system

Abstract

We provide the system. [Solution] A means for receiving biometric data collected from users, Means for preprocessing the biological data and converting it into an analyzable format, A means for analyzing the individual health status of a user based on data preprocessed using a generative AI model, A means for generating an optimal health management plan for the user based on the analysis results, A means for transmitting the health management plan to the user's terminal, A health management system that includes this.

Description

Technical Field

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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, including 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 conventional health management systems, there is a lack of personalization based on the health status of individual users, and a uniform health management approach is often adopted. As a result, there has been a problem that it is difficult for users to grasp and appropriately manage their own health status in real time. Also, in enterprises, since the health management of employees is carried out uniformly without considering individual situations, there is a problem that it is difficult to implement efficient health management measures. As a result, the importance of preventive medicine is not fully exerted, and there is a risk of an increase in medical costs and a decrease in productivity.

Means for Solving the Problems

[0005] This invention is characterized by first providing means for receiving biometric data from a user and pre-processing said biometric data into an analyzable format. Furthermore, it provides means for analyzing the user's health status based on this pre-processed data using a generation AI model and automatically generating an optimal health management plan for each individual user based on the analysis results. In addition, by transmitting this plan to the user's terminal in real time, the user can efficiently manage their personal health. Furthermore, by collecting feedback from the user and utilizing it for future plan generation, the accuracy of the plan can be improved. In addition, for companies, it provides means for providing administrators with aggregated biometric data of all employees, making it easier for companies to implement health management measures. This is expected to enhance the effectiveness of preventive medicine and lead to reduced medical costs and improved productivity.

[0006] "Biometric data" refers to physiological information used to measure an individual's health status, such as a user's heart rate, steps taken, calorie consumption, and sleep data.

[0007] "Preprocessing" refers to processes such as noise reduction and data normalization performed to prepare biological data into an analyzable format.

[0008] A "generative AI model" refers to an artificial intelligence algorithm that uses collected data to analyze a user's health status and generate an optimal health management plan.

[0009] A "health management plan" is a specific action plan, such as recommended exercise, diet, and sleep, that is generated according to each user's individual health condition.

[0010] "Real-time" refers to a state where data processing and notifications occur almost instantly, allowing users to receive information immediately.

[0011] "Feedback" is the process by which users report their evaluations and results of their actual health management plans to the system.

[0012] "Biometric data for the entire workforce" refers to aggregated biometric data collected from multiple employees within a company.

[0013] "Aggregated information" refers to data compiled by statistically processing individual biometric data and presenting it in a format useful for a company's health initiatives. [Brief explanation of the drawing]

[0014] [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 Embodiment 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.

Mode for Carrying Out the Invention

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] 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 disks (e.g., hard disks), or magnetic tapes, etc.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system for optimizing user health management, providing a customized health plan using individual biometric data. This system acquires information about the user's health status using wearable devices and smartphone apps, and analyzes this information on a cloud-based server, enabling users to manage their daily health.

[0036] First, the device collects the user's biometric data. This data includes heart rate, steps taken, calories burned, and sleep quality. The collected data is sent from the device to a server. The server preprocesses this received data and prepares it for analysis. The preprocessed data is then input into a generating AI model, which analyzes the user's health status. The AI ​​model considers various data points and identifies health risk factors that require particular attention.

[0037] Based on the analysis, the server assesses the user's current health status and generates a personalized health management plan. This plan consists of specific advice and guidelines aimed at improving the user's daily life, including diet, exercise, and sleep. For example, if a user's heart rate data indicates an increased stress level, the AI ​​may suggest stretches to promote relaxation.

[0038] The generated health plan is sent to the user's device in real time, allowing the user to review it and incorporate it into their daily life. This gives users the opportunity to proactively manage and improve their own health. Furthermore, the feedback reported by the user is sent back to the server and used for future analysis. This improves the accuracy of the generated plan, allowing it to more precisely meet the user's needs.

[0039] Furthermore, as a feature for businesses, the server aggregates employee biometric data and provides administrators with a real-time overview of their health status. This allows companies to monitor the health of their entire workforce and efficiently plan and implement necessary health promotion activities and risk management measures. In this way, the present invention provides an effective means of technically supporting individual and organizational health management.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device collects biometric data from the user's wearable devices and smartphone apps. This data may include heart rate, steps taken, calorie consumption, and sleep patterns.

[0043] Step 2:

[0044] The device transmits the collected data to the server in real time. Data transfers are performed periodically and communicated over a secure channel.

[0045] Step 3:

[0046] The server preprocesses the received biometric data into an analyzable format. Preprocessing includes imputing missing values, denoising, and formatting time-series data.

[0047] Step 4:

[0048] The server inputs pre-processed data into a generating AI model to analyze the user's current health status. Machine learning algorithms are used here to detect anomalies and analyze risk factors.

[0049] Step 5:

[0050] Based on the analysis results, the server generates a personalized health management plan tailored to the user's health condition. The plan includes specific exercise recommendations, nutritional guidance, and suggestions for improving sleep.

[0051] Step 6:

[0052] The server immediately sends the generated health management plan to the user's device. The plan is then made visible to the user through notifications and apps.

[0053] Step 7:

[0054] Users adjust their daily lives based on the health management plan they receive. Feedback on the user's actions and activities is recorded on the device.

[0055] Step 8:

[0056] The device then sends the user's feedback back to the server. The server analyzes this feedback and uses it to improve the quality of future health management plans.

[0057] Step 9:

[0058] As a feature for enterprises, the server generates aggregated biometric data from all employees and visualizes it on an administrator dashboard. Based on this information, companies can develop employee health management strategies.

[0059] (Example 1)

[0060] 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."

[0061] In modern society, personal health management is a crucial issue, but it is not easy for users to accurately understand their daily physical condition and develop appropriate health management plans. Many health monitoring systems have challenges in how to interpret the collected data and translate it into concrete improvement measures. Furthermore, companies lack effective means to efficiently understand the health status of their entire workforce and to plan and implement health promotion activities.

[0062] 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.

[0063] In this invention, the server includes means for receiving biometric information collected from a user, means for preprocessing the biometric information and converting it into an analyzable format, means for analyzing the user's individual health status based on the preprocessed information using a generation AI algorithm, means for generating an optimal health management plan for the user based on the analysis results, means for transmitting the health management plan to the user's device, and means for receiving user feedback and utilizing it to improve the quality of the health management plan. This enables users to understand their own health status in real time and implement an individualized health management plan. Furthermore, organizations can efficiently manage the health status of all employees and appropriately plan and implement necessary health promotion activities.

[0064] A "user" refers to an individual who uses this system and provides their own health information.

[0065] "Biometric information" refers to data that indicates the user's health status, and specifically includes heart rate, steps taken, calories burned, and sleep quality.

[0066] "Devices" refer to devices that users use to receive or provide information, such as smartphones and wearable devices.

[0067] A "generative AI algorithm" refers to a processing method that utilizes artificial intelligence to analyze biometric information obtained from users and generate health status assessments and health management plans.

[0068] A "health management plan" is actionable advice and suggestions created based on the user's individual health condition, providing specific guidelines regarding diet, exercise, sleep, and other related matters.

[0069] "Feedback" refers to the act of users responding to the health management plan provided by the system with their own experiences and opinions, which are used to improve the system.

[0070] An "organization" refers to a group of individuals or entities that have multiple participants (such as employees) and use a health management system to monitor and manage the health status of all its members.

[0071] This health management system uses users' biometric information to provide personalized health management plans. The system collects biometric data using wearable devices and smartphone applications, and transfers this information to a cloud-based server for processing.

[0072] First, the device collects various biometric information from the user, such as heart rate, steps taken, calories burned, and sleep quality. These devices include, for example, the latest smartwatches and smartphone apps with health monitoring capabilities. When the device collects data, it utilizes the device's sensor technology and the application's data processing capabilities to ensure that the information is recorded accurately and regularly.

[0073] Next, the collected data is transmitted to a server in real time. This server preprocesses the received biometric information and prepares it for analysis. Specifically, it imputes missing values ​​and filters out outliers. Data analysis software such as Python or R may be used in this process.

[0074] Furthermore, the server uses a generative AI model to comprehensively analyze the user's health status based on the pre-processed information. The AI ​​model takes multiple data points into consideration to identify more critical health risks. In this process, widely used machine learning frameworks such as TENSORFLOW® and PyTorch may be utilized. As a concrete example, the AI ​​model operates in response to a prompt such as, "Estimate the user's stress level from their weekly exercise and sleep data, and generate necessary health advice."

[0075] Based on the analysis results of the AI ​​model, the server generates a health management plan optimized for the user. The plan includes specific details such as meal suggestions, exercise routines, and sleep improvement measures. The generated plan is sent to the user's device in real time, allowing the user to review it and incorporate it into their daily life.

[0076] Users implement the provided health management plan and send the results as feedback via their device. This feedback is collected on a server and used for subsequent AI model analysis and plan generation. This allows the system to continuously learn and provide services that are more adapted to the user's needs.

[0077] This system allows users to proactively manage and improve their own health. Meanwhile, organizations can understand the health status of individual members and effectively plan and implement overall health promotion activities.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The device collects the user's biometric information. Specifically, it uses wearable devices and smartphones worn by the user to acquire data such as heart rate, steps taken, calories burned, and sleep quality. This information is recorded in real time within the device. The input is raw data acquired from sensors in wearable devices and smartphones, and the output is formatted biometric information.

[0081] Step 2:

[0082] The device transmits the collected biometric information to the server. Transmission typically occurs via an internet connection (Wi-Fi or mobile data). The input is formatted biometric information from the device, and the output is data packets sent to the server. Specifically, the data is encrypted during transmission to protect user privacy.

[0083] Step 3:

[0084] The server preprocesses the received biometric data. This involves imputing missing data points and filtering out abnormal values. This process formats the data into an analyzable format. The input is biometric data packets sent from the terminal, and the output is a clean, consistent dataset. Specifically, for example, the Python pandas library is used to imputate missing data points with historical data.

[0085] Step 4:

[0086] The server inputs pre-processed data into a generating AI model to analyze the user's health status. The AI ​​model performs calculations based on prompts such as, "Estimate the stress level from the user's weekly exercise and sleep data, and generate necessary health advice," and identifies risk factors. The input is a formatted biometric data set, and the output is the analysis result, i.e., an assessment of the user's health status. Specifically, pattern recognition is performed using a machine learning model based on TensorFlow.

[0087] Step 5:

[0088] The server generates a health management plan based on the analysis results of the AI ​​model. This plan includes specific advice on diet, exercise, and sleep improvement. The input is the AI ​​model's health status assessment, and the output is a personalized health management plan. Specifically, the user is provided with advice text created using natural language processing.

[0089] Step 6:

[0090] The server sends the generated health management plan to the user's device. The user can receive and review this plan and incorporate it into their daily life. The input is the generated health management plan, and the output is the plan notification sent to the user's device. Specifically, notifications are sent periodically via a mobile app.

[0091] Step 7:

[0092] Users provide feedback on the effectiveness and areas for improvement of their health management plan. This feedback is sent from the device to the server and used for subsequent analysis and plan generation. The input is user-based feedback information, and the output is an update to the system's learning database. Specifically, user feedback is obtained through an in-app form.

[0093] (Application Example 1)

[0094] 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."

[0095] In the industrial sector, the health status of workers directly impacts work efficiency and safety. Conventional methods do not adequately utilize individual biometric data for real-time health monitoring or optimization of the work environment, making it difficult to prevent health risks to workers and declines in production efficiency. Therefore, the present invention aims to provide a technology that monitors workers' health status in real time and provides an optimal work environment.

[0096] 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.

[0097] In this invention, the server includes means for receiving biometric data collected from users, means for preprocessing the biometric data and converting it into an analyzable format, means for analyzing the individual health status of users based on the preprocessed data using a generating AI model, and means for generating an optimal health management plan for users based on the analysis results. This enables the optimization of the work environment by continuously monitoring the health status of workers and recommending appropriate breaks and adjustments to workload in real time.

[0098] A "user" refers to an individual who provides biometric data and utilizes a health management system.

[0099] "Biometric data" refers to information related to the user's health status, such as heart rate, body temperature, steps taken, and sleep patterns.

[0100] "Preprocessing" refers to the process of shaping and correcting biological data in order to convert it into an analyzable format.

[0101] "Generative AI models" refer to artificial intelligence technology used to analyze a user's biometric data and assess their health status.

[0102] A "health management plan" refers to a plan that includes specific action guidelines for maintaining or improving the user's health, based on the analysis results.

[0103] "Device" refers to the device that a user uses to receive and view their health management plan.

[0104] "Health monitoring" refers to the process of continuously collecting a user's biometric data and analyzing and evaluating that data in real time.

[0105] "Work environment management" refers to a method of optimizing the work environment by adjusting workload and rest periods based on workers' health data.

[0106] This invention is a system for monitoring workers' health status in real time and optimizing the work environment. The server collects biometric data from wearable devices or smartphones worn by the user and transmits it to the cloud. The hardware used includes wearable devices (e.g., fitness bands or smartwatches), and the data processing is handled by cloud servers (e.g., AWS® or Azure®).

[0107] The server preprocesses the collected biometric data and converts it into an analyzable format. This preprocessing includes noise reduction and data formatting. Next, the server analyzes the preprocessed data using a generative AI model to assess the user's health status. This analysis generates a personalized health management plan for each user. The generated plan is sent to the user's device and provided as specific guidance for improving their health.

[0108] For example, if the server detects that a worker's heart rate is higher than normal, it sends an alert to the worker's terminal prompting them to take a break. This allows for timely adjustments to workload and rest, reducing health risks for workers. The system also aggregates biometric data from across the organization and provides administrators with a real-time overview of health conditions. Administrators can use this information to plan improvements to the work environment and prevent health risks.

[0109] An example of a prompt message might be, "The user's heart rate is above normal. Please generate recommended health actions based on this condition." By sending this prompt to the AI ​​model, an appropriate health management plan is generated and provided in real time.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The terminal collects biometric data from the user's wearable device. This data includes heart rate, body temperature, and steps taken. The terminal then prepares this biometric data to send to a cloud server. The input is the biometric data from the wearable device, and the output is data formatted for transmission to the cloud server.

[0113] Step 2:

[0114] The server receives biometric data transmitted from the terminal. The server preprocesses the received data, removing noise and converting it into an analyzable data format. In this step, the input is the biometric data transmitted from the terminal, and the output is the preprocessed, clean data.

[0115] Step 3:

[0116] The server uses a generative AI model to analyze pre-processed data. The server inputs data into the AI ​​model and evaluates the user's health status. This process identifies health risks that require attention. The input is pre-processed data, and the output is the health status evaluation as a result of the analysis.

[0117] Step 4:

[0118] The server generates an optimal health management plan for the user based on the analysis results. The generated plan includes specific guidelines for diet, exercise, and rest. The input for this step is the analysis results, and the output is the health management plan.

[0119] Step 5:

[0120] The server sends the generated health management plan to the user's terminal. The user's terminal receives this plan and displays it on the screen for the user to review. The input is the health management plan, and the output is the plan displayed on the terminal.

[0121] Step 6:

[0122] The user inputs feedback based on their health management plan into a terminal. The terminal sends the feedback to a server, which uses the feedback to generate future plans. The input in this step is user feedback, and the output is feedback data sent to the server.

[0123] 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.

[0124] This invention is a system that recognizes a user's emotional state based on their biometric data and uses this information to adjust their health management plan. The system analyzes the user's biometric data collected using wearable devices and smartphone apps, and provides a personalized health plan that takes into account both their physical and emotional state.

[0125] First, the device collects the user's biometric data, such as heart rate, steps taken, sleep quality, facial expressions, and voice data. This data is then transmitted to a server via the device. The transmitted biometric data is preprocessed on the server and prepared into an analyzable format.

[0126] The server inputs pre-processed data into a generating AI model to analyze the user's health status and recognize their emotional state using an emotion engine. The emotion engine can identify emotions from the user's physiological responses, voice, and facial recognition data, and estimate states such as relaxation, stress, happiness, and anxiety.

[0127] Based on the analysis results, the server generates an optimal health management plan for the user. This plan takes into account the user's current health and emotional state, and includes guidance and suggestions for future actions. For example, a user experiencing stress might be recommended relaxation exercises, while a user in a comfortable emotional state might be provided with a health maintenance plan to help maintain that state.

[0128] The generated health plan is sent to the user's device in real time, allowing the user to immediately review it and incorporate it into their daily life. The user can then take action based on the plan and input feedback into their device. This feedback information is sent to the server and used to generate the next plan.

[0129] As a feature for businesses, the server aggregates employees' biometric data and emotional states and provides administrators with a real-time summary. This allows businesses to develop employee care plans that consider both psychological and physical health. Thus, this invention enhances the quality of health management for both users and businesses, supporting comprehensive health promotion that also considers emotional aspects.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The device collects biometric data along with data such as the user's voice and facial expressions. This data includes heart rate, steps taken, calories burned, changes in facial expressions, and voice modulation.

[0133] Step 2:

[0134] The device transmits the collected data to the server in real time. This data is transferred using a secure communication protocol.

[0135] Step 3:

[0136] The server preprocesses the received biometric and emotional data, converting it into an analyzable format. Specifically, it performs data normalization, noise reduction, and interpolation.

[0137] Step 4:

[0138] The server inputs pre-processed data into a generating AI model to analyze the user's overall health status. The AI ​​model uses machine learning algorithms to identify the user's health risks.

[0139] Step 5:

[0140] The server uses an emotion engine to recognize the user's emotional state from collected voice and facial expression data. It estimates emotions such as stress, joy, anger, and anxiety.

[0141] Step 6:

[0142] The server generates an optimal health management plan for the user based on their analyzed health and emotional state. The health plan combines exercise, nutrition, and relaxation methods that address the user's emotional state.

[0143] Step 7:

[0144] The server sends the generated health management plan to the user's device in real time. The device notifies the user of the plan, making it easy for the user to review.

[0145] Step 8:

[0146] Users perform activities based on their health management plan and input the results and feedback into their device. For example, if they perform a suggested exercise, they record their progress.

[0147] Step 9:

[0148] The device sends user feedback data to the server. The server uses this feedback for future data analysis and to improve the health plan.

[0149] Step 10:

[0150] The server acts as a corporate health management function, aggregating employee biometric and emotional data and providing the information through an administrator dashboard. This allows companies to understand the health and emotional state of all employees and take appropriate measures.

[0151] (Example 2)

[0152] 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".

[0153] To provide a system that effectively analyzes users' biometric data and emotional states to deliver individually optimized health management plans. Furthermore, for companies, to provide integrated information to understand the overall health and emotional state of employees, thereby addressing the challenge of comprehensively improving employee health and well-being.

[0154] 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.

[0155] In this invention, the server includes means for receiving biometric data collected from a user, means for preprocessing the biometric data and converting it into an analyzable format, and means for analyzing the user's individual health and emotional state based on the preprocessed data using a generative AI model. This makes it possible to provide the user with a personalized health management plan.

[0156] "Biometric data" refers to a collection of numerical data and information that indicates a user's physical and psychological state, such as heart rate, steps taken, sleep quality, facial expressions, and voice data.

[0157] "Preprocessing" is the process of converting biological data into an analyzable format by removing noise, normalizing, and imputing missing values.

[0158] A "generative AI model" is a program that uses artificial intelligence technology to analyze a user's health status, emotional state, and other factors from input data.

[0159] An "emotion engine" is an algorithm that identifies emotions from a user's voice and facial expression data and estimates states such as relaxation, stress, happiness, and anxiety.

[0160] A "health management plan" is a plan that includes personalized suggestions and action guidelines to maintain and improve the user's health, based on the user's analyzed health and emotional state.

[0161] "Feedback data" refers to information that records the actions taken by users based on their health management plans, as well as the effects and changes they experienced as a result.

[0162] A "terminal" is an electronic device or apparatus used to collect a user's biometric data and communicate data with a server.

[0163] "Corporate administrator" refers to the individual or department responsible for comprehensively managing the health and emotional well-being of employees within an organization and for developing and implementing optimized employee care plans.

[0164] This invention is a system that analyzes a user's health and emotional state based on their biometric data and provides a personalized health management plan. This system primarily operates through the collaboration of a server and a terminal.

[0165] The device uses wearable devices and smartphone apps to collect biometric data such as the user's heart rate, steps taken, sleep quality, facial expressions, and voice. This data is transmitted to a server in real time, and the server receives the data via a secure communication protocol.

[0166] The server preprocesses the received biometric data, removing noise and normalizing it to prepare it for analysis. The preprocessed data is then input into a generative AI model. This model is built to analyze the user's health status and uses machine learning algorithms to analyze the data.

[0167] An emotion engine is used to recognize emotional states. This engine estimates the user's emotional state based on voice data and facial expression data, determining states such as relaxation, stress, happiness, and anxiety. For example, if a user's heart rate decreases while listening to calming music, the emotion engine detects a relaxed state.

[0168] Based on the analyzed results, the server generates a personalized health management plan for the user. For example, a user experiencing stress might be suggested relaxation exercises, while a user in a comfortable emotional state might be provided with a plan to maintain that state. This health management plan is sent from the server to the user's device in real time, allowing the user to review it immediately.

[0169] Furthermore, users can input feedback data on their completed health management plans into their devices. This feedback is sent to the server and used to generate future plans. By utilizing this feedback, the system can provide more precise and user-friendly health management over time.

[0170] As a concrete example of a prompt, the following could be used as input to the generative AI model: "Estimate the emotional state of the user when their heart rate is 75 bpm, their sleep quality is good, and their facial expression is smiling. Then, suggest an appropriate health management plan."

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The device collects data such as the user's heart rate, steps taken, sleep quality, facial expressions, and voice via wearable devices and smartphone apps. The input is biometric data obtained from sensors. The device measures this data in real time and records it as a digital signal. The output is the recorded biometric data, ready to be sent to a server for further processing.

[0174] Step 2:

[0175] The device transmits the collected biometric data to the server. The input is the biometric data recorded earlier. This data is encrypted using a secure protocol such as HTTPS and sent to the server. The output is the biometric data received on the server side. This ensures that the data is delivered securely to the server.

[0176] Step 3:

[0177] The server preprocesses the received biometric data. The input is biometric data transmitted from the terminal. The server performs noise reduction and data normalization to prepare the data for analysis. The output is clean biometric data after preprocessing. This process yields data of a quality suitable for analysis.

[0178] Step 4:

[0179] The server inputs pre-processed data into a generative AI model to analyze the user's health and emotional state. The input is pre-processed biometric data. The generative AI model uses machine learning to assess the health state and estimate emotions using an emotion engine. The output is the analyzed health and emotional state. Specifically, it calculates health scores, relaxation levels, stress levels, etc.

[0180] Step 5:

[0181] The server generates a health management plan based on the analysis results. The input is the analysis results of health status and emotional state. The server uses these results to assemble an individualized health management plan. The output is a personalized health management plan for the user. For example, if stress levels are high, it might suggest yoga for relaxation.

[0182] Step 6:

[0183] The server sends the generated health management plan to the user's device. The input is the created health management plan. The plan is sent to the device using push notifications so that the user can review it immediately. The output is the health management plan displayed on the device. This allows the user to take appropriate actions in their daily life.

[0184] Step 7:

[0185] The user acts according to the provided health management plan and inputs feedback into the device. The input consists of the user's activities and their results. The device records the feedback and prepares to send it to the server. The output is the recorded feedback data.

[0186] Step 8:

[0187] The terminal sends the collected feedback data to the server. The input is the user's feedback data. This data is then sent back to the server using a secure communication protocol. The output is the feedback data that has arrived at the server. This is then used to generate the next health management plan, improving the system's accuracy.

[0188] (Application Example 2)

[0189] 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".

[0190] In modern manufacturing, the health and emotional state of workers significantly impacts work safety and efficiency. In particular, accumulated stress and fatigue can lead to work errors and workplace accidents. However, currently, there are limited means to monitor these conditions in real time and provide appropriate health management plans. Therefore, there is a need to analyze emotional states based on workers' biometric data and provide a safe and efficient work environment.

[0191] 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.

[0192] In this invention, the server includes means for receiving biometric information collected from a user, means for preprocessing the biometric information and converting it into an analyzable format, means for analyzing the health status based on the preprocessed information using a generating AI model, means for generating a health management plan based on the analysis results, means for transmitting the health management plan to a terminal, means for collecting biometric information from a worker and analyzing their emotional state, and means for proposing a safe and efficient work environment to the worker according to their emotional state. This enables the provision of real-time feedback and personalized health management plans based on the worker's health and emotional state.

[0193] "Biometric information" refers to data about an individual's physical activity and physiological state, such as heart rate, steps taken, and sleep quality.

[0194] "Preprocessing" refers to the initial data organization process used to convert data into an analyzable format.

[0195] A "generative AI model" is an algorithm that performs predictions and analyses based on a large amount of data, and in this invention, it is used to analyze individual health conditions.

[0196] "Health status" refers to indicators of physical and mental condition, and in this invention, it serves as a criterion for generating an optimal health management plan for an individual.

[0197] A "health management plan" is a set of behavioral guidelines and activity plans proposed based on an individual's health and emotional state.

[0198] A "terminal" is a device operated by the user to send and receive biometric information and display health management plans.

[0199] "Worker" refers to an individual who is actually engaged in work at a factory or work site.

[0200] "Emotional state" is an evaluation index that indicates the psychological and emotional state of a worker.

[0201] A "safe and efficient work environment" refers to a workplace environment that is designed to allow workers to perform their duties comfortably.

[0202] To implement this invention, users collect biometric information in real time using wearable devices or smartphones. This includes heart rate, steps taken, sleep quality, and facial recognition data. This information is transmitted to a server via the terminal. The server preprocesses the received biometric information using Python's Pandas library and converts it into an analyzable format. Then, a generative AI model using TensorFlow analyzes the user's health and emotional state based on the preprocessed data.

[0203] Based on the analysis results, the server generates an individually optimized health management plan and sends it to the terminal. This plan is tailored to the worker's emotional state and includes guidelines for action to provide a safe and efficient work environment. For example, if a worker has a high heart rate and is under stress, they will be advised to take appropriate breaks.

[0204] As a concrete example, suppose a factory worker feels fatigued during their work. At that time, a wearable device they are wearing detects a sudden increase in their heart rate. Based on this data, the server analyzes that the worker is in a stressed state and immediately sends a notification to the device prompting them to take a break. This allows the worker to continue working with peace of mind.

[0205] An example of a prompt message for the generating AI model would be, "Based on biometric information, analyze the emotional state in real time and propose an appropriate health management plan to the worker." This would allow for comprehensive management of the worker's health status and contribute to improved work efficiency.

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] Users collect biometric information such as heart rate, steps taken, and sleep quality using wearable devices and smartphones. This information is transmitted to a server via Bluetooth or the internet through the device. The input is biometric data, and the output is biometric data transmitted to the server.

[0209] Step 2:

[0210] The server preprocesses the received biometric data using Python's Pandas library. Specifically, it cleans the data, imputes missing values, and formats it into an analyzable format. In this step, the input is the transmitted biometric data, and the output is the preprocessed, analyzable biometric data.

[0211] Step 3:

[0212] The server inputs pre-processed data into a generating AI model and uses TensorFlow to analyze individual health and emotional states. The analysis results in an estimation of the user's current health and emotional state. The input is pre-processed biometric data, and the output is the analysis results of the health and emotional state.

[0213] Step 4:

[0214] The server generates an optimal health management plan for the user based on the analysis results. The generated health management plan includes specific action guidelines tailored to the user's condition. The input is the analysis results, and the output is the health management plan.

[0215] Step 5:

[0216] The server sends the generated health management plan to the user's terminal in real time. The user can immediately check the plan on their terminal and incorporate it into their daily life. The input is the health management plan, and the output is the health management plan displayed on the terminal.

[0217] Step 6:

[0218] The user performs activities based on their health management plan and inputs feedback into a terminal. The terminal sends the feedback information to a server, which is used to generate the next plan. The input is the user's feedback, and the output is data used to generate the next health management plan.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] This invention is a system for optimizing user health management, providing a customized health plan using individual biometric data. This system acquires information about the user's health status using wearable devices and smartphone apps, and analyzes this information on a cloud-based server, enabling users to manage their daily health.

[0236] First, the device collects the user's biometric data. This data includes heart rate, steps taken, calories burned, and sleep quality. The collected data is sent from the device to a server. The server preprocesses this received data and prepares it for analysis. The preprocessed data is then input into a generating AI model, which analyzes the user's health status. The AI ​​model considers various data points and identifies health risk factors that require particular attention.

[0237] Based on the analysis, the server assesses the user's current health status and generates a personalized health management plan. This plan consists of specific advice and guidelines aimed at improving the user's daily life, including diet, exercise, and sleep. For example, if a user's heart rate data indicates an increased stress level, the AI ​​may suggest stretches to promote relaxation.

[0238] The generated health plan is sent to the user's device in real time, allowing the user to review it and incorporate it into their daily life. This gives users the opportunity to proactively manage and improve their own health. Furthermore, the feedback reported by the user is sent back to the server and used for future analysis. This improves the accuracy of the generated plan, allowing it to more precisely meet the user's needs.

[0239] Furthermore, as a feature for businesses, the server aggregates employee biometric data and provides administrators with a real-time overview of their health status. This allows companies to monitor the health of their entire workforce and efficiently plan and implement necessary health promotion activities and risk management measures. In this way, the present invention provides an effective means of technically supporting individual and organizational health management.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] The device collects biometric data from the user's wearable devices and smartphone apps. This data may include heart rate, steps taken, calorie consumption, and sleep patterns.

[0243] Step 2:

[0244] The device transmits the collected data to the server in real time. Data transfers are performed periodically and communicated over a secure channel.

[0245] Step 3:

[0246] The server preprocesses the received biometric data into an analyzable format. Preprocessing includes imputing missing values, denoising, and formatting time-series data.

[0247] Step 4:

[0248] The server inputs pre-processed data into a generating AI model to analyze the user's current health status. Machine learning algorithms are used here to detect anomalies and analyze risk factors.

[0249] Step 5:

[0250] Based on the analysis results, the server generates a personalized health management plan tailored to the user's health condition. The plan includes specific exercise recommendations, nutritional guidance, and suggestions for improving sleep.

[0251] Step 6:

[0252] The server immediately sends the generated health management plan to the user's device. The plan is then made visible to the user through notifications and apps.

[0253] Step 7:

[0254] Users adjust their daily lives based on the health management plan they receive. Feedback on the user's actions and activities is recorded on the device.

[0255] Step 8:

[0256] The device then sends the user's feedback back to the server. The server analyzes this feedback and uses it to improve the quality of future health management plans.

[0257] Step 9:

[0258] As a feature for enterprises, the server generates aggregated biometric data from all employees and visualizes it on an administrator dashboard. Based on this information, companies can develop employee health management strategies.

[0259] (Example 1)

[0260] 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."

[0261] In modern society, personal health management is a crucial issue, but it is not easy for users to accurately understand their daily physical condition and develop appropriate health management plans. Many health monitoring systems have challenges in how to interpret the collected data and translate it into concrete improvement measures. Furthermore, companies lack effective means to efficiently understand the health status of their entire workforce and to plan and implement health promotion activities.

[0262] 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.

[0263] In this invention, the server includes means for receiving biometric information collected from a user, means for preprocessing the biometric information and converting it into an analyzable format, means for analyzing the user's individual health status based on the preprocessed information using a generation AI algorithm, means for generating an optimal health management plan for the user based on the analysis results, means for transmitting the health management plan to the user's device, and means for receiving user feedback and utilizing it to improve the quality of the health management plan. This enables users to understand their own health status in real time and implement an individualized health management plan. Furthermore, organizations can efficiently manage the health status of all employees and appropriately plan and implement necessary health promotion activities.

[0264] A "user" refers to an individual who uses this system and provides their own health information.

[0265] "Biometric information" refers to data that indicates the user's health status, and specifically includes heart rate, steps taken, calories burned, and sleep quality.

[0266] "Devices" refer to devices that users use to receive or provide information, such as smartphones and wearable devices.

[0267] A "generative AI algorithm" refers to a processing method that utilizes artificial intelligence to analyze biometric information obtained from users and generate health status assessments and health management plans.

[0268] A "health management plan" is actionable advice and suggestions created based on the user's individual health condition, providing specific guidelines regarding diet, exercise, sleep, and other related matters.

[0269] "Feedback" refers to the act of users responding to the health management plan provided by the system with their own experiences and opinions, which are used to improve the system.

[0270] An "organization" refers to a group of individuals or entities that have multiple participants (such as employees) and use a health management system to monitor and manage the health status of all its members.

[0271] This health management system uses users' biometric information to provide personalized health management plans. The system collects biometric data using wearable devices and smartphone applications, and transfers this information to a cloud-based server for processing.

[0272] First, the device collects various biometric information from the user, such as heart rate, steps taken, calories burned, and sleep quality. These devices include, for example, the latest smartwatches and smartphone apps with health monitoring capabilities. When the device collects data, it utilizes the device's sensor technology and the application's data processing capabilities to ensure that the information is recorded accurately and regularly.

[0273] Next, the collected data is transmitted to a server in real time. This server preprocesses the received biometric information and prepares it for analysis. Specifically, it imputes missing values ​​and filters out outliers. Data analysis software such as Python or R may be used in this process.

[0274] Furthermore, the server uses a generative AI model to comprehensively analyze the user's health status based on the pre-processed information. The AI ​​model takes multiple data points into consideration to identify more critical health risks. In this process, widely used machine learning frameworks such as TensorFlow and PyTorch may be utilized. As a concrete example, the AI ​​model operates in response to a prompt such as, "Estimate the user's stress level from their weekly exercise and sleep data, and generate necessary health advice."

[0275] Based on the analysis results of the AI ​​model, the server generates a health management plan optimized for the user. The plan includes specific details such as meal suggestions, exercise routines, and sleep improvement measures. The generated plan is sent to the user's device in real time, allowing the user to review it and incorporate it into their daily life.

[0276] Users implement the provided health management plan and send the results as feedback via their device. This feedback is collected on a server and used for subsequent AI model analysis and plan generation. This allows the system to continuously learn and provide services that are more adapted to the user's needs.

[0277] This system allows users to proactively manage and improve their own health. Meanwhile, organizations can understand the health status of individual members and effectively plan and implement overall health promotion activities.

[0278] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0279] Step 1:

[0280] The device collects the user's biometric information. Specifically, it uses wearable devices and smartphones worn by the user to acquire data such as heart rate, steps taken, calories burned, and sleep quality. This information is recorded in real time within the device. The input is raw data acquired from sensors in wearable devices and smartphones, and the output is formatted biometric information.

[0281] Step 2:

[0282] The terminal sends the collected biometric information to the server. The transmission is usually carried out through an Internet connection (Wi-Fi or mobile data communication). The input is the formatted biometric information from the terminal, and the output is the data packet sent to the server. Specifically, the data is encrypted and transmitted to protect the user's privacy.

[0283] Step 3:

[0284] The server preprocesses the received biometric information. Here, the missing parts of the data are complemented, and abnormal values are filtered. Through this process, the data is formatted into an analyzable form. The input is the biometric information data packet sent from the terminal, and the output is a clean and consistent dataset. Specifically, for example, the missing values of the data are complemented with past historical data using the pandas library in Python.

[0285] Step 4:

[0286] The server inputs the preprocessed data into a generative AI model to analyze the user's health status. The AI model performs calculations based on a prompt sentence such as "Estimate the stress level from the user's weekly exercise and sleep data and generate necessary health advice", and identifies risk factors. The input is the formatted biometric information dataset, and the output is the analysis result, that is, the user's health status evaluation. As a specific operation, pattern recognition is performed by a machine learning model using TensorFlow.

[0287] Step 5:

[0288] The server generates a health management plan based on the analysis result of the AI model. This plan includes specific advice on diet, exercise, and sleep improvement. The input is the health status evaluation of the AI model, and the output is an individualized health management plan. Specifically, advice sentences created by natural language processing are provided to the user.

[0289] Step 6:

[0290] The server sends the generated health management plan to the user's device. The user can receive and review this plan and incorporate it into their daily life. The input is the generated health management plan, and the output is the plan notification sent to the user's device. Specifically, notifications are sent periodically via a mobile app.

[0291] Step 7:

[0292] Users provide feedback on the effectiveness and areas for improvement of their health management plan. This feedback is sent from the device to the server and used for subsequent analysis and plan generation. The input is user-based feedback information, and the output is an update to the system's learning database. Specifically, user feedback is obtained through an in-app form.

[0293] (Application Example 1)

[0294] 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."

[0295] In the industrial sector, the health status of workers directly impacts work efficiency and safety. Conventional methods do not adequately utilize individual biometric data for real-time health monitoring or optimization of the work environment, making it difficult to prevent health risks to workers and declines in production efficiency. Therefore, the present invention aims to provide a technology that monitors workers' health status in real time and provides an optimal work environment.

[0296] 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.

[0297] In this invention, the server includes means for receiving biometric data collected from users, means for preprocessing the biometric data and converting it into an analyzable format, means for analyzing the individual health status of users based on the preprocessed data using a generating AI model, and means for generating an optimal health management plan for users based on the analysis results. This enables the optimization of the work environment by continuously monitoring the health status of workers and recommending appropriate breaks and adjustments to workload in real time.

[0298] A "user" refers to an individual who provides biometric data and utilizes a health management system.

[0299] "Biometric data" refers to information related to the user's health status, such as heart rate, body temperature, steps taken, and sleep patterns.

[0300] "Preprocessing" refers to the process of shaping and correcting biological data in order to convert it into an analyzable format.

[0301] "Generative AI models" refer to artificial intelligence technology used to analyze a user's biometric data and assess their health status.

[0302] A "health management plan" refers to a plan that includes specific action guidelines for maintaining or improving the user's health, based on the analysis results.

[0303] "Device" refers to the device that a user uses to receive and view their health management plan.

[0304] "Health monitoring" refers to the process of continuously collecting a user's biometric data and analyzing and evaluating that data in real time.

[0305] "Work environment management" refers to a method of optimizing the work environment by adjusting workload and rest periods based on workers' health data.

[0306] This invention is a system for monitoring the health status of workers in real time and optimizing the working environment. The server collects biometric data from wearable devices and smartphones worn by users and transmits it to the cloud. The hardware used includes wearable devices (such as fitness bands and smartwatches), and the data processing is done by cloud servers (such as AWS and Azure).

[0307] The server preprocesses the collected biometric data and converts it into an analyzable format. This preprocessing includes noise removal and data shaping. Next, the server analyzes the preprocessed data using a generated AI model to evaluate the health status of the user. Based on this analysis, an individual health management plan is generated for each user. The generated plan is transmitted to the user's terminal and provided as specific guidelines for health improvement.

[0308] As a specific example, when the server detects that the worker's heart rate is higher than normal, it sends an alert prompting the worker to take a break to the worker's terminal. This enables adjustment of the workload and rest at appropriate times, reducing the health risks of the worker. In addition, this system also has the function of aggregating the biometric data of the entire organization and providing an overview of the health status to the administrator in real time. Based on this information, the administrator can plan improvements to the working environment and preventive measures for health risks.

[0309] As an example of a prompt sentence, an instruction such as "The user's heart rate exceeds normal. Generate the recommended health actions from this state." can be considered. By sending this prompt to the AI model, an appropriate health management plan is generated and provided in real time.

[0310] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0311] Step 1:

[0312] The terminal collects biometric data from the user's wearable device. This data includes heart rate, body temperature, and steps taken. The terminal then prepares this biometric data to send to a cloud server. The input is the biometric data from the wearable device, and the output is data formatted for transmission to the cloud server.

[0313] Step 2:

[0314] The server receives biometric data transmitted from the terminal. The server preprocesses the received data, removing noise and converting it into an analyzable data format. In this step, the input is the biometric data transmitted from the terminal, and the output is the preprocessed, clean data.

[0315] Step 3:

[0316] The server uses a generative AI model to analyze pre-processed data. The server inputs data into the AI ​​model and evaluates the user's health status. This process identifies health risks that require attention. The input is pre-processed data, and the output is the health status evaluation as a result of the analysis.

[0317] Step 4:

[0318] The server generates an optimal health management plan for the user based on the analysis results. The generated plan includes specific guidelines for diet, exercise, and rest. The input for this step is the analysis results, and the output is the health management plan.

[0319] Step 5:

[0320] The server sends the generated health management plan to the user's terminal. The user's terminal receives this plan and displays it on the screen for the user to review. The input is the health management plan, and the output is the plan displayed on the terminal.

[0321] Step 6:

[0322] The user inputs feedback based on their health management plan into a terminal. The terminal sends the feedback to a server, which uses the feedback to generate future plans. The input in this step is user feedback, and the output is feedback data sent to the server.

[0323] 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.

[0324] This invention is a system that recognizes a user's emotional state based on their biometric data and uses this information to adjust their health management plan. The system analyzes the user's biometric data collected using wearable devices and smartphone apps, and provides a personalized health plan that takes into account both their physical and emotional state.

[0325] First, the device collects the user's biometric data, such as heart rate, steps taken, sleep quality, facial expressions, and voice data. This data is then transmitted to a server via the device. The transmitted biometric data is preprocessed on the server and prepared into an analyzable format.

[0326] The server inputs pre-processed data into a generating AI model to analyze the user's health status and recognize their emotional state using an emotion engine. The emotion engine can identify emotions from the user's physiological responses, voice, and facial recognition data, and estimate states such as relaxation, stress, happiness, and anxiety.

[0327] Based on the analysis results, the server generates an optimal health management plan for the user. This plan takes into account the user's current health and emotional state, and includes guidance and suggestions for future actions. For example, a user experiencing stress might be recommended relaxation exercises, while a user in a comfortable emotional state might be provided with a health maintenance plan to help maintain that state.

[0328] The generated health plan is sent to the user's device in real time, allowing the user to immediately review it and incorporate it into their daily life. The user can then take action based on the plan and input feedback into their device. This feedback information is sent to the server and used to generate the next plan.

[0329] As a feature for businesses, the server aggregates employees' biometric data and emotional states and provides administrators with a real-time summary. This allows businesses to develop employee care plans that consider both psychological and physical health. Thus, this invention enhances the quality of health management for both users and businesses, supporting comprehensive health promotion that also considers emotional aspects.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] The device collects biometric data along with data such as the user's voice and facial expressions. This data includes heart rate, steps taken, calories burned, changes in facial expressions, and voice modulation.

[0333] Step 2:

[0334] The device transmits the collected data to the server in real time. This data is transferred using a secure communication protocol.

[0335] Step 3:

[0336] The server preprocesses the received biometric and emotional data, converting it into an analyzable format. Specifically, it performs data normalization, noise reduction, and interpolation.

[0337] Step 4:

[0338] The server inputs pre-processed data into a generating AI model to analyze the user's overall health status. The AI ​​model uses machine learning algorithms to identify the user's health risks.

[0339] Step 5:

[0340] The server uses an emotion engine to recognize the user's emotional state from collected voice and facial expression data. It estimates emotions such as stress, joy, anger, and anxiety.

[0341] Step 6:

[0342] The server generates an optimal health management plan for the user based on their analyzed health and emotional state. The health plan combines exercise, nutrition, and relaxation methods that address the user's emotional state.

[0343] Step 7:

[0344] The server sends the generated health management plan to the user's device in real time. The device notifies the user of the plan, making it easy for the user to review.

[0345] Step 8:

[0346] Users perform activities based on their health management plan and input the results and feedback into their device. For example, if they perform a suggested exercise, they record their progress.

[0347] Step 9:

[0348] The device sends user feedback data to the server. The server uses this feedback for future data analysis and to improve the health plan.

[0349] Step 10:

[0350] The server acts as a corporate health management function, aggregating employee biometric and emotional data and providing the information through an administrator dashboard. This allows companies to understand the health and emotional state of all employees and take appropriate measures.

[0351] (Example 2)

[0352] 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".

[0353] To provide a system that effectively analyzes users' biometric data and emotional states to deliver individually optimized health management plans. Furthermore, for companies, to provide integrated information to understand the overall health and emotional state of employees, thereby addressing the challenge of comprehensively improving employee health and well-being.

[0354] 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.

[0355] In this invention, the server includes means for receiving biometric data collected from a user, means for preprocessing the biometric data and converting it into an analyzable format, and means for analyzing the user's individual health and emotional state based on the preprocessed data using a generative AI model. This makes it possible to provide the user with a personalized health management plan.

[0356] "Biometric data" refers to a collection of numerical data and information that indicates a user's physical and psychological state, such as heart rate, steps taken, sleep quality, facial expressions, and voice data.

[0357] "Preprocessing" is the process of converting biological data into an analyzable format by removing noise, normalizing, and imputing missing values.

[0358] A "generative AI model" is a program that uses artificial intelligence technology to analyze a user's health status, emotional state, and other factors from input data.

[0359] An "emotion engine" is an algorithm that identifies emotions from a user's voice and facial expression data and estimates states such as relaxation, stress, happiness, and anxiety.

[0360] A "health management plan" is a plan that includes personalized suggestions and action guidelines to maintain and improve the user's health, based on the user's analyzed health and emotional state.

[0361] "Feedback data" refers to information that records the actions taken by users based on their health management plans, as well as the effects and changes they experienced as a result.

[0362] A "terminal" is an electronic device or apparatus used to collect a user's biometric data and communicate data with a server.

[0363] "Corporate administrator" refers to the individual or department responsible for comprehensively managing the health and emotional well-being of employees within an organization and for developing and implementing optimized employee care plans.

[0364] This invention is a system that analyzes a user's health and emotional state based on their biometric data and provides a personalized health management plan. This system primarily operates through the collaboration of a server and a terminal.

[0365] The device uses wearable devices and smartphone apps to collect biometric data such as the user's heart rate, steps taken, sleep quality, facial expressions, and voice. This data is transmitted to a server in real time, and the server receives the data via a secure communication protocol.

[0366] The server preprocesses the received biometric data, removing noise and normalizing it to prepare it for analysis. The preprocessed data is then input into a generative AI model. This model is built to analyze the user's health status and uses machine learning algorithms to analyze the data.

[0367] An emotion engine is used to recognize emotional states. This engine estimates the user's emotional state based on voice data and facial expression data, determining states such as relaxation, stress, happiness, and anxiety. For example, if a user's heart rate decreases while listening to calming music, the emotion engine detects a relaxed state.

[0368] Based on the analyzed results, the server generates a personalized health management plan for the user. For example, a user experiencing stress might be suggested relaxation exercises, while a user in a comfortable emotional state might be provided with a plan to maintain that state. This health management plan is sent from the server to the user's device in real time, allowing the user to review it immediately.

[0369] Furthermore, users can input feedback data on their completed health management plans into their devices. This feedback is sent to the server and used to generate future plans. By utilizing this feedback, the system can provide more precise and user-friendly health management over time.

[0370] As a concrete example of a prompt, the following could be used as input to the generative AI model: "Estimate the emotional state of the user when their heart rate is 75 bpm, their sleep quality is good, and their facial expression is smiling. Then, suggest an appropriate health management plan."

[0371] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0372] Step 1:

[0373] The device collects data such as the user's heart rate, steps taken, sleep quality, facial expressions, and voice via wearable devices and smartphone apps. The input is biometric data obtained from sensors. The device measures this data in real time and records it as a digital signal. The output is the recorded biometric data, ready to be sent to a server for further processing.

[0374] Step 2:

[0375] The device transmits the collected biometric data to the server. The input is the biometric data recorded earlier. This data is encrypted using a secure protocol such as HTTPS and sent to the server. The output is the biometric data received on the server side. This ensures that the data is delivered securely to the server.

[0376] Step 3:

[0377] The server preprocesses the received biometric data. The input is biometric data transmitted from the terminal. The server performs noise reduction and data normalization to prepare the data for analysis. The output is clean biometric data after preprocessing. This process yields data of a quality suitable for analysis.

[0378] Step 4:

[0379] The server inputs pre-processed data into a generative AI model to analyze the user's health and emotional state. The input is pre-processed biometric data. The generative AI model uses machine learning to assess the health state and estimate emotions using an emotion engine. The output is the analyzed health and emotional state. Specifically, it calculates health scores, relaxation levels, stress levels, etc.

[0380] Step 5:

[0381] The server generates a health management plan based on the analysis results. The input is the analysis results of health status and emotional state. The server uses these results to assemble an individualized health management plan. The output is a personalized health management plan for the user. For example, if stress levels are high, it might suggest yoga for relaxation.

[0382] Step 6:

[0383] The server sends the generated health management plan to the user's device. The input is the created health management plan. The plan is sent to the device using push notifications so that the user can review it immediately. The output is the health management plan displayed on the device. This allows the user to take appropriate actions in their daily life.

[0384] Step 7:

[0385] The user acts according to the provided health management plan and inputs feedback into the device. The input consists of the user's activities and their results. The device records the feedback and prepares to send it to the server. The output is the recorded feedback data.

[0386] Step 8:

[0387] The terminal sends the collected feedback data to the server. The input is the user's feedback data. This data is then sent back to the server using a secure communication protocol. The output is the feedback data that has arrived at the server. This is then used to generate the next health management plan, improving the system's accuracy.

[0388] (Application Example 2)

[0389] 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."

[0390] In modern manufacturing, the health and emotional state of workers significantly impacts work safety and efficiency. In particular, accumulated stress and fatigue can lead to work errors and workplace accidents. However, currently, there are limited means to monitor these conditions in real time and provide appropriate health management plans. Therefore, there is a need to analyze emotional states based on workers' biometric data and provide a safe and efficient work environment.

[0391] 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.

[0392] In this invention, the server includes means for receiving biometric information collected from a user, means for preprocessing the biometric information and converting it into an analyzable format, means for analyzing the health status based on the preprocessed information using a generating AI model, means for generating a health management plan based on the analysis results, means for transmitting the health management plan to a terminal, means for collecting biometric information from a worker and analyzing their emotional state, and means for proposing a safe and efficient work environment to the worker according to their emotional state. This enables the provision of real-time feedback and personalized health management plans based on the worker's health and emotional state.

[0393] "Biometric information" refers to data about an individual's physical activity and physiological state, such as heart rate, steps taken, and sleep quality.

[0394] "Preprocessing" refers to the initial data organization process used to convert data into an analyzable format.

[0395] A "generative AI model" is an algorithm that performs predictions and analyses based on a large amount of data, and in this invention, it is used to analyze individual health conditions.

[0396] "Health status" refers to indicators of physical and mental condition, and in this invention, it serves as a criterion for generating an optimal health management plan for an individual.

[0397] A "health management plan" is a set of behavioral guidelines and activity plans proposed based on an individual's health and emotional state.

[0398] A "terminal" is a device operated by the user to send and receive biometric information and display health management plans.

[0399] "Worker" refers to an individual who is actually engaged in work at a factory or work site.

[0400] "Emotional state" is an evaluation index that indicates the psychological and emotional state of a worker.

[0401] A "safe and efficient work environment" refers to a workplace environment that is designed to allow workers to perform their duties comfortably.

[0402] To implement this invention, users collect biometric information in real time using wearable devices or smartphones. This includes heart rate, steps taken, sleep quality, and facial recognition data. This information is transmitted to a server via the terminal. The server preprocesses the received biometric information using Python's Pandas library and converts it into an analyzable format. Then, a generative AI model using TensorFlow analyzes the user's health and emotional state based on the preprocessed data.

[0403] Based on the analysis results, the server generates an individually optimized health management plan and sends it to the terminal. This plan is tailored to the worker's emotional state and includes guidelines for action to provide a safe and efficient work environment. For example, if a worker has a high heart rate and is under stress, they will be advised to take appropriate breaks.

[0404] As a concrete example, suppose a factory worker feels fatigued during their work. At that time, a wearable device they are wearing detects a sudden increase in their heart rate. Based on this data, the server analyzes that the worker is in a stressed state and immediately sends a notification to the device prompting them to take a break. This allows the worker to continue working with peace of mind.

[0405] An example of a prompt message for the generating AI model would be, "Based on biometric information, analyze the emotional state in real time and propose an appropriate health management plan to the worker." This would allow for comprehensive management of the worker's health status and contribute to improved work efficiency.

[0406] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0407] Step 1:

[0408] Users collect biometric information such as heart rate, steps taken, and sleep quality using wearable devices and smartphones. This information is transmitted to a server via Bluetooth or the internet through the device. The input is biometric data, and the output is biometric data transmitted to the server.

[0409] Step 2:

[0410] The server preprocesses the received biometric data using Python's Pandas library. Specifically, it cleans the data, imputes missing values, and formats it into an analyzable format. In this step, the input is the transmitted biometric data, and the output is the preprocessed, analyzable biometric data.

[0411] Step 3:

[0412] The server inputs pre-processed data into a generating AI model and uses TensorFlow to analyze individual health and emotional states. The analysis results in an estimation of the user's current health and emotional state. The input is pre-processed biometric data, and the output is the analysis results of the health and emotional state.

[0413] Step 4:

[0414] The server generates an optimal health management plan for the user based on the analysis results. The generated health management plan includes specific action guidelines tailored to the user's condition. The input is the analysis results, and the output is the health management plan.

[0415] Step 5:

[0416] The server sends the generated health management plan to the user's terminal in real time. The user can immediately check the plan on their terminal and incorporate it into their daily life. The input is the health management plan, and the output is the health management plan displayed on the terminal.

[0417] Step 6:

[0418] The user performs activities based on their health management plan and inputs feedback into a terminal. The terminal sends the feedback information to a server, which is used to generate the next plan. The input is the user's feedback, and the output is data used to generate the next health management plan.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] [Third Embodiment]

[0423] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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".

[0435] This invention is a system for optimizing user health management, providing a customized health plan using individual biometric data. This system acquires information about the user's health status using wearable devices and smartphone apps, and analyzes this information on a cloud-based server, enabling users to manage their daily health.

[0436] First, the device collects the user's biometric data. This data includes heart rate, steps taken, calories burned, and sleep quality. The collected data is sent from the device to a server. The server preprocesses this received data and prepares it for analysis. The preprocessed data is then input into a generating AI model, which analyzes the user's health status. The AI ​​model considers various data points and identifies health risk factors that require particular attention.

[0437] Based on the analysis, the server assesses the user's current health status and generates a personalized health management plan. This plan consists of specific advice and guidelines aimed at improving the user's daily life, including diet, exercise, and sleep. For example, if a user's heart rate data indicates an increased stress level, the AI ​​may suggest stretches to promote relaxation.

[0438] The generated health plan is sent to the user's device in real time, allowing the user to review it and incorporate it into their daily life. This gives users the opportunity to proactively manage and improve their own health. Furthermore, the feedback reported by the user is sent back to the server and used for future analysis. This improves the accuracy of the generated plan, allowing it to more precisely meet the user's needs.

[0439] Furthermore, as a feature for businesses, the server aggregates employee biometric data and provides administrators with a real-time overview of their health status. This allows companies to monitor the health of their entire workforce and efficiently plan and implement necessary health promotion activities and risk management measures. In this way, the present invention provides an effective means of technically supporting individual and organizational health management.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] The device collects biometric data from the user's wearable devices and smartphone apps. This data may include heart rate, steps taken, calorie consumption, and sleep patterns.

[0443] Step 2:

[0444] The device transmits the collected data to the server in real time. Data transfers are performed periodically and communicated over a secure channel.

[0445] Step 3:

[0446] The server preprocesses the received biometric data into an analyzable format. Preprocessing includes imputing missing values, denoising, and formatting time-series data.

[0447] Step 4:

[0448] The server inputs pre-processed data into a generating AI model to analyze the user's current health status. Machine learning algorithms are used here to detect anomalies and analyze risk factors.

[0449] Step 5:

[0450] Based on the analysis results, the server generates a personalized health management plan tailored to the user's health condition. The plan includes specific exercise recommendations, nutritional guidance, and suggestions for improving sleep.

[0451] Step 6:

[0452] The server immediately sends the generated health management plan to the user's device. The plan is then made visible to the user through notifications and apps.

[0453] Step 7:

[0454] Users adjust their daily lives based on the health management plan they receive. Feedback on the user's actions and activities is recorded on the device.

[0455] Step 8:

[0456] The device then sends the user's feedback back to the server. The server analyzes this feedback and uses it to improve the quality of future health management plans.

[0457] Step 9:

[0458] As a feature for enterprises, the server generates aggregated biometric data from all employees and visualizes it on an administrator dashboard. Based on this information, companies can develop employee health management strategies.

[0459] (Example 1)

[0460] 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."

[0461] In modern society, personal health management is a crucial issue, but it is not easy for users to accurately understand their daily physical condition and develop appropriate health management plans. Many health monitoring systems have challenges in how to interpret the collected data and translate it into concrete improvement measures. Furthermore, companies lack effective means to efficiently understand the health status of their entire workforce and to plan and implement health promotion activities.

[0462] 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.

[0463] In this invention, the server includes means for receiving biometric information collected from a user, means for preprocessing the biometric information and converting it into an analyzable format, means for analyzing the user's individual health status based on the preprocessed information using a generation AI algorithm, means for generating an optimal health management plan for the user based on the analysis results, means for transmitting the health management plan to the user's device, and means for receiving user feedback and utilizing it to improve the quality of the health management plan. This enables users to understand their own health status in real time and implement an individualized health management plan. Furthermore, organizations can efficiently manage the health status of all employees and appropriately plan and implement necessary health promotion activities.

[0464] A "user" refers to an individual who uses this system and provides their own health information.

[0465] "Biometric information" refers to data that indicates the user's health status, and specifically includes heart rate, steps taken, calories burned, and sleep quality.

[0466] "Devices" refer to devices that users use to receive or provide information, such as smartphones and wearable devices.

[0467] A "generative AI algorithm" refers to a processing method that utilizes artificial intelligence to analyze biometric information obtained from users and generate health status assessments and health management plans.

[0468] A "health management plan" is actionable advice and suggestions created based on the user's individual health condition, providing specific guidelines regarding diet, exercise, sleep, and other related matters.

[0469] "Feedback" refers to the act of users responding to the health management plan provided by the system with their own experiences and opinions, which are used to improve the system.

[0470] An "organization" refers to a group of individuals or entities that have multiple participants (such as employees) and use a health management system to monitor and manage the health status of all its members.

[0471] This health management system uses users' biometric information to provide personalized health management plans. The system collects biometric data using wearable devices and smartphone applications, and transfers this information to a cloud-based server for processing.

[0472] First, the device collects various biometric information from the user, such as heart rate, steps taken, calories burned, and sleep quality. These devices include, for example, the latest smartwatches and smartphone apps with health monitoring capabilities. When the device collects data, it utilizes the device's sensor technology and the application's data processing capabilities to ensure that the information is recorded accurately and regularly.

[0473] Next, the collected data is transmitted to a server in real time. This server preprocesses the received biometric information and prepares it for analysis. Specifically, it imputes missing values ​​and filters out outliers. Data analysis software such as Python or R may be used in this process.

[0474] Furthermore, the server uses a generative AI model to comprehensively analyze the user's health status based on the pre-processed information. The AI ​​model takes multiple data points into consideration to identify more critical health risks. In this process, widely used machine learning frameworks such as TensorFlow and PyTorch may be utilized. As a concrete example, the AI ​​model operates in response to a prompt such as, "Estimate the user's stress level from their weekly exercise and sleep data, and generate necessary health advice."

[0475] Based on the analysis results of the AI ​​model, the server generates a health management plan optimized for the user. The plan includes specific details such as meal suggestions, exercise routines, and sleep improvement measures. The generated plan is sent to the user's device in real time, allowing the user to review it and incorporate it into their daily life.

[0476] Users implement the provided health management plan and send the results as feedback via their device. This feedback is collected on a server and used for subsequent AI model analysis and plan generation. This allows the system to continuously learn and provide services that are more adapted to the user's needs.

[0477] This system allows users to proactively manage and improve their own health. Meanwhile, organizations can understand the health status of individual members and effectively plan and implement overall health promotion activities.

[0478] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0479] Step 1:

[0480] The device collects the user's biometric information. Specifically, it uses wearable devices and smartphones worn by the user to acquire data such as heart rate, steps taken, calories burned, and sleep quality. This information is recorded in real time within the device. The input is raw data acquired from sensors in wearable devices and smartphones, and the output is formatted biometric information.

[0481] Step 2:

[0482] The device transmits the collected biometric information to the server. Transmission typically occurs via an internet connection (Wi-Fi or mobile data). The input is formatted biometric information from the device, and the output is data packets sent to the server. Specifically, the data is encrypted during transmission to protect user privacy.

[0483] Step 3:

[0484] The server preprocesses the received biometric data. This involves imputing missing data points and filtering out abnormal values. This process formats the data into an analyzable format. The input is biometric data packets sent from the terminal, and the output is a clean, consistent dataset. Specifically, for example, the Python pandas library is used to imputate missing data points with historical data.

[0485] Step 4:

[0486] The server inputs pre-processed data into a generating AI model to analyze the user's health status. The AI ​​model performs calculations based on prompts such as, "Estimate the stress level from the user's weekly exercise and sleep data, and generate necessary health advice," and identifies risk factors. The input is a formatted biometric data set, and the output is the analysis result, i.e., an assessment of the user's health status. Specifically, pattern recognition is performed using a machine learning model based on TensorFlow.

[0487] Step 5:

[0488] The server generates a health management plan based on the analysis results of the AI ​​model. This plan includes specific advice on diet, exercise, and sleep improvement. The input is the AI ​​model's health status assessment, and the output is a personalized health management plan. Specifically, the user is provided with advice text created using natural language processing.

[0489] Step 6:

[0490] The server sends the generated health management plan to the user's device. The user can receive and review this plan and incorporate it into their daily life. The input is the generated health management plan, and the output is the plan notification sent to the user's device. Specifically, notifications are sent periodically via a mobile app.

[0491] Step 7:

[0492] Users provide feedback on the effectiveness and areas for improvement of their health management plan. This feedback is sent from the device to the server and used for subsequent analysis and plan generation. The input is user-based feedback information, and the output is an update to the system's learning database. Specifically, user feedback is obtained through an in-app form.

[0493] (Application Example 1)

[0494] 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."

[0495] In the industrial sector, the health status of workers directly impacts work efficiency and safety. Conventional methods do not adequately utilize individual biometric data for real-time health monitoring or optimization of the work environment, making it difficult to prevent health risks to workers and declines in production efficiency. Therefore, the present invention aims to provide a technology that monitors workers' health status in real time and provides an optimal work environment.

[0496] 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.

[0497] In this invention, the server includes means for receiving biometric data collected from users, means for preprocessing the biometric data and converting it into an analyzable format, means for analyzing the individual health status of users based on the preprocessed data using a generating AI model, and means for generating an optimal health management plan for users based on the analysis results. This enables the optimization of the work environment by continuously monitoring the health status of workers and recommending appropriate breaks and adjustments to workload in real time.

[0498] A "user" refers to an individual who provides biometric data and utilizes a health management system.

[0499] "Biometric data" refers to information related to the user's health status, such as heart rate, body temperature, steps taken, and sleep patterns.

[0500] "Preprocessing" refers to the process of shaping and correcting biological data in order to convert it into an analyzable format.

[0501] "Generative AI models" refer to artificial intelligence technology used to analyze a user's biometric data and assess their health status.

[0502] A "health management plan" refers to a plan that includes specific action guidelines for maintaining or improving the user's health, based on the analysis results.

[0503] "Device" refers to the device that a user uses to receive and view their health management plan.

[0504] "Health monitoring" refers to the process of continuously collecting a user's biometric data and analyzing and evaluating that data in real time.

[0505] "Work environment management" refers to a method of optimizing the work environment by adjusting workload and rest periods based on workers' health data.

[0506] This invention is a system for monitoring workers' health status in real time and optimizing the work environment. The server collects biometric data from wearable devices or smartphones worn by the user and transmits it to the cloud. The hardware used includes wearable devices (e.g., fitness bands or smartwatches), and the data processing is handled by cloud servers (e.g., AWS or Azure).

[0507] The server preprocesses the collected biometric data and converts it into an analyzable format. This preprocessing includes noise reduction and data formatting. Next, the server analyzes the preprocessed data using a generative AI model to assess the user's health status. This analysis generates a personalized health management plan for each user. The generated plan is sent to the user's device and provided as specific guidance for improving their health.

[0508] For example, if the server detects that a worker's heart rate is higher than normal, it sends an alert to the worker's terminal prompting them to take a break. This allows for timely adjustments to workload and rest, reducing health risks for workers. The system also aggregates biometric data from across the organization and provides administrators with a real-time overview of health conditions. Administrators can use this information to plan improvements to the work environment and prevent health risks.

[0509] An example of a prompt message might be, "The user's heart rate is above normal. Please generate recommended health actions based on this condition." By sending this prompt to the AI ​​model, an appropriate health management plan is generated and provided in real time.

[0510] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0511] Step 1:

[0512] The terminal collects biometric data from the user's wearable device. This data includes heart rate, body temperature, and steps taken. The terminal then prepares this biometric data to send to a cloud server. The input is the biometric data from the wearable device, and the output is data formatted for transmission to the cloud server.

[0513] Step 2:

[0514] The server receives biometric data transmitted from the terminal. The server preprocesses the received data, removing noise and converting it into an analyzable data format. In this step, the input is the biometric data transmitted from the terminal, and the output is the preprocessed, clean data.

[0515] Step 3:

[0516] The server uses a generative AI model to analyze pre-processed data. The server inputs data into the AI ​​model and evaluates the user's health status. This process identifies health risks that require attention. The input is pre-processed data, and the output is the health status evaluation as a result of the analysis.

[0517] Step 4:

[0518] The server generates an optimal health management plan for the user based on the analysis results. The generated plan includes specific guidelines for diet, exercise, and rest. The input for this step is the analysis results, and the output is the health management plan.

[0519] Step 5:

[0520] The server sends the generated health management plan to the user's terminal. The user's terminal receives this plan and displays it on the screen for the user to review. The input is the health management plan, and the output is the plan displayed on the terminal.

[0521] Step 6:

[0522] The user inputs feedback based on their health management plan into a terminal. The terminal sends the feedback to a server, which uses the feedback to generate future plans. The input in this step is user feedback, and the output is feedback data sent to the server.

[0523] 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.

[0524] This invention is a system that recognizes a user's emotional state based on their biometric data and uses this information to adjust their health management plan. The system analyzes the user's biometric data collected using wearable devices and smartphone apps, and provides a personalized health plan that takes into account both their physical and emotional state.

[0525] First, the device collects the user's biometric data, such as heart rate, steps taken, sleep quality, facial expressions, and voice data. This data is then transmitted to a server via the device. The transmitted biometric data is preprocessed on the server and prepared into an analyzable format.

[0526] The server inputs pre-processed data into a generating AI model to analyze the user's health status and recognize their emotional state using an emotion engine. The emotion engine can identify emotions from the user's physiological responses, voice, and facial recognition data, and estimate states such as relaxation, stress, happiness, and anxiety.

[0527] Based on the analysis results, the server generates an optimal health management plan for the user. This plan takes into account the user's current health and emotional state, and includes guidance and suggestions for future actions. For example, a user experiencing stress might be recommended relaxation exercises, while a user in a comfortable emotional state might be provided with a health maintenance plan to help maintain that state.

[0528] The generated health plan is sent to the user's device in real time, allowing the user to immediately review it and incorporate it into their daily life. The user can then take action based on the plan and input feedback into their device. This feedback information is sent to the server and used to generate the next plan.

[0529] As a feature for businesses, the server aggregates employees' biometric data and emotional states and provides administrators with a real-time summary. This allows businesses to develop employee care plans that consider both psychological and physical health. Thus, this invention enhances the quality of health management for both users and businesses, supporting comprehensive health promotion that also considers emotional aspects.

[0530] The following describes the processing flow.

[0531] Step 1:

[0532] The device collects biometric data along with data such as the user's voice and facial expressions. This data includes heart rate, steps taken, calories burned, changes in facial expressions, and voice modulation.

[0533] Step 2:

[0534] The device transmits the collected data to the server in real time. This data is transferred using a secure communication protocol.

[0535] Step 3:

[0536] The server preprocesses the received biometric and emotional data, converting it into an analyzable format. Specifically, it performs data normalization, noise reduction, and interpolation.

[0537] Step 4:

[0538] The server inputs pre-processed data into a generating AI model to analyze the user's overall health status. The AI ​​model uses machine learning algorithms to identify the user's health risks.

[0539] Step 5:

[0540] The server uses an emotion engine to recognize the user's emotional state from collected voice and facial expression data. It estimates emotions such as stress, joy, anger, and anxiety.

[0541] Step 6:

[0542] The server generates an optimal health management plan for the user based on their analyzed health and emotional state. The health plan combines exercise, nutrition, and relaxation methods that address the user's emotional state.

[0543] Step 7:

[0544] The server sends the generated health management plan to the user's device in real time. The device notifies the user of the plan, making it easy for the user to review.

[0545] Step 8:

[0546] Users perform activities based on their health management plan and input the results and feedback into their device. For example, if they perform a suggested exercise, they record their progress.

[0547] Step 9:

[0548] The device sends user feedback data to the server. The server uses this feedback for future data analysis and to improve the health plan.

[0549] Step 10:

[0550] The server acts as a corporate health management function, aggregating employee biometric and emotional data and providing the information through an administrator dashboard. This allows companies to understand the health and emotional state of all employees and take appropriate measures.

[0551] (Example 2)

[0552] 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."

[0553] To provide a system that effectively analyzes users' biometric data and emotional states to deliver individually optimized health management plans. Furthermore, for companies, to provide integrated information to understand the overall health and emotional state of employees, thereby addressing the challenge of comprehensively improving employee health and well-being.

[0554] 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.

[0555] In this invention, the server includes means for receiving biometric data collected from a user, means for preprocessing the biometric data and converting it into an analyzable format, and means for analyzing the user's individual health and emotional state based on the preprocessed data using a generative AI model. This makes it possible to provide the user with a personalized health management plan.

[0556] "Biometric data" refers to a collection of numerical data and information that indicates a user's physical and psychological state, such as heart rate, steps taken, sleep quality, facial expressions, and voice data.

[0557] "Preprocessing" is the process of converting biological data into an analyzable format by removing noise, normalizing, and imputing missing values.

[0558] A "generative AI model" is a program that uses artificial intelligence technology to analyze a user's health status, emotional state, and other factors from input data.

[0559] An "emotion engine" is an algorithm that identifies emotions from a user's voice and facial expression data and estimates states such as relaxation, stress, happiness, and anxiety.

[0560] A "health management plan" is a plan that includes personalized suggestions and action guidelines to maintain and improve the user's health, based on the user's analyzed health and emotional state.

[0561] "Feedback data" refers to information that records the actions taken by users based on their health management plans, as well as the effects and changes they experienced as a result.

[0562] A "terminal" is an electronic device or apparatus used to collect a user's biometric data and communicate data with a server.

[0563] "Corporate administrator" refers to the individual or department responsible for comprehensively managing the health and emotional well-being of employees within an organization and for developing and implementing optimized employee care plans.

[0564] This invention is a system that analyzes a user's health and emotional state based on their biometric data and provides a personalized health management plan. This system primarily operates through the collaboration of a server and a terminal.

[0565] The device uses wearable devices and smartphone apps to collect biometric data such as the user's heart rate, steps taken, sleep quality, facial expressions, and voice. This data is transmitted to a server in real time, and the server receives the data via a secure communication protocol.

[0566] The server preprocesses the received biometric data, removing noise and normalizing it to prepare it for analysis. The preprocessed data is then input into a generative AI model. This model is built to analyze the user's health status and uses machine learning algorithms to analyze the data.

[0567] An emotion engine is used to recognize emotional states. This engine estimates the user's emotional state based on voice data and facial expression data, determining states such as relaxation, stress, happiness, and anxiety. For example, if a user's heart rate decreases while listening to calming music, the emotion engine detects a relaxed state.

[0568] Based on the analyzed results, the server generates a personalized health management plan for the user. For example, a user experiencing stress might be suggested relaxation exercises, while a user in a comfortable emotional state might be provided with a plan to maintain that state. This health management plan is sent from the server to the user's device in real time, allowing the user to review it immediately.

[0569] Furthermore, users can input feedback data on their completed health management plans into their devices. This feedback is sent to the server and used to generate future plans. By utilizing this feedback, the system can provide more precise and user-friendly health management over time.

[0570] As a concrete example of a prompt, the following could be used as input to the generative AI model: "Estimate the emotional state of the user when their heart rate is 75 bpm, their sleep quality is good, and their facial expression is smiling. Then, suggest an appropriate health management plan."

[0571] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0572] Step 1:

[0573] The device collects data such as the user's heart rate, steps taken, sleep quality, facial expressions, and voice via wearable devices and smartphone apps. The input is biometric data obtained from sensors. The device measures this data in real time and records it as a digital signal. The output is the recorded biometric data, ready to be sent to a server for further processing.

[0574] Step 2:

[0575] The device transmits the collected biometric data to the server. The input is the biometric data recorded earlier. This data is encrypted using a secure protocol such as HTTPS and sent to the server. The output is the biometric data received on the server side. This ensures that the data is delivered securely to the server.

[0576] Step 3:

[0577] The server preprocesses the received biometric data. The input is biometric data transmitted from the terminal. The server performs noise reduction and data normalization to prepare the data for analysis. The output is clean biometric data after preprocessing. This process yields data of a quality suitable for analysis.

[0578] Step 4:

[0579] The server inputs pre-processed data into a generative AI model to analyze the user's health and emotional state. The input is pre-processed biometric data. The generative AI model uses machine learning to assess the health state and estimate emotions using an emotion engine. The output is the analyzed health and emotional state. Specifically, it calculates health scores, relaxation levels, stress levels, etc.

[0580] Step 5:

[0581] The server generates a health management plan based on the analysis results. The input is the analysis results of health status and emotional state. The server uses these results to assemble an individualized health management plan. The output is a personalized health management plan for the user. For example, if stress levels are high, it might suggest yoga for relaxation.

[0582] Step 6:

[0583] The server sends the generated health management plan to the user's device. The input is the created health management plan. The plan is sent to the device using push notifications so that the user can review it immediately. The output is the health management plan displayed on the device. This allows the user to take appropriate actions in their daily life.

[0584] Step 7:

[0585] The user acts according to the provided health management plan and inputs feedback into the device. The input consists of the user's activities and their results. The device records the feedback and prepares to send it to the server. The output is the recorded feedback data.

[0586] Step 8:

[0587] The terminal sends the collected feedback data to the server. The input is the user's feedback data. This data is then sent back to the server using a secure communication protocol. The output is the feedback data that has arrived at the server. This is then used to generate the next health management plan, improving the system's accuracy.

[0588] (Application Example 2)

[0589] 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."

[0590] In modern manufacturing, the health and emotional state of workers significantly impacts work safety and efficiency. In particular, accumulated stress and fatigue can lead to work errors and workplace accidents. However, currently, there are limited means to monitor these conditions in real time and provide appropriate health management plans. Therefore, there is a need to analyze emotional states based on workers' biometric data and provide a safe and efficient work environment.

[0591] 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.

[0592] In this invention, the server includes means for receiving biometric information collected from a user, means for preprocessing the biometric information and converting it into an analyzable format, means for analyzing the health status based on the preprocessed information using a generating AI model, means for generating a health management plan based on the analysis results, means for transmitting the health management plan to a terminal, means for collecting biometric information from a worker and analyzing their emotional state, and means for proposing a safe and efficient work environment to the worker according to their emotional state. This enables the provision of real-time feedback and personalized health management plans based on the worker's health and emotional state.

[0593] "Biometric information" refers to data about an individual's physical activity and physiological state, such as heart rate, steps taken, and sleep quality.

[0594] "Preprocessing" refers to the initial data organization process used to convert data into an analyzable format.

[0595] A "generative AI model" is an algorithm that performs predictions and analyses based on a large amount of data, and in this invention, it is used to analyze individual health conditions.

[0596] "Health status" refers to indicators of physical and mental condition, and in this invention, it serves as a criterion for generating an optimal health management plan for an individual.

[0597] A "health management plan" is a set of behavioral guidelines and activity plans proposed based on an individual's health and emotional state.

[0598] A "terminal" is a device operated by the user to send and receive biometric information and display health management plans.

[0599] "Worker" refers to an individual who is actually engaged in work at a factory or work site.

[0600] "Emotional state" is an evaluation index that indicates the psychological and emotional state of a worker.

[0601] A "safe and efficient work environment" refers to a workplace environment that is designed to allow workers to perform their duties comfortably.

[0602] To implement this invention, users collect biometric information in real time using wearable devices or smartphones. This includes heart rate, steps taken, sleep quality, and facial recognition data. This information is transmitted to a server via the terminal. The server preprocesses the received biometric information using Python's Pandas library and converts it into an analyzable format. Then, a generative AI model using TensorFlow analyzes the user's health and emotional state based on the preprocessed data.

[0603] Based on the analysis results, the server generates an individually optimized health management plan and sends it to the terminal. This plan is tailored to the worker's emotional state and includes guidelines for action to provide a safe and efficient work environment. For example, if a worker has a high heart rate and is under stress, they will be advised to take appropriate breaks.

[0604] As a concrete example, suppose a factory worker feels fatigued during their work. At that time, a wearable device they are wearing detects a sudden increase in their heart rate. Based on this data, the server analyzes that the worker is in a stressed state and immediately sends a notification to the device prompting them to take a break. This allows the worker to continue working with peace of mind.

[0605] An example of a prompt message for the generating AI model would be, "Based on biometric information, analyze the emotional state in real time and propose an appropriate health management plan to the worker." This would allow for comprehensive management of the worker's health status and contribute to improved work efficiency.

[0606] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0607] Step 1:

[0608] Users collect biometric information such as heart rate, steps taken, and sleep quality using wearable devices and smartphones. This information is transmitted to a server via Bluetooth or the internet through the device. The input is biometric data, and the output is biometric data transmitted to the server.

[0609] Step 2:

[0610] The server preprocesses the received biometric data using Python's Pandas library. Specifically, it cleans the data, imputes missing values, and formats it into an analyzable format. In this step, the input is the transmitted biometric data, and the output is the preprocessed, analyzable biometric data.

[0611] Step 3:

[0612] The server inputs pre-processed data into a generating AI model and uses TensorFlow to analyze individual health and emotional states. The analysis results in an estimation of the user's current health and emotional state. The input is pre-processed biometric data, and the output is the analysis results of the health and emotional state.

[0613] Step 4:

[0614] The server generates an optimal health management plan for the user based on the analysis results. The generated health management plan includes specific action guidelines tailored to the user's condition. The input is the analysis results, and the output is the health management plan.

[0615] Step 5:

[0616] The server sends the generated health management plan to the user's terminal in real time. The user can immediately check the plan on their terminal and incorporate it into their daily life. The input is the health management plan, and the output is the health management plan displayed on the terminal.

[0617] Step 6:

[0618] The user performs activities based on their health management plan and inputs feedback into a terminal. The terminal sends the feedback information to a server, which is used to generate the next plan. The input is the user's feedback, and the output is data used to generate the next health management plan.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] [Fourth Embodiment]

[0623] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0624] 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.

[0625] 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).

[0626] 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.

[0627] 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.

[0628] 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).

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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".

[0636] This invention is a system for optimizing user health management, providing a customized health plan using individual biometric data. This system acquires information about the user's health status using wearable devices and smartphone apps, and analyzes this information on a cloud-based server, enabling users to manage their daily health.

[0637] First, the device collects the user's biometric data. This data includes heart rate, steps taken, calories burned, and sleep quality. The collected data is sent from the device to a server. The server preprocesses this received data and prepares it for analysis. The preprocessed data is then input into a generating AI model, which analyzes the user's health status. The AI ​​model considers various data points and identifies health risk factors that require particular attention.

[0638] Based on the analysis, the server assesses the user's current health status and generates a personalized health management plan. This plan consists of specific advice and guidelines aimed at improving the user's daily life, including diet, exercise, and sleep. For example, if a user's heart rate data indicates an increased stress level, the AI ​​may suggest stretches to promote relaxation.

[0639] The generated health plan is sent to the user's device in real time, allowing the user to review it and incorporate it into their daily life. This gives users the opportunity to proactively manage and improve their own health. Furthermore, the feedback reported by the user is sent back to the server and used for future analysis. This improves the accuracy of the generated plan, allowing it to more precisely meet the user's needs.

[0640] Furthermore, as a feature for businesses, the server aggregates employee biometric data and provides administrators with a real-time overview of their health status. This allows companies to monitor the health of their entire workforce and efficiently plan and implement necessary health promotion activities and risk management measures. In this way, the present invention provides an effective means of technically supporting individual and organizational health management.

[0641] The following describes the processing flow.

[0642] Step 1:

[0643] The device collects biometric data from the user's wearable devices and smartphone apps. This data may include heart rate, steps taken, calorie consumption, and sleep patterns.

[0644] Step 2:

[0645] The device transmits the collected data to the server in real time. Data transfers are performed periodically and communicated over a secure channel.

[0646] Step 3:

[0647] The server preprocesses the received biometric data into an analyzable format. Preprocessing includes imputing missing values, denoising, and formatting time-series data.

[0648] Step 4:

[0649] The server inputs pre-processed data into a generating AI model to analyze the user's current health status. Machine learning algorithms are used here to detect anomalies and analyze risk factors.

[0650] Step 5:

[0651] Based on the analysis results, the server generates a personalized health management plan tailored to the user's health condition. The plan includes specific exercise recommendations, nutritional guidance, and suggestions for improving sleep.

[0652] Step 6:

[0653] The server immediately sends the generated health management plan to the user's device. The plan is then made visible to the user through notifications and apps.

[0654] Step 7:

[0655] Users adjust their daily lives based on the health management plan they receive. Feedback on the user's actions and activities is recorded on the device.

[0656] Step 8:

[0657] The device then sends the user's feedback back to the server. The server analyzes this feedback and uses it to improve the quality of future health management plans.

[0658] Step 9:

[0659] As a feature for enterprises, the server generates aggregated biometric data from all employees and visualizes it on an administrator dashboard. Based on this information, companies can develop employee health management strategies.

[0660] (Example 1)

[0661] 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".

[0662] In modern society, personal health management is a crucial issue, but it is not easy for users to accurately understand their daily physical condition and develop appropriate health management plans. Many health monitoring systems have challenges in how to interpret the collected data and translate it into concrete improvement measures. Furthermore, companies lack effective means to efficiently understand the health status of their entire workforce and to plan and implement health promotion activities.

[0663] 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.

[0664] In this invention, the server includes means for receiving biometric information collected from a user, means for preprocessing the biometric information and converting it into an analyzable format, means for analyzing the user's individual health status based on the preprocessed information using a generation AI algorithm, means for generating an optimal health management plan for the user based on the analysis results, means for transmitting the health management plan to the user's device, and means for receiving user feedback and utilizing it to improve the quality of the health management plan. This enables users to understand their own health status in real time and implement an individualized health management plan. Furthermore, organizations can efficiently manage the health status of all employees and appropriately plan and implement necessary health promotion activities.

[0665] A "user" refers to an individual who uses this system and provides their own health information.

[0666] "Biometric information" refers to data that indicates the user's health status, and specifically includes heart rate, steps taken, calories burned, and sleep quality.

[0667] "Devices" refer to devices that users use to receive or provide information, such as smartphones and wearable devices.

[0668] A "generative AI algorithm" refers to a processing method that utilizes artificial intelligence to analyze biometric information obtained from users and generate health status assessments and health management plans.

[0669] A "health management plan" is actionable advice and suggestions created based on the user's individual health condition, providing specific guidelines regarding diet, exercise, sleep, and other related matters.

[0670] "Feedback" refers to the act of users responding to the health management plan provided by the system with their own experiences and opinions, which are used to improve the system.

[0671] An "organization" refers to a group of individuals or entities that have multiple participants (such as employees) and use a health management system to monitor and manage the health status of all its members.

[0672] This health management system uses users' biometric information to provide personalized health management plans. The system collects biometric data using wearable devices and smartphone applications, and transfers this information to a cloud-based server for processing.

[0673] First, the device collects various biometric information from the user, such as heart rate, steps taken, calories burned, and sleep quality. These devices include, for example, the latest smartwatches and smartphone apps with health monitoring capabilities. When the device collects data, it utilizes the device's sensor technology and the application's data processing capabilities to ensure that the information is recorded accurately and regularly.

[0674] Next, the collected data is transmitted to a server in real time. This server preprocesses the received biometric information and prepares it for analysis. Specifically, it imputes missing values ​​and filters out outliers. Data analysis software such as Python or R may be used in this process.

[0675] Furthermore, the server uses a generative AI model to comprehensively analyze the user's health status based on the pre-processed information. The AI ​​model takes multiple data points into consideration to identify more critical health risks. In this process, widely used machine learning frameworks such as TensorFlow and PyTorch may be utilized. As a concrete example, the AI ​​model operates in response to a prompt such as, "Estimate the user's stress level from their weekly exercise and sleep data, and generate necessary health advice."

[0676] Based on the analysis results of the AI ​​model, the server generates a health management plan optimized for the user. The plan includes specific details such as meal suggestions, exercise routines, and sleep improvement measures. The generated plan is sent to the user's device in real time, allowing the user to review it and incorporate it into their daily life.

[0677] Users implement the provided health management plan and send the results as feedback via their device. This feedback is collected on a server and used for subsequent AI model analysis and plan generation. This allows the system to continuously learn and provide services that are more adapted to the user's needs.

[0678] This system allows users to proactively manage and improve their own health. Meanwhile, organizations can understand the health status of individual members and effectively plan and implement overall health promotion activities.

[0679] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0680] Step 1:

[0681] The device collects the user's biometric information. Specifically, it uses wearable devices and smartphones worn by the user to acquire data such as heart rate, steps taken, calories burned, and sleep quality. This information is recorded in real time within the device. The input is raw data acquired from sensors in wearable devices and smartphones, and the output is formatted biometric information.

[0682] Step 2:

[0683] The device transmits the collected biometric information to the server. Transmission typically occurs via an internet connection (Wi-Fi or mobile data). The input is formatted biometric information from the device, and the output is data packets sent to the server. Specifically, the data is encrypted during transmission to protect user privacy.

[0684] Step 3:

[0685] The server preprocesses the received biometric data. This involves imputing missing data points and filtering out abnormal values. This process formats the data into an analyzable format. The input is biometric data packets sent from the terminal, and the output is a clean, consistent dataset. Specifically, for example, the Python pandas library is used to imputate missing data points with historical data.

[0686] Step 4:

[0687] The server inputs pre-processed data into a generating AI model to analyze the user's health status. The AI ​​model performs calculations based on prompts such as, "Estimate the stress level from the user's weekly exercise and sleep data, and generate necessary health advice," and identifies risk factors. The input is a formatted biometric data set, and the output is the analysis result, i.e., an assessment of the user's health status. Specifically, pattern recognition is performed using a machine learning model based on TensorFlow.

[0688] Step 5:

[0689] The server generates a health management plan based on the analysis results of the AI ​​model. This plan includes specific advice on diet, exercise, and sleep improvement. The input is the AI ​​model's health status assessment, and the output is a personalized health management plan. Specifically, the user is provided with advice text created using natural language processing.

[0690] Step 6:

[0691] The server sends the generated health management plan to the user's device. The user can receive and review this plan and incorporate it into their daily life. The input is the generated health management plan, and the output is the plan notification sent to the user's device. Specifically, notifications are sent periodically via a mobile app.

[0692] Step 7:

[0693] Users provide feedback on the effectiveness and areas for improvement of their health management plan. This feedback is sent from the device to the server and used for subsequent analysis and plan generation. The input is user-based feedback information, and the output is an update to the system's learning database. Specifically, user feedback is obtained through an in-app form.

[0694] (Application Example 1)

[0695] 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".

[0696] In the industrial sector, the health status of workers directly impacts work efficiency and safety. Conventional methods do not adequately utilize individual biometric data for real-time health monitoring or optimization of the work environment, making it difficult to prevent health risks to workers and declines in production efficiency. Therefore, the present invention aims to provide a technology that monitors workers' health status in real time and provides an optimal work environment.

[0697] 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.

[0698] In this invention, the server includes means for receiving biometric data collected from users, means for preprocessing the biometric data and converting it into an analyzable format, means for analyzing the individual health status of users based on the preprocessed data using a generating AI model, and means for generating an optimal health management plan for users based on the analysis results. This enables the optimization of the work environment by continuously monitoring the health status of workers and recommending appropriate breaks and adjustments to workload in real time.

[0699] A "user" refers to an individual who provides biometric data and utilizes a health management system.

[0700] "Biometric data" refers to information related to the user's health status, such as heart rate, body temperature, steps taken, and sleep patterns.

[0701] "Preprocessing" refers to the process of shaping and correcting biological data in order to convert it into an analyzable format.

[0702] "Generative AI models" refer to artificial intelligence technology used to analyze a user's biometric data and assess their health status.

[0703] A "health management plan" refers to a plan that includes specific action guidelines for maintaining or improving the user's health, based on the analysis results.

[0704] "Device" refers to the device that a user uses to receive and view their health management plan.

[0705] "Health monitoring" refers to the process of continuously collecting a user's biometric data and analyzing and evaluating that data in real time.

[0706] "Work environment management" refers to a method of optimizing the work environment by adjusting workload and rest periods based on workers' health data.

[0707] This invention is a system for monitoring workers' health status in real time and optimizing the work environment. The server collects biometric data from wearable devices or smartphones worn by the user and transmits it to the cloud. The hardware used includes wearable devices (e.g., fitness bands or smartwatches), and the data processing is handled by cloud servers (e.g., AWS or Azure).

[0708] The server preprocesses the collected biometric data and converts it into an analyzable format. This preprocessing includes noise reduction and data formatting. Next, the server analyzes the preprocessed data using a generative AI model to assess the user's health status. This analysis generates a personalized health management plan for each user. The generated plan is sent to the user's device and provided as specific guidance for improving their health.

[0709] For example, if the server detects that a worker's heart rate is higher than normal, it sends an alert to the worker's terminal prompting them to take a break. This allows for timely adjustments to workload and rest, reducing health risks for workers. The system also aggregates biometric data from across the organization and provides administrators with a real-time overview of health conditions. Administrators can use this information to plan improvements to the work environment and prevent health risks.

[0710] An example of a prompt message might be, "The user's heart rate is above normal. Please generate recommended health actions based on this condition." By sending this prompt to the AI ​​model, an appropriate health management plan is generated and provided in real time.

[0711] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0712] Step 1:

[0713] The terminal collects biometric data from the user's wearable device. This data includes heart rate, body temperature, and steps taken. The terminal then prepares this biometric data to send to a cloud server. The input is the biometric data from the wearable device, and the output is data formatted for transmission to the cloud server.

[0714] Step 2:

[0715] The server receives biometric data transmitted from the terminal. The server preprocesses the received data, removing noise and converting it into an analyzable data format. In this step, the input is the biometric data transmitted from the terminal, and the output is the preprocessed, clean data.

[0716] Step 3:

[0717] The server uses a generative AI model to analyze pre-processed data. The server inputs data into the AI ​​model and evaluates the user's health status. This process identifies health risks that require attention. The input is pre-processed data, and the output is the health status evaluation as a result of the analysis.

[0718] Step 4:

[0719] The server generates an optimal health management plan for the user based on the analysis results. The generated plan includes specific guidelines for diet, exercise, and rest. The input for this step is the analysis results, and the output is the health management plan.

[0720] Step 5:

[0721] The server sends the generated health management plan to the user's terminal. The user's terminal receives this plan and displays it on the screen for the user to review. The input is the health management plan, and the output is the plan displayed on the terminal.

[0722] Step 6:

[0723] The user inputs feedback based on their health management plan into a terminal. The terminal sends the feedback to a server, which uses the feedback to generate future plans. The input in this step is user feedback, and the output is feedback data sent to the server.

[0724] 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.

[0725] This invention is a system that recognizes a user's emotional state based on their biometric data and uses this information to adjust their health management plan. The system analyzes the user's biometric data collected using wearable devices and smartphone apps, and provides a personalized health plan that takes into account both their physical and emotional state.

[0726] First, the device collects the user's biometric data, such as heart rate, steps taken, sleep quality, facial expressions, and voice data. This data is then transmitted to a server via the device. The transmitted biometric data is preprocessed on the server and prepared into an analyzable format.

[0727] The server inputs pre-processed data into a generating AI model to analyze the user's health status and recognize their emotional state using an emotion engine. The emotion engine can identify emotions from the user's physiological responses, voice, and facial recognition data, and estimate states such as relaxation, stress, happiness, and anxiety.

[0728] Based on the analysis results, the server generates an optimal health management plan for the user. This plan takes into account the user's current health and emotional state, and includes guidance and suggestions for future actions. For example, a user experiencing stress might be recommended relaxation exercises, while a user in a comfortable emotional state might be provided with a health maintenance plan to help maintain that state.

[0729] The generated health plan is sent to the user's device in real time, allowing the user to immediately review it and incorporate it into their daily life. The user can then take action based on the plan and input feedback into their device. This feedback information is sent to the server and used to generate the next plan.

[0730] As a feature for businesses, the server aggregates employees' biometric data and emotional states and provides administrators with a real-time summary. This allows businesses to develop employee care plans that consider both psychological and physical health. Thus, this invention enhances the quality of health management for both users and businesses, supporting comprehensive health promotion that also considers emotional aspects.

[0731] The following describes the processing flow.

[0732] Step 1:

[0733] The device collects biometric data along with data such as the user's voice and facial expressions. This data includes heart rate, steps taken, calories burned, changes in facial expressions, and voice modulation.

[0734] Step 2:

[0735] The device transmits the collected data to the server in real time. This data is transferred using a secure communication protocol.

[0736] Step 3:

[0737] The server preprocesses the received biometric and emotional data, converting it into an analyzable format. Specifically, it performs data normalization, noise reduction, and interpolation.

[0738] Step 4:

[0739] The server inputs pre-processed data into a generating AI model to analyze the user's overall health status. The AI ​​model uses machine learning algorithms to identify the user's health risks.

[0740] Step 5:

[0741] The server uses an emotion engine to recognize the user's emotional state from collected voice and facial expression data. It estimates emotions such as stress, joy, anger, and anxiety.

[0742] Step 6:

[0743] The server generates an optimal health management plan for the user based on their analyzed health and emotional state. The health plan combines exercise, nutrition, and relaxation methods that address the user's emotional state.

[0744] Step 7:

[0745] The server sends the generated health management plan to the user's device in real time. The device notifies the user of the plan, making it easy for the user to review.

[0746] Step 8:

[0747] Users perform activities based on their health management plan and input the results and feedback into their device. For example, if they perform a suggested exercise, they record their progress.

[0748] Step 9:

[0749] The device sends user feedback data to the server. The server uses this feedback for future data analysis and to improve the health plan.

[0750] Step 10:

[0751] The server acts as a corporate health management function, aggregating employee biometric and emotional data and providing the information through an administrator dashboard. This allows companies to understand the health and emotional state of all employees and take appropriate measures.

[0752] (Example 2)

[0753] 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".

[0754] To provide a system that effectively analyzes users' biometric data and emotional states to deliver individually optimized health management plans. Furthermore, for companies, to provide integrated information to understand the overall health and emotional state of employees, thereby addressing the challenge of comprehensively improving employee health and well-being.

[0755] 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.

[0756] In this invention, the server includes means for receiving biometric data collected from a user, means for preprocessing the biometric data and converting it into an analyzable format, and means for analyzing the user's individual health and emotional state based on the preprocessed data using a generative AI model. This makes it possible to provide the user with a personalized health management plan.

[0757] "Biometric data" refers to a collection of numerical data and information that indicates a user's physical and psychological state, such as heart rate, steps taken, sleep quality, facial expressions, and voice data.

[0758] "Preprocessing" is the process of converting biological data into an analyzable format by removing noise, normalizing, and imputing missing values.

[0759] A "generative AI model" is a program that uses artificial intelligence technology to analyze a user's health status, emotional state, and other factors from input data.

[0760] An "emotion engine" is an algorithm that identifies emotions from a user's voice and facial expression data and estimates states such as relaxation, stress, happiness, and anxiety.

[0761] A "health management plan" is a plan that includes personalized suggestions and action guidelines to maintain and improve the user's health, based on the user's analyzed health and emotional state.

[0762] "Feedback data" refers to information that records the actions taken by users based on their health management plans, as well as the effects and changes they experienced as a result.

[0763] A "terminal" is an electronic device or apparatus used to collect a user's biometric data and communicate data with a server.

[0764] "Corporate administrator" refers to the individual or department responsible for comprehensively managing the health and emotional well-being of employees within an organization and for developing and implementing optimized employee care plans.

[0765] This invention is a system that analyzes a user's health and emotional state based on their biometric data and provides a personalized health management plan. This system primarily operates through the collaboration of a server and a terminal.

[0766] The device uses wearable devices and smartphone apps to collect biometric data such as the user's heart rate, steps taken, sleep quality, facial expressions, and voice. This data is transmitted to a server in real time, and the server receives the data via a secure communication protocol.

[0767] The server preprocesses the received biometric data, removing noise and normalizing it to prepare it for analysis. The preprocessed data is then input into a generative AI model. This model is built to analyze the user's health status and uses machine learning algorithms to analyze the data.

[0768] An emotion engine is used to recognize emotional states. This engine estimates the user's emotional state based on voice data and facial expression data, determining states such as relaxation, stress, happiness, and anxiety. For example, if a user's heart rate decreases while listening to calming music, the emotion engine detects a relaxed state.

[0769] Based on the analyzed results, the server generates a personalized health management plan for the user. For example, a user experiencing stress might be suggested relaxation exercises, while a user in a comfortable emotional state might be provided with a plan to maintain that state. This health management plan is sent from the server to the user's device in real time, allowing the user to review it immediately.

[0770] Furthermore, users can input feedback data on their completed health management plans into their devices. This feedback is sent to the server and used to generate future plans. By utilizing this feedback, the system can provide more precise and user-friendly health management over time.

[0771] As a concrete example of a prompt, the following could be used as input to the generative AI model: "Estimate the emotional state of the user when their heart rate is 75 bpm, their sleep quality is good, and their facial expression is smiling. Then, suggest an appropriate health management plan."

[0772] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0773] Step 1:

[0774] The device collects data such as the user's heart rate, steps taken, sleep quality, facial expressions, and voice via wearable devices and smartphone apps. The input is biometric data obtained from sensors. The device measures this data in real time and records it as a digital signal. The output is the recorded biometric data, ready to be sent to a server for further processing.

[0775] Step 2:

[0776] The device transmits the collected biometric data to the server. The input is the biometric data recorded earlier. This data is encrypted using a secure protocol such as HTTPS and sent to the server. The output is the biometric data received on the server side. This ensures that the data is delivered securely to the server.

[0777] Step 3:

[0778] The server preprocesses the received biometric data. The input is biometric data transmitted from the terminal. The server performs noise reduction and data normalization to prepare the data for analysis. The output is clean biometric data after preprocessing. This process yields data of a quality suitable for analysis.

[0779] Step 4:

[0780] The server inputs pre-processed data into a generative AI model to analyze the user's health and emotional state. The input is pre-processed biometric data. The generative AI model uses machine learning to assess the health state and estimate emotions using an emotion engine. The output is the analyzed health and emotional state. Specifically, it calculates health scores, relaxation levels, stress levels, etc.

[0781] Step 5:

[0782] The server generates a health management plan based on the analysis results. The input is the analysis results of health status and emotional state. The server uses these results to assemble an individualized health management plan. The output is a personalized health management plan for the user. For example, if stress levels are high, it might suggest yoga for relaxation.

[0783] Step 6:

[0784] The server sends the generated health management plan to the user's device. The input is the created health management plan. The plan is sent to the device using push notifications so that the user can review it immediately. The output is the health management plan displayed on the device. This allows the user to take appropriate actions in their daily life.

[0785] Step 7:

[0786] The user acts according to the provided health management plan and inputs feedback into the device. The input consists of the user's activities and their results. The device records the feedback and prepares to send it to the server. The output is the recorded feedback data.

[0787] Step 8:

[0788] The terminal sends the collected feedback data to the server. The input is the user's feedback data. This data is then sent back to the server using a secure communication protocol. The output is the feedback data that has arrived at the server. This is then used to generate the next health management plan, improving the system's accuracy.

[0789] (Application Example 2)

[0790] 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".

[0791] In modern manufacturing, the health and emotional state of workers significantly impacts work safety and efficiency. In particular, accumulated stress and fatigue can lead to work errors and workplace accidents. However, currently, there are limited means to monitor these conditions in real time and provide appropriate health management plans. Therefore, there is a need to analyze emotional states based on workers' biometric data and provide a safe and efficient work environment.

[0792] 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.

[0793] In this invention, the server includes means for receiving biometric information collected from a user, means for preprocessing the biometric information and converting it into an analyzable format, means for analyzing the health status based on the preprocessed information using a generating AI model, means for generating a health management plan based on the analysis results, means for transmitting the health management plan to a terminal, means for collecting biometric information from a worker and analyzing their emotional state, and means for proposing a safe and efficient work environment to the worker according to their emotional state. This enables the provision of real-time feedback and personalized health management plans based on the worker's health and emotional state.

[0794] "Biometric information" refers to data about an individual's physical activity and physiological state, such as heart rate, steps taken, and sleep quality.

[0795] "Preprocessing" refers to the initial data organization process used to convert data into an analyzable format.

[0796] A "generative AI model" is an algorithm that performs predictions and analyses based on a large amount of data, and in this invention, it is used to analyze individual health conditions.

[0797] "Health status" refers to indicators of physical and mental condition, and in this invention, it serves as a criterion for generating an optimal health management plan for an individual.

[0798] A "health management plan" is a set of behavioral guidelines and activity plans proposed based on an individual's health and emotional state.

[0799] A "terminal" is a device operated by the user to send and receive biometric information and display health management plans.

[0800] "Worker" refers to an individual who is actually engaged in work at a factory or work site.

[0801] "Emotional state" is an evaluation index that indicates the psychological and emotional state of a worker.

[0802] A "safe and efficient work environment" refers to a workplace environment that is designed to allow workers to perform their duties comfortably.

[0803] To implement this invention, users collect biometric information in real time using wearable devices or smartphones. This includes heart rate, steps taken, sleep quality, and facial recognition data. This information is transmitted to a server via the terminal. The server preprocesses the received biometric information using Python's Pandas library and converts it into an analyzable format. Then, a generative AI model using TensorFlow analyzes the user's health and emotional state based on the preprocessed data.

[0804] Based on the analysis results, the server generates an individually optimized health management plan and sends it to the terminal. This plan is tailored to the worker's emotional state and includes guidelines for action to provide a safe and efficient work environment. For example, if a worker has a high heart rate and is under stress, they will be advised to take appropriate breaks.

[0805] As a concrete example, suppose a factory worker feels fatigued during their work. At that time, a wearable device they are wearing detects a sudden increase in their heart rate. Based on this data, the server analyzes that the worker is in a stressed state and immediately sends a notification to the device prompting them to take a break. This allows the worker to continue working with peace of mind.

[0806] An example of a prompt message for the generating AI model would be, "Based on biometric information, analyze the emotional state in real time and propose an appropriate health management plan to the worker." This would allow for comprehensive management of the worker's health status and contribute to improved work efficiency.

[0807] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0808] Step 1:

[0809] Users collect biometric information such as heart rate, steps taken, and sleep quality using wearable devices and smartphones. This information is transmitted to a server via Bluetooth or the internet through the device. The input is biometric data, and the output is biometric data transmitted to the server.

[0810] Step 2:

[0811] The server preprocesses the received biometric data using Python's Pandas library. Specifically, it cleans the data, imputes missing values, and formats it into an analyzable format. In this step, the input is the transmitted biometric data, and the output is the preprocessed, analyzable biometric data.

[0812] Step 3:

[0813] The server inputs pre-processed data into a generating AI model and uses TensorFlow to analyze individual health and emotional states. The analysis results in an estimation of the user's current health and emotional state. The input is pre-processed biometric data, and the output is the analysis results of the health and emotional state.

[0814] Step 4:

[0815] The server generates an optimal health management plan for the user based on the analysis results. The generated health management plan includes specific action guidelines tailored to the user's condition. The input is the analysis results, and the output is the health management plan.

[0816] Step 5:

[0817] The server sends the generated health management plan to the user's terminal in real time. The user can immediately check the plan on their terminal and incorporate it into their daily life. The input is the health management plan, and the output is the health management plan displayed on the terminal.

[0818] Step 6:

[0819] The user performs activities based on their health management plan and inputs feedback into a terminal. The terminal sends the feedback information to a server, which is used to generate the next plan. The input is the user's feedback, and the output is data used to generate the next health management plan.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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."

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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 to be incorporated by reference.

[0841] The following is further disclosed regarding the embodiments described above.

[0842] (Claim 1)

[0843] A means for receiving biometric data collected from users,

[0844] Means for preprocessing the biological data and converting it into an analyzable format,

[0845] A means for analyzing the individual health status of a user based on data preprocessed using a generative AI model,

[0846] A means for generating an optimal health management plan for the user based on the analysis results,

[0847] A means for transmitting the health management plan to the user's terminal,

[0848] A health management system that includes this.

[0849] (Claim 2)

[0850] A health management system according to claim 1, comprising means for collecting user feedback and utilizing it when generating future health management plans.

[0851] (Claim 3)

[0852] A health management system according to claim 1, comprising means for providing a company administrator with aggregated information of biometric data of all employees.

[0853] "Example 1"

[0854] (Claim 1)

[0855] A means for receiving biometric information collected from a user,

[0856] Means for preprocessing the biological information and converting it into an analyzable format,

[0857] A means for analyzing a user's individual health status based on information preprocessed using a generative AI algorithm,

[0858] A means for generating an optimal health management plan for the user based on the analysis results,

[0859] Means for transmitting the health management plan to the user's device,

[0860] A means of receiving user feedback and using it to improve the quality of health management plans,

[0861] A system that includes this.

[0862] (Claim 2)

[0863] The system according to claim 1, comprising means for providing the administrator of the organization with aggregated data of biometric information of all participants.

[0864] (Claim 3)

[0865] The system according to claim 1, comprising means for identifying health risks and providing necessary advice based on the user's biometric information.

[0866] "Application Example 1"

[0867] (Claim 1)

[0868] A means for receiving biometric data collected from users,

[0869] Means for preprocessing the biological data and converting it into an analyzable format,

[0870] A means for analyzing the individual health status of users based on data preprocessed using a generative AI model,

[0871] A means for generating an optimal health management plan for the user based on the analysis results,

[0872] Means for transmitting the health management plan to the user's device,

[0873] A means of managing the work environment and monitoring the health status of workers using user health data,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, further comprising means for collecting user feedback and utilizing it when generating future health management plans.

[0877] (Claim 3)

[0878] The system according to claim 1, comprising means for providing the administrator of the organization with aggregated information of biometric data of all employees.

[0879] "Example 2 of combining an emotion engine"

[0880] (Claim 1)

[0881] A means for receiving biometric data collected from users,

[0882] Means for preprocessing the biological data and converting it into an analyzable format,

[0883] A means for analyzing the individual health and emotional state of a user based on data preprocessed using a generative AI model,

[0884] A means for generating an optimal health management plan for the user based on the analysis results and transmitting it to the user's device in real time,

[0885] A means of receiving user feedback data and using it to generate the next health management plan,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, comprising means for providing a company administrator with aggregated information on the biometric data and emotional state of all employees.

[0889] (Claim 3)

[0890] The system according to claim 1, comprising means for using an emotion engine to analyze the emotional state of a user.

[0891] "Application example 2 when combining with an emotional engine"

[0892] (Claim 1)

[0893] A means for receiving biometric information collected from a user,

[0894] Means for preprocessing the biological information and converting it into an analyzable format,

[0895] A means for analyzing individual health status based on information preprocessed using a generative AI model,

[0896] A means for generating an optimal health management plan based on analysis results,

[0897] A means for transmitting the health management plan to a terminal,

[0898] A means of collecting biometric information from workers and analyzing their emotional state,

[0899] A means of proposing a safe and efficient work environment to the worker according to their emotional state,

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, further comprising means for collecting feedback and utilizing it when generating future health management plans.

[0903] (Claim 3)

[0904] The system according to claim 1, comprising means for providing an administrator with aggregated information of the biometric data of all employees. [Explanation of Symbols]

[0905] 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 means for receiving biometric data collected from users, Means for preprocessing the biological data and converting it into an analyzable format, A means for analyzing the individual health status of a user based on data preprocessed using a generative AI model, A means for generating an optimal health management plan for the user based on the analysis results, A means for transmitting the health management plan to the user's terminal, A health management system that includes this.

2. A health management system according to claim 1, comprising means for collecting user feedback and utilizing it when generating future health management plans.

3. The health management system according to claim 1, comprising means for providing a company administrator with information that aggregates the biometric data of all employees.

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