System

Abstract

A system is provided.SOLUTION: A system includes a means for acquiring body composition data, a means for providing an artificial intelligence model for analyzing a health condition, a means for generating and displaying an avatar image on the basis of an analysis result, a means for providing simple advice based on the avatar image and the analysis result to a user, and a charging service for providing detailed health advice.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] In modern society, the aging population is progressing, making health management and prevention of pre-diseases important issues. In particular, there is a need for means to provide appropriate health information and support self-management for people who have few opportunities to undergo regular health checkups at work. Furthermore, it is necessary to effectively utilize collected health data and enable users to visually understand their own health status. To address these issues, the present invention aims to provide a system that utilizes body composition data to analyze health status, visualizes it as an avatar image, and provides detailed health advice. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for acquiring body composition data, a means for providing an artificial intelligence model for analyzing health status, a means for generating and displaying an avatar image based on the analysis results, a means for providing the user with simple advice based on the avatar image and the analysis results, and a billable service for providing detailed health advice. The system further includes a means for predicting the user's future health risks using big data and a means for continuously monitoring changes in the user's health status and providing appropriate feedback. This allows users to visually understand their own health status and effectively manage their health as needed.

[0006] "Body composition data" refers to data relating to the composition of the body, such as weight, body fat percentage, bone density, and heart rate.

[0007] An "artificial intelligence model" refers to a group of algorithms that use technologies such as machine learning and deep learning to analyze specific patterns and trends from input data and make predictions.

[0008] "Avatar image" refers to a visual character image that reflects the user's physical characteristics and health condition.

[0009] "Simple advice" refers to simple suggestions for maintaining and improving health that users can put into practice in their daily lives, based on the analysis results.

[0010] "Charged service" refers to a service that is provided on the condition that the user pays money in order to use additional detailed services or information.

[0011] "Big data" refers to a large and diverse set of data that, when analyzed, can yield insights and predictions that could not be discovered using conventional data processing methods.

[0012] "Health risk prediction" is the process of predicting future health conditions and possible risks based on current data and past statistics.

[0013] "Monitoring" means continuously collecting and analyzing data to keep track of the user's condition and changes.

[0014] "Feedback" refers to the act of providing users with guidelines for action and areas for improvement based on collected and analyzed data. [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 acquires body composition data through a dedicated app, analyzes health status using generative AI based on the data, and provides the results to users as a visualized avatar image and simple advice. It also includes a paid service for providing detailed health advice and a function for predicting future health risks using big data. The present invention is specifically implemented using the following means.

[0037] System configuration

[0038] 1. Data acquisition means: A module for acquiring data from body composition monitors and healthcare apps.

[0039] 2. Analysis tools: Artificial intelligence models to analyze the acquired data.

[0040] 3. Visualization means: An interface for generating and displaying avatar images based on the analysis results.

[0041] 4. Advice Providing Tool: A module for providing simple and detailed advice to users.

[0042] 5. Billing service means: A billing system for providing detailed health advice.

[0043] 6. Continuous monitoring measures: The ability to track changes in health status and provide feedback.

[0044] Specific Examples of the Invention

[0045] Program processing explanation

[0046] 1. Data acquisition and integration settings

[0047] Server: To obtain the user's body composition data through a dedicated app, the server connects to the body composition monitor and healthcare app, collecting data such as weight, body fat percentage, bone density, and heart rate.

[0048] Device: A dedicated app is installed on the user's smartphone, and a screen is provided for setting up the connection with the body composition scale and healthcare app.

[0049] User: Follow the instructions in the dedicated app and allow it to connect to the body composition scale and healthcare app.

[0050] 2. Data analysis using generative AI

[0051] Server: The acquired data is input into an AI model to analyze the user's health condition, thereby calculating health indicators such as biological age and bone density.

[0052] Server: Generates an avatar image based on the analysis results, visually reflecting the user's health condition.

[0053] 3. Display of results and simple advice

[0054] On the device, the generated avatar image and a description of the user's health condition are displayed. For example, if the user's biological age is higher than their actual age, the device will inform the user that they need to exercise less or improve their diet.

[0055] On your device: Displays simple advice based on your health status, such as "To increase your daily step count, aim for 10,000 steps a day."

[0056] 4. Detailed advice through paid services

[0057] User: Follows a link provided within the app and purchases in-depth advice using a paid service.

[0058] Server: Once the charge is successful, the server generates personalized health check results based on detailed health data, a dietary improvement plan, future health predictions, and an appropriate exercise program.

[0059] On the device: The detailed advice provided with the purchase is displayed to the user, providing specific instructions such as, "Your calcium intake is low, so drink milk or yogurt every day."

[0060] 5. Continuous monitoring and feedback

[0061] Device: Periodically sends new health data to the server to reflect updated health status.

[0062] Server: Continuously monitors the user's health data and provides immediate feedback if any abnormalities are detected.

[0063] On the device: Continuously updates avatars, health advice, and notifies users.

[0064] In this way, the present invention provides a system that analyzes and visualizes health conditions based on body composition data, and supports effective health management for users.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] Users: Install the dedicated app on their smartphone and create an account.

[0068] Step 2:

[0069] Device: Display the settings screen for linking with a body composition scale or healthcare app.

[0070] Step 3:

[0071] User: Allows integration with body composition scales and healthcare apps.

[0072] Step 4:

[0073] Device: After authorization, data such as weight, body fat percentage, bone density, and heart rate will be collected.

[0074] Step 5:

[0075] Terminal: Sends the acquired data to the server.

[0076] Step 6:

[0077] Server: Receives the transmitted data and organizes it for each user.

[0078] Step 7:

[0079] Server: Inputs the organized data into an artificial intelligence model to analyze the user's health condition.

[0080] Step 8:

[0081] Server: Based on the analysis results, an avatar image is generated that reflects the user's biological age, bone density, etc.

[0082] Step 9:

[0083] Server: Sends the generated avatar image and health status description data to the device.

[0084] Step 10:

[0085] Terminal: Displays an avatar image and health status description to the user.

[0086] Step 11:

[0087] On your device: Displays simple advice based on your health status in the form of a comment (e.g., "You are not getting enough exercise. We recommend walking for 30 minutes three times a week.").

[0088] Step 12:

[0089] Users: Click on the billing service link and select additional detailed advice.

[0090] Step 13:

[0091] Terminal: Displays a payment screen and prompts the user to enter billing information.

[0092] Step 14:

[0093] Terminal: Processes the payment and, if successful, sends the information to the server.

[0094] Step 15:

[0095] Server: Based on the user's detailed data, it generates personalized health checkup results, dietary improvement plans, future health predictions, and appropriate exercise programs.

[0096] Step 16:

[0097] Server: Sends the generated detailed advice to the device.

[0098] Step 17:

[0099] On the device: Display detailed health advice to the user (e.g., "Your calcium intake is low, so drink milk or yogurt every day").

[0100] Step 18:

[0101] Device: Periodically sends new health data to the server.

[0102] Step 19:

[0103] Server: Analyzes new data and updates the avatar and adjusts advice based on changes in health.

[0104] Step 20:

[0105] On the device: Displays continuously updated avatar images, advice, and notifications to the user.

[0106] In this way, the entire process starts with acquiring the user's body composition data, then visualization and advice based on the analysis results, and includes a fee-based service for detailed advice and continuous monitoring, allowing users to track and manage their own health status.

[0107] Example 1

[0108] 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."

[0109] In modern society, the importance of individual health management is increasing. However, many people find it difficult to manage their daily health on their own, and have limited opportunities to receive professional advice. Furthermore, there are insufficient tools for accumulating health data and assessing long-term health risks. To solve these issues, a system is needed that can accurately grasp each individual's health status, visualize it in an easy-to-understand manner, and provide continuous monitoring and feedback.

[0110] 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.

[0111] In this invention, the server includes means for acquiring body composition data, means for providing an artificial intelligence model for analyzing health conditions, and means for generating and displaying an avatar image based on the analysis results, thereby enabling users to visually grasp their individual health conditions and receive easy-to-understand feedback, thereby enabling them to more effectively manage their own health conditions.

[0112] "Body composition data" refers to data related to the composition of the user's body, such as weight, body fat percentage, bone density, and heart rate.

[0113] An "artificial intelligence model" is a program or system that uses machine learning and data analysis algorithms to analyze health conditions based on acquired data.

[0114] An "avatar image" is a graphical image of a person that visually displays the user's health condition based on the analysis results.

[0115] "Simple advice" is a message that provides basic health management guidelines that can be put into practice in daily life based on the user's health condition.

[0116] A "billing system" is a system that collects fees from users for providing detailed health advice or additional services.

[0117] "Big data" refers to large amounts of health data collected from many users, a dataset that can be analyzed to gain valuable insights.

[0118] "Health risks" are the risk of health problems or diseases that may occur in the future based on the user's current health condition and lifestyle habits.

[0119] "Continuous monitoring" is the process of regularly and continuously collecting and analyzing a user's health data to track changes.

[0120] "Feedback" refers to information such as advice, warnings, and recommendations provided to users based on analysis results and monitoring data.

[0121] The present invention is a system that uses body composition data to analyze health conditions and provides visual feedback and specific health advice. Specific embodiments of this system will be described below.

[0122] Data acquisition and integration settings

[0123] The server configures the connection with the body composition monitor and healthcare app to obtain the user's body composition data through a dedicated app. This connection is granted by OAuth authentication, etc., and the server obtains data such as the user's weight, body fat percentage, bone density, and heart rate.

[0124] The device provides a screen for installing a dedicated app on the user's smartphone and displays an interface for setting up connectivity with the body composition scale and healthcare app. For example, the app can connect to the body composition scale using Bluetooth or Wi-Fi.

[0125] The user follows the instructions in the dedicated app to allow the body composition scale to connect to the healthcare app. Specifically, this involves pressing the corresponding button on the body composition scale within the app to have the device recognized.

[0126] Data analysis with generative AI

[0127] The server inputs the acquired body composition data into an artificial intelligence model (hereinafter referred to as the "generative AI model"). For example, data such as weight, body fat percentage, bone density, and heart rate measured by the user in the morning are sent to the server.

[0128] The server then feeds this data into a generative AI model, which analyzes the user's health from multiple angles. Specifically, the AI ​​model uses machine learning algorithms to calculate factors such as biological age and bone density.

[0129] The server generates an avatar image based on the analysis results, visually reflecting the user's health status. For example, if the user has a high body fat percentage, the avatar will be displayed as slightly overweight.

[0130] Display of results and simple advice

[0131] The device displays the generated avatar image and a description of the user's health status. For example, the app screen might say, "Your biological age is 35."

[0132] The device will display simple advice based on your health status, such as "To increase your daily step count, aim for 10,000 steps per day," and may also encourage users to drink at least eight glasses of water per day.

[0133] Detailed advice via paid services

[0134] The user clicks on a link provided within the app to proceed to a billing service screen, which may say, for example, "Please pay to receive detailed health checkup results."

[0135] Once the user's payment is successful, the server generates personalized health checkup results and dietary improvement plans based on the detailed health data. Specifically, the generative AI model performs further detailed analysis and generates advice based on the user's lifestyle habits.

[0136] The device will then display detailed advice to the user about the purchase, including specific instructions such as "Your calcium intake is low, so drink milk or yogurt every day."

[0137] Ongoing monitoring and feedback

[0138] The device periodically sends new health data to the server. For example, a user measures their weight and body fat percentage every Sunday and uploads the data to the server.

[0139] The server continuously monitors the user's health data and provides immediate feedback if any abnormalities are detected. For example, if a sudden weight gain is detected, an alert will be sent saying, "Your weight has increased rapidly recently. Please be careful about your diet and exercise."

[0140] The device continuously updates the avatar and health advice and notifies the user. For example, every time health data is updated, the device notifies the user of "how their health status has changed" along with the latest avatar image.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Step 1:

[0143] Data acquisition and integration settings

[0144] The server acquires the user's body composition data from a body composition scale or healthcare app connected to the dedicated app. The acquired data includes weight, body fat percentage, bone density, heart rate, etc. This provides basic data for accurately understanding the user's physical condition.

[0145] The device displays a screen for installing a dedicated app on the user's smartphone and provides an interface for setting up linkage with the body composition scale and healthcare app. Specifically, the app connects to the body composition scale using Bluetooth or Wi-Fi and associates it with the user ID.

[0146] The user follows the instructions in the dedicated app to allow the body composition scale to connect to the healthcare app. Specifically, the user presses the corresponding button on the body composition scale within the app to have the device recognized.

[0147] Step 2:

[0148] Data analysis with generative AI

[0149] The server inputs the acquired body composition data into the generative AI model. This input data includes weight, body fat percentage, bone density, heart rate, etc. The AI ​​model uses this data to analyze the user's health condition and calculate health indicators such as biological age and bone density. Specifically, it uses a machine learning algorithm to perform the analysis and compare the user's data with other databases to evaluate their health condition.

[0150] As an output, the AI ​​model calculates health indicators and returns them to the server, providing the results as numerical data reflecting the user's health status.

[0151] Step 3:

[0152] Avatar image generation and result display

[0153] The server generates an avatar image based on the analysis results. This avatar image visually reflects the user's health condition, and includes operations such as "if the body fat percentage is high, the avatar will be displayed slightly overweight."

[0154] The device displays the generated avatar image and a description of the user's health condition. Specifically, the app screen displays a message such as "Your biological age is 35" along with the avatar image.

[0155] Step 4:

[0156] Providing simple advice

[0157] The device will display simple advice to the user based on their health status, such as "To increase your daily step count, aim for 10,000 steps per day," and will also display a message encouraging the user to drink at least eight glasses of water per day.

[0158] This advice is developed based on the analysis results of the generative AI model and is notified to the user via the app.

[0159] Step 5:

[0160] Chargeable service with detailed advice

[0161] The user clicks on a link provided within the app to proceed to a billing service screen, which may say, for example, "Please pay to receive detailed health checkup results."

[0162] Once the user has successfully completed the payment, the server generates personalized health checkup results and a diet plan based on the user's detailed health data. The AI ​​model then performs further detailed analysis and generates advice based on the user's lifestyle habits.

[0163] The device will then display detailed advice to the user about the product they purchased, such as "Your calcium intake is low, so drink milk or yogurt every day."

[0164] Step 6:

[0165] Ongoing monitoring and feedback

[0166] The device periodically sends new health data to the server. For example, a user measures their weight and body fat percentage every Sunday and uploads the data to the server.

[0167] The server continuously monitors the user's health data and provides immediate feedback if any abnormalities are detected. For example, if a sudden weight gain is detected, it will send an alert saying, "Your weight has increased rapidly recently. Please be careful about your diet and exercise."

[0168] The device continuously updates the avatar and health advice and notifies the user. Each time the data is updated, the device notifies the user of the latest avatar image and how their health status has changed.

[0169] (Application example 1)

[0170] 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."

[0171] Health management is an important issue in today's world, but conventional systems have struggled to quickly and effectively analyze users' body composition data and present it in an intuitively understandable format. Furthermore, they lacked the functionality to provide continuous monitoring and individual feedback, making it difficult to grasp users' health status and implement improvement measures. Furthermore, there was a lack of billing services to provide detailed health advice, and a system that integratedly enabled users to set health goals and track their progress. These issues need to be resolved.

[0172] 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.

[0173] In this invention, the server includes means for acquiring body composition data, means for providing an artificial intelligence model for analyzing health status, means for generating and displaying an avatar image based on the analysis results, means for providing the user with simple advice based on the avatar image and the analysis results, means for including a billing service for providing detailed health advice, means for continuously monitoring user data and providing appropriate feedback, means for setting the user's health goals and tracking progress toward those goals, additional means for displaying the visualization results of the health status, and means for performing billing service processing. This enables analysis and visualization of the user's health status based on the user's body composition data, individualized health advice, continuous health monitoring and feedback, provision of detailed advice, and health goal setting and progress management.

[0174] "Body composition data" refers to data relating to the composition of the human body, such as weight, body fat percentage, bone density, and heart rate.

[0175] An "artificial intelligence model" is an algorithm or machine learning model used to analyze a user's health condition.

[0176] An "avatar image" is a virtual image that visually reflects the user's health condition.

[0177] "Quick Advice" is basic health recommendations provided to the user.

[0178] A "billing service" is a system that charges users a fee for providing detailed health advice and individual plans.

[0179] "Continuous user data monitoring" refers to the regular collection of a user's health data over time and tracking changes.

[0180] "Feedback" refers to advice or warnings provided to users based on analysis results and monitoring data.

[0181] "Setting health goals" means defining specific health indicators and action plans that a user aims to achieve.

[0182] "Progress tracking" refers to monitoring and recording a user's progress toward defined health goals.

[0183] The "visualization result of health status" is information that displays the user's health status based on the analysis results in an intuitive and easy-to-understand manner.

[0184] "Billing service processing" refers to managing the process by which users pay for detailed health services and advice.

[0185] MODE FOR CARRYING OUT THE INVENTION

[0186] As an embodiment of the present invention, a description will be given of how to specifically realize an application example.

[0187] Server Roles

[0188] The server uses the following hardware and software to perform processes such as acquiring, analyzing, visualizing, and providing advice on various types of data.

[0189] 1. Data Acquisition:

[0190] Hardware: Body composition scale, smartphone, wearable device.

[0191] Software: A dedicated application for collecting healthcare data.

[0192] Description: The server obtains the user's body composition data (weight, body fat percentage, bone density, heart rate, etc.) from a body composition scale or wearable device.

[0193] 2. Data Analysis:

[0194] Hardware: Server.

[0195] Software: Generative AI model (HealthAIModel).

[0196] Description: The server inputs the acquired body composition data into a generative AI model to analyze the user's health condition, thereby calculating health indicators such as biological age and bone density.

[0197] 3. Visualization:

[0198] Hardware: Server, user's smartphone.

[0199] Software: Avatar Generator.

[0200] Description: An avatar image is generated based on the analysis results and displayed on the user's smartphone.

[0201] 4. Providing advice:

[0202] Hardware: Server, user's smartphone.

[0203] Software: Advice delivery module.

[0204] Description: The server provides simple advice to the user based on the analysis results. In addition, the server provides detailed health advice to the user by paying for a service.

[0205] 5. Continuous monitoring and feedback:

[0206] Hardware: Servers, smartphones.

[0207] Software: Monitoring and feedback module.

[0208] Description: The server continuously monitors changes in the user's health status and provides immediate feedback if any abnormalities are detected. It periodically retrieves new health data and reflects the updated health status.

[0209] 6. Billing Service Processing:

[0210] Hardware: Smartphones, servers.

[0211] Software: Payment Gateway.

[0212] Description: When a user purchases detailed health advice, the server processes the billing and, if successful, provides the detailed advice.

[0213] Specific examples

[0214] Take the example of an application provided by a fitness gym. When a user steps on a body composition scale, the data is automatically sent to a dedicated application. The server acquires this data and analyzes it using a generative AI model. The resulting avatar image is displayed on the user's smartphone, and basic health advice is provided. If the user desires additional, detailed advice, it can be obtained through an in-app purchase.

[0215] Example prompts for input to a generative AI model:

[0216] Weight: 70kg, body fat percentage: 20%, bone density: 1.2g / cm3, heart rate: 70bpm

[0217] Analyze the user's health status and provide appropriate advice.

[0218] Thus, based on the specific form for implementing the invention, it is possible to analyze and visualize a user's health status, provide personalized health advice, provide continuous monitoring and feedback, provide detailed advice, and set health goals and manage their progress.

[0219] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0220] Program processing steps

[0221] Step 1:

[0222] Data Acquisition

[0223] The server acquires body composition data. When a user uses a body composition scale or wearable device, the body composition data (weight, body fat percentage, bone density, heart rate, etc.) is sent from these devices to the server via a dedicated application. The input is the user's body composition scale data, and the output is the body composition data accumulated on the server. This data is saved in a database for use in later analysis.

[0224] Step 2:

[0225] Data analysis

[0226] The server inputs the acquired body composition data into a generative AI model (HealthAIModel) to analyze the user's health condition. Based on a series of input data, the AI ​​model processes the data and calculates health indicators such as biological age, bone density, and heart rate. The input here is the body composition data stored on the server, and the output is the analysis results. Data processing involves normalizing the data and extracting features, and converting it into a format suitable for the AI ​​model.

[0227] Step 3:

[0228] Visualization

[0229] The server generates an avatar image based on the analysis results. An avatar generation tool (AvatarGenerator) is used to create an avatar image that visually represents the analyzed health indicators. The input to this step is the analysis results, and the output is an avatar image. Specifically, the analysis results are input as prompts into the avatar generation tool, and the tool automatically generates an avatar image.

[0230] Step 4:

[0231] Providing advice

[0232] The server provides the generated avatar image and simple advice to the user. Based on the analysis results, the AI ​​model automatically generates prompts to provide appropriate health advice to the user. For example, advice such as "To increase your daily step count, aim for 10,000 steps a day" is displayed. The input for this step is the health analysis results and prompts, and the output is simple advice and a visualized avatar image.

[0233] Step 5:

[0234] Detailed advice and billing services

[0235] If a user wants detailed health advice, they use the billing service through their terminal. The server processes the billing using a payment gateway. If the billing is successful, detailed advice is displayed on the user's terminal. Specifically, the server receives a billing request from the user and makes the payment via the payment gateway. The input is the user's billing request information, and the output is the result of the billing process and detailed advice.

[0236] Step 6:

[0237] Ongoing monitoring and feedback

[0238] The server continuously monitors changes in the user's health condition. It periodically acquires the user's body composition data and reanalyzes the health condition based on that data. If an abnormality is detected, it provides immediate feedback. The input to this step is the continuously collected body composition data, and the output is an updated health condition report and feedback. Specifically, the server automatically acquires and analyzes data according to a regular data collection schedule.

[0239] Prompt Sentence Examples

[0240] Weight: 70kg, body fat percentage: 20%, bone density: 1.2g / cm3, heart rate: 70bpm

[0241] Analyze the user's health status and provide appropriate advice.

[0242] 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.

[0243] The present invention is a system that acquires body composition data through a dedicated app, analyzes health status using generative AI based on the data, and provides the results to the user as a visualized avatar image and simple advice. In addition, by combining this with an emotion engine that recognizes the user's emotions, more personalized advice and feedback can be provided. The present invention is specifically implemented using the following means.

[0244] System configuration

[0245] 1. Data acquisition means: A module for acquiring data from body composition monitors and healthcare apps.

[0246] 2. Analysis tools: Artificial intelligence models to analyze the acquired data.

[0247] 3. Visualization means: An interface for generating and displaying avatar images based on the analysis results.

[0248] 4. Emotion Recognition Means: An emotion engine that recognizes the user's emotions.

[0249] 5. Advice Providing Tool: A module for providing simple and detailed advice to users.

[0250] 6. Billing service means: A billing system for providing detailed health advice.

[0251] 7. Continuous monitoring measures: The ability to track changes in health status and provide feedback.

[0252] Specific Examples of the Invention

[0253] Program processing explanation

[0254] 1. Data acquisition and integration settings

[0255] Server: To obtain the user's body composition data through a dedicated app, the server connects to the body composition monitor and healthcare app, collecting data such as weight, body fat percentage, bone density, and heart rate.

[0256] Device: A dedicated app is installed on the user's smartphone, and a screen is provided for setting up the connection with the body composition scale and healthcare app.

[0257] User: Follow the instructions in the dedicated app and allow it to connect to the body composition scale and healthcare app.

[0258] 2. Data analysis using generative AI

[0259] Server: The acquired data is input into an AI model to analyze the user's health condition, thereby calculating health indicators such as biological age and bone density.

[0260] Server: Generates an avatar image based on the analysis results, visually reflecting the user's health condition.

[0261] 3. Emotion Recognition by Emotion Engine

[0262] Device: Using the built-in camera and microphone, the device recognizes emotions from the user's facial expressions and tone of voice. For example, facial expression analysis and voice analysis can be used to determine whether the user is happy or stressed.

[0263] Server: Analyzes the emotion recognition results and stores the user's current emotional state in a database.

[0264] 4. Display of results and simple advice

[0265] On the device: The generated avatar image and a description of the health condition are displayed to the user. Comments are added in an appropriate tone based on the results of the emotion engine.

[0266] On your device: It displays simple advice based on your health status, adjusting to your emotions and providing softer comments like "Try to increase your daily step count by aiming for 10,000 steps a day."

[0267] 5. Detailed advice through paid services

[0268] User: Follows a link provided within the app and purchases in-depth advice using a paid service.

[0269] Server: Once the charge is successful, the server generates personalized health check results based on detailed health data, a dietary improvement plan, future health predictions, and an appropriate exercise program.

[0270] On the device: The detailed advice provided with the purchase is displayed to the user, providing specific instructions such as, "Your calcium intake is low, so drink milk or yogurt every day."

[0271] 6. Continuous monitoring and feedback

[0272] Device: Periodically sends new health data to the server to reflect updated health status.

[0273] Server: Continuously monitors the user's health data and provides immediate feedback if any abnormalities are detected.

[0274] On the device: Continuously updates avatars, health advice, and notifies users.

[0275] In this way, the present invention provides a system that analyzes and visualizes health status based on body composition data, and recognizes emotions using an emotion engine, thereby supporting more effective and personalized health management for users.

[0276] The processing flow will be explained below.

[0277] Step 1:

[0278] Users: Install the dedicated app on their smartphone and create an account.

[0279] Step 2:

[0280] Device: Display the settings screen for linking with a body composition scale or healthcare app.

[0281] Step 3:

[0282] User: Allows integration with body composition scales and healthcare apps.

[0283] Step 4:

[0284] Device: Obtain data such as weight, body fat percentage, bone density, and heart rate from a body composition scale or health app.

[0285] Step 5:

[0286] Terminal: Sends the acquired data to the server.

[0287] Step 6:

[0288] Server: Receives the transmitted data and organizes it for each user.

[0289] Step 7:

[0290] Server: Inputs the organized data into an artificial intelligence model to analyze the user's health condition.

[0291] Step 8:

[0292] Server: Based on the analysis results, an avatar image is generated that reflects the user's biological age, bone density, etc.

[0293] Step 9:

[0294] Server: Sends the generated avatar image and health status description data to the device.

[0295] Step 10:

[0296] Terminal: Displays an avatar image and health status description to the user.

[0297] Step 11:

[0298] On your device: Displays simple advice based on your health status in the form of a comment (e.g., "You are not getting enough exercise. We recommend walking for 30 minutes three times a week.").

[0299] Step 12:

[0300] On the device: Using the built-in camera or microphone, the emotion engine analyzes the user's facial expressions and tone of voice.

[0301] Step 13:

[0302] Server: Receives the analysis results of the emotion engine and stores the user's emotional state in a database.

[0303] Step 14:

[0304] Server: Adjusts the content and display of advice based on the results of emotion recognition. For example, if the user is feeling stressed, it displays advice encouraging relaxation.

[0305] Step 15:

[0306] User: Click on the billing service link within the app and select additional detailed advice.

[0307] Step 16:

[0308] Terminal: Displays a payment screen and prompts the user to enter billing information.

[0309] Step 17:

[0310] Terminal: Processes the payment and, if successful, sends the information to the server.

[0311] Step 18:

[0312] Server: Based on the user's detailed data, it generates personalized health checkup results, dietary improvement plans, future health predictions, and appropriate exercise programs.

[0313] Step 19:

[0314] Server: Sends the generated detailed advice and emotion-based advice adjustments to the device.

[0315] Step 20:

[0316] On-device: Display the purchased detailed advice (e.g., "Your calcium intake is low, so drink milk or yogurt every day") to the user, and add a comment that matches their sentiment.

[0317] Step 21:

[0318] Device: Periodically sends new health and emotion data to the server.

[0319] Step 22:

[0320] Server: Analyzes new data and updates the avatar and adjusts advice based on changes in health status. Emotional data is also analyzed and feedback is adjusted.

[0321] Step 23:

[0322] On the device: Displays continuously updated avatar images, advice, and notifications to the user, along with emotion-based feedback.

[0323] In this way, the entire processing flow starts with acquiring the user's body composition data, then visualization and advice based on the analysis results, followed by a paid service for detailed advice, continuous monitoring, and emotion recognition using an emotion engine, allowing users to comprehensively manage and understand their own health condition and emotions and take appropriate action.

[0324] Example 2

[0325] 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."

[0326] Health management is an important issue in modern society, and many people want to understand their own health status and receive appropriate advice. However, it is difficult to provide personalized advice based on each individual's health and emotional state. Conventional systems only provide uniform advice without considering the user's emotions, which has the problem of not being effective enough. Furthermore, there is a lack of systems that can provide detailed advice, which makes it difficult to provide in-depth health management.

[0327] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring body composition data, means for providing an artificial intelligence model for analyzing health status, and means for generating and displaying an avatar image based on the analysis results. This enables detailed analysis and visualization of the user's health status based on the user's body composition data. Furthermore, by including means for providing personalized advice using an emotion engine, appropriate feedback according to the user's emotional state can be provided. Furthermore, by including a billing service for providing detailed health advice, an environment is provided in which the user can perform in-depth health management.

[0328] "Body composition data" is numerical data that indicates an individual's physical condition, such as weight, body fat percentage, bone density, and heart rate.

[0329] An "artificial intelligence model" is a technology that includes machine learning algorithms and neural networks to analyze collected data and assess a user's health status.

[0330] An "avatar image" is a virtual image that reflects the user's condition and visually displays analyzed health data.

[0331] The "emotion engine" is a technology that analyzes the user's facial expressions, tone of voice, etc. to recognize their current emotional state.

[0332] "Simple advice" is health-related advice that can be understood in a short amount of time and is provided based on the user's current health and emotional state.

[0333] "Paid Services" are paid services that provide detailed health advice, individual health checkup results, dietary improvement plans, etc.

[0334] "Big data" refers to large amounts of data collected from diverse sources and used to derive valuable information through analysis and statistics.

[0335] "Future health risks" refers to the risk of poor health or disease that may occur in the future based on current data and trends.

[0336] "Monitoring" is a surveillance method that regularly tracks changes in the user's health status and immediately notifies them of any abnormalities.

[0337] "Feedback" refers to advice or notifications provided to users based on analysis results and monitoring data.

[0338] This system acquires the user's body composition data through a dedicated app, analyzes the user's health condition using a generative AI model, visualizes the results, and provides simple advice.Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it provides personalized advice.

[0339] Data Acquisition

[0340] Server: A dedicated app is used to set up a connection to obtain the user's body composition data. This connection is made between the body composition monitor and existing healthcare apps (e.g., Apple Health or Google Fit). The server receives data such as weight, body fat percentage, bone density, and heart rate and stores it in a database.

[0341] Device: A dedicated app is installed on the user's smartphone, and when launched for the first time, a screen for setting up the connection with the body composition scale and healthcare app is displayed. The user follows the guide and enters the necessary API key and Bluetooth connection information.

[0342] User: Follow the instructions in the dedicated app and allow the device to connect to the body composition monitor and Health app. Specifically, tap the "Sync Data" button to connect to the iOS Health app.

[0343] Data analysis with generative AI

[0344] Server: The acquired body composition data is input into an AI model for analysis. The generative AI model used compares it with past data to calculate changes in anatomical age and bone density. This is achieved by using machine learning algorithms and neural networks.

[0345] Server: Generates an avatar image based on the analysis results, visually reflecting the user's health status. For example, if the user's health status is good, the avatar is displayed in a bright color, and if the user's health status is deteriorating, the avatar is displayed in a dark color.

[0346] Emotion recognition by emotion engine

[0347] On-device: Captures the user's facial expressions and voice using the built-in camera and microphone, capturing emotional changes in real time. This feature is automatically turned on when the user starts using the app.

[0348] Device: The emotion engine analyzes emotions such as "happiness," "sadness," and "anger" from changes in facial expressions and tone of voice. The analysis results are sent from the device to the server.

[0349] Server: Stores the emotion recognition results in a database so that advice can be provided according to the user's emotional state.

[0350] Display of results and simple advice

[0351] Device: Launches an interface that displays the generated avatar image and analysis results to the user. The avatar image is displayed on the home screen, along with the user's biological age and bone density.

[0352] On your device: Display simple advice based on your health status. Add comments in an appropriate tone based on emotion recognition results. For example, display a goal in a soft tone, such as "To increase your daily activity, aim for 10,000 steps a day."

[0353] User: Review the advice provided and incorporate it into their daily routine, for example by taking more walks to reach their step goal.

[0354] Detailed advice via paid services

[0355] User: Follows the link in the app and navigates to the screen where they can purchase detailed health advice. They then click the "Purchase detailed advice" button to proceed with the payment process.

[0356] Server: Once the payment is completed, the server generates detailed advice, including personalized health check results, a diet plan, and an exercise program.

[0357] On the device: The device displays detailed advice about the purchase to the user, providing specific instructions such as "Your calcium intake is insufficient, so drink milk or yogurt every day."

[0358] Ongoing monitoring and feedback

[0359] On your device: Send new health data to the server periodically, for example, automatically sync your data every day at 9:00 AM.

[0360] Server: Continuously monitors the user's health data and provides anomaly alerts if an abnormality is detected. The server notifies the user in real time that "abnormalities have been found in recent data, so we recommend that you see a doctor."

[0361] On the device: Provide regular health updates and advice to users, including providing a monthly report showing health progress.

[0362] Specific examples

[0363] The user downloads and installs the dedicated app, and when they launch it for the first time, they set up the connection with the body composition scale. Data is then sent from the body composition scale to the server, where it is analyzed using a generative AI model. An avatar image is generated based on the analysis results and displayed on the home screen of the dedicated app. The app uses an emotion engine to recognize the user's emotions and provides advice based on the results. If the user desires more detailed advice, they can use a paid service to receive specific improvement instructions.

[0364] Prompt Sentence Examples

[0365] "Analyze this user's health status based on their latest body composition data and provide advice."

[0366] "Analyze the user's latest sentiment and provide advice based on it."

[0367] "Generate and display an avatar image based on the latest health and emotional data."

[0368] In this way, through the configuration of the present invention, the user can more accurately understand his or her own health condition and receive personalized advice.

[0369] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0370] Step 1: Data Acquisition and Integration Settings

[0371] Device: A dedicated app is installed on the user's smartphone, and when the app is launched for the first time, a screen for setting up linkage with the body composition scale and healthcare app is displayed. Specifically, an interface is provided that prompts the user to enter Bluetooth and API keys. The input in this step is the connection information entered by the user, and the output is a notification of successful linkage.

[0372] User: Follow the instructions in the dedicated app to allow linking with the body composition scale and healthcare app. For example, tap the "Sync Data" button to link with the iOS healthcare app. The input in this step is the user's permission action, and the output is a screen display indicating that the link setup is complete.

[0373] Server: Receives the connection setting information sent from the device and establishes a connection with the API of the body composition scale and healthcare app. The input in this step is the connection information from the device, and the output is a log record of successful connection.

[0374] Step 2: Data analysis with generative AI

[0375] Server: The body composition data acquired through the dedicated app is formatted and input into the AI ​​model. The input in this step is weight, body fat percentage, bone density, and heart rate data, and the output is a standardized dataset required for analysis. Specifically, the data is standardized and converted into a format suitable for the generative AI model.

[0376] Server: Analyzes the acquired data using a generative AI model and evaluates the user's health condition. The input in this step is the formatted body composition data, and the output is health indicators such as biological age and bone density. Specifically, health indicators are calculated by comparing with past data.

[0377] Server: Generates an avatar image based on the analysis results and visualizes the user's health condition. The input in this step is the analysis results of the generative AI model, and the output is an avatar image that reflects the user's unique health condition.

[0378] Step 3: Emotion recognition by the emotion engine

[0379] Device: The built-in camera and microphone are used to capture the user's facial expressions and tone of voice. Specifically, the camera and microphone are automatically turned on when the user uses the app. The input in this step is audio and video data obtained from the camera and microphone, and the output is the user's facial expressions and voice data captured in real time.

[0380] On the device: An emotion recognition algorithm is used to analyze the user's emotions from the captured data. The input in this step is the captured facial expression and voice data, and the output is an emotion label such as "happiness," "sadness," or "anger." Specifically, changes in facial expressiveness and voice tone are analyzed.

[0381] Server: Stores the emotion recognition results in a database. The input to this step is the analysis result of the emotion recognition algorithm, and the output is a record of the emotional state stored in the database.

[0382] Step 4: Displaying results and giving simple advice

[0383] Device: The generated avatar image and the health status analysis results are displayed to the user. The input in this step is the avatar image and health indicators sent from the server, and the output is the visual and text information displayed on the app screen. Specifically, the avatar image is displayed on the home screen and the data is presented visually.

[0384] Device: Displays simple advice based on health status. The inputs in this step are the user's health indicators and emotion recognition results, and the output is an advice message to be displayed to the user. Specifically, a message such as "Today's goal is to walk 10,000 steps a day" is displayed in a soft tone.

[0385] User: Check the displayed advice and incorporate it into their daily life. For example, increase their activity by following a goal of "walking 10,000 steps a day."

[0386] Step 5: Detailed advice from a billing service

[0387] User: Follows a link within the app and operates the screen to purchase detailed health advice. The input at this step is the user's action to complete the purchase, and the output is a notification of successful payment and completion of the purchase of detailed advice. Specifically, the user taps the "Purchase detailed advice" button.

[0388] Server: Once the billing process is complete, detailed health advice is generated. The inputs at this step are the purchase information for the detailed advice and the user's health data, and the output is individual health checkup results and an improvement plan. Specifically, a diet improvement plan and exercise program are generated based on the user's data.

[0389] Terminal: Display the detailed advice of the purchase to the user. The input in this step is the detailed advice information sent from the server, and the output is the specific instructions or plan displayed to the user. For example, the advice displayed might be, "Your calcium intake is insufficient, so drink milk or yogurt every day."

[0390] Step 6: Ongoing monitoring and feedback

[0391] Device: Periodically send new health data to the server. The input in this step is the newly acquired body composition data, and the output is the data to be sent to the server. For example, automatically synchronize data every day at 9:00 AM.

[0392] Server: Continuously monitors the user's health data and provides immediate feedback if an abnormality is detected. The input in this step is the continuously received health data, and the output is a warning message in the event of an abnormality. Specifically, it notifies the user that "an abnormality has been found in recent data, so we recommend that you see a doctor."

[0393] Device: Continuously notify the user of the latest health status and advice. The input in this step is the latest health information and advice sent from the server, and the output is the updated information displayed on the app screen. For example, a monthly report summarizing the latest health status.

[0394] (Application example 2)

[0395] 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."

[0396] Health management for industrial workers is important, and there is a need to appropriately monitor their physical and mental health. However, conventional systems have difficulty understanding individual health and emotional states in real time and allocating appropriate breaks and tasks based on that information. Therefore, there is a need for a system that can effectively and efficiently monitor the health and emotional states of industrial workers and respond to their individual needs.

[0397] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring body composition data, means for providing an artificial intelligence model for analyzing health conditions, means for generating and displaying an avatar image based on the analysis results, means for providing the user with simple advice based on the avatar image and the analysis results, means for recognizing the user's emotional state using an emotion recognition engine, and means for monitoring the health and emotional states of industrial workers in real time and suggesting appropriate breaks and task assignments. This enables effective and efficient health management of industrial workers.

[0398] "Body composition data" refers to data relating to the composition of a human body, such as weight, body fat percentage, bone density, and heart rate.

[0399] An "artificial intelligence model" is a machine learning algorithm that analyzes data and extracts specific patterns and trends.

[0400] An "avatar image" is a digital image that visually represents a user's health status or other attributes.

[0401] "Simple advice" refers to basic and immediate health guidance and suggestions provided to users.

[0402] "Charging Service" means a fee-collection system used to provide detailed health advice to users.

[0403] An "emotion recognition engine" is a combination of software and hardware that identifies emotions from a user's facial expressions, tone of voice, etc.

[0404] "Industrial workers" refers to people who perform manual labor in factories, manufacturing facilities, etc.

[0405] "Real-time monitoring" means being able to instantly observe ongoing events and conditions and take prompt action based on that information.

[0406] A "health management system" refers to a set of technologies and tools that monitor a user's health status and provide appropriate advice and feedback.

[0407] The present invention is a system for monitoring the health and emotional state of industrial workers in real time and providing personalized advice. Specific embodiments of the system will be described below.

[0408] System configuration and operation

[0409] 1. Data Acquisition Method

[0410] The server collects body composition data from industrial workers through specialized applications, including data from body composition monitors and healthcare apps, such as weight, body fat percentage, bone density, and heart rate. The terminals are equipped with the functionality to transmit this data in real time.

[0411] 2. Health status analysis tools

[0412] The server inputs the acquired body composition data into an artificial intelligence model (AI model) to analyze health status. This AI model can use machine learning frameworks such as TensorFlow and PyTorch. As a result of the analysis, health indicators such as biological age and bone density are calculated.

[0413] 3. Avatar Image Generation Method

[0414] The server generates and displays an avatar image based on the analysis results, which visually reflects the user's health condition on the user's smartphone or tablet.

[0415] 4. Emotion recognition means

[0416] The device uses a built-in camera and microphone to recognize emotions from the user's facial expressions and tone of voice. An emotion recognition model is used for facial expression and voice analysis. The server analyzes the emotion recognition results and records the user's current emotional state.

[0417] 5. Display of results and means of providing simple advice

[0418] The device displays the generated avatar image and a description of the user's health status to the user. In addition, based on the results of emotion recognition, the device provides simple, appropriate advice in a soft tone, such as "To increase your daily step count, aim for 10,000 steps a day."

[0419] 6. Means of providing detailed advice through paid services

[0420] By using the paid service, detailed health advice is provided. Users can purchase detailed advice by following the link provided in the app and using the paid service. The server generates individual advice and health check results according to the purchase, and provides dietary improvement plans and appropriate exercise programs.

[0421] 7. Ongoing monitoring and feedback measures

[0422] The device periodically sends new health data to the server to update the user's health status. The server continuously monitors the user's health data and provides immediate feedback if any abnormalities are detected, thereby supporting the user's health management.

[0423] Specific examples

[0424] An industrial worker uses a body composition scale to collect data such as weight, body fat percentage, bone density, and heart rate, and then sends that data to an application. The device's camera also captures facial expressions in real time, and if the emotion analysis model recognizes the worker as being in a "stressed state," the AI ​​model analyzes the data and generates an avatar image based on the results. This avatar image and simple advice are displayed to the worker, suggesting appropriate tasks and breaks based on his health condition and emotions.

[0425] Prompt Sentence Examples

[0426] "Analyze the health status of a worker based on the following body composition data:

[0427] Weight: 70kg

[0428] Body fat percentage: 15%

[0429] Bone density: 1.2g / cm3

[0430] Heart rate: 75 bpm

[0431] Also consider the following sentiment analysis data:

[0432] Facial Expressions: Stressed

[0433] Based on the analysis results, generate an avatar image of the worker's health status and provide appropriate health advice."

[0434] Thus, the present invention is a system that comprehensively monitors the health and emotional state of industrial workers and provides specific advice and feedback tailored to their individual needs.

[0435] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0436] Step 1:

[0437] Users collect body composition data using a dedicated body composition monitor or healthcare app. This data includes weight, body fat percentage, bone density, heart rate, etc. Users then send the collected data to a dedicated application installed on their smartphone or tablet.

[0438] Input: Body composition data obtained from a body composition analyzer

[0439] Output: Body composition data sent to a dedicated application

[0440] Step 2:

[0441] The device transmits the user's body composition data to the server, which receives the data and stores it in a database.

[0442] Input: Body composition data sent from the dedicated application

[0443] Output: Body composition data stored in a database

[0444] Step 3:

[0445] The server inputs the stored body composition data into an artificial intelligence model (AI model) that uses machine learning frameworks such as TensorFlow and PyTorch to analyze the user's health condition.

[0446] Input: Body composition data stored in the database

[0447] Output: Health status analysis results

[0448] Step 4:

[0449] The server generates an avatar image that visually represents the user's health condition based on the analysis results.

[0450] Input: Health status analysis results

[0451] Output: Avatar image

[0452] Step 5:

[0453] The device uses a built-in camera and microphone to recognize the user's emotions from their facial expressions and tone of voice. An emotion recognition model analyzes their facial expressions and voice to determine the user's emotional state.

[0454] Input: Facial expression and voice data captured by the built-in camera and microphone

[0455] Output: Emotional state analysis result

[0456] Step 6:

[0457] The server analyzes the results of the emotion recognition engine and stores the user's current emotional state in a database.

[0458] Input: Emotional state analysis results

[0459] Output: Emotional state data stored in a database

[0460] Step 7:

[0461] The device displays the generated avatar image, a description of the user's health status, and simple advice based on the user's emotions. For example, it may display a comment such as, "To increase your daily step count, aim for 10,000 steps a day."

[0462] Input: Avatar image, health status description, emotion recognition results

[0463] Output: Avatar image and advice displayed on the device

[0464] Step 8:

[0465] The user uses the billing service to obtain detailed health advice within the app. When billing is successful, the server generates detailed advice and health check results and provides them to the user.

[0466] Input: Billing service usage information

[0467] Output: Detailed health check results, dietary improvement plan, exercise program

[0468] Step 9:

[0469] The device periodically sends new health data to the server, updating the user's health status. The server continuously monitors the device and provides feedback if any abnormalities are detected.

[0470] Input: New health data

[0471] Output: Updated health status data, feedback

[0472] In this way, a system is created that monitors the health and emotional state of industrial workers in real time and provides personalized advice, enabling effective health management.

[0473] 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.

[0474] 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.

[0475] 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.

[0476] [Second embodiment]

[0477] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0478] 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.

[0479] 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).

[0480] 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.

[0481] 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.

[0482] 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).

[0483] 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.

[0484] 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.

[0485] 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.

[0486] 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.

[0487] 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.

[0488] 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."

[0489] The present invention is a system that acquires body composition data through a dedicated app, analyzes health status using generative AI based on the data, and provides the results to users as a visualized avatar image and simple advice. It also includes a paid service for providing detailed health advice and a function for predicting future health risks using big data. The present invention is specifically implemented using the following means.

[0490] System configuration

[0491] 1. Data acquisition means: A module for acquiring data from body composition monitors and healthcare apps.

[0492] 2. Analysis tools: Artificial intelligence models to analyze the acquired data.

[0493] 3. Visualization means: An interface for generating and displaying avatar images based on the analysis results.

[0494] 4. Advice Providing Tool: A module for providing simple and detailed advice to users.

[0495] 5. Billing service means: A billing system for providing detailed health advice.

[0496] 6. Continuous monitoring measures: The ability to track changes in health status and provide feedback.

[0497] Specific Examples of the Invention

[0498] Program processing explanation

[0499] 1. Data acquisition and integration settings

[0500] Server: To obtain the user's body composition data through a dedicated app, the server connects to the body composition monitor and healthcare app, collecting data such as weight, body fat percentage, bone density, and heart rate.

[0501] Device: A dedicated app is installed on the user's smartphone, and a screen is provided for setting up the connection with the body composition scale and healthcare app.

[0502] User: Follow the instructions in the dedicated app and allow it to connect to the body composition scale and healthcare app.

[0503] 2. Data analysis using generative AI

[0504] Server: The acquired data is input into an AI model to analyze the user's health condition, thereby calculating health indicators such as biological age and bone density.

[0505] Server: Generates an avatar image based on the analysis results, visually reflecting the user's health condition.

[0506] 3. Display of results and simple advice

[0507] On the device, the generated avatar image and a description of the user's health condition are displayed. For example, if the user's biological age is higher than their actual age, the device will inform the user that they need to exercise less or improve their diet.

[0508] On your device: Displays simple advice based on your health status, such as "To increase your daily step count, aim for 10,000 steps a day."

[0509] 4. Detailed advice through paid services

[0510] User: Follows a link provided within the app and purchases in-depth advice using a paid service.

[0511] Server: Once the charge is successful, the server generates personalized health check results based on detailed health data, a dietary improvement plan, future health predictions, and an appropriate exercise program.

[0512] On the device: The detailed advice provided with the purchase is displayed to the user, providing specific instructions such as, "Your calcium intake is low, so drink milk or yogurt every day."

[0513] 5. Continuous monitoring and feedback

[0514] Device: Periodically sends new health data to the server to reflect updated health status.

[0515] Server: Continuously monitors the user's health data and provides immediate feedback if any abnormalities are detected.

[0516] On the device: Continuously updates avatars, health advice, and notifies users.

[0517] In this way, the present invention provides a system that analyzes and visualizes health conditions based on body composition data, and supports effective health management for users.

[0518] The processing flow will be explained below.

[0519] Step 1:

[0520] Users: Install the dedicated app on their smartphone and create an account.

[0521] Step 2:

[0522] Device: Display the settings screen for linking with a body composition scale or healthcare app.

[0523] Step 3:

[0524] User: Allows integration with body composition scales and healthcare apps.

[0525] Step 4:

[0526] Device: After authorization, data such as weight, body fat percentage, bone density, and heart rate will be collected.

[0527] Step 5:

[0528] Terminal: Sends the acquired data to the server.

[0529] Step 6:

[0530] Server: Receives the transmitted data and organizes it for each user.

[0531] Step 7:

[0532] Server: Inputs the organized data into an artificial intelligence model to analyze the user's health condition.

[0533] Step 8:

[0534] Server: Based on the analysis results, an avatar image is generated that reflects the user's biological age, bone density, etc.

[0535] Step 9:

[0536] Server: Sends the generated avatar image and health status description data to the device.

[0537] Step 10:

[0538] Terminal: Displays an avatar image and health status description to the user.

[0539] Step 11:

[0540] On your device: Displays simple advice based on your health status in the form of a comment (e.g., "You are not getting enough exercise. We recommend walking for 30 minutes three times a week.").

[0541] Step 12:

[0542] Users: Click on the billing service link and select additional detailed advice.

[0543] Step 13:

[0544] Terminal: Displays a payment screen and prompts the user to enter billing information.

[0545] Step 14:

[0546] Terminal: Processes the payment and, if successful, sends the information to the server.

[0547] Step 15:

[0548] Server: Based on the user's detailed data, it generates personalized health checkup results, dietary improvement plans, future health predictions, and appropriate exercise programs.

[0549] Step 16:

[0550] Server: Sends the generated detailed advice to the device.

[0551] Step 17:

[0552] On the device: Display detailed health advice to the user (e.g., "Your calcium intake is low, so drink milk or yogurt every day").

[0553] Step 18:

[0554] Device: Periodically sends new health data to the server.

[0555] Step 19:

[0556] Server: Analyzes new data and updates the avatar and adjusts advice based on changes in health.

[0557] Step 20:

[0558] On the device: Displays continuously updated avatar images, advice, and notifications to the user.

[0559] In this way, the entire process starts with acquiring the user's body composition data, then visualization and advice based on the analysis results, and includes a fee-based service for detailed advice and continuous monitoring, allowing users to track and manage their own health status.

[0560] Example 1

[0561] 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."

[0562] In modern society, the importance of individual health management is increasing. However, many people find it difficult to manage their daily health on their own, and have limited opportunities to receive professional advice. Furthermore, there are insufficient tools for accumulating health data and assessing long-term health risks. To solve these issues, a system is needed that can accurately grasp each individual's health status, visualize it in an easy-to-understand manner, and provide continuous monitoring and feedback.

[0563] 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.

[0564] In this invention, the server includes means for acquiring body composition data, means for providing an artificial intelligence model for analyzing health conditions, and means for generating and displaying an avatar image based on the analysis results, thereby enabling users to visually grasp their individual health conditions and receive easy-to-understand feedback, thereby enabling them to more effectively manage their own health conditions.

[0565] "Body composition data" refers to data related to the composition of the user's body, such as weight, body fat percentage, bone density, and heart rate.

[0566] An "artificial intelligence model" is a program or system that uses machine learning and data analysis algorithms to analyze health conditions based on acquired data.

[0567] An "avatar image" is a graphical image of a person that visually displays the user's health condition based on the analysis results.

[0568] "Simple advice" is a message that provides basic health management guidelines that can be put into practice in daily life based on the user's health condition.

[0569] A "billing system" is a system that collects fees from users for providing detailed health advice or additional services.

[0570] "Big data" refers to large amounts of health data collected from many users, a dataset that can be analyzed to gain valuable insights.

[0571] "Health risks" are the risk of health problems or diseases that may occur in the future based on the user's current health condition and lifestyle habits.

[0572] "Continuous monitoring" is the process of regularly and continuously collecting and analyzing a user's health data to track changes.

[0573] "Feedback" refers to information such as advice, warnings, and recommendations provided to users based on analysis results and monitoring data.

[0574] The present invention is a system that uses body composition data to analyze health conditions and provides visual feedback and specific health advice. Specific embodiments of this system will be described below.

[0575] Data acquisition and integration settings

[0576] The server configures the connection with the body composition monitor and healthcare app to obtain the user's body composition data through a dedicated app. This connection is granted by OAuth authentication, etc., and the server obtains data such as the user's weight, body fat percentage, bone density, and heart rate.

[0577] The device provides a screen for installing a dedicated app on the user's smartphone and displays an interface for setting up connectivity with the body composition scale and healthcare app. For example, the app can connect to the body composition scale using Bluetooth or Wi-Fi.

[0578] The user follows the instructions in the dedicated app to allow the body composition scale to connect to the healthcare app. Specifically, this involves pressing the corresponding button on the body composition scale within the app to have the device recognized.

[0579] Data analysis with generative AI

[0580] The server inputs the acquired body composition data into an artificial intelligence model (hereinafter referred to as the "generative AI model"). For example, data such as weight, body fat percentage, bone density, and heart rate measured by the user in the morning are sent to the server.

[0581] The server then feeds this data into a generative AI model, which analyzes the user's health from multiple angles. Specifically, the AI ​​model uses machine learning algorithms to calculate factors such as biological age and bone density.

[0582] The server generates an avatar image based on the analysis results, visually reflecting the user's health status. For example, if the user has a high body fat percentage, the avatar will be displayed as slightly overweight.

[0583] Display of results and simple advice

[0584] The device displays the generated avatar image and a description of the user's health status. For example, the app screen might say, "Your biological age is 35."

[0585] The device will display simple advice based on your health status, such as "To increase your daily step count, aim for 10,000 steps per day," and may also encourage users to drink at least eight glasses of water per day.

[0586] Detailed advice via paid services

[0587] The user clicks on a link provided within the app to proceed to a billing service screen, which may say, for example, "Please pay to receive detailed health checkup results."

[0588] Once the user's payment is successful, the server generates personalized health checkup results and dietary improvement plans based on the detailed health data. Specifically, the generative AI model performs further detailed analysis and generates advice based on the user's lifestyle habits.

[0589] The device will then display detailed advice to the user about the purchase, including specific instructions such as "Your calcium intake is low, so drink milk or yogurt every day."

[0590] Ongoing monitoring and feedback

[0591] The device periodically sends new health data to the server. For example, a user measures their weight and body fat percentage every Sunday and uploads the data to the server.

[0592] The server continuously monitors the user's health data and provides immediate feedback if any abnormalities are detected. For example, if a sudden weight gain is detected, an alert will be sent saying, "Your weight has increased rapidly recently. Please be careful about your diet and exercise."

[0593] The device continuously updates the avatar and health advice and notifies the user. For example, every time health data is updated, the device notifies the user of "how their health status has changed" along with the latest avatar image.

[0594] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0595] Step 1:

[0596] Data acquisition and integration settings

[0597] The server acquires the user's body composition data from a body composition scale or healthcare app connected to the dedicated app. The acquired data includes weight, body fat percentage, bone density, heart rate, etc. This provides basic data for accurately understanding the user's physical condition.

[0598] The device displays a screen for installing a dedicated app on the user's smartphone and provides an interface for setting up linkage with the body composition scale and healthcare app. Specifically, the app connects to the body composition scale using Bluetooth or Wi-Fi and associates it with the user ID.

[0599] The user follows the instructions in the dedicated app to allow the body composition scale to connect to the healthcare app. Specifically, the user presses the corresponding button on the body composition scale within the app to have the device recognized.

[0600] Step 2:

[0601] Data analysis with generative AI

[0602] The server inputs the acquired body composition data into the generative AI model. This input data includes weight, body fat percentage, bone density, heart rate, etc. The AI ​​model uses this data to analyze the user's health condition and calculate health indicators such as biological age and bone density. Specifically, it uses a machine learning algorithm to perform the analysis and compare the user's data with other databases to evaluate their health condition.

[0603] As an output, the AI ​​model calculates health indicators and returns them to the server, providing the results as numerical data reflecting the user's health status.

[0604] Step 3:

[0605] Avatar image generation and result display

[0606] The server generates an avatar image based on the analysis results. This avatar image visually reflects the user's health condition, and includes operations such as "if the body fat percentage is high, the avatar will be displayed slightly overweight."

[0607] The device displays the generated avatar image and a description of the user's health condition. Specifically, the app screen displays a message such as "Your biological age is 35" along with the avatar image.

[0608] Step 4:

[0609] Providing simple advice

[0610] The device will display simple advice to the user based on their health status, such as "To increase your daily step count, aim for 10,000 steps per day," and will also display a message encouraging the user to drink at least eight glasses of water per day.

[0611] This advice is developed based on the analysis results of the generative AI model and is notified to the user via the app.

[0612] Step 5:

[0613] Chargeable service with detailed advice

[0614] The user clicks on a link provided within the app to proceed to a billing service screen, which may say, for example, "Please pay to receive detailed health checkup results."

[0615] Once the user has successfully completed the payment, the server generates personalized health checkup results and a diet plan based on the user's detailed health data. The AI ​​model then performs further detailed analysis and generates advice based on the user's lifestyle habits.

[0616] The device will then display detailed advice to the user about the product they purchased, such as "Your calcium intake is low, so drink milk or yogurt every day."

[0617] Step 6:

[0618] Ongoing monitoring and feedback

[0619] The device periodically sends new health data to the server. For example, a user measures their weight and body fat percentage every Sunday and uploads the data to the server.

[0620] The server continuously monitors the user's health data and provides immediate feedback if any abnormalities are detected. For example, if a sudden weight gain is detected, it will send an alert saying, "Your weight has increased rapidly recently. Please be careful about your diet and exercise."

[0621] The device continuously updates the avatar and health advice and notifies the user. Each time the data is updated, the device notifies the user of the latest avatar image and how their health status has changed.

[0622] (Application example 1)

[0623] 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."

[0624] Health management is an important issue in today's world, but conventional systems have struggled to quickly and effectively analyze users' body composition data and present it in an intuitively understandable format. Furthermore, they lacked the functionality to provide continuous monitoring and individual feedback, making it difficult to grasp users' health status and implement improvement measures. Furthermore, there was a lack of billing services to provide detailed health advice, and a system that integratedly enabled users to set health goals and track their progress. These issues need to be resolved.

[0625] 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.

[0626] In this invention, the server includes means for acquiring body composition data, means for providing an artificial intelligence model for analyzing health status, means for generating and displaying an avatar image based on the analysis results, means for providing the user with simple advice based on the avatar image and the analysis results, means for including a billing service for providing detailed health advice, means for continuously monitoring user data and providing appropriate feedback, means for setting the user's health goals and tracking progress toward those goals, additional means for displaying the visualization results of the health status, and means for performing billing service processing. This enables analysis and visualization of the user's health status based on the user's body composition data, individualized health advice, continuous health monitoring and feedback, provision of detailed advice, and health goal setting and progress management.

[0627] "Body composition data" refers to data relating to the composition of the human body, such as weight, body fat percentage, bone density, and heart rate.

[0628] An "artificial intelligence model" is an algorithm or machine learning model used to analyze a user's health condition.

[0629] An "avatar image" is a virtual image that visually reflects the user's health condition.

[0630] "Quick Advice" is basic health recommendations provided to the user.

[0631] A "billing service" is a system that charges users a fee for providing detailed health advice and individual plans.

[0632] "Continuous user data monitoring" refers to the regular collection of a user's health data over time and tracking changes.

[0633] "Feedback" refers to advice or warnings provided to users based on analysis results and monitoring data.

[0634] "Setting health goals" means defining specific health indicators and action plans that a user aims to achieve.

[0635] "Progress tracking" refers to monitoring and recording a user's progress toward defined health goals.

[0636] The "visualization result of health status" is information that displays the user's health status based on the analysis results in an intuitive and easy-to-understand manner.

[0637] "Billing service processing" refers to managing the process by which users pay for detailed health services and advice.

[0638] MODE FOR CARRYING OUT THE INVENTION

[0639] As an embodiment of the present invention, a description will be given of how to specifically realize an application example.

[0640] Server Roles

[0641] The server uses the following hardware and software to perform processes such as acquiring, analyzing, visualizing, and providing advice on various types of data.

[0642] 1. Data Acquisition:

[0643] Hardware: Body composition scale, smartphone, wearable device.

[0644] Software: A dedicated application for collecting healthcare data.

[0645] Description: The server obtains the user's body composition data (weight, body fat percentage, bone density, heart rate, etc.) from a body composition scale or wearable device.

[0646] 2. Data Analysis:

[0647] Hardware: Server.

[0648] Software: Generative AI model (HealthAIModel).

[0649] Description: The server inputs the acquired body composition data into a generative AI model to analyze the user's health condition, thereby calculating health indicators such as biological age and bone density.

[0650] 3. Visualization:

[0651] Hardware: Server, user's smartphone.

[0652] Software: Avatar Generator.

[0653] Description: An avatar image is generated based on the analysis results and displayed on the user's smartphone.

[0654] 4. Providing advice:

[0655] Hardware: Server, user's smartphone.

[0656] Software: Advice delivery module.

[0657] Description: The server provides simple advice to the user based on the analysis results. In addition, the server provides detailed health advice to the user by paying for a service.

[0658] 5. Continuous monitoring and feedback:

[0659] Hardware: Servers, smartphones.

[0660] Software: Monitoring and feedback module.

[0661] Description: The server continuously monitors changes in the user's health status and provides immediate feedback if any abnormalities are detected. It periodically retrieves new health data and reflects the updated health status.

[0662] 6. Billing Service Processing:

[0663] Hardware: Smartphones, servers.

[0664] Software: Payment Gateway.

[0665] Description: When a user purchases detailed health advice, the server processes the billing and, if successful, provides the detailed advice.

[0666] Specific examples

[0667] Take the example of an application provided by a fitness gym. When a user steps on a body composition scale, the data is automatically sent to a dedicated application. The server acquires this data and analyzes it using a generative AI model. The resulting avatar image is displayed on the user's smartphone, and basic health advice is provided. If the user desires additional, detailed advice, it can be obtained through an in-app purchase.

[0668] Example prompts for input to a generative AI model:

[0669] Weight: 70kg, body fat percentage: 20%, bone density: 1.2g / cm3, heart rate: 70bpm

[0670] Analyze the user's health status and provide appropriate advice.

[0671] Thus, based on the specific form for implementing the invention, it is possible to analyze and visualize a user's health status, provide personalized health advice, provide continuous monitoring and feedback, provide detailed advice, and set health goals and manage their progress.

[0672] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0673] Program processing steps

[0674] Step 1:

[0675] Data Acquisition

[0676] The server acquires body composition data. When a user uses a body composition scale or wearable device, the body composition data (weight, body fat percentage, bone density, heart rate, etc.) is sent from these devices to the server via a dedicated application. The input is the user's body composition scale data, and the output is the body composition data accumulated on the server. This data is saved in a database for use in later analysis.

[0677] Step 2:

[0678] Data analysis

[0679] The server inputs the acquired body composition data into a generative AI model (HealthAIModel) to analyze the user's health condition. Based on a series of input data, the AI ​​model processes the data and calculates health indicators such as biological age, bone density, and heart rate. The input here is the body composition data stored on the server, and the output is the analysis results. Data processing involves normalizing the data and extracting features, and converting it into a format suitable for the AI ​​model.

[0680] Step 3:

[0681] Visualization

[0682] The server generates an avatar image based on the analysis results. An avatar generation tool (AvatarGenerator) is used to create an avatar image that visually represents the analyzed health indicators. The input to this step is the analysis results, and the output is an avatar image. Specifically, the analysis results are input as prompts into the avatar generation tool, and the tool automatically generates an avatar image.

[0683] Step 4:

[0684] Providing advice

[0685] The server provides the generated avatar image and simple advice to the user. Based on the analysis results, the AI ​​model automatically generates prompts to provide appropriate health advice to the user. For example, advice such as "To increase your daily step count, aim for 10,000 steps a day" is displayed. The input for this step is the health analysis results and prompts, and the output is simple advice and a visualized avatar image.

[0686] Step 5:

[0687] Detailed advice and billing services

[0688] If a user wants detailed health advice, they use the billing service through their terminal. The server processes the billing using a payment gateway. If the billing is successful, detailed advice is displayed on the user's terminal. Specifically, the server receives a billing request from the user and makes the payment via the payment gateway. The input is the user's billing request information, and the output is the result of the billing process and detailed advice.

[0689] Step 6:

[0690] Ongoing monitoring and feedback

[0691] The server continuously monitors changes in the user's health condition. It periodically acquires the user's body composition data and reanalyzes the health condition based on that data. If an abnormality is detected, it provides immediate feedback. The input to this step is the continuously collected body composition data, and the output is an updated health condition report and feedback. Specifically, the server automatically acquires and analyzes data according to a regular data collection schedule.

[0692] Prompt Sentence Examples

[0693] Weight: 70kg, body fat percentage: 20%, bone density: 1.2g / cm3, heart rate: 70bpm

[0694] Analyze the user's health status and provide appropriate advice.

[0695] 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.

[0696] The present invention is a system that acquires body composition data through a dedicated app, analyzes health status using generative AI based on the data, and provides the results to the user as a visualized avatar image and simple advice. In addition, by combining this with an emotion engine that recognizes the user's emotions, more personalized advice and feedback can be provided. The present invention is specifically implemented using the following means.

[0697] System configuration

[0698] 1. Data acquisition means: A module for acquiring data from body composition monitors and healthcare apps.

[0699] 2. Analysis tools: Artificial intelligence models to analyze the acquired data.

[0700] 3. Visualization means: An interface for generating and displaying avatar images based on the analysis results.

[0701] 4. Emotion Recognition Means: An emotion engine that recognizes the user's emotions.

[0702] 5. Advice Providing Tool: A module for providing simple and detailed advice to users.

[0703] 6. Billing service means: A billing system for providing detailed health advice.

[0704] 7. Continuous monitoring measures: The ability to track changes in health status and provide feedback.

[0705] Specific Examples of the Invention

[0706] Program processing explanation

[0707] 1. Data acquisition and integration settings

[0708] Server: To obtain the user's body composition data through a dedicated app, the server connects to the body composition monitor and healthcare app, collecting data such as weight, body fat percentage, bone density, and heart rate.

[0709] Device: A dedicated app is installed on the user's smartphone, and a screen is provided for setting up the connection with the body composition scale and healthcare app.

[0710] User: Follow the instructions in the dedicated app and allow it to connect to the body composition scale and healthcare app.

[0711] 2. Data analysis using generative AI

[0712] Server: The acquired data is input into an AI model to analyze the user's health condition, thereby calculating health indicators such as biological age and bone density.

[0713] Server: Generates an avatar image based on the analysis results, visually reflecting the user's health condition.

[0714] 3. Emotion Recognition by Emotion Engine

[0715] Device: Using the built-in camera and microphone, the device recognizes emotions from the user's facial expressions and tone of voice. For example, facial expression analysis and voice analysis can be used to determine whether the user is happy or stressed.

[0716] Server: Analyzes the emotion recognition results and stores the user's current emotional state in a database.

[0717] 4. Display of results and simple advice

[0718] On the device: The generated avatar image and a description of the health condition are displayed to the user. Comments are added in an appropriate tone based on the results of the emotion engine.

[0719] On your device: It displays simple advice based on your health status, adjusting to your emotions and providing softer comments like "Try to increase your daily step count by aiming for 10,000 steps a day."

[0720] 5. Detailed advice through paid services

[0721] User: Follows a link provided within the app and purchases in-depth advice using a paid service.

[0722] Server: Once the charge is successful, the server generates personalized health check results based on detailed health data, a dietary improvement plan, future health predictions, and an appropriate exercise program.

[0723] On the device: The detailed advice provided with the purchase is displayed to the user, providing specific instructions such as, "Your calcium intake is low, so drink milk or yogurt every day."

[0724] 6. Continuous monitoring and feedback

[0725] Device: Periodically sends new health data to the server to reflect updated health status.

[0726] Server: Continuously monitors the user's health data and provides immediate feedback if any abnormalities are detected.

[0727] On the device: Continuously updates avatars, health advice, and notifies users.

[0728] In this way, the present invention provides a system that analyzes and visualizes health status based on body composition data, and recognizes emotions using an emotion engine, thereby supporting more effective and personalized health management for users.

[0729] The processing flow will be explained below.

[0730] Step 1:

[0731] Users: Install the dedicated app on their smartphone and create an account.

[0732] Step 2:

[0733] Device: Display the settings screen for linking with a body composition scale or healthcare app.

[0734] Step 3:

[0735] User: Allows integration with body composition scales and healthcare apps.

[0736] Step 4:

[0737] Device: Obtain data such as weight, body fat percentage, bone density, and heart rate from a body composition scale or health app.

[0738] Step 5:

[0739] Terminal: Sends the acquired data to the server.

[0740] Step 6:

[0741] Server: Receives the transmitted data and organizes it for each user.

[0742] Step 7:

[0743] Server: Inputs the organized data into an artificial intelligence model to analyze the user's health condition.

[0744] Step 8:

[0745] Server: Based on the analysis results, an avatar image is generated that reflects the user's biological age, bone density, etc.

[0746] Step 9:

[0747] Server: Sends the generated avatar image and health status description data to the device.

[0748] Step 10:

[0749] Terminal: Displays an avatar image and health status description to the user.

[0750] Step 11:

[0751] On your device: Displays simple advice based on your health status in the form of a comment (e.g., "You are not getting enough exercise. We recommend walking for 30 minutes three times a week.").

[0752] Step 12:

[0753] On the device: Using the built-in camera or microphone, the emotion engine analyzes the user's facial expressions and tone of voice.

[0754] Step 13:

[0755] Server: Receives the analysis results of the emotion engine and stores the user's emotional state in a database.

[0756] Step 14:

[0757] Server: Adjusts the content and display of advice based on the results of emotion recognition. For example, if the user is feeling stressed, it displays advice encouraging relaxation.

[0758] Step 15:

[0759] User: Click on the billing service link within the app and select additional detailed advice.

[0760] Step 16:

[0761] Terminal: Displays a payment screen and prompts the user to enter billing information.

[0762] Step 17:

[0763] Terminal: Processes the payment and, if successful, sends the information to the server.

[0764] Step 18:

[0765] Server: Based on the user's detailed data, it generates personalized health checkup results, dietary improvement plans, future health predictions, and appropriate exercise programs.

[0766] Step 19:

[0767] Server: Sends the generated detailed advice and emotion-based advice adjustments to the device.

[0768] Step 20:

[0769] On-device: Display the purchased detailed advice (e.g., "Your calcium intake is low, so drink milk or yogurt every day") to the user, and add a comment that matches their sentiment.

[0770] Step 21:

[0771] Device: Periodically sends new health and emotion data to the server.

[0772] Step 22:

[0773] Server: Analyzes new data and updates the avatar and adjusts advice based on changes in health status. Emotional data is also analyzed and feedback is adjusted.

[0774] Step 23:

[0775] On the device: Displays continuously updated avatar images, advice, and notifications to the user, along with emotion-based feedback.

[0776] In this way, the entire processing flow starts with acquiring the user's body composition data, then visualization and advice based on the analysis results, followed by a paid service for detailed advice, continuous monitoring, and emotion recognition using an emotion engine, allowing users to comprehensively manage and understand their own health condition and emotions and take appropriate action.

[0777] Example 2

[0778] 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."

[0779] Health management is an important issue in modern society, and many people want to understand their own health status and receive appropriate advice. However, it is difficult to provide personalized advice based on each individual's health and emotional state. Conventional systems only provide uniform advice without considering the user's emotions, which has the problem of not being effective enough. Furthermore, there is a lack of systems that can provide detailed advice, which makes it difficult to provide in-depth health management.

[0780] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring body composition data, means for providing an artificial intelligence model for analyzing health status, and means for generating and displaying an avatar image based on the analysis results. This enables detailed analysis and visualization of the user's health status based on the user's body composition data. Furthermore, by including means for providing personalized advice using an emotion engine, appropriate feedback according to the user's emotional state can be provided. Furthermore, by including a billing service for providing detailed health advice, an environment is provided in which the user can perform in-depth health management.

[0781] "Body composition data" is numerical data that indicates an individual's physical condition, such as weight, body fat percentage, bone density, and heart rate.

[0782] An "artificial intelligence model" is a technology that includes machine learning algorithms and neural networks to analyze collected data and assess a user's health status.

[0783] An "avatar image" is a virtual image that reflects the user's condition and visually displays analyzed health data.

[0784] The "emotion engine" is a technology that analyzes the user's facial expressions, tone of voice, etc. to recognize their current emotional state.

[0785] "Simple advice" is health-related advice that can be understood in a short amount of time and is provided based on the user's current health and emotional state.

[0786] "Paid Services" are paid services that provide detailed health advice, individual health checkup results, dietary improvement plans, etc.

[0787] "Big data" refers to large amounts of data collected from diverse sources and used to derive valuable information through analysis and statistics.

[0788] "Future health risks" refers to the risk of poor health or disease that may occur in the future based on current data and trends.

[0789] "Monitoring" is a surveillance method that regularly tracks changes in the user's health status and immediately notifies them of any abnormalities.

[0790] "Feedback" refers to advice or notifications provided to users based on analysis results and monitoring data.

[0791] This system acquires the user's body composition data through a dedicated app, analyzes the user's health condition using a generative AI model, visualizes the results, and provides simple advice.Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it provides personalized advice.

[0792] Data Acquisition

[0793] Server: A dedicated app is used to set up a connection to obtain the user's body composition data. This connection is made between the body composition monitor and existing healthcare apps (e.g., Apple Health or Google Fit). The server receives data such as weight, body fat percentage, bone density, and heart rate and stores it in a database.

[0794] Device: A dedicated app is installed on the user's smartphone, and when launched for the first time, a screen for setting up the connection with the body composition scale and healthcare app is displayed. The user follows the guide and enters the necessary API key and Bluetooth connection information.

[0795] User: Follow the instructions in the dedicated app and allow the device to connect to the body composition monitor and Health app. Specifically, tap the "Sync Data" button to connect to the iOS Health app.

[0796] Data analysis with generative AI

[0797] Server: The acquired body composition data is input into an AI model for analysis. The generative AI model used compares it with past data to calculate changes in anatomical age and bone density. This is achieved by using machine learning algorithms and neural networks.

[0798] Server: Generates an avatar image based on the analysis results, visually reflecting the user's health status. For example, if the user's health status is good, the avatar is displayed in a bright color, and if the user's health status is deteriorating, the avatar is displayed in a dark color.

[0799] Emotion recognition by emotion engine

[0800] On-device: Captures the user's facial expressions and voice using the built-in camera and microphone, capturing emotional changes in real time. This feature is automatically turned on when the user starts using the app.

[0801] Device: The emotion engine analyzes emotions such as "happiness," "sadness," and "anger" from changes in facial expressions and tone of voice. The analysis results are sent from the device to the server.

[0802] Server: Stores the emotion recognition results in a database so that advice can be provided according to the user's emotional state.

[0803] Display of results and simple advice

[0804] Device: Launches an interface that displays the generated avatar image and analysis results to the user. The avatar image is displayed on the home screen, along with the user's biological age and bone density.

[0805] On your device: Display simple advice based on your health status. Add comments in an appropriate tone based on emotion recognition results. For example, display a goal in a soft tone, such as "To increase your daily activity, aim for 10,000 steps a day."

[0806] User: Review the advice provided and incorporate it into their daily routine, for example by taking more walks to reach their step goal.

[0807] Detailed advice via paid services

[0808] User: Follows the link in the app and navigates to the screen where they can purchase detailed health advice. They then click the "Purchase detailed advice" button to proceed with the payment process.

[0809] Server: Once the payment is completed, the server generates detailed advice, including personalized health check results, a diet plan, and an exercise program.

[0810] On the device: The device displays detailed advice about the purchase to the user, providing specific instructions such as "Your calcium intake is insufficient, so drink milk or yogurt every day."

[0811] Ongoing monitoring and feedback

[0812] On your device: Send new health data to the server periodically, for example, automatically sync your data every day at 9:00 AM.

[0813] Server: Continuously monitors the user's health data and provides anomaly alerts if an abnormality is detected. The server notifies the user in real time that "abnormalities have been found in recent data, so we recommend that you see a doctor."

[0814] On the device: Provide regular health updates and advice to users, including providing a monthly report showing health progress.

[0815] Specific examples

[0816] The user downloads and installs the dedicated app, and when they launch it for the first time, they set up the connection with the body composition scale. Data is then sent from the body composition scale to the server, where it is analyzed using a generative AI model. An avatar image is generated based on the analysis results and displayed on the home screen of the dedicated app. The app uses an emotion engine to recognize the user's emotions and provides advice based on the results. If the user desires more detailed advice, they can use a paid service to receive specific improvement instructions.

[0817] Prompt Sentence Examples

[0818] "Analyze this user's health status based on their latest body composition data and provide advice."

[0819] "Analyze the user's latest sentiment and provide advice based on it."

[0820] "Generate and display an avatar image based on the latest health and emotional data."

[0821] In this way, through the configuration of the present invention, the user can more accurately understand his or her own health condition and receive personalized advice.

[0822] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0823] Step 1: Data Acquisition and Integration Settings

[0824] Device: A dedicated app is installed on the user's smartphone, and when the app is launched for the first time, a screen for setting up linkage with the body composition scale and healthcare app is displayed. Specifically, an interface is provided that prompts the user to enter Bluetooth and API keys. The input in this step is the connection information entered by the user, and the output is a notification of successful linkage.

[0825] User: Follow the instructions in the dedicated app to allow linking with the body composition scale and healthcare app. For example, tap the "Sync Data" button to link with the iOS healthcare app. The input in this step is the user's permission action, and the output is a screen display indicating that the link setup is complete.

[0826] Server: Receives the connection setting information sent from the device and establishes a connection with the API of the body composition scale and healthcare app. The input in this step is the connection information from the device, and the output is a log record of successful connection.

[0827] Step 2: Data analysis with generative AI

[0828] Server: The body composition data acquired through the dedicated app is formatted and input into the AI ​​model. The input in this step is weight, body fat percentage, bone density, and heart rate data, and the output is a standardized dataset required for analysis. Specifically, the data is standardized and converted into a format suitable for the generative AI model.

[0829] Server: Analyzes the acquired data using a generative AI model and evaluates the user's health condition. The input in this step is the formatted body composition data, and the output is health indicators such as biological age and bone density. Specifically, health indicators are calculated by comparing with past data.

[0830] Server: Generates an avatar image based on the analysis results and visualizes the user's health condition. The input in this step is the analysis results of the generative AI model, and the output is an avatar image that reflects the user's unique health condition.

[0831] Step 3: Emotion recognition by the emotion engine

[0832] Device: The built-in camera and microphone are used to capture the user's facial expressions and tone of voice. Specifically, the camera and microphone are automatically turned on when the user uses the app. The input in this step is audio and video data obtained from the camera and microphone, and the output is the user's facial expressions and voice data captured in real time.

[0833] On the device: An emotion recognition algorithm is used to analyze the user's emotions from the captured data. The input in this step is the captured facial expression and voice data, and the output is an emotion label such as "happiness," "sadness," or "anger." Specifically, changes in facial expressiveness and voice tone are analyzed.

[0834] Server: Stores the emotion recognition results in a database. The input to this step is the analysis result of the emotion recognition algorithm, and the output is a record of the emotional state stored in the database.

[0835] Step 4: Displaying results and giving simple advice

[0836] Device: The generated avatar image and the health status analysis results are displayed to the user. The input in this step is the avatar image and health indicators sent from the server, and the output is the visual and text information displayed on the app screen. Specifically, the avatar image is displayed on the home screen and the data is presented visually.

[0837] Device: Displays simple advice based on health status. The inputs in this step are the user's health indicators and emotion recognition results, and the output is an advice message to be displayed to the user. Specifically, a message such as "Today's goal is to walk 10,000 steps a day" is displayed in a soft tone.

[0838] User: Check the displayed advice and incorporate it into their daily life. For example, increase their activity by following a goal of "walking 10,000 steps a day."

[0839] Step 5: Detailed advice from a billing service

[0840] User: Follows a link within the app and operates the screen to purchase detailed health advice. The input at this step is the user's action to complete the purchase, and the output is a notification of successful payment and completion of the purchase of detailed advice. Specifically, the user taps the "Purchase detailed advice" button.

[0841] Server: Once the billing process is complete, detailed health advice is generated. The inputs at this step are the purchase information for the detailed advice and the user's health data, and the output is individual health checkup results and an improvement plan. Specifically, a diet improvement plan and exercise program are generated based on the user's data.

[0842] Terminal: Display the detailed advice of the purchase to the user. The input in this step is the detailed advice information sent from the server, and the output is the specific instructions or plan displayed to the user. For example, the advice displayed might be, "Your calcium intake is insufficient, so drink milk or yogurt every day."

[0843] Step 6: Ongoing monitoring and feedback

[0844] Device: Periodically send new health data to the server. The input in this step is the newly acquired body composition data, and the output is the data to be sent to the server. For example, automatically synchronize data every day at 9:00 AM.

[0845] Server: Continuously monitors the user's health data and provides immediate feedback if an abnormality is detected. The input in this step is the continuously received health data, and the output is a warning message in the event of an abnormality. Specifically, it notifies the user that "an abnormality has been found in recent data, so we recommend that you see a doctor."

[0846] Device: Continuously notify the user of the latest health status and advice. The input in this step is the latest health information and advice sent from the server, and the output is the updated information displayed on the app screen. For example, a monthly report summarizing the latest health status.

[0847] (Application example 2)

[0848] 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."

[0849] Health management for industrial workers is important, and there is a need to appropriately monitor their physical and mental health. However, conventional systems have difficulty understanding individual health and emotional states in real time and allocating appropriate breaks and tasks based on that information. Therefore, there is a need for a system that can effectively and efficiently monitor the health and emotional states of industrial workers and respond to their individual needs.

[0850] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring body composition data, means for providing an artificial intelligence model for analyzing health conditions, means for generating and displaying an avatar image based on the analysis results, means for providing the user with simple advice based on the avatar image and the analysis results, means for recognizing the user's emotional state using an emotion recognition engine, and means for monitoring the health and emotional states of industrial workers in real time and suggesting appropriate breaks and task assignments. This enables effective and efficient health management of industrial workers.

[0851] "Body composition data" refers to data relating to the composition of a human body, such as weight, body fat percentage, bone density, and heart rate.

[0852] An "artificial intelligence model" is a machine learning algorithm that analyzes data and extracts specific patterns and trends.

[0853] An "avatar image" is a digital image that visually represents a user's health status or other attributes.

[0854] "Simple advice" refers to basic and immediate health guidance and suggestions provided to users.

[0855] "Charging Service" means a fee-collection system used to provide detailed health advice to users.

[0856] An "emotion recognition engine" is a combination of software and hardware that identifies emotions from a user's facial expressions, tone of voice, etc.

[0857] "Industrial workers" refers to people who perform manual labor in factories, manufacturing facilities, etc.

[0858] "Real-time monitoring" means being able to instantly observe ongoing events and conditions and take prompt action based on that information.

[0859] A "health management system" refers to a set of technologies and tools that monitor a user's health status and provide appropriate advice and feedback.

[0860] The present invention is a system for monitoring the health and emotional state of industrial workers in real time and providing personalized advice. Specific embodiments of the system will be described below.

[0861] System configuration and operation

[0862] 1. Data Acquisition Method

[0863] The server collects body composition data from industrial workers through specialized applications, including data from body composition monitors and healthcare apps, such as weight, body fat percentage, bone density, and heart rate. The terminals are equipped with the functionality to transmit this data in real time.

[0864] 2. Health status analysis tools

[0865] The server inputs the acquired body composition data into an artificial intelligence model (AI model) to analyze health status. This AI model can use machine learning frameworks such as TensorFlow and PyTorch. As a result of the analysis, health indicators such as biological age and bone density are calculated.

[0866] 3. Avatar Image Generation Method

[0867] The server generates and displays an avatar image based on the analysis results, which visually reflects the user's health condition on the user's smartphone or tablet.

[0868] 4. Emotion recognition means

[0869] The device uses a built-in camera and microphone to recognize emotions from the user's facial expressions and tone of voice. An emotion recognition model is used for facial expression and voice analysis. The server analyzes the emotion recognition results and records the user's current emotional state.

[0870] 5. Display of results and means of providing simple advice

[0871] The device displays the generated avatar image and a description of the user's health status to the user. In addition, based on the results of emotion recognition, the device provides simple, appropriate advice in a soft tone, such as "To increase your daily step count, aim for 10,000 steps a day."

[0872] 6. Means of providing detailed advice through paid services

[0873] By using the paid service, detailed health advice is provided. Users can purchase detailed advice by following the link provided in the app and using the paid service. The server generates individual advice and health check results according to the purchase, and provides dietary improvement plans and appropriate exercise programs.

[0874] 7. Ongoing monitoring and feedback measures

[0875] The device periodically sends new health data to the server to update the user's health status. The server continuously monitors the user's health data and provides immediate feedback if any abnormalities are detected, thereby supporting the user's health management.

[0876] Specific examples

[0877] An industrial worker uses a body composition scale to collect data such as weight, body fat percentage, bone density, and heart rate, and then sends that data to an application. The device's camera also captures facial expressions in real time, and if the emotion analysis model recognizes the worker as being in a "stressed state," the AI ​​model analyzes the data and generates an avatar image based on the results. This avatar image and simple advice are displayed to the worker, suggesting appropriate tasks and breaks based on his health condition and emotions.

[0878] Prompt Sentence Examples

[0879] "Analyze the health status of a worker based on the following body composition data:

[0880] Weight: 70kg

[0881] Body fat percentage: 15%

[0882] Bone density: 1.2g / cm3

[0883] Heart rate: 75 bpm

[0884] Also consider the following sentiment analysis data:

[0885] Facial Expressions: Stressed

[0886] Based on the analysis results, generate an avatar image of the worker's health status and provide appropriate health advice."

[0887] Thus, the present invention is a system that comprehensively monitors the health and emotional state of industrial workers and provides specific advice and feedback tailored to their individual needs.

[0888] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0889] Step 1:

[0890] Users collect body composition data using a dedicated body composition monitor or healthcare app. This data includes weight, body fat percentage, bone density, heart rate, etc. Users then send the collected data to a dedicated application installed on their smartphone or tablet.

[0891] Input: Body composition data obtained from a body composition analyzer

[0892] Output: Body composition data sent to a dedicated application

[0893] Step 2:

[0894] The device transmits the user's body composition data to the server, which receives the data and stores it in a database.

[0895] Input: Body composition data sent from the dedicated application

[0896] Output: Body composition data stored in a database

[0897] Step 3:

[0898] The server inputs the stored body composition data into an artificial intelligence model (AI model) that uses machine learning frameworks such as TensorFlow and PyTorch to analyze the user's health condition.

[0899] Input: Body composition data stored in the database

[0900] Output: Health status analysis results

[0901] Step 4:

[0902] The server generates an avatar image that visually represents the user's health condition based on the analysis results.

[0903] Input: Health status analysis results

[0904] Output: Avatar image

[0905] Step 5:

[0906] The device uses a built-in camera and microphone to recognize the user's emotions from their facial expressions and tone of voice. An emotion recognition model analyzes their facial expressions and voice to determine the user's emotional state.

[0907] Input: Facial expression and voice data captured by the built-in camera and microphone

[0908] Output: Emotional state analysis result

[0909] Step 6:

[0910] The server analyzes the results of the emotion recognition engine and stores the user's current emotional state in a database.

[0911] Input: Emotional state analysis results

[0912] Output: Emotional state data stored in a database

[0913] Step 7:

[0914] The device displays the generated avatar image, a description of the user's health status, and simple advice based on the user's emotions. For example, it may display a comment such as, "To increase your daily step count, aim for 10,000 steps a day."

[0915] Input: Avatar image, health status description, emotion recognition results

[0916] Output: Avatar image and advice displayed on the device

[0917] Step 8:

[0918] The user uses the billing service to obtain detailed health advice within the app. When billing is successful, the server generates detailed advice and health check results and provides them to the user.

[0919] Input: Billing service usage information

[0920] Output: Detailed health check results, dietary improvement plan, exercise program

[0921] Step 9:

[0922] The device periodically sends new health data to the server, updating the user's health status. The server continuously monitors the device and provides feedback if any abnormalities are detected.

[0923] Input: New health data

[0924] Output: Updated health status data, feedback

[0925] In this way, a system is created that monitors the health and emotional state of industrial workers in real time and provides personalized advice, enabling effective health management.

[0926] 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.

[0927] 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.

[0928] 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.

[0929] [Third embodiment]

[0930] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0931] 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.

[0932] 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).

[0933] 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.

[0934] 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.

[0935] 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).

[0936] 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.

[0937] 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.

[0938] 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.

[0939] 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.

[0940] 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.

[0941] 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."

[0942] The present invention is a system that acquires body composition data through a dedicated app, analyzes health status using generative AI based on the data, and provides the results to users as a visualized avatar image and simple advice. It also includes a paid service for providing detailed health advice and a function for predicting future health risks using big data. The present invention is specifically implemented using the following means.

[0943] System configuration

[0944] 1. Data acquisition means: A module for acquiring data from body composition monitors and healthcare apps.

[0945] 2. Analysis tools: Artificial intelligence models to analyze the acquired data.

[0946] 3. Visualization means: An interface for generating and displaying avatar images based on the analysis results.

[0947] 4. Advice Providing Tool: A module for providing simple and detailed advice to users.

[0948] 5. Billing service means: A billing system for providing detailed health advice.

[0949] 6. Continuous monitoring measures: The ability to track changes in health status and provide feedback.

[0950] Specific Examples of the Invention

[0951] Program processing explanation

[0952] 1. Data acquisition and integration settings

[0953] Server: To obtain the user's body composition data through a dedicated app, the server connects to the body composition monitor and healthcare app, collecting data such as weight, body fat percentage, bone density, and heart rate.

[0954] Device: A dedicated app is installed on the user's smartphone, and a screen is provided for setting up the connection with the body composition scale and healthcare app.

[0955] User: Follow the instructions in the dedicated app and allow it to connect to the body composition scale and healthcare app.

[0956] 2. Data analysis using generative AI

[0957] Server: The acquired data is input into an AI model to analyze the user's health condition, thereby calculating health indicators such as biological age and bone density.

[0958] Server: Generates an avatar image based on the analysis results, visually reflecting the user's health condition.

[0959] 3. Display of results and simple advice

[0960] On the device, the generated avatar image and a description of the user's health condition are displayed. For example, if the user's biological age is higher than their actual age, the device will inform the user that they need to exercise less or improve their diet.

[0961] On your device: Displays simple advice based on your health status, such as "To increase your daily step count, aim for 10,000 steps a day."

[0962] 4. Detailed advice through paid services

[0963] User: Follows a link provided within the app and purchases in-depth advice using a paid service.

[0964] Server: Once the charge is successful, the server generates personalized health check results based on detailed health data, a dietary improvement plan, future health predictions, and an appropriate exercise program.

[0965] On the device: The detailed advice provided with the purchase is displayed to the user, providing specific instructions such as, "Your calcium intake is low, so drink milk or yogurt every day."

[0966] 5. Continuous monitoring and feedback

[0967] Device: Periodically sends new health data to the server to reflect updated health status.

[0968] Server: Continuously monitors the user's health data and provides immediate feedback if any abnormalities are detected.

[0969] On the device: Continuously updates avatars, health advice, and notifies users.

[0970] In this way, the present invention provides a system that analyzes and visualizes health conditions based on body composition data, and supports effective health management for users.

[0971] The processing flow will be explained below.

[0972] Step 1:

[0973] Users: Install the dedicated app on their smartphone and create an account.

[0974] Step 2:

[0975] Device: Display the settings screen for linking with a body composition scale or healthcare app.

[0976] Step 3:

[0977] User: Allows integration with body composition scales and healthcare apps.

[0978] Step 4:

[0979] Device: After authorization, data such as weight, body fat percentage, bone density, and heart rate will be collected.

[0980] Step 5:

[0981] Terminal: Sends the acquired data to the server.

[0982] Step 6:

[0983] Server: Receives the transmitted data and organizes it for each user.

[0984] Step 7:

[0985] Server: Inputs the organized data into an artificial intelligence model to analyze the user's health condition.

[0986] Step 8:

[0987] Server: Based on the analysis results, an avatar image is generated that reflects the user's biological age, bone density, etc.

[0988] Step 9:

[0989] Server: Sends the generated avatar image and health status description data to the device.

[0990] Step 10:

[0991] Terminal: Displays an avatar image and health status description to the user.

[0992] Step 11:

[0993] On your device: Displays simple advice based on your health status in the form of a comment (e.g., "You are not getting enough exercise. We recommend walking for 30 minutes three times a week.").

[0994] Step 12:

[0995] Users: Click on the billing service link and select additional detailed advice.

[0996] Step 13:

[0997] Terminal: Displays a payment screen and prompts the user to enter billing information.

[0998] Step 14:

[0999] Terminal: Processes the payment and, if successful, sends the information to the server.

[1000] Step 15:

[1001] Server: Based on the user's detailed data, it generates personalized health checkup results, dietary improvement plans, future health predictions, and appropriate exercise programs.

[1002] Step 16:

[1003] Server: Sends the generated detailed advice to the device.

[1004] Step 17:

[1005] On the device: Display detailed health advice to the user (e.g., "Your calcium intake is low, so drink milk or yogurt every day").

[1006] Step 18:

[1007] Device: Periodically sends new health data to the server.

[1008] Step 19:

[1009] Server: Analyzes new data and updates the avatar and adjusts advice based on changes in health.

[1010] Step 20:

[1011] On the device: Displays continuously updated avatar images, advice, and notifications to the user.

[1012] In this way, the entire process starts with acquiring the user's body composition data, then visualization and advice based on the analysis results, and includes a fee-based service for detailed advice and continuous monitoring, allowing users to track and manage their own health status.

[1013] Example 1

[1014] 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."

[1015] In modern society, the importance of individual health management is increasing. However, many people find it difficult to manage their daily health on their own, and have limited opportunities to receive professional advice. Furthermore, there are insufficient tools for accumulating health data and assessing long-term health risks. To solve these issues, a system is needed that can accurately grasp each individual's health status, visualize it in an easy-to-understand manner, and provide continuous monitoring and feedback.

[1016] 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.

[1017] In this invention, the server includes means for acquiring body composition data, means for providing an artificial intelligence model for analyzing health conditions, and means for generating and displaying an avatar image based on the analysis results, thereby enabling users to visually grasp their individual health conditions and receive easy-to-understand feedback, thereby enabling them to more effectively manage their own health conditions.

[1018] "Body composition data" refers to data related to the composition of the user's body, such as weight, body fat percentage, bone density, and heart rate.

[1019] An "artificial intelligence model" is a program or system that uses machine learning and data analysis algorithms to analyze health conditions based on acquired data.

[1020] An "avatar image" is a graphical image of a person that visually displays the user's health condition based on the analysis results.

[1021] "Simple advice" is a message that provides basic health management guidelines that can be put into practice in daily life based on the user's health condition.

[1022] A "billing system" is a system that collects fees from users for providing detailed health advice or additional services.

[1023] "Big data" refers to large amounts of health data collected from many users, a dataset that can be analyzed to gain valuable insights.

[1024] "Health risks" are the risk of health problems or diseases that may occur in the future based on the user's current health condition and lifestyle habits.

[1025] "Continuous monitoring" is the process of regularly and continuously collecting and analyzing a user's health data to track changes.

[1026] "Feedback" refers to information such as advice, warnings, and recommendations provided to users based on analysis results and monitoring data.

[1027] The present invention is a system that uses body composition data to analyze health conditions and provides visual feedback and specific health advice. Specific embodiments of this system will be described below.

[1028] Data acquisition and integration settings

[1029] The server configures the connection with the body composition monitor and healthcare app to obtain the user's body composition data through a dedicated app. This connection is granted by OAuth authentication, etc., and the server obtains data such as the user's weight, body fat percentage, bone density, and heart rate.

[1030] The device provides a screen for installing a dedicated app on the user's smartphone and displays an interface for setting up connectivity with the body composition scale and healthcare app. For example, the app can connect to the body composition scale using Bluetooth or Wi-Fi.

[1031] The user follows the instructions in the dedicated app to allow the body composition scale to connect to the healthcare app. Specifically, this involves pressing the corresponding button on the body composition scale within the app to have the device recognized.

[1032] Data analysis with generative AI

[1033] The server inputs the acquired body composition data into an artificial intelligence model (hereinafter referred to as the "generative AI model"). For example, data such as weight, body fat percentage, bone density, and heart rate measured by the user in the morning are sent to the server.

[1034] The server then feeds this data into a generative AI model, which analyzes the user's health from multiple angles. Specifically, the AI ​​model uses machine learning algorithms to calculate factors such as biological age and bone density.

[1035] The server generates an avatar image based on the analysis results, visually reflecting the user's health status. For example, if the user has a high body fat percentage, the avatar will be displayed as slightly overweight.

[1036] Display of results and simple advice

[1037] The device displays the generated avatar image and a description of the user's health status. For example, the app screen might say, "Your biological age is 35."

[1038] The device will display simple advice based on your health status, such as "To increase your daily step count, aim for 10,000 steps per day," and may also encourage users to drink at least eight glasses of water per day.

[1039] Detailed advice via paid services

[1040] The user clicks on a link provided within the app to proceed to a billing service screen, which may say, for example, "Please pay to receive detailed health checkup results."

[1041] Once the user's payment is successful, the server generates personalized health checkup results and dietary improvement plans based on the detailed health data. Specifically, the generative AI model performs further detailed analysis and generates advice based on the user's lifestyle habits.

[1042] The device will then display detailed advice to the user about the purchase, including specific instructions such as "Your calcium intake is low, so drink milk or yogurt every day."

[1043] Ongoing monitoring and feedback

[1044] The device periodically sends new health data to the server. For example, a user measures their weight and body fat percentage every Sunday and uploads the data to the server.

[1045] The server continuously monitors the user's health data and provides immediate feedback if any abnormalities are detected. For example, if a sudden weight gain is detected, an alert will be sent saying, "Your weight has increased rapidly recently. Please be careful about your diet and exercise."

[1046] The device continuously updates the avatar and health advice and notifies the user. For example, every time health data is updated, the device notifies the user of "how their health status has changed" along with the latest avatar image.

[1047] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1048] Step 1:

[1049] Data acquisition and integration settings

[1050] The server acquires the user's body composition data from a body composition scale or healthcare app connected to the dedicated app. The acquired data includes weight, body fat percentage, bone density, heart rate, etc. This provides basic data for accurately understanding the user's physical condition.

[1051] The device displays a screen for installing a dedicated app on the user's smartphone and provides an interface for setting up linkage with the body composition scale and healthcare app. Specifically, the app connects to the body composition scale using Bluetooth or Wi-Fi and associates it with the user ID.

[1052] The user follows the instructions in the dedicated app to allow the body composition scale to connect to the healthcare app. Specifically, the user presses the corresponding button on the body composition scale within the app to have the device recognized.

[1053] Step 2:

[1054] Data analysis with generative AI

[1055] The server inputs the acquired body composition data into the generative AI model. This input data includes weight, body fat percentage, bone density, heart rate, etc. The AI ​​model uses this data to analyze the user's health condition and calculate health indicators such as biological age and bone density. Specifically, it uses a machine learning algorithm to perform the analysis and compare the user's data with other databases to evaluate their health condition.

[1056] As an output, the AI ​​model calculates health indicators and returns them to the server, providing the results as numerical data reflecting the user's health status.

[1057] Step 3:

[1058] Avatar image generation and result display

[1059] The server generates an avatar image based on the analysis results. This avatar image visually reflects the user's health condition, and includes operations such as "if the body fat percentage is high, the avatar will be displayed slightly overweight."

[1060] The device displays the generated avatar image and a description of the user's health condition. Specifically, the app screen displays a message such as "Your biological age is 35" along with the avatar image.

[1061] Step 4:

[1062] Providing simple advice

[1063] The device will display simple advice to the user based on their health status, such as "To increase your daily step count, aim for 10,000 steps per day," and will also display a message encouraging the user to drink at least eight glasses of water per day.

[1064] This advice is developed based on the analysis results of the generative AI model and is notified to the user via the app.

[1065] Step 5:

[1066] Chargeable service with detailed advice

[1067] The user clicks on a link provided within the app to proceed to a billing service screen, which may say, for example, "Please pay to receive detailed health checkup results."

[1068] Once the user has successfully completed the payment, the server generates personalized health checkup results and a diet plan based on the user's detailed health data. The AI ​​model then performs further detailed analysis and generates advice based on the user's lifestyle habits.

[1069] The device will then display detailed advice to the user about the product they purchased, such as "Your calcium intake is low, so drink milk or yogurt every day."

[1070] Step 6:

[1071] Ongoing monitoring and feedback

[1072] The device periodically sends new health data to the server. For example, a user measures their weight and body fat percentage every Sunday and uploads the data to the server.

[1073] The server continuously monitors the user's health data and provides immediate feedback if any abnormalities are detected. For example, if a sudden weight gain is detected, it will send an alert saying, "Your weight has increased rapidly recently. Please be careful about your diet and exercise."

[1074] The device continuously updates the avatar and health advice and notifies the user. Each time the data is updated, the device notifies the user of the latest avatar image and how their health status has changed.

[1075] (Application example 1)

[1076] 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."

[1077] Health management is an important issue in today's world, but conventional systems have struggled to quickly and effectively analyze users' body composition data and present it in an intuitively understandable format. Furthermore, they lacked the functionality to provide continuous monitoring and individual feedback, making it difficult to grasp users' health status and implement improvement measures. Furthermore, there was a lack of billing services to provide detailed health advice, and a system that integratedly enabled users to set health goals and track their progress. These issues need to be resolved.

[1078] 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.

[1079] In this invention, the server includes means for acquiring body composition data, means for providing an artificial intelligence model for analyzing health status, means for generating and displaying an avatar image based on the analysis results, means for providing the user with simple advice based on the avatar image and the analysis results, means for including a billing service for providing detailed health advice, means for continuously monitoring user data and providing appropriate feedback, means for setting the user's health goals and tracking progress toward those goals, additional means for displaying the visualization results of the health status, and means for performing billing service processing. This enables analysis and visualization of the user's health status based on the user's body composition data, individualized health advice, continuous health monitoring and feedback, provision of detailed advice, and health goal setting and progress management.

[1080] "Body composition data" refers to data relating to the composition of the human body, such as weight, body fat percentage, bone density, and heart rate.

[1081] An "artificial intelligence model" is an algorithm or machine learning model used to analyze a user's health condition.

[1082] An "avatar image" is a virtual image that visually reflects the user's health condition.

[1083] "Quick Advice" is basic health recommendations provided to the user.

[1084] A "billing service" is a system that charges users a fee for providing detailed health advice and individual plans.

[1085] "Continuous user data monitoring" refers to the regular collection of a user's health data over time and tracking changes.

[1086] "Feedback" refers to advice or warnings provided to users based on analysis results and monitoring data.

[1087] "Setting health goals" means defining specific health indicators and action plans that a user aims to achieve.

[1088] "Progress tracking" refers to monitoring and recording a user's progress toward defined health goals.

[1089] The "visualization result of health status" is information that displays the user's health status based on the analysis results in an intuitive and easy-to-understand manner.

[1090] "Billing service processing" refers to managing the process by which users pay for detailed health services and advice.

[1091] MODE FOR CARRYING OUT THE INVENTION

[1092] As an embodiment of the present invention, a description will be given of how to specifically realize an application example.

[1093] Server Roles

[1094] The server uses the following hardware and software to perform processes such as acquiring, analyzing, visualizing, and providing advice on various types of data.

[1095] 1. Data Acquisition:

[1096] Hardware: Body composition scale, smartphone, wearable device.

[1097] Software: A dedicated application for collecting healthcare data.

[1098] Description: The server obtains the user's body composition data (weight, body fat percentage, bone density, heart rate, etc.) from a body composition scale or wearable device.

[1099] 2. Data Analysis:

[1100] Hardware: Server.

[1101] Software: Generative AI model (HealthAIModel).

[1102] Description: The server inputs the acquired body composition data into a generative AI model to analyze the user's health condition, thereby calculating health indicators such as biological age and bone density.

[1103] 3. Visualization:

[1104] Hardware: Server, user's smartphone.

[1105] Software: Avatar Generator.

[1106] Description: An avatar image is generated based on the analysis results and displayed on the user's smartphone.

[1107] 4. Providing advice:

[1108] Hardware: Server, user's smartphone.

[1109] Software: Advice delivery module.

[1110] Description: The server provides simple advice to the user based on the analysis results. In addition, the server provides detailed health advice to the user by paying for a service.

[1111] 5. Continuous monitoring and feedback:

[1112] Hardware: Servers, smartphones.

[1113] Software: Monitoring and feedback module.

[1114] Description: The server continuously monitors changes in the user's health status and provides immediate feedback if any abnormalities are detected. It periodically retrieves new health data and reflects the updated health status.

[1115] 6. Billing Service Processing:

[1116] Hardware: Smartphones, servers.

[1117] Software: Payment Gateway.

[1118] Description: When a user purchases detailed health advice, the server processes the billing and, if successful, provides the detailed advice.

[1119] Specific examples

[1120] Take the example of an application provided by a fitness gym. When a user steps on a body composition scale, the data is automatically sent to a dedicated application. The server acquires this data and analyzes it using a generative AI model. The resulting avatar image is displayed on the user's smartphone, and basic health advice is provided. If the user desires additional, detailed advice, it can be obtained through an in-app purchase.

[1121] Example prompts for input to a generative AI model:

[1122] Weight: 70kg, body fat percentage: 20%, bone density: 1.2g / cm3, heart rate: 70bpm

[1123] Analyze the user's health status and provide appropriate advice.

[1124] Thus, based on the specific form for implementing the invention, it is possible to analyze and visualize a user's health status, provide personalized health advice, provide continuous monitoring and feedback, provide detailed advice, and set health goals and manage their progress.

[1125] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1126] Program processing steps

[1127] Step 1:

[1128] Data Acquisition

[1129] The server acquires body composition data. When a user uses a body composition scale or wearable device, the body composition data (weight, body fat percentage, bone density, heart rate, etc.) is sent from these devices to the server via a dedicated application. The input is the user's body composition scale data, and the output is the body composition data accumulated on the server. This data is saved in a database for use in later analysis.

[1130] Step 2:

[1131] Data analysis

[1132] The server inputs the acquired body composition data into a generative AI model (HealthAIModel) to analyze the user's health condition. Based on a series of input data, the AI ​​model processes the data and calculates health indicators such as biological age, bone density, and heart rate. The input here is the body composition data stored on the server, and the output is the analysis results. Data processing involves normalizing the data and extracting features, and converting it into a format suitable for the AI ​​model.

[1133] Step 3:

[1134] Visualization

[1135] The server generates an avatar image based on the analysis results. An avatar generation tool (AvatarGenerator) is used to create an avatar image that visually represents the analyzed health indicators. The input to this step is the analysis results, and the output is an avatar image. Specifically, the analysis results are input as prompts into the avatar generation tool, and the tool automatically generates an avatar image.

[1136] Step 4:

[1137] Providing advice

[1138] The server provides the generated avatar image and simple advice to the user. Based on the analysis results, the AI ​​model automatically generates prompts to provide appropriate health advice to the user. For example, advice such as "To increase your daily step count, aim for 10,000 steps a day" is displayed. The input for this step is the health analysis results and prompts, and the output is simple advice and a visualized avatar image.

[1139] Step 5:

[1140] Detailed advice and billing services

[1141] If a user wants detailed health advice, they use the billing service through their terminal. The server processes the billing using a payment gateway. If the billing is successful, detailed advice is displayed on the user's terminal. Specifically, the server receives a billing request from the user and makes the payment via the payment gateway. The input is the user's billing request information, and the output is the result of the billing process and detailed advice.

[1142] Step 6:

[1143] Ongoing monitoring and feedback

[1144] The server continuously monitors changes in the user's health condition. It periodically acquires the user's body composition data and reanalyzes the health condition based on that data. If an abnormality is detected, it provides immediate feedback. The input to this step is the continuously collected body composition data, and the output is an updated health condition report and feedback. Specifically, the server automatically acquires and analyzes data according to a regular data collection schedule.

[1145] Prompt Sentence Examples

[1146] Weight: 70kg, body fat percentage: 20%, bone density: 1.2g / cm3, heart rate: 70bpm

[1147] Analyze the user's health status and provide appropriate advice.

[1148] 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.

[1149] The present invention is a system that acquires body composition data through a dedicated app, analyzes health status using generative AI based on the data, and provides the results to the user as a visualized avatar image and simple advice. In addition, by combining this with an emotion engine that recognizes the user's emotions, more personalized advice and feedback can be provided. The present invention is specifically implemented using the following means.

[1150] System configuration

[1151] 1. Data acquisition means: A module for acquiring data from body composition monitors and healthcare apps.

[1152] 2. Analysis tools: Artificial intelligence models to analyze the acquired data.

[1153] 3. Visualization means: An interface for generating and displaying avatar images based on the analysis results.

[1154] 4. Emotion Recognition Means: An emotion engine that recognizes the user's emotions.

[1155] 5. Advice Providing Tool: A module for providing simple and detailed advice to users.

[1156] 6. Billing service means: A billing system for providing detailed health advice.

[1157] 7. Continuous monitoring measures: The ability to track changes in health status and provide feedback.

[1158] Specific Examples of the Invention

[1159] Program processing explanation

[1160] 1. Data acquisition and integration settings

[1161] Server: To obtain the user's body composition data through a dedicated app, the server connects to the body composition monitor and healthcare app, collecting data such as weight, body fat percentage, bone density, and heart rate.

[1162] Device: A dedicated app is installed on the user's smartphone, and a screen is provided for setting up the connection with the body composition scale and healthcare app.

[1163] User: Follow the instructions in the dedicated app and allow it to connect to the body composition scale and healthcare app.

[1164] 2. Data analysis using generative AI

[1165] Server: The acquired data is input into an AI model to analyze the user's health condition, thereby calculating health indicators such as biological age and bone density.

[1166] Server: Generates an avatar image based on the analysis results, visually reflecting the user's health condition.

[1167] 3. Emotion Recognition by Emotion Engine

[1168] Device: Using the built-in camera and microphone, the device recognizes emotions from the user's facial expressions and tone of voice. For example, facial expression analysis and voice analysis can be used to determine whether the user is happy or stressed.

[1169] Server: Analyzes the emotion recognition results and stores the user's current emotional state in a database.

[1170] 4. Display of results and simple advice

[1171] On the device: The generated avatar image and a description of the health condition are displayed to the user. Comments are added in an appropriate tone based on the results of the emotion engine.

[1172] On your device: It displays simple advice based on your health status, adjusting to your emotions and providing softer comments like "Try to increase your daily step count by aiming for 10,000 steps a day."

[1173] 5. Detailed advice through paid services

[1174] User: Follows a link provided within the app and purchases in-depth advice using a paid service.

[1175] Server: Once the charge is successful, the server generates personalized health check results based on detailed health data, a dietary improvement plan, future health predictions, and an appropriate exercise program.

[1176] On the device: The detailed advice provided with the purchase is displayed to the user, providing specific instructions such as, "Your calcium intake is low, so drink milk or yogurt every day."

[1177] 6. Continuous monitoring and feedback

[1178] Device: Periodically sends new health data to the server to reflect updated health status.

[1179] Server: Continuously monitors the user's health data and provides immediate feedback if any abnormalities are detected.

[1180] On the device: Continuously updates avatars, health advice, and notifies users.

[1181] In this way, the present invention provides a system that analyzes and visualizes health status based on body composition data, and recognizes emotions using an emotion engine, thereby supporting more effective and personalized health management for users.

[1182] The processing flow will be explained below.

[1183] Step 1:

[1184] Users: Install the dedicated app on their smartphone and create an account.

[1185] Step 2:

[1186] Device: Display the settings screen for linking with a body composition scale or healthcare app.

[1187] Step 3:

[1188] User: Allows integration with body composition scales and healthcare apps.

[1189] Step 4:

[1190] Device: Obtain data such as weight, body fat percentage, bone density, and heart rate from a body composition scale or health app.

[1191] Step 5:

[1192] Terminal: Sends the acquired data to the server.

[1193] Step 6:

[1194] Server: Receives the transmitted data and organizes it for each user.

[1195] Step 7:

[1196] Server: Inputs the organized data into an artificial intelligence model to analyze the user's health condition.

[1197] Step 8:

[1198] Server: Based on the analysis results, an avatar image is generated that reflects the user's biological age, bone density, etc.

[1199] Step 9:

[1200] Server: Sends the generated avatar image and health status description data to the device.

[1201] Step 10:

[1202] Terminal: Displays an avatar image and health status description to the user.

[1203] Step 11:

[1204] On your device: Displays simple advice based on your health status in the form of a comment (e.g., "You are not getting enough exercise. We recommend walking for 30 minutes three times a week.").

[1205] Step 12:

[1206] On the device: Using the built-in camera or microphone, the emotion engine analyzes the user's facial expressions and tone of voice.

[1207] Step 13:

[1208] Server: Receives the analysis results of the emotion engine and stores the user's emotional state in a database.

[1209] Step 14:

[1210] Server: Adjusts the content and display of advice based on the results of emotion recognition. For example, if the user is feeling stressed, it displays advice encouraging relaxation.

[1211] Step 15:

[1212] User: Click on the billing service link within the app and select additional detailed advice.

[1213] Step 16:

[1214] Terminal: Displays a payment screen and prompts the user to enter billing information.

[1215] Step 17:

[1216] Terminal: Processes the payment and, if successful, sends the information to the server.

[1217] Step 18:

[1218] Server: Based on the user's detailed data, it generates personalized health checkup results, dietary improvement plans, future health predictions, and appropriate exercise programs.

[1219] Step 19:

[1220] Server: Sends the generated detailed advice and emotion-based advice adjustments to the device.

[1221] Step 20:

[1222] On-device: Display the purchased detailed advice (e.g., "Your calcium intake is low, so drink milk or yogurt every day") to the user, and add a comment that matches their sentiment.

[1223] Step 21:

[1224] Device: Periodically sends new health and emotion data to the server.

[1225] Step 22:

[1226] Server: Analyzes new data and updates the avatar and adjusts advice based on changes in health status. Emotional data is also analyzed and feedback is adjusted.

[1227] Step 23:

[1228] On the device: Displays continuously updated avatar images, advice, and notifications to the user, along with emotion-based feedback.

[1229] In this way, the entire processing flow starts with acquiring the user's body composition data, then visualization and advice based on the analysis results, followed by a paid service for detailed advice, continuous monitoring, and emotion recognition using an emotion engine, allowing users to comprehensively manage and understand their own health condition and emotions and take appropriate action.

[1230] Example 2

[1231] 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."

[1232] Health management is an important issue in modern society, and many people want to understand their own health status and receive appropriate advice. However, it is difficult to provide personalized advice based on each individual's health and emotional state. Conventional systems only provide uniform advice without considering the user's emotions, which has the problem of not being effective enough. Furthermore, there is a lack of systems that can provide detailed advice, which makes it difficult to provide in-depth health management.

[1233] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring body composition data, means for providing an artificial intelligence model for analyzing health status, and means for generating and displaying an avatar image based on the analysis results. This enables detailed analysis and visualization of the user's health status based on the user's body composition data. Furthermore, by including means for providing personalized advice using an emotion engine, appropriate feedback according to the user's emotional state can be provided. Furthermore, by including a billing service for providing detailed health advice, an environment is provided in which the user can perform in-depth health management.

[1234] "Body composition data" is numerical data that indicates an individual's physical condition, such as weight, body fat percentage, bone density, and heart rate.

[1235] An "artificial intelligence model" is a technology that includes machine learning algorithms and neural networks to analyze collected data and assess a user's health status.

[1236] An "avatar image" is a virtual image that reflects the user's condition and visually displays analyzed health data.

[1237] The "emotion engine" is a technology that analyzes the user's facial expressions, tone of voice, etc. to recognize their current emotional state.

[1238] "Simple advice" is health-related advice that can be understood in a short amount of time and is provided based on the user's current health and emotional state.

[1239] "Paid Services" are paid services that provide detailed health advice, individual health checkup results, dietary improvement plans, etc.

[1240] "Big data" refers to large amounts of data collected from diverse sources and used to derive valuable information through analysis and statistics.

[1241] "Future health risks" refers to the risk of poor health or disease that may occur in the future based on current data and trends.

[1242] "Monitoring" is a surveillance method that regularly tracks changes in the user's health status and immediately notifies them of any abnormalities.

[1243] "Feedback" refers to advice or notifications provided to users based on analysis results and monitoring data.

[1244] This system acquires the user's body composition data through a dedicated app, analyzes the user's health condition using a generative AI model, visualizes the results, and provides simple advice.Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it provides personalized advice.

[1245] Data Acquisition

[1246] Server: A dedicated app is used to set up a connection to obtain the user's body composition data. This connection is made between the body composition monitor and existing healthcare apps (e.g., Apple Health or Google Fit). The server receives data such as weight, body fat percentage, bone density, and heart rate and stores it in a database.

[1247] Device: A dedicated app is installed on the user's smartphone, and when launched for the first time, a screen for setting up the connection with the body composition scale and healthcare app is displayed. The user follows the guide and enters the necessary API key and Bluetooth connection information.

[1248] User: Follow the instructions in the dedicated app and allow the device to connect to the body composition monitor and Health app. Specifically, tap the "Sync Data" button to connect to the iOS Health app.

[1249] Data analysis with generative AI

[1250] Server: The acquired body composition data is input into an AI model for analysis. The generative AI model used compares it with past data to calculate changes in anatomical age and bone density. This is achieved by using machine learning algorithms and neural networks.

[1251] Server: Generates an avatar image based on the analysis results, visually reflecting the user's health status. For example, if the user's health status is good, the avatar is displayed in a bright color, and if the user's health status is deteriorating, the avatar is displayed in a dark color.

[1252] Emotion recognition by emotion engine

[1253] On-device: Captures the user's facial expressions and voice using the built-in camera and microphone, capturing emotional changes in real time. This feature is automatically turned on when the user starts using the app.

[1254] Device: The emotion engine analyzes emotions such as "happiness," "sadness," and "anger" from changes in facial expressions and tone of voice. The analysis results are sent from the device to the server.

[1255] Server: Stores the emotion recognition results in a database so that advice can be provided according to the user's emotional state.

[1256] Display of results and simple advice

[1257] Device: Launches an interface that displays the generated avatar image and analysis results to the user. The avatar image is displayed on the home screen, along with the user's biological age and bone density.

[1258] On-device: Display simple advice based on your health status. Add comments in an appropriate tone based on emotion recognition results. For example, display a goal in a soft tone, such as "To increase your daily activity, aim for 10,000 steps a day."

[1259] User: Review the advice provided and incorporate it into their daily routine, for example by taking more walks to reach their step goal.

[1260] Detailed advice via paid services

[1261] User: Follows the link in the app and navigates to the screen where they can purchase detailed health advice. They then click the "Purchase detailed advice" button to proceed with the payment process.

[1262] Server: Once the payment is completed, the server generates detailed advice, including personalized health check results, a diet plan, and an exercise program.

[1263] On the device: The device displays detailed advice about the purchase to the user, providing specific instructions such as "Your calcium intake is insufficient, so drink milk or yogurt every day."

[1264] Ongoing monitoring and feedback

[1265] On your device: Send new health data to the server periodically, for example, automatically sync your data every day at 9:00 AM.

[1266] Server: Continuously monitors the user's health data and provides anomaly alerts if an abnormality is detected. The server notifies the user in real time that "abnormalities have been found in recent data, so we recommend that you see a doctor."

[1267] On the device: Provide regular health updates and advice to users, including providing a monthly report showing health progress.

[1268] Specific examples

[1269] The user downloads and installs the dedicated app, and when they launch it for the first time, they set up the connection with the body composition scale. Data is then sent from the body composition scale to the server, where it is analyzed using a generative AI model. An avatar image is generated based on the analysis results and displayed on the home screen of the dedicated app. The app uses an emotion engine to recognize the user's emotions and provides advice based on the results. If the user desires more detailed advice, they can use a paid service to receive specific improvement instructions.

[1270] Prompt Sentence Examples

[1271] "Analyze this user's health status based on their latest body composition data and provide advice."

[1272] "Analyze the user's latest sentiment and provide advice based on it."

[1273] "Generate and display an avatar image based on the latest health and emotional data."

[1274] In this way, through the configuration of the present invention, the user can more accurately understand his or her own health condition and receive personalized advice.

[1275] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1276] Step 1: Data Acquisition and Integration Settings

[1277] Device: A dedicated app is installed on the user's smartphone, and when the app is launched for the first time, a screen for setting up linkage with the body composition scale and healthcare app is displayed. Specifically, an interface is provided that prompts the user to enter Bluetooth and API keys. The input in this step is the connection information entered by the user, and the output is a notification of successful linkage.

[1278] User: Follow the instructions in the dedicated app to allow linking with the body composition scale and healthcare app. For example, tap the "Sync Data" button to link with the iOS healthcare app. The input in this step is the user's permission action, and the output is a screen display indicating that the link setup is complete.

[1279] Server: Receives the connection setting information sent from the device and establishes a connection with the API of the body composition scale and healthcare app. The input in this step is the connection information from the device, and the output is a log record of successful connection.

[1280] Step 2: Data analysis with generative AI

[1281] Server: The body composition data acquired through the dedicated app is formatted and input into the AI ​​model. The input in this step is weight, body fat percentage, bone density, and heart rate data, and the output is a standardized dataset required for analysis. Specifically, the data is standardized and converted into a format suitable for the generative AI model.

[1282] Server: Analyzes the acquired data using a generative AI model and evaluates the user's health condition. The input in this step is the formatted body composition data, and the output is health indicators such as biological age and bone density. Specifically, health indicators are calculated by comparing with past data.

[1283] Server: Generates an avatar image based on the analysis results and visualizes the user's health condition. The input in this step is the analysis results of the generative AI model, and the output is an avatar image that reflects the user's unique health condition.

[1284] Step 3: Emotion recognition by the emotion engine

[1285] Device: The built-in camera and microphone are used to capture the user's facial expressions and tone of voice. Specifically, the camera and microphone are automatically turned on when the user uses the app. The input in this step is audio and video data obtained from the camera and microphone, and the output is the user's facial expressions and voice data captured in real time.

[1286] On the device: An emotion recognition algorithm is used to analyze the user's emotions from the captured data. The input in this step is the captured facial expression and voice data, and the output is an emotion label such as "happiness," "sadness," or "anger." Specifically, changes in facial expressiveness and voice tone are analyzed.

[1287] Server: Stores the emotion recognition results in a database. The input to this step is the analysis result of the emotion recognition algorithm, and the output is a record of the emotional state stored in the database.

[1288] Step 4: Displaying results and giving simple advice

[1289] Device: The generated avatar image and the health status analysis results are displayed to the user. The input in this step is the avatar image and health indicators sent from the server, and the output is the visual and text information displayed on the app screen. Specifically, the avatar image is displayed on the home screen and the data is presented visually.

[1290] Device: Displays simple advice based on health status. The inputs in this step are the user's health indicators and emotion recognition results, and the output is an advice message to be displayed to the user. Specifically, a message such as "Today's goal is to walk 10,000 steps a day" is displayed in a soft tone.

[1291] User: Check the displayed advice and incorporate it into their daily life. For example, increase their activity by following a goal of "walking 10,000 steps a day."

[1292] Step 5: Detailed advice from a billing service

[1293] User: Follows a link within the app and operates the screen to purchase detailed health advice. The input at this step is the user's action to complete the purchase, and the output is a notification of successful payment and completion of the purchase of detailed advice. Specifically, the user taps the "Purchase detailed advice" button.

[1294] Server: Once the billing process is complete, detailed health advice is generated. The inputs at this step are the purchase information for the detailed advice and the user's health data, and the output is individual health checkup results and an improvement plan. Specifically, a diet improvement plan and exercise program are generated based on the user's data.

[1295] Terminal: Display the detailed advice of the purchase to the user. The input in this step is the detailed advice information sent from the server, and the output is the specific instructions or plan displayed to the user. For example, the advice displayed might be, "Your calcium intake is insufficient, so drink milk or yogurt every day."

[1296] Step 6: Ongoing monitoring and feedback

[1297] Device: Periodically send new health data to the server. The input in this step is the newly acquired body composition data, and the output is the data to be sent to the server. For example, automatically synchronize data every day at 9:00 AM.

[1298] Server: Continuously monitors the user's health data and provides immediate feedback if an abnormality is detected. The input in this step is the continuously received health data, and the output is a warning message in the event of an abnormality. Specifically, it notifies the user that "an abnormality has been found in recent data, so we recommend that you see a doctor."

[1299] Device: Continuously notify the user of the latest health status and advice. The input in this step is the latest health information and advice sent from the server, and the output is the updated information displayed on the app screen. For example, a monthly report summarizing the latest health status.

[1300] (Application example 2)

[1301] 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."

[1302] Health management for industrial workers is important, and there is a need to appropriately monitor their physical and mental health. However, conventional systems have difficulty understanding individual health and emotional states in real time and allocating appropriate breaks and tasks based on that information. Therefore, there is a need for a system that can effectively and efficiently monitor the health and emotional states of industrial workers and respond to their individual needs.

[1303] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring body composition data, means for providing an artificial intelligence model for analyzing health conditions, means for generating and displaying an avatar image based on the analysis results, means for providing the user with simple advice based on the avatar image and the analysis results, means for recognizing the user's emotional state using an emotion recognition engine, and means for monitoring the health and emotional states of industrial workers in real time and suggesting appropriate breaks and task assignments. This enables effective and efficient health management of industrial workers.

[1304] "Body composition data" refers to data relating to the composition of a human body, such as weight, body fat percentage, bone density, and heart rate.

[1305] An "artificial intelligence model" is a machine learning algorithm that analyzes data and extracts specific patterns and trends.

[1306] An "avatar image" is a digital image that visually represents a user's health status or other attributes.

[1307] "Simple advice" refers to basic and immediate health guidance and suggestions provided to users.

[1308] "Charging Service" means a fee-collection system used to provide detailed health advice to users.

[1309] An "emotion recognition engine" is a combination of software and hardware that identifies emotions from a user's facial expressions, tone of voice, etc.

[1310] "Industrial workers" refers to people who perform manual labor in factories, manufacturing facilities, etc.

[1311] "Real-time monitoring" means being able to instantly observe ongoing events and conditions and take prompt action based on that information.

[1312] A "health management system" refers to a set of technologies and tools that monitor a user's health status and provide appropriate advice and feedback.

[1313] The present invention is a system for monitoring the health and emotional state of industrial workers in real time and providing personalized advice. Specific embodiments of the system will be described below.

[1314] System configuration and operation

[1315] 1. Data Acquisition Method

[1316] The server collects body composition data from industrial workers through specialized applications, including data from body composition monitors and healthcare apps, such as weight, body fat percentage, bone density, and heart rate. The terminals are equipped with the functionality to transmit this data in real time.

[1317] 2. Health status analysis tools

[1318] The server inputs the acquired body composition data into an artificial intelligence model (AI model) to analyze health status. This AI model can use machine learning frameworks such as TensorFlow and PyTorch. As a result of the analysis, health indicators such as biological age and bone density are calculated.

[1319] 3. Avatar Image Generation Method

[1320] The server generates and displays an avatar image based on the analysis results, which visually reflects the user's health condition on the user's smartphone or tablet.

[1321] 4. Emotion recognition means

[1322] The device uses a built-in camera and microphone to recognize emotions from the user's facial expressions and tone of voice. An emotion recognition model is used for facial expression and voice analysis. The server analyzes the emotion recognition results and records the user's current emotional state.

[1323] 5. Display of results and means of providing simple advice

[1324] The device displays the generated avatar image and a description of the user's health status to the user. In addition, based on the results of emotion recognition, the device provides simple, appropriate advice in a soft tone, such as "To increase your daily step count, aim for 10,000 steps a day."

[1325] 6. Means of providing detailed advice through paid services

[1326] By using the paid service, detailed health advice is provided. Users can purchase detailed advice by following the link provided in the app and using the paid service. The server generates individual advice and health check results according to the purchase, and provides dietary improvement plans and appropriate exercise programs.

[1327] 7. Ongoing monitoring and feedback measures

[1328] The device periodically sends new health data to the server to update the user's health status. The server continuously monitors the user's health data and provides immediate feedback if any abnormalities are detected, thereby supporting the user's health management.

[1329] Specific examples

[1330] An industrial worker uses a body composition scale to collect data such as weight, body fat percentage, bone density, and heart rate, and then sends that data to an application. The device's camera also captures facial expressions in real time, and if the emotion analysis model recognizes the worker as being in a "stressed state," the AI ​​model analyzes the data and generates an avatar image based on the results. This avatar image and simple advice are displayed to the worker, suggesting appropriate tasks and breaks based on his health condition and emotions.

[1331] Prompt Sentence Examples

[1332] "Analyze the health status of a worker based on the following body composition data:

[1333] Weight: 70kg

[1334] Body fat percentage: 15%

[1335] Bone density: 1.2g / cm3

[1336] Heart rate: 75 bpm

[1337] Also consider the following sentiment analysis data:

[1338] Facial Expressions: Stressed

[1339] Based on the analysis results, generate an avatar image of the worker's health status and provide appropriate health advice."

[1340] Thus, the present invention is a system that comprehensively monitors the health and emotional state of industrial workers and provides specific advice and feedback tailored to their individual needs.

[1341] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1342] Step 1:

[1343] Users collect body composition data using a dedicated body composition monitor or healthcare app. This data includes weight, body fat percentage, bone density, heart rate, etc. Users then send the collected data to a dedicated application installed on their smartphone or tablet.

[1344] Input: Body composition data obtained from a body composition analyzer

[1345] Output: Body composition data sent to a dedicated application

[1346] Step 2:

[1347] The device transmits the user's body composition data to the server, which receives the data and stores it in a database.

[1348] Input: Body composition data sent from the dedicated application

[1349] Output: Body composition data stored in a database

[1350] Step 3:

[1351] The server inputs the stored body composition data into an artificial intelligence model (AI model) that uses machine learning frameworks such as TensorFlow and PyTorch to analyze the user's health condition.

[1352] Input: Body composition data stored in the database

[1353] Output: Health status analysis results

[1354] Step 4:

[1355] The server generates an avatar image that visually represents the user's health condition based on the analysis results.

[1356] Input: Health status analysis results

[1357] Output: Avatar image

[1358] Step 5:

[1359] The device uses a built-in camera and microphone to recognize the user's emotions from their facial expressions and tone of voice. An emotion recognition model analyzes their facial expressions and voice to determine the user's emotional state.

[1360] Input: Facial expression and voice data captured by the built-in camera and microphone

[1361] Output: Emotional state analysis result

[1362] Step 6:

[1363] The server analyzes the results of the emotion recognition engine and stores the user's current emotional state in a database.

[1364] Input: Emotional state analysis results

[1365] Output: Emotional state data stored in a database

[1366] Step 7:

[1367] The device displays the generated avatar image, a description of the user's health status, and simple advice based on the user's emotions. For example, it may display a comment such as, "To increase your daily step count, aim for 10,000 steps a day."

[1368] Input: Avatar image, health status description, emotion recognition results

[1369] Output: Avatar image and advice displayed on the device

[1370] Step 8:

[1371] The user uses the billing service to obtain detailed health advice within the app. When billing is successful, the server generates detailed advice and health check results and provides them to the user.

[1372] Input: Billing service usage information

[1373] Output: Detailed health check results, dietary improvement plan, exercise program

[1374] Step 9:

[1375] The device periodically sends new health data to the server, updating the user's health status. The server continuously monitors the device and provides feedback if any abnormalities are detected.

[1376] Input: New health data

[1377] Output: Updated health status data, feedback

[1378] In this way, a system is created that monitors the health and emotional state of industrial workers in real time and provides personalized advice, enabling effective health management.

[1379] 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.

[1380] 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.

[1381] 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.

[1382] [Fourth embodiment]

[1383] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1384] 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.

[1385] 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).

[1386] 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.

[1387] 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.

[1388] 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).

[1389] 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.

[1390] 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.

[1391] 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.

[1392] 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.

[1393] 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.

[1394] 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.

[1395] 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."

[1396] The present invention is a system that acquires body composition data through a dedicated app, analyzes health status using generative AI based on the data, and provides the results to users as a visualized avatar image and simple advice. It also includes a paid service for providing detailed health advice and a function for predicting future health risks using big data. The present invention is specifically implemented using the following means.

[1397] System configuration

[1398] 1. Data acquisition means: A module for acquiring data from body composition monitors and healthcare apps.

[1399] 2. Analysis tools: Artificial intelligence models to analyze the acquired data.

[1400] 3. Visualization means: An interface for generating and displaying avatar images based on the analysis results.

[1401] 4. Advice Providing Tool: A module for providing simple and detailed advice to users.

[1402] 5. Billing service means: A billing system for providing detailed health advice.

[1403] 6. Continuous monitoring measures: The ability to track changes in health status and provide feedback.

[1404] Specific Examples of the Invention

[1405] Program processing explanation

[1406] 1. Data acquisition and integration settings

[1407] Server: To obtain the user's body composition data through a dedicated app, the server connects to the body composition monitor and healthcare app, collecting data such as weight, body fat percentage, bone density, and heart rate.

[1408] Device: A dedicated app is installed on the user's smartphone, and a screen is provided for setting up the connection with the body composition scale and healthcare app.

[1409] User: Follow the instructions in the dedicated app and allow it to connect to the body composition scale and healthcare app.

[1410] 2. Data analysis using generative AI

[1411] Server: The acquired data is input into an AI model to analyze the user's health condition, thereby calculating health indicators such as biological age and bone density.

[1412] Server: Generates an avatar image based on the analysis results, visually reflecting the user's health condition.

[1413] 3. Display of results and simple advice

[1414] On the device, the generated avatar image and a description of the user's health condition are displayed. For example, if the user's biological age is higher than their actual age, the device will inform the user that they need to exercise less or improve their diet.

[1415] On your device: Displays simple advice based on your health status, such as "To increase your daily step count, aim for 10,000 steps a day."

[1416] 4. Detailed advice through paid services

[1417] User: Follows a link provided within the app and purchases in-depth advice using a paid service.

[1418] Server: Once the charge is successful, the server generates personalized health check results based on detailed health data, a dietary improvement plan, future health predictions, and an appropriate exercise program.

[1419] On the device: The detailed advice provided with the purchase is displayed to the user, providing specific instructions such as, "Your calcium intake is low, so drink milk or yogurt every day."

[1420] 5. Continuous monitoring and feedback

[1421] Device: Periodically sends new health data to the server to reflect updated health status.

[1422] Server: Continuously monitors the user's health data and provides immediate feedback if any abnormalities are detected.

[1423] On the device: Continuously updates avatars, health advice, and notifies users.

[1424] In this way, the present invention provides a system that analyzes and visualizes health conditions based on body composition data, and supports effective health management for users.

[1425] The processing flow will be explained below.

[1426] Step 1:

[1427] Users: Install the dedicated app on their smartphone and create an account.

[1428] Step 2:

[1429] Device: Display the settings screen for linking with a body composition scale or healthcare app.

[1430] Step 3:

[1431] User: Allows integration with body composition scales and healthcare apps.

[1432] Step 4:

[1433] Device: After authorization, data such as weight, body fat percentage, bone density, and heart rate will be collected.

[1434] Step 5:

[1435] Terminal: Sends the acquired data to the server.

[1436] Step 6:

[1437] Server: Receives the transmitted data and organizes it for each user.

[1438] Step 7:

[1439] Server: Inputs the organized data into an artificial intelligence model to analyze the user's health condition.

[1440] Step 8:

[1441] Server: Based on the analysis results, an avatar image is generated that reflects the user's biological age, bone density, etc.

[1442] Step 9:

[1443] Server: Sends the generated avatar image and health status description data to the device.

[1444] Step 10:

[1445] Terminal: Displays an avatar image and health status description to the user.

[1446] Step 11:

[1447] On your device: Displays simple advice based on your health status in the form of a comment (e.g., "You are not getting enough exercise. We recommend walking for 30 minutes three times a week.").

[1448] Step 12:

[1449] Users: Click on the billing service link and select additional detailed advice.

[1450] Step 13:

[1451] Terminal: Displays a payment screen and prompts the user to enter billing information.

[1452] Step 14:

[1453] Terminal: Processes the payment and, if successful, sends the information to the server.

[1454] Step 15:

[1455] Server: Based on the user's detailed data, it generates personalized health checkup results, dietary improvement plans, future health predictions, and appropriate exercise programs.

[1456] Step 16:

[1457] Server: Sends the generated detailed advice to the device.

[1458] Step 17:

[1459] On the device: Display detailed health advice to the user (e.g., "Your calcium intake is low, so drink milk or yogurt every day").

[1460] Step 18:

[1461] Device: Periodically sends new health data to the server.

[1462] Step 19:

[1463] Server: Analyzes new data and updates the avatar and adjusts advice based on changes in health.

[1464] Step 20:

[1465] On the device: Displays continuously updated avatar images, advice, and notifications to the user.

[1466] In this way, the entire process starts with acquiring the user's body composition data, then visualization and advice based on the analysis results, and includes a fee-based service for detailed advice and continuous monitoring, allowing users to track and manage their own health status.

[1467] Example 1

[1468] 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."

[1469] In modern society, the importance of individual health management is increasing. However, many people find it difficult to manage their daily health on their own, and have limited opportunities to receive professional advice. Furthermore, there are insufficient tools for accumulating health data and assessing long-term health risks. To solve these issues, a system is needed that can accurately grasp each individual's health status, visualize it in an easy-to-understand manner, and provide continuous monitoring and feedback.

[1470] 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.

[1471] In this invention, the server includes means for acquiring body composition data, means for providing an artificial intelligence model for analyzing health conditions, and means for generating and displaying an avatar image based on the analysis results, thereby enabling users to visually grasp their individual health conditions and receive easy-to-understand feedback, thereby enabling them to more effectively manage their own health conditions.

[1472] "Body composition data" refers to data related to the composition of the user's body, such as weight, body fat percentage, bone density, and heart rate.

[1473] An "artificial intelligence model" is a program or system that uses machine learning and data analysis algorithms to analyze health conditions based on acquired data.

[1474] An "avatar image" is a graphical image of a person that visually displays the user's health condition based on the analysis results.

[1475] "Simple advice" is a message that provides basic health management guidelines that can be put into practice in daily life based on the user's health condition.

[1476] A "billing system" is a system that collects fees from users for providing detailed health advice or additional services.

[1477] "Big data" refers to large amounts of health data collected from many users, a dataset that can be analyzed to gain valuable insights.

[1478] "Health risks" are the risk of health problems or diseases that may occur in the future based on the user's current health condition and lifestyle habits.

[1479] "Continuous monitoring" is the process of regularly and continuously collecting and analyzing a user's health data to track changes.

[1480] "Feedback" refers to information such as advice, warnings, and recommendations provided to users based on analysis results and monitoring data.

[1481] The present invention is a system that uses body composition data to analyze health conditions and provides visual feedback and specific health advice. Specific embodiments of this system will be described below.

[1482] Data acquisition and integration settings

[1483] The server configures the connection with the body composition monitor and healthcare app to obtain the user's body composition data through a dedicated app. This connection is granted by OAuth authentication, etc., and the server obtains data such as the user's weight, body fat percentage, bone density, and heart rate.

[1484] The device provides a screen for installing a dedicated app on the user's smartphone and displays an interface for setting up connectivity with the body composition scale and healthcare app. For example, the app can connect to the body composition scale using Bluetooth or Wi-Fi.

[1485] The user follows the instructions in the dedicated app to allow the body composition scale to connect to the healthcare app. Specifically, this involves pressing the corresponding button on the body composition scale within the app to have the device recognized.

[1486] Data analysis with generative AI

[1487] The server inputs the acquired body composition data into an artificial intelligence model (hereinafter referred to as the "generative AI model"). For example, data such as weight, body fat percentage, bone density, and heart rate measured by the user in the morning are sent to the server.

[1488] The server then feeds this data into a generative AI model, which analyzes the user's health from multiple angles. Specifically, the AI ​​model uses machine learning algorithms to calculate factors such as biological age and bone density.

[1489] The server generates an avatar image based on the analysis results, visually reflecting the user's health status. For example, if the user has a high body fat percentage, the avatar will be displayed as slightly overweight.

[1490] Display of results and simple advice

[1491] The device displays the generated avatar image and a description of the user's health status. For example, the app screen might say, "Your biological age is 35."

[1492] The device will display simple advice based on your health status, such as "To increase your daily step count, aim for 10,000 steps per day," and may also encourage users to drink at least eight glasses of water per day.

[1493] Detailed advice via paid services

[1494] The user clicks on a link provided within the app to proceed to a billing service screen, which may say, for example, "Please pay to receive detailed health checkup results."

[1495] Once the user's payment is successful, the server generates personalized health checkup results and dietary improvement plans based on the detailed health data. Specifically, the generative AI model performs further detailed analysis and generates advice based on the user's lifestyle habits.

[1496] The device will then display detailed advice to the user about the purchase, including specific instructions such as "Your calcium intake is low, so drink milk or yogurt every day."

[1497] Ongoing monitoring and feedback

[1498] The device periodically sends new health data to the server. For example, a user measures their weight and body fat percentage every Sunday and uploads the data to the server.

[1499] The server continuously monitors the user's health data and provides immediate feedback if any abnormalities are detected. For example, if a sudden weight gain is detected, an alert will be sent saying, "Your weight has increased rapidly recently. Please be careful about your diet and exercise."

[1500] The device continuously updates the avatar and health advice and notifies the user. For example, every time health data is updated, the device notifies the user of "how their health status has changed" along with the latest avatar image.

[1501] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1502] Step 1:

[1503] Data acquisition and integration settings

[1504] The server acquires the user's body composition data from a body composition scale or healthcare app connected to the dedicated app. The acquired data includes weight, body fat percentage, bone density, heart rate, etc. This provides basic data for accurately understanding the user's physical condition.

[1505] The device displays a screen for installing a dedicated app on the user's smartphone and provides an interface for setting up linkage with the body composition scale and healthcare app. Specifically, the app connects to the body composition scale using Bluetooth or Wi-Fi and associates it with the user ID.

[1506] The user follows the instructions in the dedicated app to allow the body composition scale to connect to the healthcare app. Specifically, the user presses the corresponding button on the body composition scale within the app to have the device recognized.

[1507] Step 2:

[1508] Data analysis with generative AI

[1509] The server inputs the acquired body composition data into the generative AI model. This input data includes weight, body fat percentage, bone density, heart rate, etc. The AI ​​model uses this data to analyze the user's health condition and calculate health indicators such as biological age and bone density. Specifically, it uses a machine learning algorithm to perform the analysis and compare the user's data with other databases to evaluate their health condition.

[1510] As an output, the AI ​​model calculates health indicators and returns them to the server, providing the results as numerical data reflecting the user's health status.

[1511] Step 3:

[1512] Avatar image generation and result display

[1513] The server generates an avatar image based on the analysis results. This avatar image visually reflects the user's health condition, and includes operations such as "if the body fat percentage is high, the avatar will be displayed slightly overweight."

[1514] The device displays the generated avatar image and a description of the user's health condition. Specifically, the app screen displays a message such as "Your biological age is 35" along with the avatar image.

[1515] Step 4:

[1516] Providing simple advice

[1517] The device will display simple advice to the user based on their health status, such as "To increase your daily step count, aim for 10,000 steps per day," and will also display a message encouraging the user to drink at least eight glasses of water per day.

[1518] This advice is developed based on the analysis results of the generative AI model and is notified to the user via the app.

[1519] Step 5:

[1520] Chargeable service with detailed advice

[1521] The user clicks on a link provided within the app to proceed to a billing service screen, which may say, for example, "Please pay to receive detailed health checkup results."

[1522] Once the user has successfully completed the payment, the server generates personalized health checkup results and a diet plan based on the user's detailed health data. The AI ​​model then performs further detailed analysis and generates advice based on the user's lifestyle habits.

[1523] The device will then display detailed advice to the user about the product they purchased, such as "Your calcium intake is low, so drink milk or yogurt every day."

[1524] Step 6:

[1525] Ongoing monitoring and feedback

[1526] The device periodically sends new health data to the server. For example, a user measures their weight and body fat percentage every Sunday and uploads the data to the server.

[1527] The server continuously monitors the user's health data and provides immediate feedback if any abnormalities are detected. For example, if a sudden weight gain is detected, it will send an alert saying, "Your weight has increased rapidly recently. Please be careful about your diet and exercise."

[1528] The device continuously updates the avatar and health advice and notifies the user. Each time the data is updated, the device notifies the user of the latest avatar image and how their health status has changed.

[1529] (Application example 1)

[1530] 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."

[1531] Health management is an important issue in today's world, but conventional systems have struggled to quickly and effectively analyze users' body composition data and present it in an intuitively understandable format. Furthermore, they lacked the functionality to provide continuous monitoring and individual feedback, making it difficult to grasp users' health status and implement improvement measures. Furthermore, there was a lack of billing services to provide detailed health advice, and a system that integratedly enabled users to set health goals and track their progress. These issues need to be resolved.

[1532] 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.

[1533] In this invention, the server includes means for acquiring body composition data, means for providing an artificial intelligence model for analyzing health status, means for generating and displaying an avatar image based on the analysis results, means for providing the user with simple advice based on the avatar image and the analysis results, means for including a billing service for providing detailed health advice, means for continuously monitoring user data and providing appropriate feedback, means for setting the user's health goals and tracking progress toward those goals, additional means for displaying the visualization results of the health status, and means for performing billing service processing. This enables analysis and visualization of the user's health status based on the user's body composition data, individualized health advice, continuous health monitoring and feedback, provision of detailed advice, and health goal setting and progress management.

[1534] "Body composition data" refers to data relating to the composition of the human body, such as weight, body fat percentage, bone density, and heart rate.

[1535] An "artificial intelligence model" is an algorithm or machine learning model used to analyze a user's health condition.

[1536] An "avatar image" is a virtual image that visually reflects the user's health condition.

[1537] "Quick Advice" is basic health recommendations provided to the user.

[1538] A "billing service" is a system that charges users a fee for providing detailed health advice and individual plans.

[1539] "Continuous user data monitoring" refers to the regular collection of a user's health data over time and tracking changes.

[1540] "Feedback" refers to advice or warnings provided to users based on analysis results and monitoring data.

[1541] "Setting health goals" means defining specific health indicators and action plans that a user aims to achieve.

[1542] "Progress tracking" refers to monitoring and recording a user's progress toward defined health goals.

[1543] The "visualization result of health status" is information that displays the user's health status based on the analysis results in an intuitive and easy-to-understand manner.

[1544] "Billing service processing" refers to managing the process by which users pay for detailed health services and advice.

[1545] MODE FOR CARRYING OUT THE INVENTION

[1546] As an embodiment of the present invention, a description will be given of how to specifically realize an application example.

[1547] Server Roles

[1548] The server uses the following hardware and software to perform processes such as acquiring, analyzing, visualizing, and providing advice on various types of data.

[1549] 1. Data Acquisition:

[1550] Hardware: Body composition scale, smartphone, wearable device.

[1551] Software: A dedicated application for collecting healthcare data.

[1552] Description: The server obtains the user's body composition data (weight, body fat percentage, bone density, heart rate, etc.) from a body composition scale or wearable device.

[1553] 2. Data Analysis:

[1554] Hardware: Server.

[1555] Software: Generative AI model (HealthAIModel).

[1556] Description: The server inputs the acquired body composition data into a generative AI model to analyze the user's health condition, thereby calculating health indicators such as biological age and bone density.

[1557] 3. Visualization:

[1558] Hardware: Server, user's smartphone.

[1559] Software: Avatar Generator.

[1560] Description: An avatar image is generated based on the analysis results and displayed on the user's smartphone.

[1561] 4. Providing advice:

[1562] Hardware: Server, user's smartphone.

[1563] Software: Advice delivery module.

[1564] Description: The server provides simple advice to the user based on the analysis results. In addition, the server provides detailed health advice to the user by paying for a service.

[1565] 5. Continuous monitoring and feedback:

[1566] Hardware: Servers, smartphones.

[1567] Software: Monitoring and feedback module.

[1568] Description: The server continuously monitors changes in the user's health status and provides immediate feedback if any abnormalities are detected. It periodically retrieves new health data and reflects the updated health status.

[1569] 6. Billing Service Processing:

[1570] Hardware: Smartphones, servers.

[1571] Software: Payment Gateway.

[1572] Description: When a user purchases detailed health advice, the server processes the billing and, if successful, provides the detailed advice.

[1573] Specific examples

[1574] Take the example of an application provided by a fitness gym. When a user steps on a body composition scale, the data is automatically sent to a dedicated application. The server acquires this data and analyzes it using a generative AI model. The resulting avatar image is displayed on the user's smartphone, and basic health advice is provided. If the user desires additional, detailed advice, it can be obtained through an in-app purchase.

[1575] Example prompts for input to a generative AI model:

[1576] Weight: 70kg, body fat percentage: 20%, bone density: 1.2g / cm3, heart rate: 70bpm

[1577] Analyze the user's health status and provide appropriate advice.

[1578] Thus, based on the specific form for implementing the invention, it is possible to analyze and visualize a user's health status, provide personalized health advice, provide continuous monitoring and feedback, provide detailed advice, and set health goals and manage their progress.

[1579] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1580] Program processing steps

[1581] Step 1:

[1582] Data Acquisition

[1583] The server acquires body composition data. When a user uses a body composition scale or wearable device, the body composition data (weight, body fat percentage, bone density, heart rate, etc.) is sent from these devices to the server via a dedicated application. The input is the user's body composition scale data, and the output is the body composition data accumulated on the server. This data is saved in a database for use in later analysis.

[1584] Step 2:

[1585] Data analysis

[1586] The server inputs the acquired body composition data into a generative AI model (HealthAIModel) to analyze the user's health condition. Based on a series of input data, the AI ​​model processes the data and calculates health indicators such as biological age, bone density, and heart rate. The input here is the body composition data stored on the server, and the output is the analysis results. Data processing involves normalizing the data and extracting features, and converting it into a format suitable for the AI ​​model.

[1587] Step 3:

[1588] Visualization

[1589] The server generates an avatar image based on the analysis results. An avatar generation tool (AvatarGenerator) is used to create an avatar image that visually represents the analyzed health indicators. The input to this step is the analysis results, and the output is an avatar image. Specifically, the analysis results are input as prompts into the avatar generation tool, and the tool automatically generates an avatar image.

[1590] Step 4:

[1591] Providing advice

[1592] The server provides the generated avatar image and simple advice to the user. Based on the analysis results, the AI ​​model automatically generates prompts to provide appropriate health advice to the user. For example, advice such as "To increase your daily step count, aim for 10,000 steps a day" is displayed. The input for this step is the health analysis results and prompts, and the output is simple advice and a visualized avatar image.

[1593] Step 5:

[1594] Detailed advice and billing services

[1595] If a user wants detailed health advice, they use the billing service through their terminal. The server processes the billing using a payment gateway. If the billing is successful, detailed advice is displayed on the user's terminal. Specifically, the server receives a billing request from the user and makes the payment via the payment gateway. The input is the user's billing request information, and the output is the result of the billing process and detailed advice.

[1596] Step 6:

[1597] Ongoing monitoring and feedback

[1598] The server continuously monitors changes in the user's health condition. It periodically acquires the user's body composition data and reanalyzes the health condition based on that data. If an abnormality is detected, it provides immediate feedback. The input to this step is the continuously collected body composition data, and the output is an updated health condition report and feedback. Specifically, the server automatically acquires and analyzes data according to a regular data collection schedule.

[1599] Prompt Sentence Examples

[1600] Weight: 70kg, body fat percentage: 20%, bone density: 1.2g / cm3, heart rate: 70bpm

[1601] Analyze the user's health status and provide appropriate advice.

[1602] 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.

[1603] The present invention is a system that acquires body composition data through a dedicated app, analyzes health status using generative AI based on the data, and provides the results to the user as a visualized avatar image and simple advice. In addition, by combining this with an emotion engine that recognizes the user's emotions, more personalized advice and feedback can be provided. The present invention is specifically implemented using the following means.

[1604] System configuration

[1605] 1. Data acquisition means: A module for acquiring data from body composition monitors and healthcare apps.

[1606] 2. Analysis tools: Artificial intelligence models to analyze the acquired data.

[1607] 3. Visualization means: An interface for generating and displaying avatar images based on the analysis results.

[1608] 4. Emotion Recognition Means: An emotion engine that recognizes the user's emotions.

[1609] 5. Advice Providing Tool: A module for providing simple and detailed advice to users.

[1610] 6. Billing service means: A billing system for providing detailed health advice.

[1611] 7. Continuous monitoring measures: The ability to track changes in health status and provide feedback.

[1612] Specific Examples of the Invention

[1613] Program processing explanation

[1614] 1. Data acquisition and integration settings

[1615] Server: To obtain the user's body composition data through a dedicated app, the server connects to the body composition monitor and healthcare app, collecting data such as weight, body fat percentage, bone density, and heart rate.

[1616] Device: A dedicated app is installed on the user's smartphone, and a screen is provided for setting up the connection with the body composition scale and healthcare app.

[1617] User: Follow the instructions in the dedicated app and allow it to connect to the body composition scale and healthcare app.

[1618] 2. Data analysis using generative AI

[1619] Server: The acquired data is input into an AI model to analyze the user's health condition, thereby calculating health indicators such as biological age and bone density.

[1620] Server: Generates an avatar image based on the analysis results, visually reflecting the user's health condition.

[1621] 3. Emotion Recognition by Emotion Engine

[1622] Device: Using the built-in camera and microphone, the device recognizes emotions from the user's facial expressions and tone of voice. For example, facial expression analysis and voice analysis can be used to determine whether the user is happy or stressed.

[1623] Server: Analyzes the emotion recognition results and stores the user's current emotional state in a database.

[1624] 4. Display of results and simple advice

[1625] On the device: The generated avatar image and a description of the health condition are displayed to the user. Comments are added in an appropriate tone based on the results of the emotion engine.

[1626] On your device: It displays simple advice based on your health status, adjusting to your emotions and providing softer comments like "Try to increase your daily step count by aiming for 10,000 steps a day."

[1627] 5. Detailed advice through paid services

[1628] User: Follows a link provided within the app and purchases in-depth advice using a paid service.

[1629] Server: Once the charge is successful, the server generates personalized health check results based on detailed health data, a dietary improvement plan, future health predictions, and an appropriate exercise program.

[1630] On the device: The detailed advice provided with the purchase is displayed to the user, providing specific instructions such as, "Your calcium intake is low, so drink milk or yogurt every day."

[1631] 6. Continuous monitoring and feedback

[1632] Device: Periodically sends new health data to the server to reflect updated health status.

[1633] Server: Continuously monitors the user's health data and provides immediate feedback if any abnormalities are detected.

[1634] On the device: Continuously updates avatars, health advice, and notifies users.

[1635] In this way, the present invention provides a system that analyzes and visualizes health status based on body composition data, and recognizes emotions using an emotion engine, thereby supporting more effective and personalized health management for users.

[1636] The processing flow will be explained below.

[1637] Step 1:

[1638] Users: Install the dedicated app on their smartphone and create an account.

[1639] Step 2:

[1640] Device: Display the settings screen for linking with a body composition scale or healthcare app.

[1641] Step 3:

[1642] User: Allows integration with body composition scales and healthcare apps.

[1643] Step 4:

[1644] Device: Obtain data such as weight, body fat percentage, bone density, and heart rate from a body composition scale or health app.

[1645] Step 5:

[1646] Terminal: Sends the acquired data to the server.

[1647] Step 6:

[1648] Server: Receives the transmitted data and organizes it for each user.

[1649] Step 7:

[1650] Server: Inputs the organized data into an artificial intelligence model to analyze the user's health condition.

[1651] Step 8:

[1652] Server: Based on the analysis results, an avatar image is generated that reflects the user's biological age, bone density, etc.

[1653] Step 9:

[1654] Server: Sends the generated avatar image and health status description data to the device.

[1655] Step 10:

[1656] Terminal: Displays an avatar image and health status description to the user.

[1657] Step 11:

[1658] On your device: Displays simple advice based on your health status in the form of a comment (e.g., "You are not getting enough exercise. We recommend walking for 30 minutes three times a week.").

[1659] Step 12:

[1660] On the device: Using the built-in camera or microphone, the emotion engine analyzes the user's facial expressions and tone of voice.

[1661] Step 13:

[1662] Server: Receives the analysis results of the emotion engine and stores the user's emotional state in a database.

[1663] Step 14:

[1664] Server: Adjusts the content and display of advice based on the results of emotion recognition. For example, if the user is feeling stressed, it displays advice encouraging relaxation.

[1665] Step 15:

[1666] User: Click on the billing service link within the app and select additional detailed advice.

[1667] Step 16:

[1668] Terminal: Displays a payment screen and prompts the user to enter billing information.

[1669] Step 17:

[1670] Terminal: Processes the payment and, if successful, sends the information to the server.

[1671] Step 18:

[1672] Server: Based on the user's detailed data, it generates personalized health checkup results, dietary improvement plans, future health predictions, and appropriate exercise programs.

[1673] Step 19:

[1674] Server: Sends the generated detailed advice and emotion-based advice adjustments to the device.

[1675] Step 20:

[1676] On-device: Display the purchased detailed advice (e.g., "Your calcium intake is low, so drink milk or yogurt every day") to the user, and add a comment that matches their sentiment.

[1677] Step 21:

[1678] Device: Periodically sends new health and emotion data to the server.

[1679] Step 22:

[1680] Server: Analyzes new data and updates the avatar and adjusts advice based on changes in health status. Emotional data is also analyzed and feedback is adjusted.

[1681] Step 23:

[1682] On the device: Displays continuously updated avatar images, advice, and notifications to the user, along with emotion-based feedback.

[1683] In this way, the entire processing flow starts with acquiring the user's body composition data, then visualization and advice based on the analysis results, followed by a paid service for detailed advice, continuous monitoring, and emotion recognition using an emotion engine, allowing users to comprehensively manage and understand their own health condition and emotions and take appropriate action.

[1684] Example 2

[1685] 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."

[1686] Health management is an important issue in modern society, and many people want to understand their own health status and receive appropriate advice. However, it is difficult to provide personalized advice based on each individual's health and emotional state. Conventional systems only provide uniform advice without considering the user's emotions, which has the problem of not being effective enough. Furthermore, there is a lack of systems that can provide detailed advice, which makes it difficult to provide in-depth health management.

[1687] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring body composition data, means for providing an artificial intelligence model for analyzing health status, and means for generating and displaying an avatar image based on the analysis results. This enables detailed analysis and visualization of the user's health status based on the user's body composition data. Furthermore, by including means for providing personalized advice using an emotion engine, appropriate feedback according to the user's emotional state can be provided. Furthermore, by including a billing service for providing detailed health advice, an environment is provided in which the user can perform in-depth health management.

[1688] "Body composition data" is numerical data that indicates an individual's physical condition, such as weight, body fat percentage, bone density, and heart rate.

[1689] An "artificial intelligence model" is a technology that includes machine learning algorithms and neural networks to analyze collected data and assess a user's health status.

[1690] An "avatar image" is a virtual image that reflects the user's condition and visually displays analyzed health data.

[1691] The "emotion engine" is a technology that analyzes the user's facial expressions, tone of voice, etc. to recognize their current emotional state.

[1692] "Simple advice" is health-related advice that can be understood in a short amount of time and is provided based on the user's current health and emotional state.

[1693] "Paid Services" are paid services that provide detailed health advice, individual health checkup results, dietary improvement plans, etc.

[1694] "Big data" refers to large amounts of data collected from diverse sources and used to derive valuable information through analysis and statistics.

[1695] "Future health risks" refers to the risk of poor health or disease that may occur in the future based on current data and trends.

[1696] "Monitoring" is a surveillance method that regularly tracks changes in the user's health status and immediately notifies them of any abnormalities.

[1697] "Feedback" refers to advice or notifications provided to users based on analysis results and monitoring data.

[1698] This system acquires the user's body composition data through a dedicated app, analyzes the user's health condition using a generative AI model, visualizes the results, and provides simple advice.Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it provides personalized advice.

[1699] Data Acquisition

[1700] Server: A dedicated app is used to set up a connection to obtain the user's body composition data. This connection is made between the body composition monitor and existing healthcare apps (e.g., Apple Health or Google Fit). The server receives data such as weight, body fat percentage, bone density, and heart rate and stores it in a database.

[1701] Device: A dedicated app is installed on the user's smartphone, and when launched for the first time, a screen for setting up the connection with the body composition scale and healthcare app is displayed. The user follows the guide and enters the necessary API key and Bluetooth connection information.

[1702] User: Follow the instructions in the dedicated app and allow the device to connect to the body composition monitor and Health app. Specifically, tap the "Sync Data" button to connect to the iOS Health app.

[1703] Data analysis with generative AI

[1704] Server: The acquired body composition data is input into an AI model for analysis. The generative AI model used compares it with past data to calculate changes in anatomical age and bone density. This is achieved by using machine learning algorithms and neural networks.

[1705] Server: Generates an avatar image based on the analysis results, visually reflecting the user's health status. For example, if the user's health status is good, the avatar is displayed in a bright color, and if the user's health status is deteriorating, the avatar is displayed in a dark color.

[1706] Emotion recognition by emotion engine

[1707] On-device: Captures the user's facial expressions and voice using the built-in camera and microphone, capturing emotional changes in real time. This feature is automatically turned on when the user starts using the app.

[1708] Device: The emotion engine analyzes emotions such as "happiness," "sadness," and "anger" from changes in facial expressions and tone of voice. The analysis results are sent from the device to the server.

[1709] Server: Stores the emotion recognition results in a database so that advice can be provided according to the user's emotional state.

[1710] Display of results and simple advice

[1711] Device: Launches an interface that displays the generated avatar image and analysis results to the user. The avatar image is displayed on the home screen, along with the user's biological age and bone density.

[1712] On-device: Display simple advice based on your health status. Add comments in an appropriate tone based on emotion recognition results. For example, display a goal in a soft tone, such as "To increase your daily activity, aim for 10,000 steps a day."

[1713] User: Review the advice provided and incorporate it into their daily routine, for example by taking more walks to reach their step goal.

[1714] Detailed advice via paid services

[1715] User: Follows the link in the app and navigates to the screen where they can purchase detailed health advice. They then click the "Purchase detailed advice" button to proceed with the payment process.

[1716] Server: Once the payment is completed, the server generates detailed advice, including personalized health check results, a diet plan, and an exercise program.

[1717] On the device: The device displays detailed advice about the purchase to the user, providing specific instructions such as "Your calcium intake is insufficient, so drink milk or yogurt every day."

[1718] Ongoing monitoring and feedback

[1719] On your device: Send new health data to the server periodically, for example, automatically sync your data every day at 9:00 AM.

[1720] Server: Continuously monitors the user's health data and provides anomaly alerts if an abnormality is detected. The server notifies the user in real time that "abnormalities have been found in recent data, so we recommend that you see a doctor."

[1721] On the device: Provide regular health updates and advice to users, including providing a monthly report showing health progress.

[1722] Specific examples

[1723] The user downloads and installs the dedicated app, and when they launch it for the first time, they set up the connection with the body composition scale. Data is then sent from the body composition scale to the server, where it is analyzed using a generative AI model. An avatar image is generated based on the analysis results and displayed on the home screen of the dedicated app. The app uses an emotion engine to recognize the user's emotions and provides advice based on the results. If the user desires more detailed advice, they can use a paid service to receive specific improvement instructions.

[1724] Prompt Sentence Examples

[1725] "Analyze this user's health status based on their latest body composition data and provide advice."

[1726] "Analyze the user's latest sentiment and provide advice based on it."

[1727] "Generate and display an avatar image based on the latest health and emotional data."

[1728] In this way, through the configuration of the present invention, the user can more accurately understand his or her own health condition and receive personalized advice.

[1729] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1730] Step 1: Data Acquisition and Integration Settings

[1731] Device: A dedicated app is installed on the user's smartphone, and when the app is launched for the first time, a screen for setting up linkage with the body composition scale and healthcare app is displayed. Specifically, an interface is provided that prompts the user to enter Bluetooth and API keys. The input in this step is the connection information entered by the user, and the output is a notification of successful linkage.

[1732] User: Follow the instructions in the dedicated app to allow linking with the body composition scale and healthcare app. For example, tap the "Sync Data" button to link with the iOS healthcare app. The input in this step is the user's permission action, and the output is a screen display indicating that the link setup is complete.

[1733] Server: Receives the connection setting information sent from the device and establishes a connection with the API of the body composition scale and healthcare app. The input in this step is the connection information from the device, and the output is a log record of successful connection.

[1734] Step 2: Data analysis with generative AI

[1735] Server: The body composition data acquired through the dedicated app is formatted and input into the AI ​​model. The input in this step is weight, body fat percentage, bone density, and heart rate data, and the output is a standardized dataset required for analysis. Specifically, the data is standardized and converted into a format suitable for the generative AI model.

[1736] Server: Analyzes the acquired data using a generative AI model and evaluates the user's health condition. The input in this step is the formatted body composition data, and the output is health indicators such as biological age and bone density. Specifically, health indicators are calculated by comparing with past data.

[1737] Server: Generates an avatar image based on the analysis results and visualizes the user's health condition. The input in this step is the analysis results of the generative AI model, and the output is an avatar image that reflects the user's unique health condition.

[1738] Step 3: Emotion recognition by the emotion engine

[1739] Device: The built-in camera and microphone are used to capture the user's facial expressions and tone of voice. Specifically, the camera and microphone are automatically turned on when the user uses the app. The input in this step is audio and video data obtained from the camera and microphone, and the output is the user's facial expressions and voice data captured in real time.

[1740] On the device: An emotion recognition algorithm is used to analyze the user's emotions from the captured data. The input in this step is the captured facial expression and voice data, and the output is an emotion label such as "happiness," "sadness," or "anger." Specifically, changes in facial expressiveness and voice tone are analyzed.

[1741] Server: Stores the emotion recognition results in a database. The input to this step is the analysis result of the emotion recognition algorithm, and the output is a record of the emotional state stored in the database.

[1742] Step 4: Displaying results and giving simple advice

[1743] Device: The generated avatar image and the health status analysis results are displayed to the user. The input in this step is the avatar image and health indicators sent from the server, and the output is the visual and text information displayed on the app screen. Specifically, the avatar image is displayed on the home screen and the data is presented visually.

[1744] Device: Displays simple advice based on health status. The inputs in this step are the user's health indicators and emotion recognition results, and the output is an advice message to be displayed to the user. Specifically, a message such as "Today's goal is to walk 10,000 steps a day" is displayed in a soft tone.

[1745] User: Check the displayed advice and incorporate it into their daily life. For example, increase their activity by following a goal of "walking 10,000 steps a day."

[1746] Step 5: Detailed advice from a billing service

[1747] User: Follows a link within the app and operates the screen to purchase detailed health advice. The input at this step is the user's action to complete the purchase, and the output is a notification of successful payment and completion of the purchase of detailed advice. Specifically, the user taps the "Purchase detailed advice" button.

[1748] Server: Once the billing process is complete, detailed health advice is generated. The inputs at this step are the purchase information for the detailed advice and the user's health data, and the output is individual health checkup results and an improvement plan. Specifically, a diet improvement plan and exercise program are generated based on the user's data.

[1749] Terminal: Display the detailed advice of the purchase to the user. The input in this step is the detailed advice information sent from the server, and the output is the specific instructions or plan displayed to the user. For example, the advice displayed might be, "Your calcium intake is insufficient, so drink milk or yogurt every day."

[1750] Step 6: Ongoing monitoring and feedback

[1751] Device: Periodically send new health data to the server. The input in this step is the newly acquired body composition data, and the output is the data to be sent to the server. For example, automatically synchronize data every day at 9:00 AM.

[1752] Server: Continuously monitors the user's health data and provides immediate feedback if an abnormality is detected. The input in this step is the continuously received health data, and the output is a warning message in the event of an abnormality. Specifically, it notifies the user that "an abnormality has been found in recent data, so we recommend that you see a doctor."

[1753] Device: Continuously notify the user of the latest health status and advice. The input in this step is the latest health information and advice sent from the server, and the output is the updated information displayed on the app screen. For example, a monthly report summarizing the latest health status.

[1754] (Application example 2)

[1755] 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."

[1756] Health management for industrial workers is important, and there is a need to appropriately monitor their physical and mental health. However, conventional systems have difficulty understanding individual health and emotional states in real time and allocating appropriate breaks and tasks based on that information. Therefore, there is a need for a system that can effectively and efficiently monitor the health and emotional states of industrial workers and respond to their individual needs.

[1757] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring body composition data, means for providing an artificial intelligence model for analyzing health conditions, means for generating and displaying an avatar image based on the analysis results, means for providing the user with simple advice based on the avatar image and the analysis results, means for recognizing the user's emotional state using an emotion recognition engine, and means for monitoring the health and emotional states of industrial workers in real time and suggesting appropriate breaks and task assignments. This enables effective and efficient health management of industrial workers.

[1758] "Body composition data" refers to data relating to the composition of a human body, such as weight, body fat percentage, bone density, and heart rate.

[1759] An "artificial intelligence model" is a machine learning algorithm that analyzes data and extracts specific patterns and trends.

[1760] An "avatar image" is a digital image that visually represents a user's health status or other attributes.

[1761] "Simple advice" refers to basic and immediate health guidance and suggestions provided to users.

[1762] "Charging Service" means a fee-collection system used to provide detailed health advice to users.

[1763] An "emotion recognition engine" is a combination of software and hardware that identifies emotions from a user's facial expressions, tone of voice, etc.

[1764] "Industrial workers" refers to people who perform manual labor in factories, manufacturing facilities, etc.

[1765] "Real-time monitoring" means being able to instantly observe ongoing events and conditions and take prompt action based on that information.

[1766] A "health management system" refers to a set of technologies and tools that monitor a user's health status and provide appropriate advice and feedback.

[1767] The present invention is a system for monitoring the health and emotional state of industrial workers in real time and providing personalized advice. Specific embodiments of the system will be described below.

[1768] System configuration and operation

[1769] 1. Data Acquisition Method

[1770] The server collects body composition data from industrial workers through specialized applications, including data from body composition monitors and healthcare apps, such as weight, body fat percentage, bone density, and heart rate. The terminals are equipped with the functionality to transmit this data in real time.

[1771] 2. Health status analysis tools

[1772] The server inputs the acquired body composition data into an artificial intelligence model (AI model) to analyze health status. This AI model can use machine learning frameworks such as TensorFlow and PyTorch. As a result of the analysis, health indicators such as biological age and bone density are calculated.

[1773] 3. Avatar Image Generation Method

[1774] The server generates and displays an avatar image based on the analysis results, which visually reflects the user's health condition on the user's smartphone or tablet.

[1775] 4. Emotion recognition means

[1776] The device uses a built-in camera and microphone to recognize emotions from the user's facial expressions and tone of voice. An emotion recognition model is used for facial expression and voice analysis. The server analyzes the emotion recognition results and records the user's current emotional state.

[1777] 5. Display of results and means of providing simple advice

[1778] The device displays the generated avatar image and a description of the user's health status to the user. In addition, based on the results of emotion recognition, the device provides simple, appropriate advice in a soft tone, such as "To increase your daily step count, aim for 10,000 steps a day."

[1779] 6. Means of providing detailed advice through paid services

[1780] By using the paid service, detailed health advice is provided. Users can purchase detailed advice by following the link provided in the app and using the paid service. The server generates individual advice and health check results according to the purchase, and provides dietary improvement plans and appropriate exercise programs.

[1781] 7. Ongoing monitoring and feedback measures

[1782] The device periodically sends new health data to the server to update the user's health status. The server continuously monitors the user's health data and provides immediate feedback if any abnormalities are detected, thereby supporting the user's health management.

[1783] Specific examples

[1784] An industrial worker uses a body composition scale to collect data such as weight, body fat percentage, bone density, and heart rate, and then sends that data to an application. The device's camera also captures facial expressions in real time, and if the emotion analysis model recognizes the worker as being in a "stressed state," the AI ​​model analyzes the data and generates an avatar image based on the results. This avatar image and simple advice are displayed to the worker, suggesting appropriate tasks and breaks based on his health condition and emotions.

[1785] Prompt Sentence Examples

[1786] "Analyze the health status of a worker based on the following body composition data:

[1787] Weight: 70kg

[1788] Body fat percentage: 15%

[1789] Bone density: 1.2g / cm3

[1790] Heart rate: 75 bpm

[1791] Also consider the following sentiment analysis data:

[1792] Facial Expressions: Stressed

[1793] Based on the analysis results, generate an avatar image of the worker's health status and provide appropriate health advice."

[1794] Thus, the present invention is a system that comprehensively monitors the health and emotional state of industrial workers and provides specific advice and feedback tailored to their individual needs.

[1795] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1796] Step 1:

[1797] Users collect body composition data using a dedicated body composition monitor or healthcare app. This data includes weight, body fat percentage, bone density, heart rate, etc. Users then send the collected data to a dedicated application installed on their smartphone or tablet.

[1798] Input: Body composition data obtained from a body composition analyzer

[1799] Output: Body composition data sent to a dedicated application

[1800] Step 2:

[1801] The device transmits the user's body composition data to the server, which receives the data and stores it in a database.

[1802] Input: Body composition data sent from the dedicated application

[1803] Output: Body composition data stored in a database

[1804] Step 3:

[1805] The server inputs the stored body composition data into an artificial intelligence model (AI model) that uses machine learning frameworks such as TensorFlow and PyTorch to analyze the user's health condition.

[1806] Input: Body composition data stored in the database

[1807] Output: Health status analysis results

[1808] Step 4:

[1809] The server generates an avatar image that visually represents the user's health condition based on the analysis results.

[1810] Input: Health status analysis results

[1811] Output: Avatar image

[1812] Step 5:

[1813] The device uses a built-in camera and microphone to recognize the user's emotions from their facial expressions and tone of voice. An emotion recognition model analyzes their facial expressions and voice to determine the user's emotional state.

[1814] Input: Facial expression and voice data captured by the built-in camera and microphone

[1815] Output: Emotional state analysis result

[1816] Step 6:

[1817] The server analyzes the results of the emotion recognition engine and stores the user's current emotional state in a database.

[1818] Input: Emotional state analysis results

[1819] Output: Emotional state data stored in a database

[1820] Step 7:

[1821] The device displays the generated avatar image, a description of the user's health status, and simple advice based on the user's emotions. For example, it may display a comment such as, "To increase your daily step count, aim for 10,000 steps a day."

[1822] Input: Avatar image, health status description, emotion recognition results

[1823] Output: Avatar image and advice displayed on the device

[1824] Step 8:

[1825] The user uses the billing service to obtain detailed health advice within the app. When billing is successful, the server generates detailed advice and health check results and provides them to the user.

[1826] Input: Billing service usage information

[1827] Output: Detailed health check results, dietary improvement plan, exercise program

[1828] Step 9:

[1829] The device periodically sends new health data to the server, updating the user's health status. The server continuously monitors the device and provides feedback if any abnormalities are detected.

[1830] Input: New health data

[1831] Output: Updated health status data, feedback

[1832] In this way, a system is created that monitors the health and emotional state of industrial workers in real time and provides personalized advice, enabling effective health management.

[1833] 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.

[1834] 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.

[1835] 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.

[1836] 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.

[1837] 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.

[1838] 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.

[1839] 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).

[1840] 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.

[1841] 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."

[1842] 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.

[1843] 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).

[1844] 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.

[1845] 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.

[1846] 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.

[1847] 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.

[1848] 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.

[1849] 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.

[1850] 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.

[1851] 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.

[1852] 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.

[1853] 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.

[1854] The following is further disclosed regarding the above embodiment.

[1855] (Claim 1)

[1856] a means for obtaining body composition data;

[1857] means for providing an artificial intelligence model for analyzing a health condition;

[1858] A means for generating and displaying an avatar image based on the analysis results;

[1859] A means for providing a user with an avatar image and simple advice based on the analysis results;

[1860] A system that includes a billing service for providing detailed health advice.

[1861] (Claim 2)

[1862] 10. The system of claim 1, further comprising means for utilizing big data to predict future health risks of a user.

[1863] (Claim 3)

[1864] 10. The system of claim 1, further comprising means for continuously monitoring changes in the user's health status and providing feedback accordingly.

[1865] "Example 1"

[1866] (Claim 1)

[1867] a means for obtaining body composition data;

[1868] means for providing an artificial intelligence model for analyzing a health condition;

[1869] A means for generating and displaying an avatar image based on the analysis results;

[1870] A means for providing a user with an avatar image and simple advice based on the analysis results;

[1871] means including a billing system for providing detailed health advice;

[1872] A means to periodically submit new health data to reflect updated health status;

[1873] A system that includes a means of providing ongoing monitoring and feedback.

[1874] (Claim 2)

[1875] 10. The system of claim 1, further comprising means for utilizing big data to predict future health risks of a user.

[1876] (Claim 3)

[1877] 10. The system of claim 1, further comprising means for continuously...

Claims

1. a means for obtaining body composition data; means for providing an artificial intelligence model for analyzing a health condition; A means for generating and displaying an avatar image based on the analysis results; A means for providing a user with an avatar image and simple advice based on the analysis results; A system that includes a billing service for providing detailed health advice.

2. The system of claim 1 , further comprising a means for utilizing big data to predict future health risks of a user.

3. The system of claim 1 , further comprising means for continuously monitoring changes in the user's health status and providing feedback accordingly.

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