system

Abstract

We provide the system. [Solution] A terminal device for aggregating information obtained from individuals, Processing device means for analyzing the aggregated information and evaluating the state of the individual, A processing device that proposes a specific management plan for an individual based on the analysis results, A means of notifying individual devices of the proposed management plan via a terminal, A processing device that continuously monitors changes in the state of an individual and issues a warning when an abnormality is detected, A means of aggregating information to be used for health promotion activities in the local community, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In personal health management, specific advice for maintaining and effectively improving daily living habits is required. However, many people do not have the time or expertise to collect and analyze their own health data, making self-management difficult. In addition, users with chronic diseases need to precisely perform daily health management to prevent the deterioration of symptoms, which requires a lot of effort and time. This invention aims to provide an individually personalized health plan for users who have problems in making good use of health data, so that health management can be carried out efficiently and quickly even in busy daily life.

Means for Solving the Problems

[0005] This invention uses a terminal to collect diverse data acquired from individuals, analyzes that data using a server, and evaluates the user's health status. Based on the analysis results, it automatically generates a specific health management plan for the user and presents it quickly via the server. Furthermore, it notifies the user of the management plan via the terminal, supporting them in easily understanding and implementing their individual health plan. Equipped with a continuous monitoring function, it monitors changes in the user's condition and promptly issues alerts when abnormalities are detected, enabling the user to take action early. In this way, it provides a means for users to understand and optimize their own health status even in their busy daily lives.

[0006] "Individual" refers to a specific person, and in this context, it means the user who is the subject of collecting and analyzing health data.

[0007] "Data" refers to information about an individual's lifestyle and health status, primarily including information such as activity levels, heart rate, diet, and sleep patterns.

[0008] "Terminal means" refers to a combination of hardware and software used for data collection and notification to users, and smartphones and wearable devices fall under this category.

[0009] "Server means" refers to a computing system that analyzes collected data and generates and provides health management plans to users.

[0010] "Analysis" is the process of performing calculations and evaluations based on collected data to understand health conditions and trends, and to detect abnormalities.

[0011] A "health management plan" is a plan provided to an individual based on analysis results, which includes specific action guidelines aimed at maintaining and improving their health.

[0012] An "alert" refers to a warning message or signal that immediately notifies an individual when a change or abnormality in their health condition is detected.

[0013] "Monitoring" is the process of continuously monitoring an individual's health data to detect abnormalities or changes that exceed established standards. [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 the 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 embodiment of the invention begins with the collection of health data by a user's device. The device periodically acquires data such as daily activity levels, heart rate, dietary information, and sleep patterns via the user's wearable device or smartphone. Manual data collection is also possible by the user entering dietary information into a smartphone app.

[0036] The device transmits the collected data to a server via the internet. The server stores diverse data and evaluates the user's health status through advanced AI analysis. Machine learning algorithms are used for the analysis, detecting patterns and predicting anomalies based on past data. The results of this analysis are used to generate an optimal health management plan for the user.

[0037] Based on the analysis results, the server automatically generates a personalized health management plan that includes suggestions for dietary improvements, exercise recommendations, and sleep advice. The generated plan is then notified to the user via their device. The device uses natural language processing to provide advice in a way that is easy for the user to understand. This allows the user to practice healthy lifestyle habits based on the presented plan.

[0038] Furthermore, the server performs continuous monitoring, constantly watching for changes in health data. If an abnormality or risk is detected, an alert notification is sent to the device. Specifically, if the user's heart rate exceeds the normal range or if an abnormal sleep pattern persists, an alert is immediately sent to the device. This alert allows the user to quickly manage their health and, if necessary, seek medical attention or review their lifestyle habits.

[0039] In this way, by using the system, users can comprehensively manage and improve their own health. This process is achieved through the coordinated operation of the terminal, server, and user.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device collects health data using the user's wearable device or smartphone. Specifically, it obtains activity levels and heart rate from the wearable device via Bluetooth, and supplements the data by allowing the user to manually input dietary information using a smartphone app.

[0043] Step 2:

[0044] The terminal uploads the collected data to the server at regular intervals. This operation involves the process of sending data packets over the network and aggregating them on the server.

[0045] Step 3:

[0046] The server aggregates the received data and begins processing it with an AI analysis engine. This uses machine learning algorithms to identify trends in the data and perform analysis to detect anomalies.

[0047] Step 4:

[0048] Based on the analysis results, the server automatically generates a personalized health management plan for each user. This plan includes suggestions for dietary improvements, exercise recommendations, and sleep advice.

[0049] Step 5:

[0050] The device notifies the user of the generated health management plan. This information is presented in an easy-to-understand format using natural language processing technology.

[0051] Step 6:

[0052] The server continuously monitors the user's health data and evaluates any changes in real time. If an anomaly or risk is detected, it immediately generates an alert.

[0053] Step 7:

[0054] The device receives alerts sent from the server and notifies the user. This allows the user to take quick action to address any abnormal health conditions.

[0055] (Example 1)

[0056] 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."

[0057] In modern society, personal health management is a crucial issue, but currently available technologies make it difficult to achieve flexible, real-time health management tailored to individual lifestyles and health conditions. In particular, there is a lack of technology that efficiently provides comprehensive support, including early detection of abnormalities and concrete improvement suggestions.

[0058] 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.

[0059] In this invention, the server includes means for processing accumulated information and evaluating the user's condition, means for presenting a management plan based on the processing results, and means for creating suggestions regarding diet, exercise, and rest using a generative AI model. This makes it possible to monitor the user's health condition in real time and issue warnings and provide appropriate support when abnormalities are detected.

[0060] "Human beings" are individuals from whom information is collected for the purpose of monitoring and improving their health status.

[0061] An "information device" is a device that collects and transmits data, including wearable devices and smartphones.

[0062] A "processing unit" refers to a device that performs calculations, such as a server, and is responsible for data analysis and processing.

[0063] A "management plan" is an advice plan for users that includes action guidelines and suggestions for improving their health.

[0064] A "generative AI model" is an artificial intelligence model that automatically generates optimal suggestions based on data.

[0065] "Wearable devices" refer to information-gathering devices that are worn on the body, such as wearable devices.

[0066] A "warning" is a notification that informs users about an anomaly or risk.

[0067] This invention provides a system to support personal health management. Specific embodiments are described below.

[0068] Users collect various health information from their daily lives using wearable devices and smartphones. This includes data such as steps taken, heart rate, calorie intake, and sleep duration. These devices automatically acquire data when worn and integrate it into smartphone applications.

[0069] The device transmits this health data collected from the user to a server via the internet. The server securely stores the received data in a database via a dedicated API. Subsequently, machine learning algorithms are applied to analyze this data. This analysis process utilizes Python libraries such as TENSORFLOW® and Scikit-learn.

[0070] Based on the analysis, the server assesses the user's health status and generates a management plan. This plan is customized using a generative AI model and includes specific suggestions regarding diet, exercise, and rest. For example, if the user is predicted to be inactive based on recent data, advice will be generated indicating how many times a week they should exercise.

[0071] The generated management plan is then communicated to the user again via the device. The device uses natural language processing to provide advice in language that is easy for the user to understand. This allows the user to incorporate the proposed action plan into their daily life.

[0072] As a concrete example, if a user inputs their daily step count and diet, this system can provide a health management plan based on that data, such as: "Please input the following information into the generating AI model: Yesterday's step count was 8,000 steps, diet was high protein and low fat, and sleep time was 7 hours. Please suggest a health management plan suitable for the user."

[0073] Thus, according to this embodiment, users can understand their health status in real time and review their lifestyle habits as needed.

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

[0075] Step 1:

[0076] Users collect their own health data using wearable devices or smartphones. Input at this stage includes data such as the user's daily steps, heart rate, diet, and sleep duration. This data is collected automatically by wearing the device or manually entered by the user into the application. The output is the set of health data acquired by the device.

[0077] Step 2:

[0078] The device transmits the collected health data to the server via the internet. The input for this process is the health data collected in step 1. The data is initially processed within the device and converted to the appropriate format. The data is then sent to the server's API via the internet. The output is the health data received by the server.

[0079] Step 3:

[0080] The server stores the received health data in a database and begins data analysis. The input for this analysis is the raw health data sent from the terminal. The server runs machine learning algorithms to evaluate the user's health status based on this data. Python libraries such as TensorFlow and Scikit-learn are used for data processing. The output is the evaluation of the user's health status based on the analysis.

[0081] Step 4:

[0082] The server generates a personalized health management plan based on the analysis results. The input for this step is the evaluation results from step 3. Using the generation AI model, a plan including dietary improvements, exercise recommendations, and rest advice is automatically generated. The output is a customized health management plan provided to the user.

[0083] Step 5:

[0084] The terminal communicates the generated health management plan to the user. The input for this processing step is the management plan sent from the server. The terminal uses natural language processing technology to convert the plan into a format that is easy for the user to understand. The output is specific health management advice displayed to the user.

[0085] Step 6:

[0086] The server performs continuous monitoring, observing changes in the user's health data in real time. In this step, health data is constantly sent to the server, and anomalies are detected. The input is continuously updated health data. If an anomaly is detected, the server issues a warning and sends a notification to the terminal. The output is the warning notification about the detected anomaly.

[0087] (Application Example 1)

[0088] 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."

[0089] In modern society, many individuals face inadequate health management and the risk of lifestyle-related diseases. Furthermore, while there is a need to improve the overall health level of communities, there is a lack of perspective on how to utilize individual data for overall health promotion activities. Therefore, providing a system that comprehensively understands the health status of individuals and communities and can propose appropriate improvement measures is essential.

[0090] 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.

[0091] In this invention, the server includes terminal means for aggregating information acquired from individuals, processing means for analyzing the aggregated information and evaluating the individual's condition, processing means for proposing a specific management plan to the individual based on the analysis results, means for notifying the individual of the proposed management plan via the terminal, processing means for continuously monitoring changes in the individual's condition and issuing a warning when an abnormality is detected, and aggregation means for utilizing the aggregated information for health promotion activities in the local community. This makes it possible to support individual health management while simultaneously contributing to the health promotion of the entire local community.

[0092] "Information obtained from individuals" refers to data collected via wearable devices and mobile information terminals, including individual activity levels, biometric information, and lifestyle data.

[0093] A "data aggregation terminal" refers to a device or software that collects data from multiple information sources and manages it centrally.

[0094] "Processing device" refers to a machine or program that has the function of analyzing and processing collected data and generating results according to a specific purpose.

[0095] A "management plan" is a specific set of action guidelines and recommended habits created based on an analysis of an individual's condition, with the aim of maintaining or improving their health.

[0096] "Monitoring and detecting anomalies" refers to a function that continuously monitors data and checks for fluctuations that exceed the normal range.

[0097] "Issuing a warning" refers to the act of sending a notification to an individual to alert them to an anomaly that has been detected.

[0098] "Data aggregation methods" refer to methods and systems that utilize data obtained from individuals to inform health promotion policies and plans for a wide range of groups or entire regions.

[0099] The system of this invention aims to effectively collect and analyze individual health data and contribute to health promotion activities throughout the community. The system mainly consists of a server, terminals, and users, and each of these entities works in cooperation with each other.

[0100] The terminal continuously collects individual biometric data, activity levels, and lifestyle data from wearable devices and personal digital assistants (PDAs). This data is transmitted to a server via the internet. The terminal uses wearable devices such as Apple Watch and Fitbit to collect data in real time. PPDAs have applications installed that allow for data display and input of additional information.

[0101] The server operates on a cloud platform (such as Cloud SQL or AWS®) and stores the collected data. Machine learning models (using TensorFlow, Keras, etc.) running on the server analyze personal data and assess activity patterns and potential health risks. Based on the analysis results, a management plan tailored to the user is automatically generated. The generated plan includes dietary improvements, exercise recommendations, and sleep advice, and is notified to the user via their device.

[0102] Furthermore, the server generates aggregated information that can be used to plan health promotion measures across the entire region. This involves analyzing the average activity levels and health status of the entire population and proposing community events and campaigns. Generative AI models can be used to gain new insights into the health of the community.

[0103] One concrete example is promoting a "10,000 Steps Daily Challenge" in which local residents participate, and sharing their progress through a system. Participants can check their progress against others via their devices, and it is expected that achieving the challenge will have a positive impact on the entire community.

[0104] An example of a prompt for a generative AI model is: "Based on the analysis of local residents' health data, please suggest events and campaigns to improve the overall health of the community."

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The device collects personal biometric data from wearable devices and personal digital assistants (PDAs). Specifically, the device synchronizes with a wearable device (e.g., a smartwatch) via Bluetooth or Wi-Fi to acquire data such as steps, heart rate, and sleep patterns. The input is biometric data from the wearable device, and the output is personal health data stored on the device.

[0108] Step 2:

[0109] The device transmits the collected health data to the server. Specifically, the device uploads the data to a server on a cloud platform via an internet connection. Encryption protocols are used for data transmission to ensure the security of personal information. The input is health data stored on the device, and the output is data stored on the server.

[0110] Step 3:

[0111] The server analyzes the received data and assesses the individual's health status. This process uses a machine learning model to analyze the collected data, including comparison with past records, detection of anomalies, and pattern recognition. The output is health assessment information based on the analysis results.

[0112] Step 4:

[0113] The server generates an optimal management plan for each individual based on the analysis results. Specifically, an algorithm using a generation AI model automatically creates improvement plans for diet and exercise. The input is the analyzed health assessment information, and the output is a specific action plan as part of the management plan.

[0114] Step 5:

[0115] The user receives notifications of proposed management plans through their device. Specifically, the device displays the plan on the user interface and provides push notifications and alerts. The input is the management plan sent from the server, and the output is the provision of visual and auditory information to the user.

[0116] Step 6:

[0117] The server aggregates individual health data and uses it for health promotion activities across the entire community. Specifically, it uses a generative AI model to generate prompts and propose measures tailored to the needs of local residents. The input is aggregated health data, and the output is suggestions for events and campaigns that will lead to improved health throughout the community.

[0118] 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.

[0119] Embodiments of this invention include the coordination of a terminal, a server, and a user, and further involve health management that takes into account the user's emotional state. First, the terminal used by the user collects health data. The terminal acquires data on activity level, heart rate, diet, and sleep patterns through the user's wearable device or smartphone. At this time, the emotion engine within the terminal analyzes the user's voice and facial expressions to recognize their emotional state.

[0120] The collected health and emotional data is sent to a server. The server uses machine learning algorithms to analyze the data and assess the user's health status. This analysis aims to reflect the user's emotional state and improve the accuracy of the health management plan. For example, if an emotion indicating stress is detected, suggestions for activities to promote relaxation will be incorporated into the plan.

[0121] The server generates a health management plan tailored to the user's condition based on the analysis results. The generated plan is customized according to the user's emotional state, as recognized by the emotion engine. The terminal notifies the user of this plan and explains it clearly using natural language processing. This allows the user to select appropriate health actions based on their emotions.

[0122] Furthermore, the server continuously monitors health and emotional data, and quickly generates alerts if anomalies or risks occur. For example, if the emotion engine detects an abnormal emotional state, the terminal sends an alert to the user, prompting them to take appropriate action. This system enables comprehensive health management that supports not only physical health but also mental health.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] The device collects biometric data such as activity levels, heart rate, and sleep patterns via the user's wearable device and smartphone. Simultaneously, an emotion engine within the device uses the camera and microphone to analyze the user's facial expressions and voice to recognize their emotional state.

[0126] Step 2:

[0127] The device transmits the collected biometric and emotional data to the server. This data transmission is performed using a secure protocol that protects privacy.

[0128] Step 3:

[0129] The server analyzes the received data. Here, a machine learning algorithm evaluates the user's health status based on biometric data and, taking emotional data into account, generates an analysis result that depends on the emotional state.

[0130] Step 4:

[0131] Based on the analysis results, the server creates a customized health management plan for the user. This plan reflects the user's emotional state; for example, if emotional data detects a high-stress state, it will include relaxation suggestions.

[0132] Step 5:

[0133] The device notifies the user of the generated health management plan. Natural language processing technology is used in the notification, and the content of the notification is adjusted to help the user understand it.

[0134] Step 6:

[0135] The server continuously monitors the user's health and emotional data. If abnormal health indicators or emotional states are detected, an alert is immediately generated on the device, requiring the user to take immediate action.

[0136] Step 7:

[0137] Users take appropriate health actions based on instructions from the device. For example, by implementing stress management techniques guided by the device, users can strive to improve their emotional state.

[0138] (Example 2)

[0139] 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 will be referred to as the "terminal."

[0140] In modern society, personal health management requires consideration not only of physical aspects but also of psychological and emotional aspects. However, conventional health management systems often only consider physiological data, making it difficult to generate management plans that adequately reflect an individual's emotional state. Furthermore, there is a lack of methods to effectively utilize emotional data and provide information that is best suited to the individual.

[0141] 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.

[0142] In this invention, the server includes means for analyzing physiological and emotional data acquired from an individual and evaluating the individual's state; means for generating a specific management plan for the individual based on the analysis results using a generative AI model; and means for customizing and notifying the individual of the generated management plan according to their emotional state. This enables comprehensive health management that takes emotional state into account and the provision of information optimized for the individual.

[0143] "Individuals" refer to users of the system, and are the subjects from whom health and emotional data are collected.

[0144] "Physiological data" refers to data that indicates an individual's physical condition, including activity level, heart rate, diet, and sleep patterns.

[0145] "Emotional data" refers to data that indicates an individual's psychological state, including emotional states analyzed from voice and facial expressions.

[0146] "Terminal means" refers to a device for collecting physiological and emotional data from an individual and transmitting it to a server, and includes smartphones and wearable electronic devices.

[0147] "Server means" refers to a computer system that analyzes received physiological and emotional data to evaluate an individual's state.

[0148] A "generative AI model" is an algorithm that utilizes artificial intelligence technology to generate management plans for individuals based on data analysis results.

[0149] A "management plan" is a plan that includes specific activities and suggestions created by a generative AI model, with the aim of maintaining and improving an individual's health.

[0150] "Natural language processing" is a technology that provides information to individuals in an easily understandable way via a device, and it is a method by which computers understand and process human language.

[0151] A "wearable electronic device" refers to a device used as part of a terminal system, which is attached to an individual's body to collect physiological data.

[0152] In order to implement this invention, it is necessary to build a system in which terminals, servers, and users work together.

[0153] First, the terminal uses devices such as smartphones and wearable electronic devices to collect physiological and emotional data from the user. This terminal collects physiological data such as the user's activity level, heart rate, diet, and sleep patterns using sensors. It also captures voice and facial expressions using cameras and microphones built into the wearable electronic device, and processes the emotional data using an emotion engine.

[0154] Next, the collected data is sent to a server. The server uses a powerful computer equipped with server software that runs machine learning algorithms. The server analyzes the received data and uses a generative AI model to assess the user's physiological and emotional state. Based on this assessment, it generates a specific health management plan for the user. This plan is optimized according to the user's health and emotional condition.

[0155] The user receives a management plan provided by the device. The device can use natural language processing technology to communicate this plan to the user in an easy-to-understand way, either in text or voice. For example, a prompt might say, "Your current stress level is high. Try incorporating a yoga session to relax."

[0156] Such a system allows users to gain a deeper understanding of their own health management and choose appropriate actions based on their emotional state.

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] The device acquires physiological and emotional data from the user. Specifically, it uses sensors via wearable devices and smartphones to collect the user's heart rate, activity level, diet, and sleep patterns. Furthermore, the device uses cameras and microphones to capture voice and facial expressions, and recognizes emotional data through an emotion engine. Inputs are the user's physiological indicators, voice, and video data, which are aggregated and stored within the device. Outputs are organized physiological and emotional data.

[0160] Step 2:

[0161] The device compresses and encrypts the collected physiological and emotional data before sending it to the server. Data encryption is crucial to protect user privacy. The input is the compressed and encrypted user data, and the output is a secure data packet sent to the server.

[0162] Step 3:

[0163] The server analyzes the received data and uses a generative AI model to assess the user's health status. The machine learning algorithm compares the user's past data patterns and similar profiles to quantify and evaluate their health status. Inputs are physiological and emotional data, and the output is detailed information about the user's current health assessment and condition.

[0164] Step 4:

[0165] The server generates a personalized health management plan based on the analysis results. The generating AI model automatically suggests activities appropriate to the user's emotional state. For example, if the stress level is determined to be high, a prompt message recommending relaxation activities will be generated. The input is the analyzed health assessment information, and the output is a customized management plan and prompt messages.

[0166] Step 5:

[0167] The terminal receives a management plan from the server and notifies the user via natural language processing technology. By clearly communicating suggestions in text or audio format, it helps users take immediate action. For example, the user might receive instructions such as, "Your current stress level is high. Try incorporating a yoga session to relax." The input is the generated management plan and prompt, while the output is the notification to the user.

[0168] Step 6:

[0169] The server continuously monitors the user's physiological and emotional data, and immediately generates an alert and notifies the terminal if an anomaly is detected. The alert aims to enable early detection of health problems and support users in taking prompt action. The input is real-time data that is continuously monitored, and the output is alert information issued when an anomaly is detected.

[0170] (Application Example 2)

[0171] 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".

[0172] In recent years, a comprehensive approach to personal health management has been required, taking into account not only physical health but also emotional state. However, conventional systems have been unable to adequately reflect emotional states, making it difficult to provide personalized health advice. Furthermore, the lack of automated means to handle health-related data and emotional data in an integrated manner has created a need for a more comprehensive and individualized health management system.

[0173] 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.

[0174] In this invention, the server includes a device for collecting biometric information acquired from an individual, an information processing device for analyzing the collected biometric information and emotional state and evaluating the individual's health status, and an information processing device for presenting a concrete health management plan to the individual based on the analysis results. This makes it possible to manage health while taking into account the individual's emotional state.

[0175] "Biometric information" refers to data that indicates an individual's physical health status, and mainly includes activity levels, heart rate, dietary content, and sleep patterns.

[0176] "Emotional state" refers to data that indicates an individual's mental state, and is obtained by analyzing voice and facial expressions.

[0177] An "information processing device" is a device that analyzes collected data and evaluates an individual's health and emotional state.

[0178] A "health management plan" is a set of concrete guidelines or action plans provided with the aim of improving or maintaining an individual's health.

[0179] An "automated machine" is a device that operates autonomously according to a specified program to provide health-related advice to an individual.

[0180] A description of the embodiment for carrying out the invention will be provided.

[0181] This system is designed to support individual health management. The device collects biometric information and emotional states through wearable devices and smartphones worn by the user. The device uses an emotion analysis engine to analyze voice and facial expressions, recognizing the user's emotional state in real time.

[0182] The collected data is sent to a server. The server, acting as an information processing device, utilizes machine learning algorithms to comprehensively analyze the collected biometric information and emotional state. This makes it possible to generate a health management plan optimized for each individual user. The server also has the function to evaluate the user's health status based on the analysis results and detect abnormalities and risks in real time.

[0183] The generated health management plan is notified to the user via the terminal. The user receives advice in an easily understandable format through an interface that utilizes natural language processing technology. The automated system provides health-related advice and helps the user take appropriate actions according to their emotional state.

[0184] For example, if the system determines that the user is fatigued due to work stress, it will suggest deep breathing or light exercise as relaxation methods. Furthermore, by utilizing a generative AI model, it can provide flexible advice based on the user's state. An example of a prompt might be, "Generate stress reduction suggestions based on the user's emotional and health data."

[0185] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0186] Step 1:

[0187] The device collects biometric information from wearable devices and smartphones. Inputs include the individual's activity level, heart rate, diet, and sleep patterns, while output is the storage of this data in digital format. To acquire this data, the device communicates with the wearable devices using Bluetooth or Wi-Fi.

[0188] Step 2:

[0189] The device recognizes the user's voice and facial expressions and analyzes their emotional state based on this information. The input is the user's voice and video data, and the output is the analyzed emotional state. Emotion analysis uses an emotion analysis engine to extract features from voice intonation and facial expressions, and then classifies emotions based on these patterns. Computer vision technology and natural language processing are used in this process.

[0190] Step 3:

[0191] The terminal transmits the collected biometric information and analyzed emotional state to the server. The input is the data obtained from steps 1 and 2, and the output is the biometric information and emotional state transmitted to the server. A secure protocol is used for data transmission to prevent data leakage.

[0192] Step 4:

[0193] The server analyzes biometric information and emotional state in combination to assess the user's health status. Inputs are transmitted biometric information and emotional state, while output is the user's health assessment data. The server applies machine learning algorithms to identify abnormalities in emotional state and potential health risks. This process utilizes big data technology to train models based on a large amount of health-related datasets.

[0194] Step 5:

[0195] The server generates an individualized health management plan based on the evaluation results. The input is the user's health evaluation data. The output is the generated health management plan, which includes specific action plans tailored to the user's characteristics. A generation AI model is used to create prompts and provide optimal suggestions based on the user's situation.

[0196] Step 6:

[0197] The server sends the generated health management plan to the terminal, which then notifies the user. The input is the health management plan data, and the output is a specific health action plan presented to the user. The terminal uses natural language processing to provide advice to the user in an easy-to-understand format.

[0198] Step 7:

[0199] The server continuously monitors changes in the user's health status and issues alerts if an anomaly is detected. Inputs are real-time updated biometric information and emotional states, while outputs are alarm messages upon anomaly detection. An anomaly detection algorithm is used to enable rapid response.

[0200] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

[0203] [Second Embodiment]

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

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

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

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

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

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

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

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

[0212] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0215] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0216] This embodiment of the invention begins with the collection of health data by a user's device. The device periodically acquires data such as daily activity levels, heart rate, dietary information, and sleep patterns via the user's wearable device or smartphone. Manual data collection is also possible by the user entering dietary information into a smartphone app.

[0217] The device transmits the collected data to a server via the internet. The server stores diverse data and evaluates the user's health status through advanced AI analysis. Machine learning algorithms are used for the analysis, detecting patterns and predicting anomalies based on past data. The results of this analysis are used to generate an optimal health management plan for the user.

[0218] Based on the analysis results, the server automatically generates a personalized health management plan that includes suggestions for dietary improvements, exercise recommendations, and sleep advice. The generated plan is then notified to the user via their device. The device uses natural language processing to provide advice in a way that is easy for the user to understand. This allows the user to practice healthy lifestyle habits based on the presented plan.

[0219] Furthermore, the server performs continuous monitoring, constantly watching for changes in health data. If an abnormality or risk is detected, an alert notification is sent to the device. Specifically, if the user's heart rate exceeds the normal range or if an abnormal sleep pattern persists, an alert is immediately sent to the device. This alert allows the user to quickly manage their health and, if necessary, seek medical attention or review their lifestyle habits.

[0220] In this way, by using the system, users can comprehensively manage and improve their own health. This process is achieved through the coordinated operation of the terminal, server, and user.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] The device collects health data using the user's wearable device or smartphone. Specifically, it obtains activity levels and heart rate from the wearable device via Bluetooth, and supplements the data by allowing the user to manually input dietary information using a smartphone app.

[0224] Step 2:

[0225] The terminal uploads the collected data to the server at regular intervals. This operation involves the process of sending data packets over the network and aggregating them on the server.

[0226] Step 3:

[0227] The server aggregates the received data and begins processing it with an AI analysis engine. This uses machine learning algorithms to identify trends in the data and perform analysis to detect anomalies.

[0228] Step 4:

[0229] Based on the analysis results, the server automatically generates a personalized health management plan for each user. This plan includes suggestions for dietary improvements, exercise recommendations, and sleep advice.

[0230] Step 5:

[0231] The device notifies the user of the generated health management plan. This information is presented in an easy-to-understand format using natural language processing technology.

[0232] Step 6:

[0233] The server continuously monitors the user's health data and evaluates any changes in real time. If an anomaly or risk is detected, it immediately generates an alert.

[0234] Step 7:

[0235] The device receives alerts sent from the server and notifies the user. This allows the user to take quick action to address any abnormal health conditions.

[0236] (Example 1)

[0237] 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."

[0238] In modern society, personal health management is a crucial issue, but currently available technologies make it difficult to achieve flexible, real-time health management tailored to individual lifestyles and health conditions. In particular, there is a lack of technology that efficiently provides comprehensive support, including early detection of abnormalities and concrete improvement suggestions.

[0239] 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.

[0240] In this invention, the server includes means for processing accumulated information and evaluating the user's condition, means for presenting a management plan based on the processing results, and means for creating suggestions regarding diet, exercise, and rest using a generative AI model. This makes it possible to monitor the user's health condition in real time and issue warnings and provide appropriate support when abnormalities are detected.

[0241] "Human beings" are individuals from whom information is collected for the purpose of monitoring and improving their health status.

[0242] An "information device" is a device that collects and transmits data, including wearable devices and smartphones.

[0243] A "processing unit" refers to a device that performs calculations, such as a server, and is responsible for data analysis and processing.

[0244] A "management plan" is an advice plan for users that includes action guidelines and suggestions for improving their health.

[0245] A "generative AI model" is an artificial intelligence model that automatically generates optimal suggestions based on data.

[0246] "Wearable devices" refer to information-gathering devices that are worn on the body, such as wearable devices.

[0247] A "warning" is a notification that informs users about an anomaly or risk.

[0248] This invention provides a system to support personal health management. Specific embodiments are described below.

[0249] Users collect various health information from their daily lives using wearable devices and smartphones. This includes data such as steps taken, heart rate, calorie intake, and sleep duration. These devices automatically acquire data when worn and integrate it into smartphone applications.

[0250] The device sends this health data collected from the user to a server via the internet. The server securely stores the received data in a database through a dedicated API. Then, machine learning algorithms are applied to analyze this data. This analysis process utilizes Python libraries such as TensorFlow and Scikit-learn.

[0251] Based on the analysis, the server assesses the user's health status and generates a management plan. This plan is customized using a generative AI model and includes specific suggestions regarding diet, exercise, and rest. For example, if the user is predicted to be inactive based on recent data, advice will be generated indicating how many times a week they should exercise.

[0252] The generated management plan is then communicated to the user again via the device. The device uses natural language processing to provide advice in language that is easy for the user to understand. This allows the user to incorporate the proposed action plan into their daily life.

[0253] As a concrete example, if a user inputs their daily step count and diet, this system can provide a health management plan based on that data, such as: "Please input the following information into the generating AI model: Yesterday's step count was 8,000 steps, diet was high protein and low fat, and sleep time was 7 hours. Please suggest a health management plan suitable for the user."

[0254] Thus, according to this embodiment, users can understand their health status in real time and review their lifestyle habits as needed.

[0255] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0256] Step 1:

[0257] Users collect their own health data using wearable devices or smartphones. Input at this stage includes data such as the user's daily steps, heart rate, diet, and sleep duration. This data is collected automatically by wearing the device or manually entered by the user into the application. The output is the set of health data acquired by the device.

[0258] Step 2:

[0259] The device transmits the collected health data to the server via the internet. The input for this process is the health data collected in step 1. The data is initially processed within the device and converted to the appropriate format. The data is then sent to the server's API via the internet. The output is the health data received by the server.

[0260] Step 3:

[0261] The server stores the received health data in a database and begins data analysis. The input for this analysis is the raw health data sent from the terminal. The server runs machine learning algorithms to evaluate the user's health status based on this data. Python libraries such as TensorFlow and Scikit-learn are used for data processing. The output is the evaluation of the user's health status based on the analysis.

[0262] Step 4:

[0263] The server generates a personalized health management plan based on the analysis results. The input for this step is the evaluation results from step 3. Using the generation AI model, a plan including dietary improvements, exercise recommendations, and rest advice is automatically generated. The output is a customized health management plan provided to the user.

[0264] Step 5:

[0265] The terminal communicates the generated health management plan to the user. The input for this processing step is the management plan sent from the server. The terminal uses natural language processing technology to convert the plan into a format that is easy for the user to understand. The output is specific health management advice displayed to the user.

[0266] Step 6:

[0267] The server performs continuous monitoring, observing changes in the user's health data in real time. In this step, health data is constantly sent to the server, and anomalies are detected. The input is continuously updated health data. If an anomaly is detected, the server issues a warning and sends a notification to the terminal. The output is the warning notification about the detected anomaly.

[0268] (Application Example 1)

[0269] 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."

[0270] In modern society, many individuals face inadequate health management and the risk of lifestyle-related diseases. Furthermore, while there is a need to improve the overall health level of communities, there is a lack of perspective on how to utilize individual data for overall health promotion activities. Therefore, providing a system that comprehensively understands the health status of individuals and communities and can propose appropriate improvement measures is essential.

[0271] 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.

[0272] In this invention, the server includes terminal means for aggregating information acquired from individuals, processing means for analyzing the aggregated information and evaluating the individual's condition, processing means for proposing a specific management plan to the individual based on the analysis results, means for notifying the individual of the proposed management plan via the terminal, processing means for continuously monitoring changes in the individual's condition and issuing a warning when an abnormality is detected, and aggregation means for utilizing the aggregated information for health promotion activities in the local community. This makes it possible to support individual health management while simultaneously contributing to the health promotion of the entire local community.

[0273] "Information obtained from individuals" refers to data collected via wearable devices and mobile information terminals, including individual activity levels, biometric information, and lifestyle data.

[0274] A "data aggregation terminal" refers to a device or software that collects data from multiple information sources and manages it centrally.

[0275] "Processing device" refers to a machine or program that has the function of analyzing and processing collected data and generating results according to a specific purpose.

[0276] A "management plan" is a specific set of action guidelines and recommended habits created based on an analysis of an individual's condition, with the aim of maintaining or improving their health.

[0277] "Monitoring and detecting anomalies" refers to a function that continuously monitors data and checks for fluctuations that exceed the normal range.

[0278] "Issuing a warning" refers to the act of sending a notification to an individual to alert them to an anomaly that has been detected.

[0279] "Data aggregation methods" refer to methods and systems that utilize data obtained from individuals to inform health promotion policies and plans for a wide range of groups or entire regions.

[0280] The system of this invention aims to effectively collect and analyze individual health data and contribute to health promotion activities throughout the community. The system mainly consists of a server, terminals, and users, and each of these entities works in cooperation with each other.

[0281] The terminal continuously collects individual biometric data, activity levels, and lifestyle data from wearable devices and personal digital assistants (PDAs). This data is transmitted to a server via the internet. The terminal uses wearable devices such as Apple Watch and Fitbit to collect data in real time. PPDAs have applications installed that allow for data display and input of additional information.

[0282] The servers operate on cloud platforms (such as Cloud SQL and AWS) and store the collected data. Machine learning models (using TensorFlow, Keras, etc.) running on the servers analyze individual data and assess activity patterns and potential health risks. Based on the analysis results, a management plan tailored to the user is automatically generated. The generated plan includes dietary improvements, exercise recommendations, and sleep advice, and is communicated to the user via their device.

[0283] Furthermore, the server generates aggregated information that can be used for planning health promotion measures across the region. To this end, it analyzes the average activity levels and health status of all residents and proposes community events and campaigns. New insights regarding the health of the local community can be obtained using the generated AI model.

[0284] As a specific example, it is conceivable to promote the "10,000 Steps a Day Challenge" in which local residents participate and share the results through the system. Participants can check their progress with others through the terminal, and it is expected that they will have a positive impact on the entire region when they achieve the challenge.

[0285] An example of a prompt sentence for the generated AI model is "Please propose events and campaigns to improve the health status of the entire community based on the analysis results of the health data of local residents."

[0286] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0287] Step 1:

[0288] The terminal collects personal biometric data from wearable devices and mobile information terminals. As a specific operation, the terminal synchronizes with a wearable device (e.g., a smartwatch) via Bluetooth or Wi-Fi and acquires data such as the number of steps, heart rate, and sleep data. The input is biometric data from the wearable device, and the output is the personal health data stored on the terminal.

[0289] Step 2:

[0290] <0, The terminal sends the collected health data to the server. Specifically, the terminal uploads the data to the server on the cloud platform via an Internet connection. At this time, an encryption protocol is used for data transmission to ensure the security of personal information. The input is the health data stored on the terminal, and the output is the data accumulated on the server.

[0291] Step 3:

[0292] The server analyzes the received data and assesses the individual's health status. This process uses a machine learning model to analyze the collected data, including comparison with past records, detection of anomalies, and pattern recognition. The output is health assessment information based on the analysis results.

[0293] Step 4:

[0294] The server generates an optimal management plan for each individual based on the analysis results. Specifically, an algorithm using a generation AI model automatically creates improvement plans for diet and exercise. The input is the analyzed health assessment information, and the output is a specific action plan as part of the management plan.

[0295] Step 5:

[0296] The user receives notifications of proposed management plans through their device. Specifically, the device displays the plan on the user interface and provides push notifications and alerts. The input is the management plan sent from the server, and the output is the provision of visual and auditory information to the user.

[0297] Step 6:

[0298] The server aggregates individual health data and uses it for health promotion activities across the entire community. Specifically, it uses a generative AI model to generate prompts and propose measures tailored to the needs of local residents. The input is aggregated health data, and the output is suggestions for events and campaigns that will lead to improved health throughout the community.

[0299] 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.

[0300] Embodiments of this invention include the coordination of a terminal, a server, and a user, and further involve health management that takes into account the user's emotional state. First, the terminal used by the user collects health data. The terminal acquires data on activity level, heart rate, diet, and sleep patterns through the user's wearable device or smartphone. At this time, the emotion engine within the terminal analyzes the user's voice and facial expressions to recognize their emotional state.

[0301] The collected health and emotional data is sent to a server. The server uses machine learning algorithms to analyze the data and assess the user's health status. This analysis aims to reflect the user's emotional state and improve the accuracy of the health management plan. For example, if an emotion indicating stress is detected, suggestions for activities to promote relaxation will be incorporated into the plan.

[0302] The server generates a health management plan tailored to the user's condition based on the analysis results. The generated plan is customized according to the user's emotional state, as recognized by the emotion engine. The terminal notifies the user of this plan and explains it clearly using natural language processing. This allows the user to select appropriate health actions based on their emotions.

[0303] Furthermore, the server continuously monitors health and emotional data, and quickly generates alerts if anomalies or risks occur. For example, if the emotion engine detects an abnormal emotional state, the terminal sends an alert to the user, prompting them to take appropriate action. This system enables comprehensive health management that supports not only physical health but also mental health.

[0304] The following describes the processing flow.

[0305] Step 1:

[0306] The terminal collects information such as activity level, heart rate, sleep pattern, etc. as biometric data via the user's wearable device and smartphone. At the same time, the emotion engine in the terminal uses the camera and microphone to recognize the emotional state by analyzing the user's expression and voice.

[0307] Step 2:

[0308] The terminal sends the collected biometric data and emotion data to the server. At this time, the data is sent using a secure protocol with privacy protected.

[0309] Step 3:

[0310] The server analyzes the received data. Here, the machine learning algorithm evaluates the user's health status based on the biometric data and generates an analysis result that depends on the emotional state by taking into account the emotion data.

[0311] Step 4:

[0312] The server creates a customized health management plan for the user based on the analysis result. This plan reflects the user's emotional state. For example, if a high-stress state is detected from the emotion data, it includes a relaxation proposal.

[0313] Step 5:

[0314] The terminal notifies the user of the generated health management plan. Natural language processing technology is used for the notification, and the notification content is adjusted to assist the user's understanding.

[0315] Step 6:

[0316] The server continuously monitors the user's health data and emotion data. If abnormal health indicators or emotional states are detected, it immediately generates an alert to the terminal, and the user is required to respond immediately.

[0317] Step 7:

[0318] Users take appropriate health actions based on instructions from the device. For example, by implementing stress management techniques guided by the device, users can strive to improve their emotional state.

[0319] (Example 2)

[0320] 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".

[0321] In modern society, personal health management requires consideration not only of physical aspects but also of psychological and emotional aspects. However, conventional health management systems often only consider physiological data, making it difficult to generate management plans that adequately reflect an individual's emotional state. Furthermore, there is a lack of methods to effectively utilize emotional data and provide information that is best suited to the individual.

[0322] 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.

[0323] In this invention, the server includes means for analyzing physiological and emotional data acquired from an individual and evaluating the individual's state; means for generating a specific management plan for the individual based on the analysis results using a generative AI model; and means for customizing and notifying the individual of the generated management plan according to their emotional state. This enables comprehensive health management that takes emotional state into account and the provision of information optimized for the individual.

[0324] "Individuals" refer to users of the system, and are the subjects from whom health and emotional data are collected.

[0325] "Physiological data" refers to data that indicates an individual's physical condition, including activity level, heart rate, diet, and sleep patterns.

[0326] "Emotional data" refers to data that indicates an individual's psychological state, including emotional states analyzed from voice and facial expressions.

[0327] "Terminal means" refers to a device for collecting physiological and emotional data from an individual and transmitting it to a server, and includes smartphones and wearable electronic devices.

[0328] "Server means" refers to a computer system that analyzes received physiological and emotional data to evaluate an individual's state.

[0329] A "generative AI model" is an algorithm that utilizes artificial intelligence technology to generate management plans for individuals based on data analysis results.

[0330] A "management plan" is a plan that includes specific activities and suggestions created by a generative AI model, with the aim of maintaining and improving an individual's health.

[0331] "Natural language processing" is a technology that provides information to individuals in an easily understandable way via a device, and it is a method by which computers understand and process human language.

[0332] A "wearable electronic device" refers to a device used as part of a terminal system, which is attached to an individual's body to collect physiological data.

[0333] In order to implement this invention, it is necessary to build a system in which terminals, servers, and users work together.

[0334] First, the terminal uses devices such as smartphones and wearable electronic devices to collect physiological and emotional data from the user. This terminal collects physiological data such as the user's activity level, heart rate, diet, and sleep patterns using sensors. It also captures voice and facial expressions using cameras and microphones built into the wearable electronic device, and processes the emotional data using an emotion engine.

[0335] Next, the collected data is sent to a server. The server uses a powerful computer equipped with server software that runs machine learning algorithms. The server analyzes the received data and uses a generative AI model to assess the user's physiological and emotional state. Based on this assessment, it generates a specific health management plan for the user. This plan is optimized according to the user's health and emotional condition.

[0336] The user receives a management plan provided by the device. The device can use natural language processing technology to communicate this plan to the user in an easy-to-understand way, either in text or voice. For example, a prompt might say, "Your current stress level is high. Try incorporating a yoga session to relax."

[0337] Such a system allows users to gain a deeper understanding of their own health management and choose appropriate actions based on their emotional state.

[0338] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0339] Step 1:

[0340] The device acquires physiological and emotional data from the user. Specifically, it uses sensors via wearable devices and smartphones to collect the user's heart rate, activity level, diet, and sleep patterns. Furthermore, the device uses cameras and microphones to capture voice and facial expressions, and recognizes emotional data through an emotion engine. Inputs are the user's physiological indicators, voice, and video data, which are aggregated and stored within the device. Outputs are organized physiological and emotional data.

[0341] Step 2:

[0342] The device compresses and encrypts the collected physiological and emotional data before sending it to the server. Data encryption is crucial to protect user privacy. The input is the compressed and encrypted user data, and the output is a secure data packet sent to the server.

[0343] Step 3:

[0344] The server analyzes the received data and uses a generative AI model to assess the user's health status. The machine learning algorithm compares the user's past data patterns and similar profiles to quantify and evaluate their health status. Inputs are physiological and emotional data, and the output is detailed information about the user's current health assessment and condition.

[0345] Step 4:

[0346] The server generates a personalized health management plan based on the analysis results. The generating AI model automatically suggests activities appropriate to the user's emotional state. For example, if the stress level is determined to be high, a prompt message recommending relaxation activities will be generated. The input is the analyzed health assessment information, and the output is a customized management plan and prompt messages.

[0347] Step 5:

[0348] The terminal receives a management plan from the server and notifies the user via natural language processing technology. By clearly communicating suggestions in text or audio format, it helps users take immediate action. For example, the user might receive instructions such as, "Your current stress level is high. Try incorporating a yoga session to relax." The input is the generated management plan and prompt, while the output is the notification to the user.

[0349] Step 6:

[0350] The server continuously monitors the user's physiological and emotional data, and immediately generates an alert and notifies the terminal if an anomaly is detected. The alert aims to enable early detection of health problems and support users in taking prompt action. The input is real-time data that is continuously monitored, and the output is alert information issued when an anomaly is detected.

[0351] (Application Example 2)

[0352] 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."

[0353] In recent years, a comprehensive approach to personal health management has been required, taking into account not only physical health but also emotional state. However, conventional systems have been unable to adequately reflect emotional states, making it difficult to provide personalized health advice. Furthermore, the lack of automated means to handle health-related data and emotional data in an integrated manner has created a need for a more comprehensive and individualized health management system.

[0354] 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.

[0355] In this invention, the server includes a device for collecting biometric information acquired from an individual, an information processing device for analyzing the collected biometric information and emotional state and evaluating the individual's health status, and an information processing device for presenting a concrete health management plan to the individual based on the analysis results. This makes it possible to manage health while taking into account the individual's emotional state.

[0356] "Biometric information" refers to data that indicates an individual's physical health status, and mainly includes activity levels, heart rate, dietary content, and sleep patterns.

[0357] "Emotional state" refers to data that indicates an individual's mental state, and is obtained by analyzing voice and facial expressions.

[0358] An "information processing device" is a device that analyzes collected data and evaluates an individual's health and emotional state.

[0359] A "health management plan" is a set of concrete guidelines or action plans provided with the aim of improving or maintaining an individual's health.

[0360] An "automated machine" is a device that operates autonomously according to a specified program to provide health-related advice to an individual.

[0361] A description of the embodiment for carrying out the invention will be provided.

[0362] This system is designed to support individual health management. The device collects biometric information and emotional states through wearable devices and smartphones worn by the user. The device uses an emotion analysis engine to analyze voice and facial expressions, recognizing the user's emotional state in real time.

[0363] The collected data is sent to a server. The server, acting as an information processing device, utilizes machine learning algorithms to comprehensively analyze the collected biometric information and emotional state. This makes it possible to generate a health management plan optimized for each individual user. The server also has the function to evaluate the user's health status based on the analysis results and detect abnormalities and risks in real time.

[0364] The generated health management plan is notified to the user via the terminal. The user receives advice in an easily understandable format through an interface that utilizes natural language processing technology. The automated system provides health-related advice and helps the user take appropriate actions according to their emotional state.

[0365] For example, if the system determines that the user is fatigued due to work stress, it will suggest deep breathing or light exercise as relaxation methods. Furthermore, by utilizing a generative AI model, it can provide flexible advice based on the user's state. An example of a prompt might be, "Generate stress reduction suggestions based on the user's emotional and health data."

[0366] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0367] Step 1:

[0368] The device collects biometric information from wearable devices and smartphones. Inputs include the individual's activity level, heart rate, diet, and sleep patterns, while output is the storage of this data in digital format. To acquire this data, the device communicates with the wearable devices using Bluetooth or Wi-Fi.

[0369] Step 2:

[0370] The device recognizes the user's voice and facial expressions and analyzes their emotional state based on this information. The input is the user's voice and video data, and the output is the analyzed emotional state. Emotion analysis uses an emotion analysis engine to extract features from voice intonation and facial expressions, and then classifies emotions based on these patterns. Computer vision technology and natural language processing are used in this process.

[0371] Step 3:

[0372] The terminal transmits the collected biometric information and analyzed emotional state to the server. The input is the data obtained from steps 1 and 2, and the output is the biometric information and emotional state transmitted to the server. A secure protocol is used for data transmission to prevent data leakage.

[0373] Step 4:

[0374] The server analyzes biometric information and emotional state in combination to assess the user's health status. Inputs are transmitted biometric information and emotional state, while output is the user's health assessment data. The server applies machine learning algorithms to identify abnormalities in emotional state and potential health risks. This process utilizes big data technology to train models based on a large amount of health-related datasets.

[0375] Step 5:

[0376] The server generates an individualized health management plan based on the evaluation results. The input is the user's health evaluation data. The output is the generated health management plan, which includes specific action plans tailored to the user's characteristics. A generation AI model is used to create prompts and provide optimal suggestions based on the user's situation.

[0377] Step 6:

[0378] The server sends the generated health management plan to the terminal, which then notifies the user. The input is the health management plan data, and the output is a specific health action plan presented to the user. The terminal uses natural language processing to provide advice to the user in an easy-to-understand format.

[0379] Step 7:

[0380] The server continuously monitors changes in the user's health status and issues alerts if an anomaly is detected. Inputs are real-time updated biometric information and emotional states, while outputs are alarm messages upon anomaly detection. An anomaly detection algorithm is used to enable rapid response.

[0381] 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.

[0382] 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.

[0383] 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.

[0384] [Third Embodiment]

[0385] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0386] 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.

[0387] 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).

[0388] 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.

[0389] 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.

[0390] 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).

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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".

[0397] This embodiment of the invention begins with the collection of health data by a user's device. The device periodically acquires data such as daily activity levels, heart rate, dietary information, and sleep patterns via the user's wearable device or smartphone. Manual data collection is also possible by the user entering dietary information into a smartphone app.

[0398] The device transmits the collected data to a server via the internet. The server stores diverse data and evaluates the user's health status through advanced AI analysis. Machine learning algorithms are used for the analysis, detecting patterns and predicting anomalies based on past data. The results of this analysis are used to generate an optimal health management plan for the user.

[0399] Based on the analysis results, the server automatically generates a personalized health management plan that includes suggestions for dietary improvements, exercise recommendations, and sleep advice. The generated plan is then notified to the user via their device. The device uses natural language processing to provide advice in a way that is easy for the user to understand. This allows the user to practice healthy lifestyle habits based on the presented plan.

[0400] Furthermore, the server performs continuous monitoring, constantly watching for changes in health data. If an abnormality or risk is detected, an alert notification is sent to the device. Specifically, if the user's heart rate exceeds the normal range or if an abnormal sleep pattern persists, an alert is immediately sent to the device. This alert allows the user to quickly manage their health and, if necessary, seek medical attention or review their lifestyle habits.

[0401] In this way, by using the system, users can comprehensively manage and improve their own health. This process is achieved through the coordinated operation of the terminal, server, and user.

[0402] The following describes the processing flow.

[0403] Step 1:

[0404] The device collects health data using the user's wearable device or smartphone. Specifically, it obtains activity levels and heart rate from the wearable device via Bluetooth, and supplements the data by allowing the user to manually input dietary information using a smartphone app.

[0405] Step 2:

[0406] The terminal uploads the collected data to the server at regular intervals. This operation involves the process of sending data packets over the network and aggregating them on the server.

[0407] Step 3:

[0408] The server aggregates the received data and begins processing it with an AI analysis engine. This uses machine learning algorithms to identify trends in the data and perform analysis to detect anomalies.

[0409] Step 4:

[0410] Based on the analysis results, the server automatically generates a personalized health management plan for each user. This plan includes suggestions for dietary improvements, exercise recommendations, and sleep advice.

[0411] Step 5:

[0412] The device notifies the user of the generated health management plan. This information is presented in an easy-to-understand format using natural language processing technology.

[0413] Step 6:

[0414] The server continuously monitors the user's health data and evaluates any changes in real time. If an anomaly or risk is detected, it immediately generates an alert.

[0415] Step 7:

[0416] The device receives alerts sent from the server and notifies the user. This allows the user to take quick action to address any abnormal health conditions.

[0417] (Example 1)

[0418] 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."

[0419] In modern society, personal health management is a crucial issue, but currently available technologies make it difficult to achieve flexible, real-time health management tailored to individual lifestyles and health conditions. In particular, there is a lack of technology that efficiently provides comprehensive support, including early detection of abnormalities and concrete improvement suggestions.

[0420] 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.

[0421] In this invention, the server includes means for processing accumulated information and evaluating the user's condition, means for presenting a management plan based on the processing results, and means for creating suggestions regarding diet, exercise, and rest using a generative AI model. This makes it possible to monitor the user's health condition in real time and issue warnings and provide appropriate support when abnormalities are detected.

[0422] "Human beings" are individuals from whom information is collected for the purpose of monitoring and improving their health status.

[0423] An "information device" is a device that collects and transmits data, including wearable devices and smartphones.

[0424] A "processing unit" refers to a device that performs calculations, such as a server, and is responsible for data analysis and processing.

[0425] A "management plan" is an advice plan for users that includes action guidelines and suggestions for improving their health.

[0426] A "generative AI model" is an artificial intelligence model that automatically generates optimal suggestions based on data.

[0427] "Wearable devices" refer to information-gathering devices that are worn on the body, such as wearable devices.

[0428] A "warning" is a notification that informs users about an anomaly or risk.

[0429] This invention provides a system to support personal health management. Specific embodiments are described below.

[0430] Users collect various health information from their daily lives using wearable devices and smartphones. This includes data such as steps taken, heart rate, calorie intake, and sleep duration. These devices automatically acquire data when worn and integrate it into smartphone applications.

[0431] The device sends this health data collected from the user to a server via the internet. The server securely stores the received data in a database through a dedicated API. Then, machine learning algorithms are applied to analyze this data. This analysis process utilizes Python libraries such as TensorFlow and Scikit-learn.

[0432] Based on the analysis, the server assesses the user's health status and generates a management plan. This plan is customized using a generative AI model and includes specific suggestions regarding diet, exercise, and rest. For example, if the user is predicted to be inactive based on recent data, advice will be generated indicating how many times a week they should exercise.

[0433] The generated management plan is then communicated to the user again via the device. The device uses natural language processing to provide advice in language that is easy for the user to understand. This allows the user to incorporate the proposed action plan into their daily life.

[0434] As a concrete example, if a user inputs their daily step count and diet, this system can provide a health management plan based on that data, such as: "Please input the following information into the generating AI model: Yesterday's step count was 8,000 steps, diet was high protein and low fat, and sleep time was 7 hours. Please suggest a health management plan suitable for the user."

[0435] Thus, according to this embodiment, users can understand their health status in real time and review their lifestyle habits as needed.

[0436] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0437] Step 1:

[0438] Users collect their own health data using wearable devices or smartphones. Input at this stage includes data such as the user's daily steps, heart rate, diet, and sleep duration. This data is collected automatically by wearing the device or manually entered by the user into the application. The output is the set of health data acquired by the device.

[0439] Step 2:

[0440] The device transmits the collected health data to the server via the internet. The input for this process is the health data collected in step 1. The data is initially processed within the device and converted to the appropriate format. The data is then sent to the server's API via the internet. The output is the health data received by the server.

[0441] Step 3:

[0442] The server stores the received health data in a database and begins data analysis. The input for this analysis is the raw health data sent from the terminal. The server runs machine learning algorithms to evaluate the user's health status based on this data. Python libraries such as TensorFlow and Scikit-learn are used for data processing. The output is the evaluation of the user's health status based on the analysis.

[0443] Step 4:

[0444] The server generates a personalized health management plan based on the analysis results. The input for this step is the evaluation results from step 3. Using the generation AI model, a plan including dietary improvements, exercise recommendations, and rest advice is automatically generated. The output is a customized health management plan provided to the user.

[0445] Step 5:

[0446] The terminal communicates the generated health management plan to the user. The input for this processing step is the management plan sent from the server. The terminal uses natural language processing technology to convert the plan into a format that is easy for the user to understand. The output is specific health management advice displayed to the user.

[0447] Step 6:

[0448] The server performs continuous monitoring, observing changes in the user's health data in real time. In this step, health data is constantly sent to the server, and anomalies are detected. The input is continuously updated health data. If an anomaly is detected, the server issues a warning and sends a notification to the terminal. The output is the warning notification about the detected anomaly.

[0449] (Application Example 1)

[0450] 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."

[0451] In modern society, many individuals face inadequate health management and the risk of lifestyle-related diseases. Furthermore, while there is a need to improve the overall health level of communities, there is a lack of perspective on how to utilize individual data for overall health promotion activities. Therefore, providing a system that comprehensively understands the health status of individuals and communities and can propose appropriate improvement measures is essential.

[0452] 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.

[0453] In this invention, the server includes terminal means for aggregating information acquired from individuals, processing means for analyzing the aggregated information and evaluating the individual's condition, processing means for proposing a specific management plan to the individual based on the analysis results, means for notifying the individual of the proposed management plan via the terminal, processing means for continuously monitoring changes in the individual's condition and issuing a warning when an abnormality is detected, and aggregation means for utilizing the aggregated information for health promotion activities in the local community. This makes it possible to support individual health management while simultaneously contributing to the health promotion of the entire local community.

[0454] "Information obtained from individuals" refers to data collected via wearable devices and mobile information terminals, including individual activity levels, biometric information, and lifestyle data.

[0455] A "data aggregation terminal" refers to a device or software that collects data from multiple information sources and manages it centrally.

[0456] "Processing device" refers to a machine or program that has the function of analyzing and processing collected data and generating results according to a specific purpose.

[0457] A "management plan" is a specific set of action guidelines and recommended habits created based on an analysis of an individual's condition, with the aim of maintaining or improving their health.

[0458] "Monitoring and detecting anomalies" refers to a function that continuously monitors data and checks for fluctuations that exceed the normal range.

[0459] "Issuing a warning" refers to the act of sending a notification to an individual to alert them to an anomaly that has been detected.

[0460] "Data aggregation methods" refer to methods and systems that utilize data obtained from individuals to inform health promotion policies and plans for a wide range of groups or entire regions.

[0461] The system of this invention aims to effectively collect and analyze individual health data and contribute to health promotion activities throughout the community. The system mainly consists of a server, terminals, and users, and each of these entities works in cooperation with each other.

[0462] The terminal continuously collects individual biometric data, activity levels, and lifestyle data from wearable devices and personal digital assistants (PDAs). This data is transmitted to a server via the internet. The terminal uses wearable devices such as Apple Watch and Fitbit to collect data in real time. PPDAs have applications installed that allow for data display and input of additional information.

[0463] The servers operate on cloud platforms (such as Cloud SQL and AWS) and store the collected data. Machine learning models (using TensorFlow, Keras, etc.) running on the servers analyze individual data and assess activity patterns and potential health risks. Based on the analysis results, a management plan tailored to the user is automatically generated. The generated plan includes dietary improvements, exercise recommendations, and sleep advice, and is communicated to the user via their device.

[0464] Furthermore, the server generates aggregated information that can be used to plan health promotion measures across the entire region. This involves analyzing the average activity levels and health status of the entire population and proposing community events and campaigns. Generative AI models can be used to gain new insights into the health of the community.

[0465] One concrete example is promoting a "10,000 Steps Daily Challenge" in which local residents participate, and sharing their progress through a system. Participants can check their progress against others via their devices, and it is expected that achieving the challenge will have a positive impact on the entire community.

[0466] An example of a prompt for a generative AI model is: "Based on the analysis of local residents' health data, please suggest events and campaigns to improve the overall health of the community."

[0467] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0468] Step 1:

[0469] The device collects personal biometric data from wearable devices and personal digital assistants (PDAs). Specifically, the device synchronizes with a wearable device (e.g., a smartwatch) via Bluetooth or Wi-Fi to acquire data such as steps, heart rate, and sleep patterns. The input is biometric data from the wearable device, and the output is personal health data stored on the device.

[0470] Step 2:

[0471] The device transmits the collected health data to the server. Specifically, the device uploads the data to a server on a cloud platform via an internet connection. Encryption protocols are used for data transmission to ensure the security of personal information. The input is health data stored on the device, and the output is data stored on the server.

[0472] Step 3:

[0473] The server analyzes the received data and assesses the individual's health status. This process uses a machine learning model to analyze the collected data, including comparison with past records, detection of anomalies, and pattern recognition. The output is health assessment information based on the analysis results.

[0474] Step 4:

[0475] The server generates an optimal management plan for each individual based on the analysis results. Specifically, an algorithm using a generation AI model automatically creates improvement plans for diet and exercise. The input is the analyzed health assessment information, and the output is a specific action plan as part of the management plan.

[0476] Step 5:

[0477] The user receives notifications of proposed management plans through their device. Specifically, the device displays the plan on the user interface and provides push notifications and alerts. The input is the management plan sent from the server, and the output is the provision of visual and auditory information to the user.

[0478] Step 6:

[0479] The server aggregates individual health data and uses it for health promotion activities across the entire community. Specifically, it uses a generative AI model to generate prompts and propose measures tailored to the needs of local residents. The input is aggregated health data, and the output is suggestions for events and campaigns that will lead to improved health throughout the community.

[0480] 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.

[0481] Embodiments of this invention include the coordination of a terminal, a server, and a user, and further involve health management that takes into account the user's emotional state. First, the terminal used by the user collects health data. The terminal acquires data on activity level, heart rate, diet, and sleep patterns through the user's wearable device or smartphone. At this time, the emotion engine within the terminal analyzes the user's voice and facial expressions to recognize their emotional state.

[0482] The collected health and emotional data is sent to a server. The server uses machine learning algorithms to analyze the data and assess the user's health status. This analysis aims to reflect the user's emotional state and improve the accuracy of the health management plan. For example, if an emotion indicating stress is detected, suggestions for activities to promote relaxation will be incorporated into the plan.

[0483] The server generates a health management plan tailored to the user's condition based on the analysis results. The generated plan is customized according to the user's emotional state, as recognized by the emotion engine. The terminal notifies the user of this plan and explains it clearly using natural language processing. This allows the user to select appropriate health actions based on their emotions.

[0484] Furthermore, the server continuously monitors health and emotional data, and quickly generates alerts if anomalies or risks occur. For example, if the emotion engine detects an abnormal emotional state, the terminal sends an alert to the user, prompting them to take appropriate action. This system enables comprehensive health management that supports not only physical health but also mental health.

[0485] The following describes the processing flow.

[0486] Step 1:

[0487] The device collects biometric data such as activity levels, heart rate, and sleep patterns via the user's wearable device and smartphone. Simultaneously, an emotion engine within the device uses the camera and microphone to analyze the user's facial expressions and voice to recognize their emotional state.

[0488] Step 2:

[0489] The device transmits the collected biometric and emotional data to the server. This data transmission is performed using a secure protocol that protects privacy.

[0490] Step 3:

[0491] The server analyzes the received data. Here, a machine learning algorithm evaluates the user's health status based on biometric data and, taking emotional data into account, generates an analysis result that depends on the emotional state.

[0492] Step 4:

[0493] Based on the analysis results, the server creates a customized health management plan for the user. This plan reflects the user's emotional state; for example, if emotional data detects a high-stress state, it will include relaxation suggestions.

[0494] Step 5:

[0495] The device notifies the user of the generated health management plan. Natural language processing technology is used in the notification, and the content of the notification is adjusted to help the user understand it.

[0496] Step 6:

[0497] The server continuously monitors the user's health and emotional data. If abnormal health indicators or emotional states are detected, an alert is immediately generated on the device, requiring the user to take immediate action.

[0498] Step 7:

[0499] Users take appropriate health actions based on instructions from the device. For example, by implementing stress management techniques guided by the device, users can strive to improve their emotional state.

[0500] (Example 2)

[0501] 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."

[0502] In modern society, personal health management requires consideration not only of physical aspects but also of psychological and emotional aspects. However, conventional health management systems often only consider physiological data, making it difficult to generate management plans that adequately reflect an individual's emotional state. Furthermore, there is a lack of methods to effectively utilize emotional data and provide information that is best suited to the individual.

[0503] 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.

[0504] In this invention, the server includes means for analyzing physiological and emotional data acquired from an individual and evaluating the individual's state; means for generating a specific management plan for the individual based on the analysis results using a generative AI model; and means for customizing and notifying the individual of the generated management plan according to their emotional state. This enables comprehensive health management that takes emotional state into account and the provision of information optimized for the individual.

[0505] "Individuals" refer to users of the system, and are the subjects from whom health and emotional data are collected.

[0506] "Physiological data" refers to data that indicates an individual's physical condition, including activity level, heart rate, diet, and sleep patterns.

[0507] "Emotional data" refers to data that indicates an individual's psychological state, including emotional states analyzed from voice and facial expressions.

[0508] "Terminal means" refers to a device for collecting physiological and emotional data from an individual and transmitting it to a server, and includes smartphones and wearable electronic devices.

[0509] "Server means" refers to a computer system that analyzes received physiological and emotional data to evaluate an individual's state.

[0510] A "generative AI model" is an algorithm that utilizes artificial intelligence technology to generate management plans for individuals based on data analysis results.

[0511] A "management plan" is a plan that includes specific activities and suggestions created by a generative AI model, with the aim of maintaining and improving an individual's health.

[0512] "Natural language processing" is a technology that provides information to individuals in an easily understandable way via a device, and it is a method by which computers understand and process human language.

[0513] A "wearable electronic device" refers to a device used as part of a terminal system, which is attached to an individual's body to collect physiological data.

[0514] In order to implement this invention, it is necessary to build a system in which terminals, servers, and users work together.

[0515] First, the terminal uses devices such as smartphones and wearable electronic devices to collect physiological and emotional data from the user. This terminal collects physiological data such as the user's activity level, heart rate, diet, and sleep patterns using sensors. It also captures voice and facial expressions using cameras and microphones built into the wearable electronic device, and processes the emotional data using an emotion engine.

[0516] Next, the collected data is sent to a server. The server uses a powerful computer equipped with server software that runs machine learning algorithms. The server analyzes the received data and uses a generative AI model to assess the user's physiological and emotional state. Based on this assessment, it generates a specific health management plan for the user. This plan is optimized according to the user's health and emotional condition.

[0517] The user receives a management plan provided by the device. The device can use natural language processing technology to communicate this plan to the user in an easy-to-understand way, either in text or voice. For example, a prompt might say, "Your current stress level is high. Try incorporating a yoga session to relax."

[0518] Such a system allows users to gain a deeper understanding of their own health management and choose appropriate actions based on their emotional state.

[0519] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0520] Step 1:

[0521] The device acquires physiological and emotional data from the user. Specifically, it uses sensors via wearable devices and smartphones to collect the user's heart rate, activity level, diet, and sleep patterns. Furthermore, the device uses cameras and microphones to capture voice and facial expressions, and recognizes emotional data through an emotion engine. Inputs are the user's physiological indicators, voice, and video data, which are aggregated and stored within the device. Outputs are organized physiological and emotional data.

[0522] Step 2:

[0523] The device compresses and encrypts the collected physiological and emotional data before sending it to the server. Data encryption is crucial to protect user privacy. The input is the compressed and encrypted user data, and the output is a secure data packet sent to the server.

[0524] Step 3:

[0525] The server analyzes the received data and uses a generative AI model to assess the user's health status. The machine learning algorithm compares the user's past data patterns and similar profiles to quantify and evaluate their health status. Inputs are physiological and emotional data, and the output is detailed information about the user's current health assessment and condition.

[0526] Step 4:

[0527] The server generates a personalized health management plan based on the analysis results. The generating AI model automatically suggests activities appropriate to the user's emotional state. For example, if the stress level is determined to be high, a prompt message recommending relaxation activities will be generated. The input is the analyzed health assessment information, and the output is a customized management plan and prompt messages.

[0528] Step 5:

[0529] The terminal receives a management plan from the server and notifies the user via natural language processing technology. By clearly communicating suggestions in text or audio format, it helps users take immediate action. For example, the user might receive instructions such as, "Your current stress level is high. Try incorporating a yoga session to relax." The input is the generated management plan and prompt, while the output is the notification to the user.

[0530] Step 6:

[0531] The server continuously monitors the user's physiological and emotional data, and immediately generates an alert and notifies the terminal if an anomaly is detected. The alert aims to enable early detection of health problems and support users in taking prompt action. The input is real-time data that is continuously monitored, and the output is alert information issued when an anomaly is detected.

[0532] (Application Example 2)

[0533] 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."

[0534] In recent years, a comprehensive approach to personal health management has been required, taking into account not only physical health but also emotional state. However, conventional systems have been unable to adequately reflect emotional states, making it difficult to provide personalized health advice. Furthermore, the lack of automated means to handle health-related data and emotional data in an integrated manner has created a need for a more comprehensive and individualized health management system.

[0535] 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.

[0536] In this invention, the server includes a device for collecting biometric information acquired from an individual, an information processing device for analyzing the collected biometric information and emotional state and evaluating the individual's health status, and an information processing device for presenting a concrete health management plan to the individual based on the analysis results. This makes it possible to manage health while taking into account the individual's emotional state.

[0537] "Biometric information" refers to data that indicates an individual's physical health status, and mainly includes activity levels, heart rate, dietary content, and sleep patterns.

[0538] "Emotional state" refers to data that indicates an individual's mental state, and is obtained by analyzing voice and facial expressions.

[0539] An "information processing device" is a device that analyzes collected data and evaluates an individual's health and emotional state.

[0540] A "health management plan" is a set of concrete guidelines or action plans provided with the aim of improving or maintaining an individual's health.

[0541] An "automated machine" is a device that operates autonomously according to a specified program to provide health-related advice to an individual.

[0542] A description of the embodiment for carrying out the invention will be provided.

[0543] This system is designed to support individual health management. The device collects biometric information and emotional states through wearable devices and smartphones worn by the user. The device uses an emotion analysis engine to analyze voice and facial expressions, recognizing the user's emotional state in real time.

[0544] The collected data is sent to a server. The server, acting as an information processing device, utilizes machine learning algorithms to comprehensively analyze the collected biometric information and emotional state. This makes it possible to generate a health management plan optimized for each individual user. The server also has the function to evaluate the user's health status based on the analysis results and detect abnormalities and risks in real time.

[0545] The generated health management plan is notified to the user via the terminal. The user receives advice in an easily understandable format through an interface that utilizes natural language processing technology. The automated system provides health-related advice and helps the user take appropriate actions according to their emotional state.

[0546] For example, if the system determines that the user is fatigued due to work stress, it will suggest deep breathing or light exercise as relaxation methods. Furthermore, by utilizing a generative AI model, it can provide flexible advice based on the user's state. An example of a prompt might be, "Generate stress reduction suggestions based on the user's emotional and health data."

[0547] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0548] Step 1:

[0549] The device collects biometric information from wearable devices and smartphones. Inputs include the individual's activity level, heart rate, diet, and sleep patterns, while output is the storage of this data in digital format. To acquire this data, the device communicates with the wearable devices using Bluetooth or Wi-Fi.

[0550] Step 2:

[0551] The device recognizes the user's voice and facial expressions and analyzes their emotional state based on this information. The input is the user's voice and video data, and the output is the analyzed emotional state. Emotion analysis uses an emotion analysis engine to extract features from voice intonation and facial expressions, and then classifies emotions based on these patterns. Computer vision technology and natural language processing are used in this process.

[0552] Step 3:

[0553] The terminal transmits the collected biometric information and analyzed emotional state to the server. The input is the data obtained from steps 1 and 2, and the output is the biometric information and emotional state transmitted to the server. A secure protocol is used for data transmission to prevent data leakage.

[0554] Step 4:

[0555] The server analyzes biometric information and emotional state in combination to assess the user's health status. Inputs are transmitted biometric information and emotional state, while output is the user's health assessment data. The server applies machine learning algorithms to identify abnormalities in emotional state and potential health risks. This process utilizes big data technology to train models based on a large amount of health-related datasets.

[0556] Step 5:

[0557] The server generates an individualized health management plan based on the evaluation results. The input is the user's health evaluation data. The output is the generated health management plan, which includes specific action plans tailored to the user's characteristics. A generation AI model is used to create prompts and provide optimal suggestions based on the user's situation.

[0558] Step 6:

[0559] The server sends the generated health management plan to the terminal, which then notifies the user. The input is the health management plan data, and the output is a specific health action plan presented to the user. The terminal uses natural language processing to provide advice to the user in an easy-to-understand format.

[0560] Step 7:

[0561] The server continuously monitors changes in the user's health status and issues alerts if an anomaly is detected. Inputs are real-time updated biometric information and emotional states, while outputs are alarm messages upon anomaly detection. An anomaly detection algorithm is used to enable rapid response.

[0562] 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.

[0563] 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.

[0564] 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.

[0565] [Fourth Embodiment]

[0566] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0567] 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.

[0568] 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).

[0569] 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.

[0570] 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.

[0571] 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).

[0572] 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.

[0573] 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.

[0574] 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.

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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".

[0579] This embodiment of the invention begins with the collection of health data by a user's device. The device periodically acquires data such as daily activity levels, heart rate, dietary information, and sleep patterns via the user's wearable device or smartphone. Manual data collection is also possible by the user entering dietary information into a smartphone app.

[0580] The device transmits the collected data to a server via the internet. The server stores diverse data and evaluates the user's health status through advanced AI analysis. Machine learning algorithms are used for the analysis, detecting patterns and predicting anomalies based on past data. The results of this analysis are used to generate an optimal health management plan for the user.

[0581] Based on the analysis results, the server automatically generates a personalized health management plan that includes suggestions for dietary improvements, exercise recommendations, and sleep advice. The generated plan is then notified to the user via their device. The device uses natural language processing to provide advice in a way that is easy for the user to understand. This allows the user to practice healthy lifestyle habits based on the presented plan.

[0582] Furthermore, the server performs continuous monitoring, constantly watching for changes in health data. If an abnormality or risk is detected, an alert notification is sent to the device. Specifically, if the user's heart rate exceeds the normal range or if an abnormal sleep pattern persists, an alert is immediately sent to the device. This alert allows the user to quickly manage their health and, if necessary, seek medical attention or review their lifestyle habits.

[0583] In this way, by using the system, users can comprehensively manage and improve their own health. This process is achieved through the coordinated operation of the terminal, server, and user.

[0584] The following describes the processing flow.

[0585] Step 1:

[0586] The device collects health data using the user's wearable device or smartphone. Specifically, it obtains activity levels and heart rate from the wearable device via Bluetooth, and supplements the data by allowing the user to manually input dietary information using a smartphone app.

[0587] Step 2:

[0588] The terminal uploads the collected data to the server at regular intervals. This operation involves the process of sending data packets over the network and aggregating them on the server.

[0589] Step 3:

[0590] The server aggregates the received data and begins processing it with an AI analysis engine. This uses machine learning algorithms to identify trends in the data and perform analysis to detect anomalies.

[0591] Step 4:

[0592] Based on the analysis results, the server automatically generates a personalized health management plan for each user. This plan includes suggestions for dietary improvements, exercise recommendations, and sleep advice.

[0593] Step 5:

[0594] The device notifies the user of the generated health management plan. This information is presented in an easy-to-understand format using natural language processing technology.

[0595] Step 6:

[0596] The server continuously monitors the user's health data and evaluates any changes in real time. If an anomaly or risk is detected, it immediately generates an alert.

[0597] Step 7:

[0598] The device receives alerts sent from the server and notifies the user. This allows the user to take quick action to address any abnormal health conditions.

[0599] (Example 1)

[0600] 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".

[0601] In modern society, personal health management is a crucial issue, but currently available technologies make it difficult to achieve flexible, real-time health management tailored to individual lifestyles and health conditions. In particular, there is a lack of technology that efficiently provides comprehensive support, including early detection of abnormalities and concrete improvement suggestions.

[0602] 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.

[0603] In this invention, the server includes means for processing accumulated information and evaluating the user's condition, means for presenting a management plan based on the processing results, and means for creating suggestions regarding diet, exercise, and rest using a generative AI model. This makes it possible to monitor the user's health condition in real time and issue warnings and provide appropriate support when abnormalities are detected.

[0604] "Human beings" are individuals from whom information is collected for the purpose of monitoring and improving their health status.

[0605] An "information device" is a device that collects and transmits data, including wearable devices and smartphones.

[0606] A "processing unit" refers to a device that performs calculations, such as a server, and is responsible for data analysis and processing.

[0607] A "management plan" is an advice plan for users that includes action guidelines and suggestions for improving their health.

[0608] A "generative AI model" is an artificial intelligence model that automatically generates optimal suggestions based on data.

[0609] "Wearable devices" refer to information-gathering devices that are worn on the body, such as wearable devices.

[0610] A "warning" is a notification that informs users about an anomaly or risk.

[0611] This invention provides a system to support personal health management. Specific embodiments are described below.

[0612] Users collect various health information from their daily lives using wearable devices and smartphones. This includes data such as steps taken, heart rate, calorie intake, and sleep duration. These devices automatically acquire data when worn and integrate it into smartphone applications.

[0613] The device sends this health data collected from the user to a server via the internet. The server securely stores the received data in a database through a dedicated API. Then, machine learning algorithms are applied to analyze this data. This analysis process utilizes Python libraries such as TensorFlow and Scikit-learn.

[0614] Based on the analysis, the server assesses the user's health status and generates a management plan. This plan is customized using a generative AI model and includes specific suggestions regarding diet, exercise, and rest. For example, if the user is predicted to be inactive based on recent data, advice will be generated indicating how many times a week they should exercise.

[0615] The generated management plan is then communicated to the user again via the device. The device uses natural language processing to provide advice in language that is easy for the user to understand. This allows the user to incorporate the proposed action plan into their daily life.

[0616] As a concrete example, if a user inputs their daily step count and diet, this system can provide a health management plan based on that data, such as: "Please input the following information into the generating AI model: Yesterday's step count was 8,000 steps, diet was high protein and low fat, and sleep time was 7 hours. Please suggest a health management plan suitable for the user."

[0617] Thus, according to this embodiment, users can understand their health status in real time and review their lifestyle habits as needed.

[0618] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0619] Step 1:

[0620] Users collect their own health data using wearable devices or smartphones. Input at this stage includes data such as the user's daily steps, heart rate, diet, and sleep duration. This data is collected automatically by wearing the device or manually entered by the user into the application. The output is the set of health data acquired by the device.

[0621] Step 2:

[0622] The device transmits the collected health data to the server via the internet. The input for this process is the health data collected in step 1. The data is initially processed within the device and converted to the appropriate format. The data is then sent to the server's API via the internet. The output is the health data received by the server.

[0623] Step 3:

[0624] The server stores the received health data in a database and begins data analysis. The input for this analysis is the raw health data sent from the terminal. The server runs machine learning algorithms to evaluate the user's health status based on this data. Python libraries such as TensorFlow and Scikit-learn are used for data processing. The output is the evaluation of the user's health status based on the analysis.

[0625] Step 4:

[0626] The server generates a personalized health management plan based on the analysis results. The input for this step is the evaluation results from step 3. Using the generation AI model, a plan including dietary improvements, exercise recommendations, and rest advice is automatically generated. The output is a customized health management plan provided to the user.

[0627] Step 5:

[0628] The terminal communicates the generated health management plan to the user. The input for this processing step is the management plan sent from the server. The terminal uses natural language processing technology to convert the plan into a format that is easy for the user to understand. The output is specific health management advice displayed to the user.

[0629] Step 6:

[0630] The server performs continuous monitoring, observing changes in the user's health data in real time. In this step, health data is constantly sent to the server, and anomalies are detected. The input is continuously updated health data. If an anomaly is detected, the server issues a warning and sends a notification to the terminal. The output is the warning notification about the detected anomaly.

[0631] (Application Example 1)

[0632] 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".

[0633] In modern society, many individuals face inadequate health management and the risk of lifestyle-related diseases. Furthermore, while there is a need to improve the overall health level of communities, there is a lack of perspective on how to utilize individual data for overall health promotion activities. Therefore, providing a system that comprehensively understands the health status of individuals and communities and can propose appropriate improvement measures is essential.

[0634] 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.

[0635] In this invention, the server includes terminal means for aggregating information acquired from individuals, processing means for analyzing the aggregated information and evaluating the individual's condition, processing means for proposing a specific management plan to the individual based on the analysis results, means for notifying the individual of the proposed management plan via the terminal, processing means for continuously monitoring changes in the individual's condition and issuing a warning when an abnormality is detected, and aggregation means for utilizing the aggregated information for health promotion activities in the local community. This makes it possible to support individual health management while simultaneously contributing to the health promotion of the entire local community.

[0636] "Information obtained from individuals" refers to data collected via wearable devices and mobile information terminals, including individual activity levels, biometric information, and lifestyle data.

[0637] A "data aggregation terminal" refers to a device or software that collects data from multiple information sources and manages it centrally.

[0638] "Processing device" refers to a machine or program that has the function of analyzing and processing collected data and generating results according to a specific purpose.

[0639] A "management plan" is a specific set of action guidelines and recommended habits created based on an analysis of an individual's condition, with the aim of maintaining or improving their health.

[0640] "Monitoring and detecting anomalies" refers to a function that continuously monitors data and checks for fluctuations that exceed the normal range.

[0641] "Issuing a warning" refers to the act of sending a notification to an individual to alert them to an anomaly that has been detected.

[0642] "Data aggregation methods" refer to methods and systems that utilize data obtained from individuals to inform health promotion policies and plans for a wide range of groups or entire regions.

[0643] The system of this invention aims to effectively collect and analyze individual health data and contribute to health promotion activities throughout the community. The system mainly consists of a server, terminals, and users, and each of these entities works in cooperation with each other.

[0644] The terminal continuously collects individual biometric data, activity levels, and lifestyle data from wearable devices and personal digital assistants (PDAs). This data is transmitted to a server via the internet. The terminal uses wearable devices such as Apple Watch and Fitbit to collect data in real time. PPDAs have applications installed that allow for data display and input of additional information.

[0645] The servers operate on cloud platforms (such as Cloud SQL and AWS) and store the collected data. Machine learning models (using TensorFlow, Keras, etc.) running on the servers analyze individual data and assess activity patterns and potential health risks. Based on the analysis results, a management plan tailored to the user is automatically generated. The generated plan includes dietary improvements, exercise recommendations, and sleep advice, and is communicated to the user via their device.

[0646] Furthermore, the server generates aggregated information that can be used to plan health promotion measures across the entire region. This involves analyzing the average activity levels and health status of the entire population and proposing community events and campaigns. Generative AI models can be used to gain new insights into the health of the community.

[0647] One concrete example is promoting a "10,000 Steps Daily Challenge" in which local residents participate, and sharing their progress through a system. Participants can check their progress against others via their devices, and it is expected that achieving the challenge will have a positive impact on the entire community.

[0648] An example of a prompt for a generative AI model is: "Based on the analysis of local residents' health data, please suggest events and campaigns to improve the overall health of the community."

[0649] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0650] Step 1:

[0651] The device collects personal biometric data from wearable devices and personal digital assistants (PDAs). Specifically, the device synchronizes with a wearable device (e.g., a smartwatch) via Bluetooth or Wi-Fi to acquire data such as steps, heart rate, and sleep patterns. The input is biometric data from the wearable device, and the output is personal health data stored on the device.

[0652] Step 2:

[0653] The device transmits the collected health data to the server. Specifically, the device uploads the data to a server on a cloud platform via an internet connection. Encryption protocols are used for data transmission to ensure the security of personal information. The input is health data stored on the device, and the output is data stored on the server.

[0654] Step 3:

[0655] The server analyzes the received data and assesses the individual's health status. This process uses a machine learning model to analyze the collected data, including comparison with past records, detection of anomalies, and pattern recognition. The output is health assessment information based on the analysis results.

[0656] Step 4:

[0657] The server generates an optimal management plan for each individual based on the analysis results. Specifically, an algorithm using a generation AI model automatically creates improvement plans for diet and exercise. The input is the analyzed health assessment information, and the output is a specific action plan as part of the management plan.

[0658] Step 5:

[0659] The user receives notifications of proposed management plans through their device. Specifically, the device displays the plan on the user interface and provides push notifications and alerts. The input is the management plan sent from the server, and the output is the provision of visual and auditory information to the user.

[0660] Step 6:

[0661] The server aggregates individual health data and uses it for health promotion activities across the entire community. Specifically, it uses a generative AI model to generate prompts and propose measures tailored to the needs of local residents. The input is aggregated health data, and the output is suggestions for events and campaigns that will lead to improved health throughout the community.

[0662] 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.

[0663] Embodiments of this invention include the coordination of a terminal, a server, and a user, and further involve health management that takes into account the user's emotional state. First, the terminal used by the user collects health data. The terminal acquires data on activity level, heart rate, diet, and sleep patterns through the user's wearable device or smartphone. At this time, the emotion engine within the terminal analyzes the user's voice and facial expressions to recognize their emotional state.

[0664] The collected health and emotional data is sent to a server. The server uses machine learning algorithms to analyze the data and assess the user's health status. This analysis aims to reflect the user's emotional state and improve the accuracy of the health management plan. For example, if an emotion indicating stress is detected, suggestions for activities to promote relaxation will be incorporated into the plan.

[0665] The server generates a health management plan tailored to the user's condition based on the analysis results. The generated plan is customized according to the user's emotional state, as recognized by the emotion engine. The terminal notifies the user of this plan and explains it clearly using natural language processing. This allows the user to select appropriate health actions based on their emotions.

[0666] Furthermore, the server continuously monitors health and emotional data, and quickly generates alerts if anomalies or risks occur. For example, if the emotion engine detects an abnormal emotional state, the terminal sends an alert to the user, prompting them to take appropriate action. This system enables comprehensive health management that supports not only physical health but also mental health.

[0667] The following describes the processing flow.

[0668] Step 1:

[0669] The device collects biometric data such as activity levels, heart rate, and sleep patterns via the user's wearable device and smartphone. Simultaneously, an emotion engine within the device uses the camera and microphone to analyze the user's facial expressions and voice to recognize their emotional state.

[0670] Step 2:

[0671] The device transmits the collected biometric and emotional data to the server. This data transmission is performed using a secure protocol that protects privacy.

[0672] Step 3:

[0673] The server analyzes the received data. Here, a machine learning algorithm evaluates the user's health status based on biometric data and, taking emotional data into account, generates an analysis result that depends on the emotional state.

[0674] Step 4:

[0675] Based on the analysis results, the server creates a customized health management plan for the user. This plan reflects the user's emotional state; for example, if emotional data detects a high-stress state, it will include relaxation suggestions.

[0676] Step 5:

[0677] The device notifies the user of the generated health management plan. Natural language processing technology is used in the notification, and the content of the notification is adjusted to help the user understand it.

[0678] Step 6:

[0679] The server continuously monitors the user's health and emotional data. If abnormal health indicators or emotional states are detected, an alert is immediately generated on the device, requiring the user to take immediate action.

[0680] Step 7:

[0681] Users take appropriate health actions based on instructions from the device. For example, by implementing stress management techniques guided by the device, users can strive to improve their emotional state.

[0682] (Example 2)

[0683] 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".

[0684] In modern society, personal health management requires consideration not only of physical aspects but also of psychological and emotional aspects. However, conventional health management systems often only consider physiological data, making it difficult to generate management plans that adequately reflect an individual's emotional state. Furthermore, there is a lack of methods to effectively utilize emotional data and provide information that is best suited to the individual.

[0685] 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.

[0686] In this invention, the server includes means for analyzing physiological and emotional data acquired from an individual and evaluating the individual's state; means for generating a specific management plan for the individual based on the analysis results using a generative AI model; and means for customizing and notifying the individual of the generated management plan according to their emotional state. This enables comprehensive health management that takes emotional state into account and the provision of information optimized for the individual.

[0687] "Individuals" refer to users of the system, and are the subjects from whom health and emotional data are collected.

[0688] "Physiological data" refers to data that indicates an individual's physical condition, including activity level, heart rate, diet, and sleep patterns.

[0689] "Emotional data" refers to data that indicates an individual's psychological state, including emotional states analyzed from voice and facial expressions.

[0690] "Terminal means" refers to a device for collecting physiological and emotional data from an individual and transmitting it to a server, and includes smartphones and wearable electronic devices.

[0691] "Server means" refers to a computer system that analyzes received physiological and emotional data to evaluate an individual's state.

[0692] A "generative AI model" is an algorithm that utilizes artificial intelligence technology to generate management plans for individuals based on data analysis results.

[0693] A "management plan" is a plan that includes specific activities and suggestions created by a generative AI model, with the aim of maintaining and improving an individual's health.

[0694] "Natural language processing" is a technology that provides information to individuals in an easily understandable way via a device, and it is a method by which computers understand and process human language.

[0695] A "wearable electronic device" refers to a device used as part of a terminal system, which is attached to an individual's body to collect physiological data.

[0696] In order to implement this invention, it is necessary to build a system in which terminals, servers, and users work together.

[0697] First, the terminal uses devices such as smartphones and wearable electronic devices to collect physiological and emotional data from the user. This terminal collects physiological data such as the user's activity level, heart rate, diet, and sleep patterns using sensors. It also captures voice and facial expressions using cameras and microphones built into the wearable electronic device, and processes the emotional data using an emotion engine.

[0698] Next, the collected data is sent to a server. The server uses a powerful computer equipped with server software that runs machine learning algorithms. The server analyzes the received data and uses a generative AI model to assess the user's physiological and emotional state. Based on this assessment, it generates a specific health management plan for the user. This plan is optimized according to the user's health and emotional condition.

[0699] The user receives a management plan provided by the device. The device can use natural language processing technology to communicate this plan to the user in an easy-to-understand way, either in text or voice. For example, a prompt might say, "Your current stress level is high. Try incorporating a yoga session to relax."

[0700] Such a system allows users to gain a deeper understanding of their own health management and choose appropriate actions based on their emotional state.

[0701] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0702] Step 1:

[0703] The device acquires physiological and emotional data from the user. Specifically, it uses sensors via wearable devices and smartphones to collect the user's heart rate, activity level, diet, and sleep patterns. Furthermore, the device uses cameras and microphones to capture voice and facial expressions, and recognizes emotional data through an emotion engine. Inputs are the user's physiological indicators, voice, and video data, which are aggregated and stored within the device. Outputs are organized physiological and emotional data.

[0704] Step 2:

[0705] The device compresses and encrypts the collected physiological and emotional data before sending it to the server. Data encryption is crucial to protect user privacy. The input is the compressed and encrypted user data, and the output is a secure data packet sent to the server.

[0706] Step 3:

[0707] The server analyzes the received data and uses a generative AI model to assess the user's health status. The machine learning algorithm compares the user's past data patterns and similar profiles to quantify and evaluate their health status. Inputs are physiological and emotional data, and the output is detailed information about the user's current health assessment and condition.

[0708] Step 4:

[0709] The server generates a personalized health management plan based on the analysis results. The generating AI model automatically suggests activities appropriate to the user's emotional state. For example, if the stress level is determined to be high, a prompt message recommending relaxation activities will be generated. The input is the analyzed health assessment information, and the output is a customized management plan and prompt messages.

[0710] Step 5:

[0711] The terminal receives a management plan from the server and notifies the user via natural language processing technology. By clearly communicating suggestions in text or audio format, it helps users take immediate action. For example, the user might receive instructions such as, "Your current stress level is high. Try incorporating a yoga session to relax." The input is the generated management plan and prompt, while the output is the notification to the user.

[0712] Step 6:

[0713] The server continuously monitors the user's physiological and emotional data, and immediately generates an alert and notifies the terminal if an anomaly is detected. The alert aims to enable early detection of health problems and support users in taking prompt action. The input is real-time data that is continuously monitored, and the output is alert information issued when an anomaly is detected.

[0714] (Application Example 2)

[0715] 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".

[0716] In recent years, a comprehensive approach to personal health management has been required, taking into account not only physical health but also emotional state. However, conventional systems have been unable to adequately reflect emotional states, making it difficult to provide personalized health advice. Furthermore, the lack of automated means to handle health-related data and emotional data in an integrated manner has created a need for a more comprehensive and individualized health management system.

[0717] 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.

[0718] In this invention, the server includes a device for collecting biometric information acquired from an individual, an information processing device for analyzing the collected biometric information and emotional state and evaluating the individual's health status, and an information processing device for presenting a concrete health management plan to the individual based on the analysis results. This makes it possible to manage health while taking into account the individual's emotional state.

[0719] "Biometric information" refers to data that indicates an individual's physical health status, and mainly includes activity levels, heart rate, dietary content, and sleep patterns.

[0720] "Emotional state" refers to data that indicates an individual's mental state, and is obtained by analyzing voice and facial expressions.

[0721] An "information processing device" is a device that analyzes collected data and evaluates an individual's health and emotional state.

[0722] A "health management plan" is a set of concrete guidelines or action plans provided with the aim of improving or maintaining an individual's health.

[0723] An "automated machine" is a device that operates autonomously according to a specified program to provide health-related advice to an individual.

[0724] A description of the embodiment for carrying out the invention will be provided.

[0725] This system is designed to support individual health management. The device collects biometric information and emotional states through wearable devices and smartphones worn by the user. The device uses an emotion analysis engine to analyze voice and facial expressions, recognizing the user's emotional state in real time.

[0726] The collected data is sent to a server. The server, acting as an information processing device, utilizes machine learning algorithms to comprehensively analyze the collected biometric information and emotional state. This makes it possible to generate a health management plan optimized for each individual user. The server also has the function to evaluate the user's health status based on the analysis results and detect abnormalities and risks in real time.

[0727] The generated health management plan is notified to the user via the terminal. The user receives advice in an easily understandable format through an interface that utilizes natural language processing technology. The automated system provides health-related advice and helps the user take appropriate actions according to their emotional state.

[0728] For example, if the system determines that the user is fatigued due to work stress, it will suggest deep breathing or light exercise as relaxation methods. Furthermore, by utilizing a generative AI model, it can provide flexible advice based on the user's state. An example of a prompt might be, "Generate stress reduction suggestions based on the user's emotional and health data."

[0729] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0730] Step 1:

[0731] The device collects biometric information from wearable devices and smartphones. Inputs include the individual's activity level, heart rate, diet, and sleep patterns, while output is the storage of this data in digital format. To acquire this data, the device communicates with the wearable devices using Bluetooth or Wi-Fi.

[0732] Step 2:

[0733] The device recognizes the user's voice and facial expressions and analyzes their emotional state based on this information. The input is the user's voice and video data, and the output is the analyzed emotional state. Emotion analysis uses an emotion analysis engine to extract features from voice intonation and facial expressions, and then classifies emotions based on these patterns. Computer vision technology and natural language processing are used in this process.

[0734] Step 3:

[0735] The terminal transmits the collected biometric information and analyzed emotional state to the server. The input is the data obtained from steps 1 and 2, and the output is the biometric information and emotional state transmitted to the server. A secure protocol is used for data transmission to prevent data leakage.

[0736] Step 4:

[0737] The server analyzes biometric information and emotional state in combination to assess the user's health status. Inputs are transmitted biometric information and emotional state, while output is the user's health assessment data. The server applies machine learning algorithms to identify abnormalities in emotional state and potential health risks. This process utilizes big data technology to train models based on a large amount of health-related datasets.

[0738] Step 5:

[0739] The server generates an individualized health management plan based on the evaluation results. The input is the user's health evaluation data. The output is the generated health management plan, which includes specific action plans tailored to the user's characteristics. A generation AI model is used to create prompts and provide optimal suggestions based on the user's situation.

[0740] Step 6:

[0741] The server sends the generated health management plan to the terminal, which then notifies the user. The input is the health management plan data, and the output is a specific health action plan presented to the user. The terminal uses natural language processing to provide advice to the user in an easy-to-understand format.

[0742] Step 7:

[0743] The server continuously monitors changes in the user's health status and issues alerts if an anomaly is detected. Inputs are real-time updated biometric information and emotional states, while outputs are alarm messages upon anomaly detection. An anomaly detection algorithm is used to enable rapid response.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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."

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] The following is further disclosed regarding the embodiments described above.

[0766] (Claim 1)

[0767] A terminal device for collecting data obtained from individuals,

[0768] A server means that analyzes the collected data and evaluates the user's status,

[0769] A server that presents a specific management plan to the user based on the analysis results,

[0770] A means of notifying the user of the proposed management plan via the device,

[0771] A server mechanism that continuously monitors changes in the user's status and issues alerts when an anomaly is detected,

[0772] A system that includes this.

[0773] (Claim 2)

[0774] The system according to claim 1, comprising means for using natural language processing for user interaction.

[0775] (Claim 3)

[0776] The system according to claim 1, wherein the terminal is equipped with means for synchronizing with a wearable device to acquire data.

[0777] "Example 1"

[0778] (Claim 1)

[0779] An information device that collects information acquired from humans,

[0780] A computing device that processes the accumulated information and evaluates the user's state,

[0781] A computing device that presents a management plan to the user based on the processing results,

[0782] A device that notifies users of the presented management plan via an information device,

[0783] A computing device that continuously monitors changes in the user's status and issues a warning if an abnormality is detected,

[0784] A generative AI model that creates suggestions about diet, exercise, and rest based on the processing results,

[0785] A system that includes this.

[0786] (Claim 2)

[0787] The system according to claim 1, comprising means for using natural language processing in interaction with users.

[0788] (Claim 3)

[0789] The system according to claim 1, wherein the information device is equipped with means for acquiring information in synchronization with a wearable device.

[0790] "Application Example 1"

[0791] (Claim 1)

[0792] A terminal device for aggregating information obtained from individuals,

[0793] Processing device means for analyzing the aggregated information and evaluating the state of the individual,

[0794] A processing device that proposes a specific management plan for an individual based on the analysis results,

[0795] A means of notifying individual devices of the proposed management plan via a terminal,

[0796] A processing device that continuously monitors changes in the state of an individual and issues a warning when an abnormality is detected,

[0797] A means of aggregating information to be used for health promotion activities in the local community,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] The system according to claim 1, comprising means for interacting with an individual using means of natural language processing.

[0801] (Claim 3)

[0802] The system according to claim 1, wherein the terminal is equipped with means for acquiring information in conjunction with a wearable terminal.

[0803] "Example 2 of combining an emotion engine"

[0804] (Claim 1)

[0805] A terminal device for collecting physiological and emotional data obtained from individuals,

[0806] A server means for analyzing the collected physiological and emotional data and evaluating the individual's state,

[0807] A server means that uses a generative AI model to generate specific management plans for individuals based on analysis results,

[0808] A means of customizing the generated management plan according to the individual's emotional state and notifying them via the device,

[0809] A server that continuously monitors an individual's physiological and emotional data and issues an alert if an abnormality is detected,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, comprising means for using natural language processing for interaction with individuals.

[0813] (Claim 3)

[0814] The system according to claim 1, wherein the terminal is equipped with means for acquiring physiological data in synchronization with a wearable electronic device.

[0815] "Application example 2 when combining with an emotional engine"

[0816] (Claim 1)

[0817] A device that collects biometric information obtained from an individual,

[0818] An information processing device that analyzes the collected biological information and emotional state and evaluates the health status of the individual,

[0819] An information processing device that presents a concrete health management plan to an individual based on the analysis results,

[0820] A device that notifies the individual of the presented health management plan,

[0821] An information processing device that continuously monitors changes in an individual's emotional state and issues an alarm if an abnormality is detected,

[0822] A system including an automated machine that adaptively provides health-related advice based on an individual's emotional and health status.

[0823] (Claim 2)

[0824] The system according to claim 1, comprising means for using natural language processing for interaction with individuals.

[0825] (Claim 3)

[0826] The system according to claim 1, wherein the device is equipped with means for acquiring biometric information in synchronization with a wearable information device. [Explanation of symbols]

[0827] 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 terminal device for aggregating information obtained from individuals, Processing device means for analyzing the aggregated information and evaluating the state of the individual, A processing device that proposes a specific management plan for an individual based on the analysis results, A means of notifying individual devices of the proposed management plan via a terminal, A processing device that continuously monitors changes in the state of an individual and issues a warning when an abnormality is detected, A means of aggregating information to be used for health promotion activities in the local community, A system that includes this.

2. The system according to claim 1, comprising means for interacting with an individual using means of natural language processing.

3. The system according to claim 1, wherein the terminal is equipped with means for acquiring information in conjunction with a wearable terminal.

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