system

Abstract

We provide the system. [Solution] A device for collecting personal health information, A processing device having artificial intelligence technology for analyzing the aforementioned health information, A display device that provides personalized health guidance based on the aforementioned analysis, A system that includes this.

Description

Technical Field

[0004] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In personal health management, general applications and systems only provide average information based on extensive population data, and it is difficult to fully correspond to the specific health conditions and lifestyle habits of individuals. Therefore, there is a need to provide accurate tracking and advice of health data considering individual physical characteristics and lifestyles. In addition, there is a demand for a system that can accurately provide action guidelines for maintaining and improving health and that allows users to easily utilize the information.

Means for Solving the Problems

[0005] The present invention solves the above problems by providing a system whose main components are a device for collecting individual health information, a processing device having artificial intelligence technology for analyzing said information, and a display device that provides customized health guidance to the individual based on the analysis results. Specifically, the system is characterized by collecting a wide range of health information, including sleep patterns, exercise data, heart rate data, dietary information, and diagnostic information from medical institutions, and by using a predictive algorithm based on past data to evaluate the individual's health status with artificial intelligence technology and propose individually optimized behavioral guidelines and health guidance. In this way, the present invention makes it possible to facilitate effective health management tailored to individual needs and to take concrete measures to improve the quality of life.

[0006] "Personal health information" refers to information that indicates an individual's physical characteristics and health status, and specifically includes sleep patterns, exercise data, heart rate data, dietary information, and diagnostic information from medical institutions.

[0007] "Device" refers to a machine or system designed to achieve a specific purpose, and in this invention, it refers to hardware or software components used to collect and analyze personal health information.

[0008] "Artificial intelligence technology" is a technology in which computer systems imitate human intellectual activity, and in this invention, it is used to evaluate individual health conditions through the analysis of health information.

[0009] A "processing device" refers to computer hardware or a program that receives information, analyzes it based on specific criteria, and generates a desired output.

[0010] A "display device" refers to hardware or software that has a screen or interface for showing analysis results or customized health guidance to the user.

[0011] A "predictive algorithm" is a computational procedure or method that uses historical data and statistical techniques to predict future trends or outcomes.

[0012] "Health guidance" refers to specific advice and action plans provided to improve or maintain an individual's health. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiment for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory where information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disk (e.g., hard disk), or magnetic tape, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0027] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] Embodiments of the present invention demonstrate a practical method for providing a system that effectively manages health tailored to individual users. The system comprises a device for collecting health information, a processing device having artificial intelligence technology for analyzing the information, and a display device that provides customized health guidance based on the analysis results.

[0035] The server is the central component for collecting health information. The server stores health data collected from individual users (e.g., steps, heart rate, sleep duration) in a database, and updates and manages it as needed. Furthermore, the server analyzes this data using artificial intelligence technology. Because historical data is used for the analysis, pattern recognition and trend identification are performed based on the various data collected. This process makes it possible to generate customized advice best suited to the user's health condition.

[0036] A terminal is a device that serves as the interface between the user and the system. The terminal receives analysis results and advice sent from the server and displays them to the user. Specifically, this includes smartphones, tablets, and computers. The application software on the terminal is designed using UI / UX design to allow users to intuitively understand the information, and is configured to enable users to quickly grasp the information they need and the recommended action plan.

[0037] Users wear and use health management devices such as wearable devices and smartphones in their daily lives to obtain their own health data. The health data obtained by the user is transmitted to a server via the device and stored there. Users can then use the detailed health analysis results and improvement suggestions obtained from this data to adjust their lifestyle to be healthier. For example, if a user achieves their daily step goal, the app on the device will continue to provide further feedback.

[0038] In this way, the system of the present invention functions as an important tool for users to efficiently manage their own health.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Users wear a wearable device and a smartphone to record their daily activities. The device automatically measures health data such as steps, heart rate, and sleep duration, and transmits it to the device.

[0042] Step 2:

[0043] The device collects health data received from the user and sends it to the server. The data sent includes metadata such as timestamps and user IDs.

[0044] Step 3:

[0045] The server saves the received health data to the database. During saving, the data integrity is checked, and it is verified to ensure there are no duplicates or errors.

[0046] Step 4:

[0047] The server uses artificial intelligence technology to analyze the stored data. The analysis process utilizes historical trend analysis and predictive algorithms to assess the user's health status and generate customized advice for improvement.

[0048] Step 5:

[0049] The server sends generated advice to the device. This information includes recommended daily activity levels and specific guidelines for dietary improvements.

[0050] Step 6:

[0051] The terminal displays advice received from the server to the user. The advice is applied to the application interface in an easy-to-understand format and presented visually.

[0052] Step 7:

[0053] The user reviews the advice provided and modifies their actions accordingly. The user then inputs the status of their implementation of the advice into the app and sends this information as feedback to the server.

[0054] Step 8:

[0055] The server receives user feedback and updates the database. The feedback information is used to generate future advice and as reference data to provide more personalized suggestions in subsequent analyses.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] In modern society, there is a growing need for individuals to comprehensively and in real time understand their own health status and take appropriate action. However, conventional health management systems have separate stages for data collection, analysis, and recommendations, making it difficult to provide rapid and individualized health support.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes an information receiving means for routinely collecting physiological information from individual users, a data management means for storing the physiological information in a database and updating and managing it as appropriate, and an analysis means using artificial intelligence technology to analyze a series of data, including past physiological information, and recognize patterns and trends related to the user's health status. This makes it possible to quickly evaluate the user's health status and generate customized suggestions individually.

[0061] "Information receiving means" is a general term for devices and software that collect physiological information from users, making it possible to collect data on a daily basis.

[0062] "Data management means" refers to functions and systems for efficiently storing collected physiological information and updating and managing it in a timely manner.

[0063] "Analysis means" refers to the process of analyzing collected data using artificial intelligence technology to recognize patterns and understand trends related to the user's health status.

[0064] "Information output means" refers to devices or interfaces used to visually or audibly communicate analysis results and suggestions to users.

[0065] "Physiological information" refers to various biometric data about the user's body, including information such as activity level, heart rate, sleep patterns, and nutritional intake.

[0066] "Artificial intelligence technology" refers to all algorithms and systems used for making predictions and analyses based on past data, and which automatically learn and infer.

[0067] A "generative AI model" refers to an artificial intelligence model used to create user-friendly suggestions based on a series of data and prompt statements.

[0068] Embodiments of the present invention provide a system that efficiently and effectively supports health management for individual users. This system mainly consists of information receiving means, data management means, analysis means, and information output means.

[0069] The server collects physiological information from users through information receiving means. The collected information includes data related to activity level, heart rate, sleep patterns, and nutritional intake, and is obtained via wearable devices and smartphone applications. This data is stored in a database and updated as needed.

[0070] After the collected data is stored, the server uses a generative AI model to analyze the data. Here, pattern recognition and trend analysis are performed based on past data to make predictions about the user's health status. Python machine learning libraries (e.g., TENSORFLOW® and Scikit-learn) are often used for the analysis. The server then provides the generated suggestions as customized feedback to the user.

[0071] The device receives feedback sent from the server and displays it visually to the user. Smartphones and tablets use intuitive UI / UX design to provide an environment where users can easily understand health information and take action.

[0072] Based on the feedback provided, users can appropriately adjust their lifestyles and strive to maintain and improve their health. For example, by inputting a prompt such as, "Generate weekly feedback and new goal suggestions to help the user achieve their goal of 10,000 steps," using a generative AI model, specific action recommendations can be generated.

[0073] Through the above, the system of the present invention functions as an important tool to support users in practicing data-driven health management in their daily lives.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] Users wear a wearable device during their daily lives to collect physiological information such as activity levels, heart rate, and sleep patterns. This data is temporarily stored via the user's smartphone application. The application periodically retrieves the collected data by connecting to the device via Bluetooth or Wi-Fi.

[0077] Step 2:

[0078] The device securely transmits the acquired physiological information to the server via the internet. Security protocols are used to maintain data integrity during this data transmission. The input for this step is physiological information from the wearable device, and the output is the data transmitted to the server.

[0079] Step 3:

[0080] The server uses an information receiving mechanism to store physiological information in a database and updates it as needed. The input is physiological information transmitted from the terminal, and the output is the updated database. The server uses a scalable database management system to efficiently organize large amounts of data.

[0081] Step 4:

[0082] The server analyzes the accumulated data by applying a generative AI model. Specifically, it performs trend analysis and pattern recognition based on historical data, and builds a predictive model using Python machine learning libraries (e.g., TensorFlow, Scikit-learn). The input is physiological information in the database, and the output is the analyzed result.

[0083] Step 5:

[0084] The server generates personalized health advice based on the analysis results. Here, prompts are input to the AI ​​model to create the advice. For example, the prompt "Generate weekly feedback and new goal suggestions to help the user achieve their 10,000-step goal" is used. The input is the analysis results, and the output is the generated advice.

[0085] Step 6:

[0086] The server uses an information output mechanism to send the generated advice to the terminal. Security protocols are again strictly enforced. The input is the generated advice, and the output is the status of the transmission to the terminal.

[0087] Step 7:

[0088] The terminal visually displays the received advice to the user. Through the application's intuitive interface, users can easily understand and act upon the advice. The input is advice from the server, and the output is health guidance information displayed to the user.

[0089] Step 8:

[0090] Based on the displayed advice, users make necessary adjustments to their lifestyle. This allows them to take action toward achieving their health goals, and data is collected again via the wearable device, leading to the next cycle. The input is the advice from the device, and the output is the user's behavioral changes.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] In modern work environments, it is crucial to monitor workers' health in real time and provide appropriate health management guidance, but there is a lack of effective systems to address this issue. This invention aims to improve work efficiency and maintain workers' health by providing individually tailored health guidance based on workers' biometric information.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes means for collecting personal biometric information, computing device means having machine learning technology for analyzing the biometric information, display device means for providing personalized health guidance to the individual based on the analysis, and means for evaluating the health status of workers in the work environment and providing guidelines for improving work styles. This enables real-time monitoring of health status and accurate, personalized health guidance.

[0096] "Personal biometric information" refers to data related to an individual's physical condition, including heart rate, activity level, and rest time.

[0097] "Analytical machine learning techniques" refer to algorithms that use collected data to detect patterns and predict future health conditions.

[0098] "Computation device means" refers to hardware and software configurations for data processing, and is a device that performs data collection and analysis.

[0099] "Display device means" refers to a tool that visually conveys analysis results and health guidance content, and typically presents information via a display device.

[0100] "Means for evaluating the health status of workers in the work environment" refers to methods and functions that analyze abnormal biological information and identify health risks to workers.

[0101] "Means of providing guidelines for improving work styles" refers to guidance and advice that recommend efficient and healthy work activities based on the analysis results.

[0102] The system realizing this invention efficiently manages health by collecting and analyzing personal biometric information in real time and providing appropriate health guidance. The server plays a central role in centrally processing the biometric information collected from individuals. The server collects data from wearable devices worn by individuals and analyzes that data using machine learning technology through a computing device.

[0103] Specifically, data such as heart rate, steps taken, and rest time are sent to a server, and this information is used to assess health status. Standard hardware includes wearable devices for collecting data and high-performance computing devices (such as cloud servers) for processing that data.

[0104] The analyzed information is provided as personalized health guidance through a display device. This display device could be a smartphone or tablet, presenting the information in a visually easy-to-understand format. For example, a virtual assistant could provide voice-based advice on exercise and rest based on the data.

[0105] Users can leverage feedback from the system to adjust their daily behaviors and pursue a healthier lifestyle. This helps workers gain a detailed understanding of their own health and maintain a safe and efficient work environment.

[0106] For the generating AI model, you can use the following example prompt to generate guidance content that supports health management.

[0107] "Consider a scenario where factory workers are using wearable devices, design a support program that analyzes their health status in real time and provides a safe and fulfilling work environment. Then, provide examples of health management feedback that could be implemented."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The user wears a wearable device. The wearable device collects biometric information such as heart rate, steps taken, and rest time in real time and transmits it to the terminal as digital data. The input data is biometric information, and the output is the collected data transmitted to the terminal.

[0111] Step 2:

[0112] The terminal receives data from the wearable device and transfers it to the server. The server stores the received data in a database and manages it appropriately. The input is the collected data, and the output is the update of the database on the server.

[0113] Step 3:

[0114] The server analyzes the accumulated data based on machine learning techniques. This analysis involves data computation, such as pattern recognition and trend analysis using historical data, to generate individual health status assessments. The input is biometric information in the database, and the output is the analyzed health status information.

[0115] Step 4:

[0116] The server generates individually adapted health guidance based on the analysis results. This generation process includes creating customized guidance content using a generative AI model. The input is the analysis results, and the output is health guidance information.

[0117] Step 5:

[0118] The terminal receives health guidance transmitted from the server and presents it to the user. This presentation includes visual or auditory feedback. The input is health guidance information, and the output is the feedback provided to the user.

[0119] Step 6:

[0120] Users adjust their daily activities and lifestyles based on health guidance provided by the device. This enables them to take specific actions to maintain or improve their health. The input is feedback, and the output is the user's adjusted activities.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] Embodiments of the present invention provide a comprehensive health management system that takes into account not only an individual's health information but also the user's emotional state. The system consists of a device for collecting health information, a processing device that recognizes and analyzes the user's emotions using an emotion engine, and a display device that provides customized health guidance based on this information.

[0123] The server collects health data sent from the user's device and also integrates emotional data sent from the emotion engine. Emotional data is collected using speech recognition and facial recognition technologies. Therefore, the server integrates and analyzes the health and emotional data to perform a comprehensive health assessment. The analysis results are generated as advice that takes into account the individual's health and emotional state.

[0124] The terminal is responsible for providing users with customized health guidance from the server. Users are presented with detailed guidance and recommended action plans based on health information and emotional assessments. The terminal's application provides an interface tailored to the user's state and enables visual and auditory feedback to present information in an intuitive and easy-to-understand manner.

[0125] Users record their daily activities using wearable devices and smartphones. This data is used not only to collect health information, but also to simultaneously record emotional information through voice and facial expressions. This allows users to receive detailed feedback not only on their physical health but also on their mental health.

[0126] For example, if a user's daily health data indicates a lack of exercise and emotional data shows a high stress level, the device will present an action plan such as, "We recommend increasing your daily exercise by 5 minutes and taking a walk outside to get some fresh air." At the same time, a supplementary message will also be displayed, such as, "Let's reduce stress by taking deep breaths."

[0127] In this way, the present invention provides an effective tool for comprehensively supporting the physical and mental health of users, thereby promoting a healthier and more balanced lifestyle.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] Users record their daily health data using wearable devices and smartphones. The devices measure data such as steps, heart rate, and sleep duration, and simultaneously collect emotional data through voice recognition and facial recognition.

[0131] Step 2:

[0132] The device sends health and emotional data collected from the user to the server. This data includes the measured timestamp and user ID.

[0133] Step 3:

[0134] The server stores the received health and emotional data in a database. During storage, the data format is checked, and any necessary adjustments are made to maintain consistency.

[0135] Step 4:

[0136] The server uses artificial intelligence technology to analyze the received data. The analysis performs pattern analysis and predictive algorithms based on past personal data, and further combines this with emotional data to assess the user's overall health status.

[0137] Step 5:

[0138] The server generates personalized advice based on health and emotional data, including suggestions regarding physical health and recommendations regarding emotional health.

[0139] Step 6:

[0140] The server sends generated advice to the terminal, which then presents it to the user. This display includes visual and audio feedback, presented in a way that is easy for the user to understand.

[0141] Step 7:

[0142] The user reviews the advice and acts according to the recommendations. The user then inputs their implementation status and feedback into the app and sends it to the server.

[0143] Step 8:

[0144] The server receives feedback from users and updates the database. This feedback information is used to personalize future analyses and advice, helping to continuously improve the user's health management process.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] Traditional health management systems typically provided health guidance based on individual biometric data, but they struggled to achieve comprehensive health management that took into account an individual's emotional state. Furthermore, if the feedback and recommendations users received were not intuitive and easy to understand, the effectiveness of health management was diminished.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes means for collecting an individual's biometric data, means for having emotion analysis technology for collecting the individual's biometric data and emotional data, and means for generating personalized health guidance by utilizing a generative AI model based on the integrated data. This makes it possible to provide customized health guidance that comprehensively considers an individual's physical health status and emotional state.

[0150] "Biometric data" refers to information related to an individual's physical activity and condition, such as exercise data and heart rate data.

[0151] "Emotional data" refers to information that reflects an individual's emotional state, and is acquired through data such as voice data and facial expression data.

[0152] "Emotional analysis technology" refers to technology that analyzes an individual's emotional state from voice and facial expression data, and enables the recognition and analysis of emotions using artificial intelligence.

[0153] A "generative AI model" is a model that uses AI technology to generate personalized health guidance and advice based on input data.

[0154] "Health guidance" refers to instruction aimed at improving or maintaining health by providing customized advice and action plans based on an individual's biometric and emotional data.

[0155] An "interface" is the means by which a user receives information provided by a system, and it refers to the presentation of information through visual and auditory feedback.

[0156] This invention is a system that comprehensively evaluates a user's physical health and emotional state and provides personalized health guidance.

[0157] Hardware and software configuration

[0158] The server collects biometric data from the user's wearable devices and smartphones. This includes exercise information and heart rate data. Wearable devices connect via Bluetooth or Wi-Fi and transmit data to the server. Furthermore, emotional data based on emotion analysis technology is acquired using voice recognition and facial recognition technologies. This allows for a detailed record of the user's physical and emotional health status.

[0159] The server utilizes a generative AI model to integrate and analyze collected biometric and emotional data. This model plays a role in generating personalized health guidance based on various data. For example, health guidance is generated by instructing the AI ​​model using a prompt such as, "Generate optimal health advice based on the user's latest health and emotional data. Example: Provide specific suggestions for increasing exercise."

[0160] Providing health guidance

[0161] The device provides users with customized health guidance sent from the server. It uses visual and auditory interfaces to provide intuitive and easy-to-understand feedback. For example, if it determines that the user is not getting enough exercise and has high stress levels, it might display an action plan such as, "We recommend increasing your daily exercise time by 5 minutes and taking a walk outside to get some fresh air." It might also display a message like, "Reduce stress by practicing deep breathing."

[0162] In this way, users can take specific actions for health management based on their physical and emotional health status. The system continuously monitors the user's health status and updates the guidance content as needed, supporting the realization of a healthier and more balanced lifestyle.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The server receives biometric data from the user's wearable device or smartphone. This input includes exercise information and heart rate information. It also acquires emotional data through voice recognition and facial recognition. The server centralizes this data and stores it in a database. Specifically, data communication is performed using Bluetooth or Wi-Fi.

[0166] Step 2:

[0167] The server integrates the collected biometric and emotional data and inputs it into a generative AI model. Here, the integrated data is used as a basis for comprehensively evaluating the user's physical and emotional state. This process involves data preprocessing and feature extraction, preparing the data for analysis.

[0168] Step 3:

[0169] The server uses a generative AI model to generate personalized health guidance from integrated data. The AI ​​model creates optimal advice and action plans for the user based on prompt statements. For example, using a prompt statement such as "Generate optimal health advice based on the user's latest health and emotional data," it can obtain specific output results.

[0170] Step 4:

[0171] The device displays health guidance received from the server to the user. Information is provided visually and audibly through the interface, ensuring intuitive understanding. For example, animations and voice guidance are used to suggest increasing exercise or relaxation techniques.

[0172] Step 5:

[0173] Users follow instructions from their device and take action accordingly. By incorporating recommended behaviors into their daily lives, they aim to improve their health. For example, they might take an extra five-minute walk each day and consciously practice deep breathing to relieve stress. The results of this practice are then recorded on the device and sent to the server.

[0174] Step 6:

[0175] The server receives user feedback and further optimizes subsequent health guidance. Through continuous data collection and analysis, it becomes possible to provide more effective advice to users. This improves the accuracy of health management and the user experience.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0178] In modern times, health management needs to consider not only an individual's physical aspects but also their mental health. Traditional health management systems are primarily limited to collecting and evaluating physical health information and do not comprehensively consider an individual's emotional state. As a result, there is a problem in that appropriate recommendations for products and services that contribute to mental health are difficult to make. Given this situation, there is a need for a method that comprehensively analyzes physical activity data and emotional state assessments to make individualized recommendations.

[0179] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0180] In this invention, the server includes means for collecting an individual's biometric information, processing means having artificial intelligence technology for integrating and analyzing the biometric information and emotional information, display means for presenting an individualized action plan based on the analysis, and means for recommending relevant products and services from a higher-level database to the individual who has received the recommendation. This enables comprehensive suggestions that simultaneously support the physical and mental health of individual users.

[0181] "Personal biometric information" refers to physical activity data, emotional state data, and their histories, and is fundamental data for evaluating an individual's health status.

[0182] The "processing means" refers to a device that has the function of comprehensively analyzing collected biometric and emotional information using artificial intelligence technology and generating suggestions optimized for the user.

[0183] A "display means" is a device that visually or audibly provides users with action plans or product / service suggestions generated based on analyzed data.

[0184] A "higher-level database" is an information aggregation device that stores data on related products and services based on an individual's analysis results and provides the relevant information as needed.

[0185] "Artificial intelligence technology" is a general term for algorithms and computational methods used to learn from large amounts of data and automatically generate personalized suggestions.

[0186] The integrated health support system according to the present invention mainly consists of the following elements: The server collects personal biometric information, including exercise data and emotional state assessments. This information is obtained from wearable devices and smartphones. The server integrates this biometric and emotional information and performs analysis using artificial intelligence technology. This analysis utilizes machine learning libraries such as TensorFlow and PyTorch, and employs a Keras-based deep learning model. Furthermore, based on the analysis results, personalized action plans and product / service suggestions are provided.

[0187] This suggestion is presented to the user visually or audibly on the device. The device is a smart device that displays the generated suggestion. In this process, the device provides an interface that visually displays customized content based on data generated by AI. For example, when the user's stress level is high, it may suggest products or services with relaxation effects.

[0188] For example, if a user asks the device, "Please recommend some products that will help me refresh today," the server can use biometric and emotional data to generate a prompt message, such as "Consider the user's past health data and stress level, and recommend products that will have a refreshing effect," and send this prompt to an AI model, which can then derive appropriate suggestions. In this way, it provides a concrete form of support for comprehensive health management for the user.

[0189] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0190] Step 1:

[0191] The server collects biometric and emotional information from the user transmitted from wearable devices and smartphones. At this stage, motion data, facial expressions, and voice data are sent as input data and stored on the server. This data serves as basic information for understanding the user's activity level and emotional state.

[0192] Step 2:

[0193] The server integrates collected biometric and emotional information and performs data analysis using artificial intelligence technology. This analysis applies machine learning algorithms using TensorFlow and PyTorch. Based on the input data, the user's current health and emotional state is evaluated, and prediction results are output by a generative AI model. The evaluation results are used as base data for personalized action plans and product / service recommendations.

[0194] Step 3:

[0195] Based on the analysis results, the server develops an individualized action plan for the user and generates prompts to suggest corresponding products and services. For example, a prompt such as "Considering the user's past health data and stress level, please recommend products with a refreshing effect" might be generated. The input is the analysis results, and the output is the instructional text for the suggestion.

[0196] Step 4:

[0197] Suggestions from the server are sent to the terminal, which then displays these suggestions to the user visually and audibly. The terminal displays health advice and information on related products on its screen and also provides audio guidance. At this stage, the user is provided with direct and easy-to-understand feedback based on the generated prompts received from the server.

[0198] Step 5:

[0199] Users select actions based on the information displayed on their devices and provide additional data as needed. The user's behavior history and feedback are resent to the server and stored in a database to improve future suggestions. This collected data is then used as input for future analysis.

[0200] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0201] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0202] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0203] [Second Embodiment]

[0204] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0205] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0206] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0207] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0208] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0209] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0210] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0211] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0212] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0213] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0214] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0215] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0216] Embodiments of the present invention demonstrate a practical method for providing a system that effectively manages health tailored to individual users. The system comprises a device for collecting health information, a processing device having artificial intelligence technology for analyzing the information, and a display device that provides customized health guidance based on the analysis results.

[0217] The server is the central component for collecting health information. The server stores health data collected from individual users (e.g., steps, heart rate, sleep duration) in a database, and updates and manages it as needed. Furthermore, the server analyzes this data using artificial intelligence technology. Because historical data is used for the analysis, pattern recognition and trend identification are performed based on the various data collected. This process makes it possible to generate customized advice best suited to the user's health condition.

[0218] A terminal is a device that serves as the interface between the user and the system. The terminal receives analysis results and advice sent from the server and displays them to the user. Specifically, this includes smartphones, tablets, and computers. The application software on the terminal is designed using UI / UX design to allow users to intuitively understand the information, and is configured to enable users to quickly grasp the information they need and the recommended action plan.

[0219] Users wear and use health management devices such as wearable devices and smartphones in their daily lives to obtain their own health data. The health data obtained by the user is transmitted to a server via the device and stored there. Users can then use the detailed health analysis results and improvement suggestions obtained from this data to adjust their lifestyle to be healthier. For example, if a user achieves their daily step goal, the app on the device will continue to provide further feedback.

[0220] In this way, the system of the present invention functions as an important tool for users to efficiently manage their own health.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] Users wear a wearable device and a smartphone to record their daily activities. The device automatically measures health data such as steps, heart rate, and sleep duration, and transmits it to the device.

[0224] Step 2:

[0225] The device collects health data received from the user and sends it to the server. The data sent includes metadata such as timestamps and user IDs.

[0226] Step 3:

[0227] The server saves the received health data to the database. During saving, the data integrity is checked, and it is verified to ensure there are no duplicates or errors.

[0228] Step 4:

[0229] The server uses artificial intelligence technology to analyze the stored data. The analysis process utilizes historical trend analysis and predictive algorithms to assess the user's health status and generate customized advice for improvement.

[0230] Step 5:

[0231] The server sends generated advice to the device. This information includes recommended daily activity levels and specific guidelines for dietary improvements.

[0232] Step 6:

[0233] The terminal displays advice received from the server to the user. The advice is applied to the application interface in an easy-to-understand format and presented visually.

[0234] Step 7:

[0235] The user reviews the advice provided and modifies their actions accordingly. The user then inputs the status of their implementation of the advice into the app and sends this information as feedback to the server.

[0236] Step 8:

[0237] The server receives user feedback and updates the database. The feedback information is used to generate future advice and as reference data to provide more personalized suggestions in subsequent analyses.

[0238] (Example 1)

[0239] Next, we will describe Example 1. 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".

[0240] In modern society, there is a growing need for individuals to comprehensively and in real time understand their own health status and take appropriate action. However, conventional health management systems have separate stages for data collection, analysis, and recommendations, making it difficult to provide rapid and individualized health support.

[0241] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0242] In this invention, the server includes an information receiving means for routinely collecting physiological information from individual users, a data management means for storing the physiological information in a database and updating and managing it as appropriate, and an analysis means using artificial intelligence technology to analyze a series of data, including past physiological information, and recognize patterns and trends related to the user's health status. This makes it possible to quickly evaluate the user's health status and generate customized suggestions individually.

[0243] "Information receiving means" is a general term for devices and software that collect physiological information from users, making it possible to collect data on a daily basis.

[0244] "Data management means" refers to functions and systems for efficiently storing collected physiological information and updating and managing it in a timely manner.

[0245] "Analysis means" refers to the process of analyzing collected data using artificial intelligence technology to recognize patterns and understand trends related to the user's health status.

[0246] "Information output means" refers to devices or interfaces used to visually or audibly communicate analysis results and suggestions to users.

[0247] "Physiological information" refers to various biometric data about the user's body, including information such as activity level, heart rate, sleep patterns, and nutritional intake.

[0248] "Artificial intelligence technology" refers to all algorithms and systems used for making predictions and analyses based on past data, and which automatically learn and infer.

[0249] A "generative AI model" refers to an artificial intelligence model used to create user-friendly suggestions based on a series of data and prompt statements.

[0250] Embodiments of the present invention provide a system that efficiently and effectively supports health management for individual users. This system mainly consists of information receiving means, data management means, analysis means, and information output means.

[0251] The server collects physiological information from users through information receiving means. The collected information includes data related to activity level, heart rate, sleep patterns, and nutritional intake, and is obtained via wearable devices and smartphone applications. This data is stored in a database and updated as needed.

[0252] After the collected data is stored, the server uses a generative AI model to analyze the data. Here, pattern recognition and trend analysis are performed based on past data to make predictions about the user's health status. Python machine learning libraries (e.g., TensorFlow and Scikit-learn) are often used for the analysis. The server then provides the generated suggestions as personalized feedback to the user.

[0253] The device receives feedback sent from the server and displays it visually to the user. Smartphones and tablets use intuitive UI / UX design to provide an environment where users can easily understand health information and take action.

[0254] Based on the feedback provided, users can appropriately adjust their lifestyles and strive to maintain and improve their health. For example, by inputting a prompt such as, "Generate weekly feedback and new goal suggestions to help the user achieve their goal of 10,000 steps," using a generative AI model, specific action recommendations can be generated.

[0255] Through the above, the system of the present invention functions as an important tool to support users in practicing data-driven health management in their daily lives.

[0256] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0257] Step 1:

[0258] Users wear a wearable device during their daily lives to collect physiological information such as activity levels, heart rate, and sleep patterns. This data is temporarily stored via the user's smartphone application. The application periodically retrieves the collected data by connecting to the device via Bluetooth or Wi-Fi.

[0259] Step 2:

[0260] The device securely transmits the acquired physiological information to the server via the internet. Security protocols are used to maintain data integrity during this data transmission. The input for this step is physiological information from the wearable device, and the output is the data transmitted to the server.

[0261] Step 3:

[0262] The server uses an information receiving mechanism to store physiological information in a database and updates it as needed. The input is physiological information transmitted from the terminal, and the output is the updated database. The server uses a scalable database management system to efficiently organize large amounts of data.

[0263] Step 4:

[0264] The server analyzes the accumulated data by applying a generative AI model. Specifically, it performs trend analysis and pattern recognition based on historical data, and builds a predictive model using Python machine learning libraries (e.g., TensorFlow, Scikit-learn). The input is physiological information in the database, and the output is the analyzed result.

[0265] Step 5:

[0266] The server generates personalized health advice based on the analysis results. Here, prompts are input to the AI ​​model to create the advice. For example, the prompt "Generate weekly feedback and new goal suggestions to help the user achieve their 10,000-step goal" is used. The input is the analysis results, and the output is the generated advice.

[0267] Step 6:

[0268] The server uses an information output mechanism to send the generated advice to the terminal. Security protocols are again strictly enforced. The input is the generated advice, and the output is the status of the transmission to the terminal.

[0269] Step 7:

[0270] The terminal visually displays the received advice to the user. Through the application's intuitive interface, users can easily understand and act upon the advice. The input is advice from the server, and the output is health guidance information displayed to the user.

[0271] Step 8:

[0272] Based on the displayed advice, users make necessary adjustments to their lifestyle. This allows them to take action toward achieving their health goals, and data is collected again via the wearable device, leading to the next cycle. The input is the advice from the device, and the output is the user's behavioral changes.

[0273] (Application Example 1)

[0274] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0275] In modern work environments, it is crucial to monitor workers' health in real time and provide appropriate health management guidance, but there is a lack of effective systems to address this issue. This invention aims to improve work efficiency and maintain workers' health by providing individually tailored health guidance based on workers' biometric information.

[0276] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0277] In this invention, the server includes means for collecting personal biometric information, computing device means having machine learning technology for analyzing the biometric information, display device means for providing personalized health guidance to the individual based on the analysis, and means for evaluating the health status of workers in the work environment and providing guidelines for improving work styles. This enables real-time monitoring of health status and accurate, personalized health guidance.

[0278] "Personal biometric information" refers to data related to an individual's physical condition, including heart rate, activity level, and rest time.

[0279] "Analytical machine learning techniques" refer to algorithms that use collected data to detect patterns and predict future health conditions.

[0280] "Computation device means" refers to hardware and software configurations for data processing, and is a device that performs data collection and analysis.

[0281] "Display device means" refers to a tool that visually conveys analysis results and health guidance content, and typically presents information via a display device.

[0282] "Means for evaluating the health status of workers in the work environment" refers to methods and functions that analyze abnormal biological information and identify health risks to workers.

[0283] The term "means for providing guidelines for workstyle improvement" refers to guidance and advice that recommends efficient and healthy work activities based on the analysis results.

[0284] The system for implementing this invention collects, analyzes, and provides appropriate health guidance for an individual's biometric information in real time in order to efficiently conduct health management. The server plays a central role in centrally processing the biometric information collected from individuals. The server collects data from wearable devices worn by individuals and analyzes the data using machine learning techniques through a computing device.

[0285] Specifically, for example, data such as heart rate, number of steps, and rest time are sent to the server, and the health status is evaluated using this information. Standard hardware includes wearable devices for collecting data and high-performance computing devices (such as cloud servers) for processing the data.

[0286] The analyzed information is provided to the individual as health guidance adapted to the individual through a display device. Smartphones, tablets, etc. are used as this display device, and the information is presented in a visually understandable format. For example, it is conceivable that a virtual assistant provides voice advice on exercise and rest based on the data.

[0287] The user can utilize the feedback from the system to adjust daily behaviors and pursue a healthier lifestyle. This helps the worker to understand their health status in detail and maintain a safe and efficient working environment.

[0288] For the generative AI model, the following example prompt sentences can be used to generate guidance content for supporting health management.

[0289] "Consider a scenario where factory workers are using wearable devices, design a support program that analyzes their health status in real time and provides a safe and fulfilling work environment. Then, provide examples of health management feedback that could be implemented."

[0290] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0291] Step 1:

[0292] The user wears a wearable device. The wearable device collects biometric information such as heart rate, steps taken, and rest time in real time and transmits it to the terminal as digital data. The input data is biometric information, and the output is the collected data transmitted to the terminal.

[0293] Step 2:

[0294] The terminal receives data from the wearable device and transfers it to the server. The server stores the received data in a database and manages it appropriately. The input is the collected data, and the output is the update of the database on the server.

[0295] Step 3:

[0296] The server analyzes the accumulated data based on machine learning techniques. This analysis involves data computation, such as pattern recognition and trend analysis using historical data, to generate individual health status assessments. The input is biometric information in the database, and the output is the analyzed health status information.

[0297] Step 4:

[0298] The server generates individually adapted health guidance based on the analysis results. This generation process includes creating customized guidance content using a generative AI model. The input is the analysis results, and the output is health guidance information.

[0299] Step 5:

[0300] The terminal receives health guidance transmitted from the server and presents it to the user. This presentation includes visual or auditory feedback. The input is health guidance information, and the output is the feedback provided to the user.

[0301] Step 6:

[0302] Users adjust their daily activities and lifestyles based on health guidance provided by the device. This enables them to take specific actions to maintain or improve their health. The input is feedback, and the output is the user's adjusted activities.

[0303] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0304] Embodiments of the present invention provide a comprehensive health management system that takes into account not only an individual's health information but also the user's emotional state. The system consists of a device for collecting health information, a processing device that recognizes and analyzes the user's emotions using an emotion engine, and a display device that provides customized health guidance based on this information.

[0305] The server collects health data sent from the user's device and also integrates emotional data sent from the emotion engine. Emotional data is collected using speech recognition and facial recognition technologies. Therefore, the server integrates and analyzes the health and emotional data to perform a comprehensive health assessment. The analysis results are generated as advice that takes into account the individual's health and emotional state.

[0306] The terminal has the role of providing the user with customized health guidance from the server. The user is presented with detailed guidance and a recommended action plan based on health information and emotional evaluation. The terminal application provides an interface according to the user's status and enables intuitive and easy-to-understand information presentation by allowing visual and auditory feedback.

[0307] The user uses wearable devices and smartphones to record daily activities. This data is not only used for collecting health information, but also emotional information is simultaneously recorded through voice and facial expressions. As a result, the user can receive detailed feedback not only on their physical health status but also on their mental health status.

[0308] For example, if the user's daily health data indicates lack of exercise and further emotional data shows a high stress level, the terminal presents an action plan such as "Increase daily exercise by 5 minutes and recommend taking a walk outside to breathe fresh air." At the same time, a complementary message such as "Let's reduce stress by doing deep breathing" is also displayed.

[0309] In this way, the present invention provides an effective tool for comprehensively supporting the user's physical and mental health and promotes the realization of a healthier and more balanced lifestyle.

[0310] The following describes the processing flow.

[0311] Step 1:

[0312] The user uses wearable devices and smartphones to record daily health data. The device measures data such as the number of steps, heart rate, and sleep time, and simultaneously collects emotional data through voice recognition and facial expression recognition.

[0313] Step 2:

[0314] The device sends health and emotional data collected from the user to the server. This data includes the measured timestamp and user ID.

[0315] Step 3:

[0316] The server stores the received health and emotional data in a database. During storage, the data format is checked, and any necessary adjustments are made to maintain consistency.

[0317] Step 4:

[0318] The server uses artificial intelligence technology to analyze the received data. The analysis performs pattern analysis and predictive algorithms based on past personal data, and further combines this with emotional data to assess the user's overall health status.

[0319] Step 5:

[0320] The server generates personalized advice based on health and emotional data, including suggestions regarding physical health and recommendations regarding emotional health.

[0321] Step 6:

[0322] The server sends generated advice to the terminal, which then presents it to the user. This display includes visual and audio feedback, presented in a way that is easy for the user to understand.

[0323] Step 7:

[0324] The user reviews the advice and acts according to the recommendations. The user then inputs their implementation status and feedback into the app and sends it to the server.

[0325] Step 8:

[0326] The server receives feedback from users and updates the database. This feedback information is used to personalize future analyses and advice, helping to continuously improve the user's health management process.

[0327] (Example 2)

[0328] Next, we will describe Example 2. 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".

[0329] Traditional health management systems typically provided health guidance based on individual biometric data, but they struggled to achieve comprehensive health management that took into account an individual's emotional state. Furthermore, if the feedback and recommendations users received were not intuitive and easy to understand, the effectiveness of health management was diminished.

[0330] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0331] In this invention, the server includes means for collecting an individual's biometric data, means for having emotion analysis technology for collecting the individual's biometric data and emotional data, and means for generating personalized health guidance by utilizing a generative AI model based on the integrated data. This makes it possible to provide customized health guidance that comprehensively considers an individual's physical health status and emotional state.

[0332] "Biometric data" refers to information related to an individual's physical activity and condition, such as exercise data and heart rate data.

[0333] "Emotional data" refers to information that reflects an individual's emotional state, and is acquired through data such as voice data and facial expression data.

[0334] "Emotional analysis technology" refers to technology that analyzes an individual's emotional state from voice and facial expression data, and enables the recognition and analysis of emotions using artificial intelligence.

[0335] A "generative AI model" is a model that uses AI technology to generate personalized health guidance and advice based on input data.

[0336] "Health guidance" refers to instruction aimed at improving or maintaining health by providing customized advice and action plans based on an individual's biometric and emotional data.

[0337] An "interface" is the means by which a user receives information provided by a system, and it refers to the presentation of information through visual and auditory feedback.

[0338] This invention is a system that comprehensively evaluates a user's physical health and emotional state and provides personalized health guidance.

[0339] Hardware and software configuration

[0340] The server collects biometric data from the user's wearable devices and smartphones. This includes exercise information and heart rate data. Wearable devices connect via Bluetooth or Wi-Fi and transmit data to the server. Furthermore, emotional data based on emotion analysis technology is acquired using voice recognition and facial recognition technologies. This allows for a detailed record of the user's physical and emotional health status.

[0341] The server utilizes a generative AI model to integrate and analyze collected biometric and emotional data. This model plays a role in generating personalized health guidance based on various data. For example, health guidance is generated by instructing the AI ​​model using a prompt such as, "Generate optimal health advice based on the user's latest health and emotional data. Example: Provide specific suggestions for increasing exercise."

[0342] Providing health guidance

[0343] The device provides users with customized health guidance sent from the server. It uses visual and auditory interfaces to provide intuitive and easy-to-understand feedback. For example, if it determines that the user is not getting enough exercise and has high stress levels, it might display an action plan such as, "We recommend increasing your daily exercise time by 5 minutes and taking a walk outside to get some fresh air." It might also display a message like, "Reduce stress by practicing deep breathing."

[0344] In this way, users can take specific actions for health management based on their physical and emotional health status. The system continuously monitors the user's health status and updates the guidance content as needed, supporting the realization of a healthier and more balanced lifestyle.

[0345] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0346] Step 1:

[0347] The server receives biometric data from the user's wearable device or smartphone. This input includes exercise information and heart rate information. It also acquires emotional data through voice recognition and facial recognition. The server centralizes this data and stores it in a database. Specifically, data communication is performed using Bluetooth or Wi-Fi.

[0348] Step 2:

[0349] The server integrates the collected biometric and emotional data and inputs it into a generative AI model. Here, the integrated data is used as a basis for comprehensively evaluating the user's physical and emotional state. This process involves data preprocessing and feature extraction, preparing the data for analysis.

[0350] Step 3:

[0351] The server uses a generative AI model to generate personalized health guidance from integrated data. The AI ​​model creates optimal advice and action plans for the user based on prompt statements. For example, using a prompt statement such as "Generate optimal health advice based on the user's latest health and emotional data," it can obtain specific output results.

[0352] Step 4:

[0353] The device displays health guidance received from the server to the user. Information is provided visually and audibly through the interface, ensuring intuitive understanding. For example, animations and voice guidance are used to suggest increasing exercise or relaxation techniques.

[0354] Step 5:

[0355] Users follow instructions from their device and take action accordingly. By incorporating recommended behaviors into their daily lives, they aim to improve their health. For example, they might take an extra five-minute walk each day and consciously practice deep breathing to relieve stress. The results of this practice are then recorded on the device and sent to the server.

[0356] Step 6:

[0357] The server receives user feedback and further optimizes subsequent health guidance. Through continuous data collection and analysis, it becomes possible to provide more effective advice to users. This improves the accuracy of health management and the user experience.

[0358] (Application Example 2)

[0359] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0360] In modern times, health management needs to consider not only an individual's physical aspects but also their mental health. Traditional health management systems are primarily limited to collecting and evaluating physical health information and do not comprehensively consider an individual's emotional state. As a result, there is a problem in that appropriate recommendations for products and services that contribute to mental health are difficult to make. Given this situation, there is a need for a method that comprehensively analyzes physical activity data and emotional state assessments to make individualized recommendations.

[0361] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0362] In this invention, the server includes means for collecting an individual's biometric information, processing means having artificial intelligence technology for integrating and analyzing the biometric information and emotional information, display means for presenting an individualized action plan based on the analysis, and means for recommending relevant products and services from a higher-level database to the individual who has received the recommendation. This enables comprehensive suggestions that simultaneously support the physical and mental health of individual users.

[0363] "Personal biometric information" refers to physical activity data, emotional state data, and their histories, and is fundamental data for evaluating an individual's health status.

[0364] The "processing means" refers to a device that has the function of comprehensively analyzing collected biometric and emotional information using artificial intelligence technology and generating suggestions optimized for the user.

[0365] A "display means" is a device that visually or audibly provides users with action plans or product / service suggestions generated based on analyzed data.

[0366] A "higher-level database" is an information aggregation device that stores data on related products and services based on an individual's analysis results and provides the relevant information as needed.

[0367] "Artificial intelligence technology" is a general term for algorithms and computational methods used to learn from large amounts of data and automatically generate personalized suggestions.

[0368] The integrated health support system according to the present invention mainly consists of the following elements: The server collects personal biometric information, including exercise data and emotional state assessments. This information is obtained from wearable devices and smartphones. The server integrates this biometric and emotional information and performs analysis using artificial intelligence technology. This analysis utilizes machine learning libraries such as TensorFlow and PyTorch, and employs a Keras-based deep learning model. Furthermore, based on the analysis results, personalized action plans and product / service suggestions are provided.

[0369] This suggestion is presented to the user visually or audibly on the device. The device is a smart device that displays the generated suggestion. In this process, the device provides an interface that visually displays customized content based on data generated by AI. For example, when the user's stress level is high, it may suggest products or services with relaxation effects.

[0370] For example, if a user asks the device, "Please recommend some products that will help me refresh today," the server can use biometric and emotional data to generate a prompt message, such as "Consider the user's past health data and stress level, and recommend products that will have a refreshing effect," and send this prompt to an AI model, which can then derive appropriate suggestions. In this way, it provides a concrete form of support for comprehensive health management for the user.

[0371] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0372] Step 1:

[0373] The server collects biometric and emotional information from the user transmitted from wearable devices and smartphones. At this stage, motion data, facial expressions, and voice data are sent as input data and stored on the server. This data serves as basic information for understanding the user's activity level and emotional state.

[0374] Step 2:

[0375] The server integrates collected biometric and emotional information and performs data analysis using artificial intelligence technology. This analysis applies machine learning algorithms using TensorFlow and PyTorch. Based on the input data, the user's current health and emotional state is evaluated, and prediction results are output by a generative AI model. The evaluation results are used as base data for personalized action plans and product / service recommendations.

[0376] Step 3:

[0377] Based on the analysis results, the server develops an individualized action plan for the user and generates prompts to suggest corresponding products and services. For example, a prompt such as "Considering the user's past health data and stress level, please recommend products with a refreshing effect" might be generated. The input is the analysis results, and the output is the instructional text for the suggestion.

[0378] Step 4:

[0379] Suggestions from the server are sent to the terminal, which then displays these suggestions to the user visually and audibly. The terminal displays health advice and information on related products on its screen and also provides audio guidance. At this stage, the user is provided with direct and easy-to-understand feedback based on the generated prompts received from the server.

[0380] Step 5:

[0381] Users select actions based on the information displayed on their devices and provide additional data as needed. The user's behavior history and feedback are resent to the server and stored in a database to improve future suggestions. This collected data is then used as input for future analysis.

[0382] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0383] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0384] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0385] [Third Embodiment]

[0386] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0387] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0388] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0389] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0390] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0391] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0392] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0393] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0394] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0395] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0396] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0397] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0398] Embodiments of the present invention demonstrate a practical method for providing a system that effectively manages health tailored to individual users. The system comprises a device for collecting health information, a processing device having artificial intelligence technology for analyzing the information, and a display device that provides customized health guidance based on the analysis results.

[0399] The server is the central component for collecting health information. The server stores health data collected from individual users (e.g., steps, heart rate, sleep duration) in a database, and updates and manages it as needed. Furthermore, the server analyzes this data using artificial intelligence technology. Because historical data is used for the analysis, pattern recognition and trend identification are performed based on the various data collected. This process makes it possible to generate customized advice best suited to the user's health condition.

[0400] A terminal is a device that serves as the interface between the user and the system. The terminal receives analysis results and advice sent from the server and displays them to the user. Specifically, this includes smartphones, tablets, and computers. The application software on the terminal is designed using UI / UX design to allow users to intuitively understand the information, and is configured to enable users to quickly grasp the information they need and the recommended action plan.

[0401] Users wear and use health management devices such as wearable devices and smartphones in their daily lives to obtain their own health data. The health data obtained by the user is transmitted to a server via the device and stored there. Users can then use the detailed health analysis results and improvement suggestions obtained from this data to adjust their lifestyle to be healthier. For example, if a user achieves their daily step goal, the app on the device will continue to provide further feedback.

[0402] In this way, the system of the present invention functions as an important tool for users to efficiently manage their own health.

[0403] The following describes the processing flow.

[0404] Step 1:

[0405] Users wear a wearable device and a smartphone to record their daily activities. The device automatically measures health data such as steps, heart rate, and sleep duration, and transmits it to the device.

[0406] Step 2:

[0407] The device collects health data received from the user and sends it to the server. The data sent includes metadata such as timestamps and user IDs.

[0408] Step 3:

[0409] The server saves the received health data to the database. During saving, the data integrity is checked, and it is verified to ensure there are no duplicates or errors.

[0410] Step 4:

[0411] The server uses artificial intelligence technology to analyze the stored data. The analysis process utilizes historical trend analysis and predictive algorithms to assess the user's health status and generate customized advice for improvement.

[0412] Step 5:

[0413] The server sends generated advice to the device. This information includes recommended daily activity levels and specific guidelines for dietary improvements.

[0414] Step 6:

[0415] The terminal displays advice received from the server to the user. The advice is applied to the application interface in an easy-to-understand format and presented visually.

[0416] Step 7:

[0417] The user reviews the advice provided and modifies their actions accordingly. The user then inputs the status of their implementation of the advice into the app and sends this information as feedback to the server.

[0418] Step 8:

[0419] The server receives user feedback and updates the database. The feedback information is used to generate future advice and as reference data to provide more personalized suggestions in subsequent analyses.

[0420] (Example 1)

[0421] Next, we will describe Example 1. 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."

[0422] In modern society, there is a growing need for individuals to comprehensively and in real time understand their own health status and take appropriate action. However, conventional health management systems have separate stages for data collection, analysis, and recommendations, making it difficult to provide rapid and individualized health support.

[0423] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0424] In this invention, the server includes an information receiving means for routinely collecting physiological information from individual users, a data management means for storing the physiological information in a database and updating and managing it as appropriate, and an analysis means using artificial intelligence technology to analyze a series of data, including past physiological information, and recognize patterns and trends related to the user's health status. This makes it possible to quickly evaluate the user's health status and generate customized suggestions individually.

[0425] "Information receiving means" is a general term for devices and software that collect physiological information from users, making it possible to collect data on a daily basis.

[0426] "Data management means" refers to functions and systems for efficiently storing collected physiological information and updating and managing it in a timely manner.

[0427] "Analysis means" refers to the process of analyzing collected data using artificial intelligence technology to recognize patterns and understand trends related to the user's health status.

[0428] "Information output means" refers to devices or interfaces used to visually or audibly communicate analysis results and suggestions to users.

[0429] "Physiological information" refers to various biometric data about the user's body, including information such as activity level, heart rate, sleep patterns, and nutritional intake.

[0430] "Artificial intelligence technology" refers to all algorithms and systems used for making predictions and analyses based on past data, and which automatically learn and infer.

[0431] A "generative AI model" refers to an artificial intelligence model used to create user-friendly suggestions based on a series of data and prompt statements.

[0432] Embodiments of the present invention provide a system that efficiently and effectively supports health management for individual users. This system mainly consists of information receiving means, data management means, analysis means, and information output means.

[0433] The server collects physiological information from users through information receiving means. The collected information includes data related to activity level, heart rate, sleep patterns, and nutritional intake, and is obtained via wearable devices and smartphone applications. This data is stored in a database and updated as needed.

[0434] After the collected data is stored, the server uses a generative AI model to analyze the data. Here, pattern recognition and trend analysis are performed based on past data to make predictions about the user's health status. Python machine learning libraries (e.g., TensorFlow and Scikit-learn) are often used for the analysis. The server then provides the generated suggestions as personalized feedback to the user.

[0435] The device receives feedback sent from the server and displays it visually to the user. Smartphones and tablets use intuitive UI / UX design to provide an environment where users can easily understand health information and take action.

[0436] Based on the feedback provided, users can appropriately adjust their lifestyles and strive to maintain and improve their health. For example, by inputting a prompt such as, "Generate weekly feedback and new goal suggestions to help the user achieve their goal of 10,000 steps," using a generative AI model, specific action recommendations can be generated.

[0437] Through the above, the system of the present invention functions as an important tool to support users in practicing data-driven health management in their daily lives.

[0438] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0439] Step 1:

[0440] Users wear a wearable device during their daily lives to collect physiological information such as activity levels, heart rate, and sleep patterns. This data is temporarily stored via the user's smartphone application. The application periodically retrieves the collected data by connecting to the device via Bluetooth or Wi-Fi.

[0441] Step 2:

[0442] The device securely transmits the acquired physiological information to the server via the internet. Security protocols are used to maintain data integrity during this data transmission. The input for this step is physiological information from the wearable device, and the output is the data transmitted to the server.

[0443] Step 3:

[0444] The server uses an information receiving mechanism to store physiological information in a database and updates it as needed. The input is physiological information transmitted from the terminal, and the output is the updated database. The server uses a scalable database management system to efficiently organize large amounts of data.

[0445] Step 4:

[0446] The server analyzes the accumulated data by applying a generative AI model. Specifically, it performs trend analysis and pattern recognition based on historical data, and builds a predictive model using Python machine learning libraries (e.g., TensorFlow, Scikit-learn). The input is physiological information in the database, and the output is the analyzed result.

[0447] Step 5:

[0448] The server generates personalized health advice based on the analysis results. Here, prompts are input to the AI ​​model to create the advice. For example, the prompt "Generate weekly feedback and new goal suggestions to help the user achieve their 10,000-step goal" is used. The input is the analysis results, and the output is the generated advice.

[0449] Step 6:

[0450] The server uses an information output mechanism to send the generated advice to the terminal. Security protocols are again strictly enforced. The input is the generated advice, and the output is the status of the transmission to the terminal.

[0451] Step 7:

[0452] The terminal visually displays the received advice to the user. Through the application's intuitive interface, users can easily understand and act upon the advice. The input is advice from the server, and the output is health guidance information displayed to the user.

[0453] Step 8:

[0454] Based on the displayed advice, users make necessary adjustments to their lifestyle. This allows them to take action toward achieving their health goals, and data is collected again via the wearable device, leading to the next cycle. The input is the advice from the device, and the output is the user's behavioral changes.

[0455] (Application Example 1)

[0456] Next, we will explain Application Example 1. In the following explanation, 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."

[0457] In modern work environments, it is crucial to monitor workers' health in real time and provide appropriate health management guidance, but there is a lack of effective systems to address this issue. This invention aims to improve work efficiency and maintain workers' health by providing individually tailored health guidance based on workers' biometric information.

[0458] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0459] In this invention, the server includes means for collecting personal biometric information, computing device means having machine learning technology for analyzing the biometric information, display device means for providing personalized health guidance to the individual based on the analysis, and means for evaluating the health status of workers in the work environment and providing guidelines for improving work styles. This enables real-time monitoring of health status and accurate, personalized health guidance.

[0460] "Personal biometric information" refers to data related to an individual's physical condition, including heart rate, activity level, and rest time.

[0461] "Analytical machine learning techniques" refer to algorithms that use collected data to detect patterns and predict future health conditions.

[0462] "Computation device means" refers to hardware and software configurations for data processing, and is a device that performs data collection and analysis.

[0463] "Display device means" refers to a tool that visually conveys analysis results and health guidance content, and typically presents information via a display device.

[0464] "Means for evaluating the health status of workers in the work environment" refers to methods and functions that analyze abnormal biological information and identify health risks to workers.

[0465] "Means of providing guidelines for improving work styles" refers to guidance and advice that recommend efficient and healthy work activities based on the analysis results.

[0466] The system realizing this invention efficiently manages health by collecting and analyzing personal biometric information in real time and providing appropriate health guidance. The server plays a central role in centrally processing the biometric information collected from individuals. The server collects data from wearable devices worn by individuals and analyzes that data using machine learning technology through a computing device.

[0467] Specifically, data such as heart rate, steps taken, and rest time are sent to a server, and this information is used to assess health status. Standard hardware includes wearable devices for collecting data and high-performance computing devices (such as cloud servers) for processing that data.

[0468] The analyzed information is provided as personalized health guidance through a display device. This display device could be a smartphone or tablet, presenting the information in a visually easy-to-understand format. For example, a virtual assistant could provide voice-based advice on exercise and rest based on the data.

[0469] Users can leverage feedback from the system to adjust their daily behaviors and pursue a healthier lifestyle. This helps workers gain a detailed understanding of their own health and maintain a safe and efficient work environment.

[0470] For the generating AI model, you can use the following example prompt to generate guidance content that supports health management.

[0471] "Consider a scenario where factory workers are using wearable devices, design a support program that analyzes their health status in real time and provides a safe and fulfilling work environment. Then, provide examples of health management feedback that could be implemented."

[0472] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0473] Step 1:

[0474] The user wears a wearable device. The wearable device collects biometric information such as heart rate, steps taken, and rest time in real time and transmits it to the terminal as digital data. The input data is biometric information, and the output is the collected data transmitted to the terminal.

[0475] Step 2:

[0476] The terminal receives data from the wearable device and transfers it to the server. The server stores the received data in a database and manages it appropriately. The input is the collected data, and the output is the update of the database on the server.

[0477] Step 3:

[0478] The server analyzes the accumulated data based on machine learning techniques. This analysis involves data computation, such as pattern recognition and trend analysis using historical data, to generate individual health status assessments. The input is biometric information in the database, and the output is the analyzed health status information.

[0479] Step 4:

[0480] The server generates individually adapted health guidance based on the analysis results. This generation process includes creating customized guidance content using a generative AI model. The input is the analysis results, and the output is health guidance information.

[0481] Step 5:

[0482] The terminal receives health guidance transmitted from the server and presents it to the user. This presentation includes visual or auditory feedback. The input is health guidance information, and the output is the feedback provided to the user.

[0483] Step 6:

[0484] Users adjust their daily activities and lifestyles based on health guidance provided by the device. This enables them to take specific actions to maintain or improve their health. The input is feedback, and the output is the user's adjusted activities.

[0485] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0486] Embodiments of the present invention provide a comprehensive health management system that takes into account not only an individual's health information but also the user's emotional state. The system consists of a device for collecting health information, a processing device that recognizes and analyzes the user's emotions using an emotion engine, and a display device that provides customized health guidance based on this information.

[0487] The server collects health data sent from the user's device and also integrates emotional data sent from the emotion engine. Emotional data is collected using speech recognition and facial recognition technologies. Therefore, the server integrates and analyzes the health and emotional data to perform a comprehensive health assessment. The analysis results are generated as advice that takes into account the individual's health and emotional state.

[0488] The terminal is responsible for providing users with customized health guidance from the server. Users are presented with detailed guidance and recommended action plans based on health information and emotional assessments. The terminal's application provides an interface tailored to the user's state and enables visual and auditory feedback to present information in an intuitive and easy-to-understand manner.

[0489] Users record their daily activities using wearable devices and smartphones. This data is used not only to collect health information, but also to simultaneously record emotional information through voice and facial expressions. This allows users to receive detailed feedback not only on their physical health but also on their mental health.

[0490] For example, if a user's daily health data indicates a lack of exercise and emotional data shows a high stress level, the device will present an action plan such as, "We recommend increasing your daily exercise by 5 minutes and taking a walk outside to get some fresh air." At the same time, a supplementary message will also be displayed, such as, "Let's reduce stress by taking deep breaths."

[0491] In this way, the present invention provides an effective tool for comprehensively supporting the physical and mental health of users, thereby promoting a healthier and more balanced lifestyle.

[0492] The following describes the processing flow.

[0493] Step 1:

[0494] Users record their daily health data using wearable devices and smartphones. The devices measure data such as steps, heart rate, and sleep duration, and simultaneously collect emotional data through voice recognition and facial recognition.

[0495] Step 2:

[0496] The device sends health and emotional data collected from the user to the server. This data includes the measured timestamp and user ID.

[0497] Step 3:

[0498] The server stores the received health and emotional data in a database. During storage, the data format is checked, and any necessary adjustments are made to maintain consistency.

[0499] Step 4:

[0500] The server uses artificial intelligence technology to analyze the received data. The analysis performs pattern analysis and predictive algorithms based on past personal data, and further combines this with emotional data to assess the user's overall health status.

[0501] Step 5:

[0502] The server generates personalized advice based on health and emotional data, including suggestions regarding physical health and recommendations regarding emotional health.

[0503] Step 6:

[0504] The server sends generated advice to the terminal, which then presents it to the user. This display includes visual and audio feedback, presented in a way that is easy for the user to understand.

[0505] Step 7:

[0506] The user reviews the advice and acts according to the recommendations. The user then inputs their implementation status and feedback into the app and sends it to the server.

[0507] Step 8:

[0508] The server receives feedback from users and updates the database. This feedback information is used to personalize future analyses and advice, helping to continuously improve the user's health management process.

[0509] (Example 2)

[0510] Next, we will describe Example 2. 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."

[0511] Traditional health management systems typically provided health guidance based on individual biometric data, but they struggled to achieve comprehensive health management that took into account an individual's emotional state. Furthermore, if the feedback and recommendations users received were not intuitive and easy to understand, the effectiveness of health management was diminished.

[0512] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0513] In this invention, the server includes means for collecting an individual's biometric data, means for having emotion analysis technology for collecting the individual's biometric data and emotional data, and means for generating personalized health guidance by utilizing a generative AI model based on the integrated data. This makes it possible to provide customized health guidance that comprehensively considers an individual's physical health status and emotional state.

[0514] "Biometric data" refers to information related to an individual's physical activity and condition, such as exercise data and heart rate data.

[0515] "Emotional data" refers to information that reflects an individual's emotional state, and is acquired through data such as voice data and facial expression data.

[0516] "Emotional analysis technology" refers to technology that analyzes an individual's emotional state from voice and facial expression data, and enables the recognition and analysis of emotions using artificial intelligence.

[0517] A "generative AI model" is a model that uses AI technology to generate personalized health guidance and advice based on input data.

[0518] "Health guidance" refers to instruction aimed at improving or maintaining health by providing customized advice and action plans based on an individual's biometric and emotional data.

[0519] An "interface" is the means by which a user receives information provided by a system, and it refers to the presentation of information through visual and auditory feedback.

[0520] This invention is a system that comprehensively evaluates a user's physical health and emotional state and provides personalized health guidance.

[0521] Hardware and software configuration

[0522] The server collects biometric data from the user's wearable devices and smartphones. This includes exercise information and heart rate data. Wearable devices connect via Bluetooth or Wi-Fi and transmit data to the server. Furthermore, emotional data based on emotion analysis technology is acquired using voice recognition and facial recognition technologies. This allows for a detailed record of the user's physical and emotional health status.

[0523] The server utilizes a generative AI model to integrate and analyze collected biometric and emotional data. This model plays a role in generating personalized health guidance based on various data. For example, health guidance is generated by instructing the AI ​​model using a prompt such as, "Generate optimal health advice based on the user's latest health and emotional data. Example: Provide specific suggestions for increasing exercise."

[0524] Providing health guidance

[0525] The device provides users with customized health guidance sent from the server. It uses visual and auditory interfaces to provide intuitive and easy-to-understand feedback. For example, if it determines that the user is not getting enough exercise and has high stress levels, it might display an action plan such as, "We recommend increasing your daily exercise time by 5 minutes and taking a walk outside to get some fresh air." It might also display a message like, "Reduce stress by practicing deep breathing."

[0526] In this way, users can take specific actions for health management based on their physical and emotional health status. The system continuously monitors the user's health status and updates the guidance content as needed, supporting the realization of a healthier and more balanced lifestyle.

[0527] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0528] Step 1:

[0529] The server receives biometric data from the user's wearable device or smartphone. This input includes exercise information and heart rate information. It also acquires emotional data through voice recognition and facial recognition. The server centralizes this data and stores it in a database. Specifically, data communication is performed using Bluetooth or Wi-Fi.

[0530] Step 2:

[0531] The server integrates the collected biometric and emotional data and inputs it into a generative AI model. Here, the integrated data is used as a basis for comprehensively evaluating the user's physical and emotional state. This process involves data preprocessing and feature extraction, preparing the data for analysis.

[0532] Step 3:

[0533] The server uses a generative AI model to generate personalized health guidance from integrated data. The AI ​​model creates optimal advice and action plans for the user based on prompt statements. For example, using a prompt statement such as "Generate optimal health advice based on the user's latest health and emotional data," it can obtain specific output results.

[0534] Step 4:

[0535] The device displays health guidance received from the server to the user. Information is provided visually and audibly through the interface, ensuring intuitive understanding. For example, animations and voice guidance are used to suggest increasing exercise or relaxation techniques.

[0536] Step 5:

[0537] Users follow instructions from their device and take action accordingly. By incorporating recommended behaviors into their daily lives, they aim to improve their health. For example, they might take an extra five-minute walk each day and consciously practice deep breathing to relieve stress. The results of this practice are then recorded on the device and sent to the server.

[0538] Step 6:

[0539] The server receives user feedback and further optimizes subsequent health guidance. Through continuous data collection and analysis, it becomes possible to provide more effective advice to users. This improves the accuracy of health management and the user experience.

[0540] (Application Example 2)

[0541] Next, we will explain application example 2. In the following explanation, 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."

[0542] In modern times, health management needs to consider not only an individual's physical aspects but also their mental health. Traditional health management systems are primarily limited to collecting and evaluating physical health information and do not comprehensively consider an individual's emotional state. As a result, there is a problem in that appropriate recommendations for products and services that contribute to mental health are difficult to make. Given this situation, there is a need for a method that comprehensively analyzes physical activity data and emotional state assessments to make individualized recommendations.

[0543] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0544] In this invention, the server includes means for collecting an individual's biometric information, processing means having artificial intelligence technology for integrating and analyzing the biometric information and emotional information, display means for presenting an individualized action plan based on the analysis, and means for recommending relevant products and services from a higher-level database to the individual who has received the recommendation. This enables comprehensive suggestions that simultaneously support the physical and mental health of individual users.

[0545] "Personal biometric information" refers to physical activity data, emotional state data, and their histories, and is fundamental data for evaluating an individual's health status.

[0546] The "processing means" refers to a device that has the function of comprehensively analyzing collected biometric and emotional information using artificial intelligence technology and generating suggestions optimized for the user.

[0547] A "display means" is a device that visually or audibly provides users with action plans or product / service suggestions generated based on analyzed data.

[0548] A "higher-level database" is an information aggregation device that stores data on related products and services based on an individual's analysis results and provides the relevant information as needed.

[0549] "Artificial intelligence technology" is a general term for algorithms and computational methods used to learn from large amounts of data and automatically generate personalized suggestions.

[0550] The integrated health support system according to the present invention mainly consists of the following elements: The server collects personal biometric information, including exercise data and emotional state assessments. This information is obtained from wearable devices and smartphones. The server integrates this biometric and emotional information and performs analysis using artificial intelligence technology. This analysis utilizes machine learning libraries such as TensorFlow and PyTorch, and employs a Keras-based deep learning model. Furthermore, based on the analysis results, personalized action plans and product / service suggestions are provided.

[0551] This suggestion is presented to the user visually or audibly on the device. The device is a smart device that displays the generated suggestion. In this process, the device provides an interface that visually displays customized content based on data generated by AI. For example, when the user's stress level is high, it may suggest products or services with relaxation effects.

[0552] For example, if a user asks the device, "Please recommend some products that will help me refresh today," the server can use biometric and emotional data to generate a prompt message, such as "Consider the user's past health data and stress level, and recommend products that will have a refreshing effect," and send this prompt to an AI model, which can then derive appropriate suggestions. In this way, it provides a concrete form of support for comprehensive health management for the user.

[0553] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0554] Step 1:

[0555] The server collects biometric and emotional information from the user transmitted from wearable devices and smartphones. At this stage, motion data, facial expressions, and voice data are sent as input data and stored on the server. This data serves as basic information for understanding the user's activity level and emotional state.

[0556] Step 2:

[0557] The server integrates collected biometric and emotional information and performs data analysis using artificial intelligence technology. This analysis applies machine learning algorithms using TensorFlow and PyTorch. Based on the input data, the user's current health and emotional state is evaluated, and prediction results are output by a generative AI model. The evaluation results are used as base data for personalized action plans and product / service recommendations.

[0558] Step 3:

[0559] Based on the analysis results, the server develops an individualized action plan for the user and generates prompts to suggest corresponding products and services. For example, a prompt such as "Considering the user's past health data and stress level, please recommend products with a refreshing effect" might be generated. The input is the analysis results, and the output is the instructional text for the suggestion.

[0560] Step 4:

[0561] Suggestions from the server are sent to the terminal, which then displays these suggestions to the user visually and audibly. The terminal displays health advice and information on related products on its screen and also provides audio guidance. At this stage, the user is provided with direct and easy-to-understand feedback based on the generated prompts received from the server.

[0562] Step 5:

[0563] Users select actions based on the information displayed on their devices and provide additional data as needed. The user's behavior history and feedback are resent to the server and stored in a database to improve future suggestions. This collected data is then used as input for future analysis.

[0564] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0565] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0566] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0567] [Fourth Embodiment]

[0568] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0569] As shown in Figure 7, the 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.

[0570] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0571] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0572] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0573] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0574] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0575] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0576] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0577] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0578] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0579] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0580] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0581] Embodiments of the present invention demonstrate a practical method for providing a system that effectively manages health tailored to individual users. The system comprises a device for collecting health information, a processing device having artificial intelligence technology for analyzing the information, and a display device that provides customized health guidance based on the analysis results.

[0582] The server is the central component for collecting health information. The server stores health data collected from individual users (e.g., steps, heart rate, sleep duration) in a database, and updates and manages it as needed. Furthermore, the server analyzes this data using artificial intelligence technology. Because historical data is used for the analysis, pattern recognition and trend identification are performed based on the various data collected. This process makes it possible to generate customized advice best suited to the user's health condition.

[0583] A terminal is a device that serves as the interface between the user and the system. The terminal receives analysis results and advice sent from the server and displays them to the user. Specifically, this includes smartphones, tablets, and computers. The application software on the terminal is designed using UI / UX design to allow users to intuitively understand the information, and is configured to enable users to quickly grasp the information they need and the recommended action plan.

[0584] Users wear and use health management devices such as wearable devices and smartphones in their daily lives to obtain their own health data. The health data obtained by the user is transmitted to a server via the device and stored there. Users can then use the detailed health analysis results and improvement suggestions obtained from this data to adjust their lifestyle to be healthier. For example, if a user achieves their daily step goal, the app on the device will continue to provide further feedback.

[0585] In this way, the system of the present invention functions as an important tool for users to efficiently manage their own health.

[0586] The following describes the processing flow.

[0587] Step 1:

[0588] Users wear a wearable device and a smartphone to record their daily activities. The device automatically measures health data such as steps, heart rate, and sleep duration, and transmits it to the device.

[0589] Step 2:

[0590] The device collects health data received from the user and sends it to the server. The data sent includes metadata such as timestamps and user IDs.

[0591] Step 3:

[0592] The server saves the received health data to the database. During saving, the data integrity is checked, and it is verified to ensure there are no duplicates or errors.

[0593] Step 4:

[0594] The server uses artificial intelligence technology to analyze the stored data. The analysis process utilizes historical trend analysis and predictive algorithms to assess the user's health status and generate customized advice for improvement.

[0595] Step 5:

[0596] The server sends generated advice to the device. This information includes recommended daily activity levels and specific guidelines for dietary improvements.

[0597] Step 6:

[0598] The terminal displays advice received from the server to the user. The advice is applied to the application interface in an easy-to-understand format and presented visually.

[0599] Step 7:

[0600] The user reviews the advice provided and modifies their actions accordingly. The user then inputs the status of their implementation of the advice into the app and sends this information as feedback to the server.

[0601] Step 8:

[0602] The server receives user feedback and updates the database. The feedback information is used to generate future advice and as reference data to provide more personalized suggestions in subsequent analyses.

[0603] (Example 1)

[0604] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0605] In modern society, there is a growing need for individuals to comprehensively and in real time understand their own health status and take appropriate action. However, conventional health management systems have separate stages for data collection, analysis, and recommendations, making it difficult to provide rapid and individualized health support.

[0606] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0607] In this invention, the server includes an information receiving means for routinely collecting physiological information from individual users, a data management means for storing the physiological information in a database and updating and managing it as appropriate, and an analysis means using artificial intelligence technology to analyze a series of data, including past physiological information, and recognize patterns and trends related to the user's health status. This makes it possible to quickly evaluate the user's health status and generate customized suggestions individually.

[0608] "Information receiving means" is a general term for devices and software that collect physiological information from users, making it possible to collect data on a daily basis.

[0609] "Data management means" refers to functions and systems for efficiently storing collected physiological information and updating and managing it in a timely manner.

[0610] "Analysis means" refers to the process of analyzing collected data using artificial intelligence technology to recognize patterns and understand trends related to the user's health status.

[0611] "Information output means" refers to devices or interfaces used to visually or audibly communicate analysis results and suggestions to users.

[0612] "Physiological information" refers to various biometric data about the user's body, including information such as activity level, heart rate, sleep patterns, and nutritional intake.

[0613] "Artificial intelligence technology" refers to all algorithms and systems used for making predictions and analyses based on past data, and which automatically learn and infer.

[0614] A "generative AI model" refers to an artificial intelligence model used to create user-friendly suggestions based on a series of data and prompt statements.

[0615] Embodiments of the present invention provide a system that efficiently and effectively supports health management for individual users. This system mainly consists of information receiving means, data management means, analysis means, and information output means.

[0616] The server collects physiological information from users through information receiving means. The collected information includes data related to activity level, heart rate, sleep patterns, and nutritional intake, and is obtained via wearable devices and smartphone applications. This data is stored in a database and updated as needed.

[0617] After the collected data is stored, the server uses a generative AI model to analyze the data. Here, pattern recognition and trend analysis are performed based on past data to make predictions about the user's health status. Python machine learning libraries (e.g., TensorFlow and Scikit-learn) are often used for the analysis. The server then provides the generated suggestions as personalized feedback to the user.

[0618] The device receives feedback sent from the server and displays it visually to the user. Smartphones and tablets use intuitive UI / UX design to provide an environment where users can easily understand health information and take action.

[0619] Based on the feedback provided, users can appropriately adjust their lifestyles and strive to maintain and improve their health. For example, by inputting a prompt such as, "Generate weekly feedback and new goal suggestions to help the user achieve their goal of 10,000 steps," using a generative AI model, specific action recommendations can be generated.

[0620] Through the above, the system of the present invention functions as an important tool to support users in practicing data-driven health management in their daily lives.

[0621] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0622] Step 1:

[0623] Users wear a wearable device during their daily lives to collect physiological information such as activity levels, heart rate, and sleep patterns. This data is temporarily stored via the user's smartphone application. The application periodically retrieves the collected data by connecting to the device via Bluetooth or Wi-Fi.

[0624] Step 2:

[0625] The device securely transmits the acquired physiological information to the server via the internet. Security protocols are used to maintain data integrity during this data transmission. The input for this step is physiological information from the wearable device, and the output is the data transmitted to the server.

[0626] Step 3:

[0627] The server uses an information receiving mechanism to store physiological information in a database and updates it as needed. The input is physiological information transmitted from the terminal, and the output is the updated database. The server uses a scalable database management system to efficiently organize large amounts of data.

[0628] Step 4:

[0629] The server analyzes the accumulated data by applying a generative AI model. Specifically, it performs trend analysis and pattern recognition based on historical data, and builds a predictive model using Python machine learning libraries (e.g., TensorFlow, Scikit-learn). The input is physiological information in the database, and the output is the analyzed result.

[0630] Step 5:

[0631] The server generates personalized health advice based on the analysis results. Here, prompts are input to the AI ​​model to create the advice. For example, the prompt "Generate weekly feedback and new goal suggestions to help the user achieve their 10,000-step goal" is used. The input is the analysis results, and the output is the generated advice.

[0632] Step 6:

[0633] The server uses an information output mechanism to send the generated advice to the terminal. Security protocols are again strictly enforced. The input is the generated advice, and the output is the status of the transmission to the terminal.

[0634] Step 7:

[0635] The terminal visually displays the received advice to the user. Through the application's intuitive interface, users can easily understand and act upon the advice. The input is advice from the server, and the output is health guidance information displayed to the user.

[0636] Step 8:

[0637] Based on the displayed advice, users make necessary adjustments to their lifestyle. This allows them to take action toward achieving their health goals, and data is collected again via the wearable device, leading to the next cycle. The input is the advice from the device, and the output is the user's behavioral changes.

[0638] (Application Example 1)

[0639] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0640] In modern work environments, it is crucial to monitor workers' health in real time and provide appropriate health management guidance, but there is a lack of effective systems to address this issue. This invention aims to improve work efficiency and maintain workers' health by providing individually tailored health guidance based on workers' biometric information.

[0641] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0642] In this invention, the server includes means for collecting personal biometric information, computing device means having machine learning technology for analyzing the biometric information, display device means for providing personalized health guidance to the individual based on the analysis, and means for evaluating the health status of workers in the work environment and providing guidelines for improving work styles. This enables real-time monitoring of health status and accurate, personalized health guidance.

[0643] "Personal biometric information" refers to data related to an individual's physical condition, including heart rate, activity level, and rest time.

[0644] "Analytical machine learning techniques" refer to algorithms that use collected data to detect patterns and predict future health conditions.

[0645] "Computation device means" refers to hardware and software configurations for data processing, and is a device that performs data collection and analysis.

[0646] "Display device means" refers to a tool that visually conveys analysis results and health guidance content, and typically presents information via a display device.

[0647] "Means for evaluating the health status of workers in the work environment" refers to methods and functions that analyze abnormal biological information and identify health risks to workers.

[0648] "Means of providing guidelines for improving work styles" refers to guidance and advice that recommend efficient and healthy work activities based on the analysis results.

[0649] The system realizing this invention efficiently manages health by collecting and analyzing personal biometric information in real time and providing appropriate health guidance. The server plays a central role in centrally processing the biometric information collected from individuals. The server collects data from wearable devices worn by individuals and analyzes that data using machine learning technology through a computing device.

[0650] Specifically, data such as heart rate, steps taken, and rest time are sent to a server, and this information is used to assess health status. Standard hardware includes wearable devices for collecting data and high-performance computing devices (such as cloud servers) for processing that data.

[0651] The analyzed information is provided as personalized health guidance through a display device. This display device could be a smartphone or tablet, presenting the information in a visually easy-to-understand format. For example, a virtual assistant could provide voice-based advice on exercise and rest based on the data.

[0652] Users can leverage feedback from the system to adjust their daily behaviors and pursue a healthier lifestyle. This helps workers gain a detailed understanding of their own health and maintain a safe and efficient work environment.

[0653] For the generating AI model, you can use the following example prompt to generate guidance content that supports health management.

[0654] "Consider a scenario where factory workers are using wearable devices, design a support program that analyzes their health status in real time and provides a safe and fulfilling work environment. Then, provide examples of health management feedback that could be implemented."

[0655] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0656] Step 1:

[0657] The user wears a wearable device. The wearable device collects biometric information such as heart rate, steps taken, and rest time in real time and transmits it to the terminal as digital data. The input data is biometric information, and the output is the collected data transmitted to the terminal.

[0658] Step 2:

[0659] The terminal receives data from the wearable device and transfers it to the server. The server stores the received data in a database and manages it appropriately. The input is the collected data, and the output is the update of the database on the server.

[0660] Step 3:

[0661] The server analyzes the accumulated data based on machine learning techniques. This analysis involves data computation, such as pattern recognition and trend analysis using historical data, to generate individual health status assessments. The input is biometric information in the database, and the output is the analyzed health status information.

[0662] Step 4:

[0663] The server generates individually adapted health guidance based on the analysis results. This generation process includes creating customized guidance content using a generative AI model. The input is the analysis results, and the output is health guidance information.

[0664] Step 5:

[0665] The terminal receives health guidance transmitted from the server and presents it to the user. This presentation includes visual or auditory feedback. The input is health guidance information, and the output is the feedback provided to the user.

[0666] Step 6:

[0667] Users adjust their daily activities and lifestyles based on health guidance provided by the device. This enables them to take specific actions to maintain or improve their health. The input is feedback, and the output is the user's adjusted activities.

[0668] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0669] Embodiments of the present invention provide a comprehensive health management system that takes into account not only an individual's health information but also the user's emotional state. The system consists of a device for collecting health information, a processing device that recognizes and analyzes the user's emotions using an emotion engine, and a display device that provides customized health guidance based on this information.

[0670] The server collects health data sent from the user's device and also integrates emotional data sent from the emotion engine. Emotional data is collected using speech recognition and facial recognition technologies. Therefore, the server integrates and analyzes the health and emotional data to perform a comprehensive health assessment. The analysis results are generated as advice that takes into account the individual's health and emotional state.

[0671] The terminal is responsible for providing users with customized health guidance from the server. Users are presented with detailed guidance and recommended action plans based on health information and emotional assessments. The terminal's application provides an interface tailored to the user's state and enables visual and auditory feedback to present information in an intuitive and easy-to-understand manner.

[0672] Users record their daily activities using wearable devices and smartphones. This data is used not only to collect health information, but also to simultaneously record emotional information through voice and facial expressions. This allows users to receive detailed feedback not only on their physical health but also on their mental health.

[0673] For example, if a user's daily health data indicates a lack of exercise and emotional data shows a high stress level, the device will present an action plan such as, "We recommend increasing your daily exercise by 5 minutes and taking a walk outside to get some fresh air." At the same time, a supplementary message will also be displayed, such as, "Let's reduce stress by taking deep breaths."

[0674] In this way, the present invention provides an effective tool for comprehensively supporting the physical and mental health of users, thereby promoting a healthier and more balanced lifestyle.

[0675] The following describes the processing flow.

[0676] Step 1:

[0677] Users record their daily health data using wearable devices and smartphones. The devices measure data such as steps, heart rate, and sleep duration, and simultaneously collect emotional data through voice recognition and facial recognition.

[0678] Step 2:

[0679] The device sends health and emotional data collected from the user to the server. This data includes the measured timestamp and user ID.

[0680] Step 3:

[0681] The server stores the received health and emotional data in a database. During storage, the data format is checked, and any necessary adjustments are made to maintain consistency.

[0682] Step 4:

[0683] The server uses artificial intelligence technology to analyze the received data. The analysis performs pattern analysis and predictive algorithms based on past personal data, and further combines this with emotional data to assess the user's overall health status.

[0684] Step 5:

[0685] The server generates personalized advice based on health and emotional data, including suggestions regarding physical health and recommendations regarding emotional health.

[0686] Step 6:

[0687] The server sends generated advice to the terminal, which then presents it to the user. This display includes visual and audio feedback, presented in a way that is easy for the user to understand.

[0688] Step 7:

[0689] The user reviews the advice and acts according to the recommendations. The user then inputs their implementation status and feedback into the app and sends it to the server.

[0690] Step 8:

[0691] The server receives feedback from users and updates the database. This feedback information is used to personalize future analyses and advice, helping to continuously improve the user's health management process.

[0692] (Example 2)

[0693] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0694] Traditional health management systems typically provided health guidance based on individual biometric data, but they struggled to achieve comprehensive health management that took into account an individual's emotional state. Furthermore, if the feedback and recommendations users received were not intuitive and easy to understand, the effectiveness of health management was diminished.

[0695] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0696] In this invention, the server includes means for collecting an individual's biometric data, means for having emotion analysis technology for collecting the individual's biometric data and emotional data, and means for generating personalized health guidance by utilizing a generative AI model based on the integrated data. This makes it possible to provide customized health guidance that comprehensively considers an individual's physical health status and emotional state.

[0697] "Biometric data" refers to information related to an individual's physical activity and condition, such as exercise data and heart rate data.

[0698] "Emotional data" refers to information that reflects an individual's emotional state, and is acquired through data such as voice data and facial expression data.

[0699] "Emotional analysis technology" refers to technology that analyzes an individual's emotional state from voice and facial expression data, and enables the recognition and analysis of emotions using artificial intelligence.

[0700] A "generative AI model" is a model that uses AI technology to generate personalized health guidance and advice based on input data.

[0701] "Health guidance" refers to instruction aimed at improving or maintaining health by providing customized advice and action plans based on an individual's biometric and emotional data.

[0702] An "interface" is the means by which a user receives information provided by a system, and it refers to the presentation of information through visual and auditory feedback.

[0703] This invention is a system that comprehensively evaluates a user's physical health and emotional state and provides personalized health guidance.

[0704] Hardware and software configuration

[0705] The server collects biometric data from the user's wearable devices and smartphones. This includes exercise information and heart rate data. Wearable devices connect via Bluetooth or Wi-Fi and transmit data to the server. Furthermore, emotional data based on emotion analysis technology is acquired using voice recognition and facial recognition technologies. This allows for a detailed record of the user's physical and emotional health status.

[0706] The server utilizes a generative AI model to integrate and analyze collected biometric and emotional data. This model plays a role in generating personalized health guidance based on various data. For example, health guidance is generated by instructing the AI ​​model using a prompt such as, "Generate optimal health advice based on the user's latest health and emotional data. Example: Provide specific suggestions for increasing exercise."

[0707] Providing health guidance

[0708] The device provides users with customized health guidance sent from the server. It uses visual and auditory interfaces to provide intuitive and easy-to-understand feedback. For example, if it determines that the user is not getting enough exercise and has high stress levels, it might display an action plan such as, "We recommend increasing your daily exercise time by 5 minutes and taking a walk outside to get some fresh air." It might also display a message like, "Reduce stress by practicing deep breathing."

[0709] In this way, users can take specific actions for health management based on their physical and emotional health status. The system continuously monitors the user's health status and updates the guidance content as needed, supporting the realization of a healthier and more balanced lifestyle.

[0710] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0711] Step 1:

[0712] The server receives biometric data from the user's wearable device or smartphone. This input includes exercise information and heart rate information. It also acquires emotional data through voice recognition and facial recognition. The server centralizes this data and stores it in a database. Specifically, data communication is performed using Bluetooth or Wi-Fi.

[0713] Step 2:

[0714] The server integrates the collected biometric and emotional data and inputs it into a generative AI model. Here, the integrated data is used as a basis for comprehensively evaluating the user's physical and emotional state. This process involves data preprocessing and feature extraction, preparing the data for analysis.

[0715] Step 3:

[0716] The server uses a generative AI model to generate personalized health guidance from integrated data. The AI ​​model creates optimal advice and action plans for the user based on prompt statements. For example, using a prompt statement such as "Generate optimal health advice based on the user's latest health and emotional data," it can obtain specific output results.

[0717] Step 4:

[0718] The device displays health guidance received from the server to the user. Information is provided visually and audibly through the interface, ensuring intuitive understanding. For example, animations and voice guidance are used to suggest increasing exercise or relaxation techniques.

[0719] Step 5:

[0720] Users follow instructions from their device and take action accordingly. By incorporating recommended behaviors into their daily lives, they aim to improve their health. For example, they might take an extra five-minute walk each day and consciously practice deep breathing to relieve stress. The results of this practice are then recorded on the device and sent to the server.

[0721] Step 6:

[0722] The server receives user feedback and further optimizes subsequent health guidance. Through continuous data collection and analysis, it becomes possible to provide more effective advice to users. This improves the accuracy of health management and the user experience.

[0723] (Application Example 2)

[0724] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0725] In modern times, health management needs to consider not only an individual's physical aspects but also their mental health. Traditional health management systems are primarily limited to collecting and evaluating physical health information and do not comprehensively consider an individual's emotional state. As a result, there is a problem in that appropriate recommendations for products and services that contribute to mental health are difficult to make. Given this situation, there is a need for a method that comprehensively analyzes physical activity data and emotional state assessments to make individualized recommendations.

[0726] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0727] In this invention, the server includes means for collecting an individual's biometric information, processing means having artificial intelligence technology for integrating and analyzing the biometric information and emotional information, display means for presenting an individualized action plan based on the analysis, and means for recommending relevant products and services from a higher-level database to the individual who has received the recommendation. This enables comprehensive suggestions that simultaneously support the physical and mental health of individual users.

[0728] "Personal biometric information" refers to physical activity data, emotional state data, and their histories, and is fundamental data for evaluating an individual's health status.

[0729] The "processing means" refers to a device that has the function of comprehensively analyzing collected biometric and emotional information using artificial intelligence technology and generating suggestions optimized for the user.

[0730] A "display means" is a device that visually or audibly provides users with action plans or product / service suggestions generated based on analyzed data.

[0731] A "higher-level database" is an information aggregation device that stores data on related products and services based on an individual's analysis results and provides the relevant information as needed.

[0732] "Artificial intelligence technology" is a general term for algorithms and computational methods used to learn from large amounts of data and automatically generate personalized suggestions.

[0733] The integrated health support system according to the present invention mainly consists of the following elements: The server collects personal biometric information, including exercise data and emotional state assessments. This information is obtained from wearable devices and smartphones. The server integrates this biometric and emotional information and performs analysis using artificial intelligence technology. This analysis utilizes machine learning libraries such as TensorFlow and PyTorch, and employs a Keras-based deep learning model. Furthermore, based on the analysis results, personalized action plans and product / service suggestions are provided.

[0734] This suggestion is presented to the user visually or audibly on the device. The device is a smart device that displays the generated suggestion. In this process, the device provides an interface that visually displays customized content based on data generated by AI. For example, when the user's stress level is high, it may suggest products or services with relaxation effects.

[0735] For example, if a user asks the device, "Please recommend some products that will help me refresh today," the server can use biometric and emotional data to generate a prompt message, such as "Consider the user's past health data and stress level, and recommend products that will have a refreshing effect," and send this prompt to an AI model, which can then derive appropriate suggestions. In this way, it provides a concrete form of support for comprehensive health management for the user.

[0736] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0737] Step 1:

[0738] The server collects biometric and emotional information from the user transmitted from wearable devices and smartphones. At this stage, motion data, facial expressions, and voice data are sent as input data and stored on the server. This data serves as basic information for understanding the user's activity level and emotional state.

[0739] Step 2:

[0740] The server integrates collected biometric and emotional information and performs data analysis using artificial intelligence technology. This analysis applies machine learning algorithms using TensorFlow and PyTorch. Based on the input data, the user's current health and emotional state is evaluated, and prediction results are output by a generative AI model. The evaluation results are used as base data for personalized action plans and product / service recommendations.

[0741] Step 3:

[0742] Based on the analysis results, the server develops an individualized action plan for the user and generates prompts to suggest corresponding products and services. For example, a prompt such as "Considering the user's past health data and stress level, please recommend products with a refreshing effect" might be generated. The input is the analysis results, and the output is the instructional text for the suggestion.

[0743] Step 4:

[0744] Suggestions from the server are sent to the terminal, which then displays these suggestions to the user visually and audibly. The terminal displays health advice and information on related products on its screen and also provides audio guidance. At this stage, the user is provided with direct and easy-to-understand feedback based on the generated prompts received from the server.

[0745] Step 5:

[0746] Users select actions based on the information displayed on their devices and provide additional data as needed. The user's behavior history and feedback are resent to the server and stored in a database to improve future suggestions. This collected data is then used as input for future analysis.

[0747] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0748] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0749] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0750] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0751] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0752] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0753] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0754] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0755] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0756] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0757] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0758] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0759] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0760] 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.

[0761] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0762] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0763] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0764] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0765] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0766] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0767] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0768] The following is further disclosed regarding the embodiments described above.

[0769] (Claim 1)

[0770] A device for collecting personal health information,

[0771] A processing device having artificial intelligence technology for analyzing the aforementioned health information,

[0772] A display device that provides personalized health guidance based on the aforementioned analysis,

[0773] A system that includes this.

[0774] (Claim 2)

[0775] The system according to claim 1, wherein the health information includes sleep patterns, exercise data, heart rate data, dietary information, and diagnostic information from a medical institution.

[0776] (Claim 3)

[0777] The system according to claim 1, wherein the artificial intelligence technology evaluates an individual's health status using a predictive algorithm based on past health data.

[0778] "Example 1"

[0779] (Claim 1)

[0780] A means of receiving information that collects physiological information from individual users on a daily basis,

[0781] A data management means for storing the aforementioned physiological information in a database and updating and managing it as appropriate,

[0782] An analysis method using artificial intelligence technology that analyzes a series of data including past physiological information to recognize patterns and understand trends related to the user's health status,

[0783] Based on the aforementioned analysis results, an information output means for generating and displaying the most suitable suggestions for the user,

[0784] A system that includes this.

[0785] (Claim 2)

[0786] The system according to claim 1, wherein the physiological information includes activity level patterns, exercise data, heart rate data, nutritional intake information, and medical diagnostic data.

[0787] (Claim 3)

[0788] The system according to claim 1, wherein the artificial intelligence technology evaluates the user's health status using a predictive algorithm based on past physiological information and generates suggestions using a generative artificial intelligence model.

[0789] "Application Example 1"

[0790] (Claim 1)

[0791] Means of collecting personal biometric information,

[0792] A computing device having machine learning technology for analyzing the aforementioned biological information,

[0793] A display device means that provides personalized health guidance based on the aforementioned analysis,

[0794] A means of evaluating the health status of workers in the work environment and providing guidelines for improving working conditions,

[0795] A system that includes this.

[0796] (Claim 2)

[0797] The system according to claim 1, wherein the biological information includes rest patterns, physical activity data, biological signal data, nutritional intake information, and diagnostic data from a medical institution.

[0798] (Claim 3)

[0799] The system according to claim 1, wherein the machine learning technology determines an individual's health status using a predictive model based on past biometric data.

[0800] "Example 2 of combining an emotion engine"

[0801] (Claim 1)

[0802] Means of collecting personal biometric data,

[0803] A means having emotion analysis technology for collecting the aforementioned biometric data and emotional data,

[0804] A means of generating personalized health guidance using an AI model based on integrated data,

[0805] An interface means for providing the aforementioned health guidance to an individual,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, wherein the biometric data includes exercise information, heart rate information, and voice data.

[0809] (Claim 3)

[0810] The system according to claim 1, wherein the generating AI model uses prompt statements that generate optimal health advice based on an individual's latest biometric and emotional data.

[0811] "Application example 2 when combining with an emotional engine"

[0812] (Claim 1)

[0813] Means of collecting personal biometric information,

[0814] Processing means having artificial intelligence technology for integrating and analyzing the aforementioned biological information and emotional information,

[0815] A display means for presenting an individualized action plan based on the above analysis,

[0816] A means of recommending relevant products and services from a higher-level database to individuals who have received the aforementioned presentation,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, wherein the biometric information includes physical activity data, emotional state evaluation data, and behavioral history.

[0820] (Claim 3)

[0821] The system according to claim 1, wherein the artificial intelligence technology uses an evaluation algorithm based on past biometric and emotional data to analyze an individual's health and emotional state using a generating AI model, and generates personalized suggestions. [Explanation of Symbols]

[0822] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device for collecting personal health information, A processing device having artificial intelligence technology for analyzing the aforementioned health information, A display device that provides personalized health guidance based on the aforementioned analysis, A system that includes this.

2. The system according to claim 1, wherein the health information includes sleep patterns, exercise data, heart rate data, dietary information, and diagnostic information from a medical institution.

3. The system according to claim 1, wherein the artificial intelligence technology evaluates an individual's health status using a predictive algorithm based on past health data.

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