%, muscle mass of 60kg, and bone density of 1.2g / cm³. The user performs stretching and balance exercises, and the device collects movement characteristic data.

[1518] The emotion engine collects the user's facial and voice patterns to recognize their emotional state (e.g., joy, fatigue, stress, etc.).

[1519] Send / Save

[1520] The collected data (physical information, movement information, and emotional information) is sent from the device to a server, which stores the data in a database.

[1521] analysis

[1522] The server analyzes the data and uses machine learning algorithms to assess the user's physical and emotional state. For example, it may determine that muscle mass needs to be increased and that stress levels are high during training. A generative AI model then creates a training plan that targets muscle gain and reduces stress.

[1523] Training plan generation

[1524] The server creates a training plan including specific exercises (e.g., weightlifting, relaxation exercises, mindfulness practice, etc.) based on the user's goals and current situation, and stores it in a database.

[1525] Real-time feedback

[1526] The user puts on the MR glasses and starts training. The device (motion sensor and MR glasses) monitors the user's movements in real time.

[1527] The server detects improper movements and forms and provides appropriate corrective instructions to the user via voice. It also monitors the user's emotional state and suggests training breaks and relaxation exercises if the user is feeling stressed.

[1528] Feedback Rating and Updates

[1529] After completing the training, the user inputs feedback (e.g., fatigue, muscle pain, emotional state).

[1530] The device sends the feedback data to a server, which then analyzes it and uses generative AI to update the training plan—for example, if muscle soreness is reported, the plan will be updated to adjust the load for the next workout.

[1531] This system allows users to efficiently train in a way that is optimal not only for their physical condition but also for their emotional state, maximizing results while minimizing the risk of injury.

[1532] The processing flow will be explained below.

[1533] Step 1:

[1534] The user enters their name, age, sex, height, weight, fitness level, and training goals into the device.

[1535] Step 2:

[1536] The device (body composition monitor) measures the user's muscle mass, body fat percentage, and bone density and collects the data.

[1537] Step 3:

[1538] The device (motion sensor and camera) measures the user's movement characteristics such as flexibility, balance ability, and range of motion, and collects the data.

[1539] Step 4:

[1540] The device (emotion engine) analyzes the user's facial expressions and voice patterns to recognize their emotional state. For example, a smile can be interpreted as "joy," while a stiff expression can be interpreted as "stress."

[1541] Step 5:

[1542] The terminal transmits the data obtained in steps 2, 3, and 4 to the server.

[1543] Step 6:

[1544] The server stores the received data in a database.

[1545] Step 7:

[1546] The server preprocesses the stored data and prepares it for input to machine learning algorithms, for example by denoising and normalizing the data.

[1547] Step 8:

[1548] The server analyzes the data using machine learning algorithms to assess the user's physical condition (muscle mass, body fat percentage, flexibility, etc.) and emotional state.

[1549] Step 9:

[1550] The server uses generative AI to generate an optimal training plan based on the analysis results and the user's emotional state. The plan includes specific exercises, a training schedule, and exercises tailored to the user's emotional state (e.g., adding relaxation exercises if stress levels are high).

[1551] Step 10:

[1552] The training plan generated by the server is stored in a database and notified to the device.

[1553] Step 11:

[1554] The user puts on MR glasses or other real-time evaluation devices and begins training.

[1555] Step 12:

[1556] The device (MR / AR glasses, motion sensor) monitors the user's movements during training in real time and evaluates whether the movements are appropriate for the training plan. The emotion engine also operates to continuously monitor the user's emotional state during training.

[1557] Step 13:

[1558] The server analyzes the monitored data and, if the user's form is improper or their emotional state fluctuates (e.g., stress increases), it provides the user with voice feedback and instructions for correction via the device. For example, it may say, "Your form is off. Please keep your body upright" or "Try breathing to relax."

[1559] Step 14:

[1560] The user finishes the training and inputs post-training feedback (e.g., fatigue, muscle pain, emotional state) into the terminal.

[1561] Step 15:

[1562] The terminal transmits the feedback data to the server.

[1563] Step 16:

[1564] The server analyzes the feedback data and emotional state and uses generative AI to update and optimize the training plan. For example, if muscle soreness is reported, the plan will be updated to adjust the load for the next workout. Or, if stress levels are reaching their peak, relaxation exercises will be added.

[1565] Step 17:

[1566] The server saves the updated training plan in a database and notifies the device.

[1567] Example 2

[1568] 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."

[1569] Conventional training systems generate training plans based solely on the user's physical information, making it difficult to provide an effective plan optimized for each individual user. Furthermore, because they do not take into account the user's movement characteristics or emotional state, there is a high risk of injury during training and it can lead to a loss of motivation. Furthermore, there are insufficient means of providing real-time feedback, making it difficult to make immediate adjustments to maximize training effectiveness.

[1570] 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.

[1571] In this invention, the server includes: means for collecting a user's physical information using an electronic device; means for generating a training plan based on the collected data using a generation AI; means for monitoring the user's movements using a motion sensor and a real-time evaluation device to measure movement characteristics and providing feedback; means for analyzing the user's facial expressions and voice patterns using an emotion engine to recognize their emotional state; and means for analyzing the collected data, feedback, and emotional state and updating the training plan using the generation AI. This allows for the generation and provision of an individually optimized training plan in real time that comprehensively takes into account the user's physical information, movement characteristics, and emotional state. This maximizes training efficiency, minimizes the risk of injury, and increases the user's motivation.

[1572] An "electronic device" is a device for collecting physical information about a user, and includes a body composition monitor and a motion characteristic measuring device.

[1573] "Generative AI" refers to artificial intelligence technology that automatically generates training plans based on collected data.

[1574] A "motion sensor" is a sensor for measuring the movement characteristics of a user and capturing specific data about the movement.

[1575] A "real-time evaluation device" is a device for evaluating a user's actions in real time and providing immediate feedback.

[1576] An "emotion engine" refers to a system that recognizes a user's emotional state by analyzing their facial expressions and voice patterns.

[1577] "Collected Data" collectively refers to physical information, movement information, and emotional information obtained through electronic devices, motion sensors, emotion engines, etc.

[1578] "Feedback" refers to advice and corrective instructions provided during and after training.

[1579] A "training plan" is an exercise or workout plan generated by the generative AI based on the user's physical information, movement characteristics, and emotional state.

[1580] "Analysis" refers to the process of evaluating and analyzing the user's physical and emotional state based on the collected data.

[1581] The present invention is a system that collects a user's physical and movement information, generates an individually optimized training plan based on this data, and provides real-time feedback. This maximizes the user's training efficiency and minimizes the risk of injury. The present invention also incorporates an emotion engine that recognizes the user's emotions, optimizing training according to their emotional state.

[1582] The system includes the following components:

[1583] 1. Electronic Devices

[1584] 2. Server

[1585] 3. Generation AI

[1586] 4. Motion Sensors and Real-Time Assessment Devices

[1587] 5. Emotion Engine

[1588] 6. Training Plan

[1589] 7. Feedback

[1590] Data collection

[1591] The device (electronic device) accurately measures the user's physical information, such as body fat percentage, muscle mass, and bone density. For example, when a user steps on a body composition scale, the device measures a body fat percentage of 25%, muscle mass of 60 kg, and bone density of 1.2 g / cm³. The device (motion sensor and camera) also records detailed movement characteristics (flexibility, balance ability, etc.) when the user performs stretching or balance exercises. It captures balance and flexibility data for each frame as the user performs specific movements. Furthermore, the device (emotion engine) analyzes the user's facial expressions and voice patterns to recognize their emotional state in real time. For example, it can detect a smile from the user's facial expression and recognize it as "happiness," or identify signs of fatigue from their voice patterns.

[1592] Data transmission and storage

[1593] The device sends the collected data (physical, movement, and emotional information) to a server in real time. For example, it sends a series of data in packets. The server immediately stores the received data in a database. After storage, the data integrity is checked to verify that there are no missing or abnormal values.

[1594] Data analysis

[1595] The server first preprocesses the data, for example by removing noise and normalizing the received data. It then runs algorithms to stabilize the data sample rate and remove outliers. The server then uses machine learning algorithms to analyze the user's physical and emotional state. Specifically, it compares the data with the user's previous data and analyzes fluctuations in physical state and emotional trends.

[1596] Training plan generation

[1597] The server uses generative AI to generate an optimal training plan based on the analysis results and the user's emotional state. For example, it combines exercises that increase the user's muscle mass while reducing stress. The plan includes specific exercises, number of sets, rest periods, etc. The generative AI model optimizes each element and outputs it.

[1598] Real-time feedback

[1599] When a user begins training, they put on MR glasses or other real-time evaluation devices, which constantly monitor their movements. The device analyzes the user's movements in real time. For example, if their squat form is improper, they will immediately receive audio feedback such as, "Push your hips back a little more." The server then reanalyzes the data, assessing whether their form is correct and whether emotional stress is present, and suggests resting instructions or relaxation exercises as needed.

[1600] Feedback Rating and Updates

[1601] After completing a workout, the user enters feedback such as fatigue, muscle soreness, and satisfaction into the device. For example, by manually entering the feedback into a feedback form. The device then sends this feedback data to the server, where it is immediately analyzed. The server then reanalyzes the feedback data and emotional state and uses a generative AI to update the next training plan. For example, if muscle soreness is reported, the load for the next workout will be adjusted appropriately.

[1602] Example prompt sentence:

[1603] "Measure your body fat percentage, muscle mass, and bone density, and do some stretching and balance exercises."

[1604] "Please tell us your current emotional state: joy, fatigue, stress"

[1605] "Please provide feedback after the training: fatigue, muscle soreness, satisfaction"

[1606] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1607] Step 1: Data collection

[1608] The terminal (electronic device) collects the user's physical information. Specifically, it measures body fat percentage, muscle mass, bone density, etc. This collected data becomes the input. For example, when a user steps on a body composition scale, the output is a body fat percentage of 25%, muscle mass of 60 kg, and bone density of 1.2 g / cm³. In addition, the terminal (motion sensor and camera) measures the user's movement characteristics (flexibility, balance ability, etc.) and obtains movement characteristic data. When the user performs stretching or balance exercises, movement data is captured for each frame and analyzed.

[1609] Step 2: Recognizing your emotional state

[1610] The device (emotion engine) analyzes the user's facial expressions and voice patterns in real time to recognize their emotional state. For example, if the user smiles, it recognizes this as "joy." Signs of fatigue are identified from the voice pattern and recorded in a database. This recognized data is input, and the emotional state is output.

[1611] Step 3: Send and save data

[1612] The device transmits the collected physical information, movement characteristic data, and emotional state data to a server. The server receives this data and stores it in a database. For example, the collected data may be sent in packets, which the server receives. This received data is the input, and the stored data is the output.

[1613] Step 4: Data Preprocessing

[1614] The server preprocesses the stored data. Specifically, it performs noise reduction and normalization on the received data. For example, it runs algorithms to remove outliers and normalize the data sample rate. This stored data is input as preprocessed data, and normalized data is output.

[1615] Step 5: Data analysis

[1616] The server uses machine learning algorithms to analyze the normalized data. The server analyzes the user's physical and emotional state. For example, it compares the data with previous data to identify fluctuations in physical state and emotional trends. The preprocessed data is used as input for the machine learning algorithm, which outputs the analysis results.

[1617] Step 6: Create a training plan

[1618] The server uses generative AI to generate an optimal training plan based on the analysis results and emotional state data. For example, it might suggest exercises that increase muscle mass while relieving stress. The analysis results and emotional state are input, and a training plan is output.

[1619] Step 7: Real-time feedback

[1620] The user puts on MR glasses or other real-time evaluation devices and begins training. The device monitors and analyzes the user's movements in real time. For example, if the user's form in a squat is incorrect, the device immediately provides audio feedback such as "pull your hips back a little more." This real-time data is the input, and the audio feedback is the output.

[1621] Step 8: Feedback evaluation and updates

[1622] After completing a workout, the user inputs feedback such as fatigue, muscle soreness, and satisfaction into the device. The device then sends this feedback data to the server, which immediately analyzes it. For example, if muscle soreness is reported, the load for the next workout can be adjusted. This feedback data is the input, and an updated training plan is the output.

[1623] (Application example 2)

[1624] 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."

[1625] Many conventional training systems and task optimization systems collect a user's physical and motion information to provide training and task plans, but often do not provide real-time feedback or update the plans to take the user's emotional state into account. This can lead to problems such as accumulated stress and fatigue in the user, preventing optimal performance or increasing the risk of injury. The present invention aims to solve these problems and support efficient and safe training and tasks by providing individually optimized plans tailored to the user's condition and real-time feedback.

[1626] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1627] In this invention, the server includes means for collecting physical information of the user using an electronic device, means for generating a training plan based on the collected data using a generation AI, means for monitoring the user's movements using a motion sensor and a real-time evaluation device and providing feedback, means for analyzing the collected data and feedback and updating the training plan using the generation AI, means for recognizing the worker's emotional state and generating an individually optimized work plan based on the analysis results, and means for evaluating the user's posture and movements during the work according to the generated work plan in real time and providing appropriate feedback. This enables the provision of an individually optimized plan based on the user's physical and emotional state and real-time feedback.

[1628] "Electronic device" refers to a device that collects physical information about the user, and is responsible for measuring the user's body composition and movement characteristics.

[1629] "Generative AI" is artificial intelligence that generates optimal training and work plans based on collected data. It uses machine learning algorithms to analyze the data and create personalized plans.

[1630] A "motion sensor" is a sensor that monitors the user's movements in real time and collects information about that movement. It can accurately measure the user's posture and movements.

[1631] A "real-time evaluation device" is a device that instantly evaluates a user's movements and provides appropriate feedback. It can analyze the user's movements in real time and provide necessary instructions.

[1632] "Emotional state" refers to the user's current emotional state, as recognized by analyzing the user's facial expressions and voice patterns. It is used to determine the user's stress, fatigue, excitement, etc.

[1633] "Training Plan" refers to an exercise plan generated based on the user's physical and movement information. It consists of individually optimized exercises and schedules.

[1634] The "work plan" is a work plan generated based on the user's physical and movement information to maximize work efficiency. It is optimized taking into account the user's health and emotional state.

[1635] "Feedback" refers to real-time instructions, such as movement or posture corrections, provided to users to help them train or perform tasks effectively and safely.

[1636] "Data analysis" is the process of analyzing collected physical, movement, and emotional information using machine learning algorithms to assess the user's condition.

[1637] "Update" is the process by which the generative AI reevaluates existing training and work plans based on collected data and feedback, and changes them to optimal plans.

[1638] This invention is a system for maximizing the training and work efficiency of factory workers. The system collects the user's physical information, movement information, and emotional state, and uses generative AI to generate optimal training and work plans based on this data and provides real-time feedback.

[1639] Hardware and software used

[1640] Hardware

[1641] Electronic devices: Examples include body composition monitors and performance measurement devices.

[1642] Motion sensor: A sensor for monitoring user movements in real time.

[1643] Real-time assessment devices: Examples include head-mounted displays (HMDs) and smart glasses. These devices assess the user's performance in real time and provide feedback.

[1644] Emotion engine: A device that analyzes the user's facial expressions and voice patterns to recognize their emotional state.

[1645] software

[1646] Generative AI: Specifically, there are MachineLearningModel and GenerateAIModel. These models are used to generate optimal plans for users based on collected data.

[1647] Data collection

[1648] When a user steps onto the body composition scale, an electronic device collects physical information such as heart rate, body fat percentage, and muscle mass. Next, as the user moves, a motion sensor collects movement characteristics. An emotion engine also collects the user's facial expressions and voice patterns to recognize their emotional state.

[1649] Data transmission and storage

[1650] The collected data is sent from the device to a server and stored in a database, which then performs preprocessing for analyzing the data.

[1651] Data analysis

[1652] The server preprocesses the stored physical, motion, and emotional information and analyzes the data using machine learning algorithms to assess the user's physical and emotional state.

[1653] Generate training and work plans

[1654] Based on the analysis results, the server uses generative AI to generate individually optimized training and work plans, including specific exercise and work schedules.

[1655] Real-time feedback

[1656] When a user begins training or performing a task, the real-time assessment device monitors the user's behavior and detects improper behavior or form. The server analyzes the data and provides appropriate feedback, such as a voice prompt to "correct your form."

[1657] Feedback Rating and Updates

[1658] After completing training or work, users input their feedback into the device. The collected feedback data is sent to the server, and the generation AI is used to update the next training plan or work plan.

[1659] Specific examples

[1660] Imagine a worker is working to carry heavy loads. Sensors collect information such as body fat percentage, heart rate, movement speed, and facial expressions, and the data is analyzed by a server. If the worker's emotional state is determined to be "feeling tired today" before starting work, the server generates instructions on safe lifting methods and the recommendation to take longer breaks. During work, motion sensors check the worker's lifting posture and provide feedback via the HMD, such as "Please bend your knees more when lifting." If the worker gives feedback after work such as "My lower back hurts a little," that data is also used for the next optimization.

[1661] Prompt Sentence Examples

[1662] User ID: worker_123, Physical information: {Heart rate: 85, Body fat percentage: 18%, Muscle mass: 70kg}, Movement information: {Movement speed: Medium, Flexibility: High}, Emotional state: Fatigue

[1663] Based on this data, generate an optimal work plan that maximizes work efficiency while reducing stress, and be sure to include appropriate breaks.

[1664] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1665] Step 1:

[1666] The server uses an electronic device to collect the user's physical information. Specifically, the user steps on a body composition scale to obtain data such as heart rate, body fat percentage, and muscle mass. This data is sent to the server as collected data. The input is physical information, and the output is the collected data.

[1667] Step 2:

[1668] The server uses a motion sensor and an emotion engine to collect the user's movement information and emotional state. The emotion engine analyzes the user's facial expressions and voice patterns to recognize the user's emotional state. Movement information includes movement speed, flexibility, balance, etc. The input is movement information and emotion information, and the output is the data.

[1669] Step 3:

[1670] The server stores the collected data in a database. The stored data includes physical information, movement information, and emotion information. The input is the collected data, and the output is storage in the database.

[1671] Step 4:

[1672] The server preprocesses the stored data, preparing it for input into the generative AI model. Preprocessing includes denoising and normalizing the data. This process transforms the data into a form suitable for analysis. The input is the stored data, and the output is the preprocessed data.

[1673] Step 5:

[1674] The server uses the preprocessed data to run machine learning algorithms to assess the user's physical and emotional states. Specifically, the analysis generates physical state assessment indicators and emotional state determinations. The input is the preprocessed data, and the output is the analysis results.

[1675] Step 6:

[1676] The server uses a generative AI model based on the analysis results to generate optimal training and work plans. The generative AI model uses prompts to suggest specific exercises and work schedules. This plan is stored in a database. The input is the analysis results and prompts, and the output is the generated plan.

[1677] Step 7:

[1678] The user wears the real-time evaluation device and begins training or work. The motion sensor monitors the user's movements and sends the information to the server. The input is real-time movement information, and the output is monitored movement information.

[1679] Step 8:

[1680] The server analyzes the monitored movement information in real time and detects improper movements or forms. The server provides feedback through the real-time evaluation device as needed. For example, it issues a voice command such as "Bend your knees more and lift." The input is the monitored movement information, and the output is the feedback.

[1681] Step 9:

[1682] The user inputs feedback after completing training or work. The feedback includes fatigue, muscle pain, emotional state, etc. This feedback is sent to the server via the terminal. The input is the feedback information, and the output is the information sent to the server.

[1683] Step 10:

[1684] The server analyzes the collected feedback data and emotional state and uses a generative AI model to update the training and work plans, which further optimizes the next training or work session. The input is the feedback data, and the output is the updated plan.

[1685] 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.

[1686] 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.

[1687] 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.

[1688] 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.

[1689] 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.

[1690] 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.

[1691] 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).

[1692] 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.

[1693] 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."

[1694] 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.

[1695] 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).

[1696] 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.

[1697] 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.

[1698] 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.

[1699] 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.

[1700] 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.

[1701] 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.

[1702] 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.

[1703] 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.

[1704] 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.

[1705] 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.

[1706] The following is further disclosed regarding the above embodiment.

[1707] (Claim 1)

[1708] A means for collecting physical information of a user using an electronic device;

[1709] a means for generating a training plan based on the collected data using a generative AI;

[1710] means for monitoring user behavior and providing feedback using motion sensors and real-time evaluation devices;

[1711] a means for analyzing the collected data and feedback and updating the training plan using generative AI;

[1712] A system including:

[1713] (Claim 2)

[1714] 10. The system of claim 1, wherein the feedback is provided as an audio output.

[1715] (Claim 3)

[1716] 10. The system of claim 1, wherein the electronic device includes a body composition monitor and a performance characteristic measuring device.

[1717] "Example 1"

[1718] (Claim 1)

[1719] A means for collecting physical information of a user using an electronic device;

[1720] a means for generating a training plan based on the collected data using a generative AI;

[1721] means for monitoring user behavior and providing feedback using motion sensors and real-time evaluation devices;

[1722] a means for analyzing the collected data and feedback and updating the training plan using generative AI;

[1723] A means for collecting user behavior information using a camera;

[1724] a means for assessing physical condition using a machine learning algorithm;

[1725] a means of storing the training plan in a database;

[1726] A system including:

[1727] (Claim 2)

[1728] 10. The system of claim 1, wherein the feedback is provided as an audio output.

[1729] (Claim 3)

[1730] 10. The system of claim 1, wherein the electronic device includes a body composition monitor and a performance characteristic measuring device.

[1731] "Application Example 1"

[1732] (Claim 1)

[1733] A means for collecting physical information of a user using an electronic device;

[1734] a means for generating a training plan based on the collected data using a generative AI;

[1735] means for monitoring user behavior and providing feedback using motion sensors and real-time evaluation devices;

[1736] a means for analyzing the collected data and feedback and updating the training plan using generative AI;

[1737] A system that monitors the physical and movement information of delivery personnel and uses that information to maximize delivery efficiency and improve safety.

[1738] (Claim 2)

[1739] 10. The system of claim 1, wherein the feedback is provided as an audio output.

[1740] (Claim 3)

[1741] 2. The system of claim 1, wherein the electronic device includes a body composition monitor, a performance characteristic measuring device, a smart watch, and smart glasses.

[1742] "Example 2: Combining Emotion Engines"

[1743] (Claim 1)

[1744] A means for collecting physical information of a user using an electronic device;

[1745] a means for generating a training plan based on the collected data using a generative AI;

[1746] means for monitoring user movements and providing feedback using motion sensors and real-time evaluation devices to measure movement characteristics;

[1747] a means for recognizing an emotional state of a user by analyzing facial expressions and voice patterns using an emotion engine;

[1748] A means of analyzing collected data, feedback, and emotional state and updating training plans using generative AI;

[1749] A system including:

[1750] (Claim 2)

[1751] 10. The system of claim 1, wherein the feedback is provided as an audio output.

[1752] (Claim 3)

[1753] 10. The system of claim 1, wherein the electronic device includes a body composition monitor and a performance characteristic measuring device.

[1754] "Application example 2 when combining emotion engines"

[1755] (Claim 1)

[1756] A means for collecting physical information of a user using an electronic device;

[1757] a means for generating a training plan based on the collected data using a generative AI;

[1758] means for monitoring user behavior and providing feedback using motion sensors and real-time evaluation devices;

[1759] a means for analyzing the collected data and feedback and updating the training plan using generative AI;

[1760] A means for recognizing the emotional state of a worker and generating an individually optimized work plan based on the analysis results;

[1761] a means for evaluating the posture and movement of the user in real time while performing the task according to the generated task plan and providing appropriate feedback;

[1762] A system including:

[1763] (Claim 2)

[1764] 10. The system of claim 1, wherein the feedback is provided as an audio output.

[1765] (Claim 3)

[1766] 10. The system of claim 1, wherein the electronic device includes a body composition monitor and a performance characteristic measuring device. [Explanation of symbols]

[1767] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting physical information of a user using an electronic device; a means for generating a training plan based on the collected data using a generative AI; means for monitoring user behavior and providing feedback using motion sensors and real-time evaluation devices; a means for analyzing the collected data and feedback and updating the training plan using generative AI; A system including:

2. 10. The system of claim 1, wherein the feedback is provided as an audio output.

3. 10. The system of claim 1, wherein the electronic device includes a body composition monitor and a performance characteristic measuring device.

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