system

Abstract

We provide the system. [Solution] A means of collecting user health status data from a sensor device, Means for transmitting the aforementioned health status data to a server via a network, The server provides means for storing the aforementioned health status data in a database, A means of analyzing stored data and generating health reports for each user, A means for delivering and notifying the user of the aforementioned health report, 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 that responds 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] There is a need for a system that can integrate various data necessary for health management, efficiently analyze it, and provide personalized health guidance to individual users. However, in the conventional technology, it has been difficult to integrate data from different devices, analyze it in real time, and perform accurate health information and risk prediction. It is necessary to solve this problem so that users can continuously manage their health in their daily lives.

Means for Solving the Problems

[0005] This invention provides a means for collecting user health status data from a sensor device and transmitting this data to a server via a network. The server has means for storing the health status data in a database and generating a health report for each user by analyzing the stored data. Furthermore, by distributing the generated health report to the user's terminal and notifying them, it becomes possible to provide personalized health guidance to the user. In this way, the system streamlines the integration and analysis of data from different devices and realizes a system that supports more accurate health management.

[0006] A "sensor device" is a device that is worn on the body or installed in the environment to measure a user's health data.

[0007] "Health status data" refers to various health-related data such as the user's heart rate, body temperature, weight, blood pressure, and composition of excretions.

[0008] "Network" refers to communication lines or the internet used to send and receive data.

[0009] A "server" is a computer system that receives, stores, and analyzes data over a network.

[0010] A "database" is a system that organizes and manages stored health status data, making it easily accessible.

[0011] "Analysis" is the process of processing collected health status data and extracting meaningful information and patterns.

[0012] A "health report" is a report that shows the user's health status obtained through analysis and includes advice for improving their lifestyle.

[0013] A "user terminal" refers to a device such as a smartphone or tablet that a user uses to receive and review health reports.

[0014] A "notification" is an alert or message displayed on the user's device, serving as a means of conveying important information to the user.

[0015] "Personalized health guidance" means providing specific health improvement measures and advice tailored to each individual user. [Brief explanation of the drawing]

[0016] [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] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include 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.

[0020] 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.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention realizes a system that provides individually customized health guidance by aggregating and analyzing diverse health data from users. This system functions through the coordinated operation of sensor devices, a server, and user terminals.

[0038] First, the sensor device, which acts as the terminal, measures the user's health data in real time. The measured data includes heart rate, blood pressure, body temperature, weight, and information on the composition of excretions. This data is transmitted to a server via the network using wireless communication.

[0039] Next, the server stores the received health data in a database. The stored data is analyzed using a machine learning model. The results of the analysis are generated as a personalized health report for each user. For example, if the server finds through the analysis that a user's fluid intake is inadequate, it will generate advice including appropriate fluid intake levels.

[0040] Health reports generated from the server are delivered to the user's device via the network. A notification appears on the user's smartphone app prompting them to view the health report. This report includes specific advice, such as, "Drink another glass of water in the morning."

[0041] Furthermore, the server periodically analyzes users' health trends and sends reminders for vaccinations and regular health checkups to the user's device. This allows users to keep track of their daily health status and proactively work towards maintaining their health.

[0042] In this way, the present invention provides a system that can support users in their continuous health management by integrating and analyzing health data from multiple sensors.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The device measures the user's health status in real time. Measurement items include heart rate, body temperature, weight, blood pressure, and excretory composition. Once measurement is complete, the data is transmitted from the device to a server via the network.

[0046] Step 2:

[0047] The server receives health status data sent from the terminal and stores it in a database. During this storage process, the data is managed by each user's ID and tagged with time and date.

[0048] Step 3:

[0049] The server performs analysis using machine learning models based on data in the database. The purpose of the analysis is to understand users' health trends and predict potential health risks.

[0050] Step 4:

[0051] Based on the analysis results, the server generates a personalized health report for each user. This report includes, for example, recommendations for the following day's diet, exercise, sleep, and hydration.

[0052] Step 5:

[0053] The server sends the generated health report to the user's terminal. The user's terminal receives this report and displays a notification to the user.

[0054] Step 6:

[0055] Users review reports on their devices and adjust and implement their health behaviors based on the advice provided. For example, they might decide to increase their daily water intake based on the report.

[0056] Step 7:

[0057] The server periodically analyzes data collected over the long term to understand health trends. If necessary, it delivers reminders for vaccinations and health checkups to user devices.

[0058] The above outlines the specific processing steps of this system. This provides users with an environment in which they can autonomously manage their daily health.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] In modern society, where individuals lead busy lives, managing one's health efficiently and effectively is not easy. Conventional health management systems only monitor individual health indicators in isolation, lacking the ability to integrate and analyze that data to provide specific health guidance. Furthermore, they lack mechanisms to automatically provide users with regular feedback and reminders, making sustainable health management difficult. To solve this problem, the development of a new system is needed.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for collecting health status data from sensor devices and storing it in a storage device, means for analyzing the stored data with a machine learning device and generating health information, and means for providing personalized feedback and reminders to the user based on that health information. This enables the user to understand their own health status in real time and receive specific advice.

[0064] A "sensor device" is a device that measures physiological indicators from the user's body and environment in real time and converts them into digital data.

[0065] "Health status data" refers to information that represents physiological indicators of the body, including heart rate, blood pressure, body temperature, weight, and information on the composition of excretions.

[0066] A "network" is a system that includes means of communication for sending and receiving data and information bidirectionally, and is often constructed through wireless communication technology.

[0067] An "information processing device" is a device used to analyze, store, or transmit received digital data, and often functions as a server.

[0068] A "storage device" is a device that has the function of storing digital data, and usually includes a database.

[0069] A "machine learning device" is a device that executes algorithms aimed at extracting patterns and knowledge from large amounts of data and performing predictions and classifications.

[0070] "Health information" refers to information including reports and recommendations generated from the analysis of health status data, and is useful for users' health management.

[0071] "Feedback" refers to the information and advice that a system provides to a user, used to encourage or modify the user's behavior.

[0072] A "reminder" is a means of notifying users of specific actions or events, and is used to promote regular health management.

[0073] This invention is a system for effectively supporting users' health management and consists of three main components: a sensor device, a server, and a user terminal.

[0074] The terminal (sensor device) continuously measures the user's physiological indicators, either by being attached to the body surface or carried by the user. Specifically, it collects data such as heart rate, blood pressure, body temperature, weight, and excretory substance composition, and transmits this data to a server using wireless communication technologies such as Bluetooth or Wi-Fi. This enables real-time data collection.

[0075] The server receives health status data transmitted from sensor devices. The received data is recorded in a database system. Widely used database management systems such as MySQL® and MongoDB are used for this purpose. Next, the data is analyzed by a machine learning system. Machine learning frameworks that utilize generative AI models (e.g., TENSORFLOW® and PyTorch) are used to analyze predictions and health status trends based on past data. The analysis results are generated as personalized health information. This includes content that assesses daily hydration habits and creates specific advice such as, "Drink another glass of water in the morning."

[0076] Users can receive the generated health information on their devices. Health information is notified and detailed reports can be viewed through smartphone and tablet applications. Furthermore, the server regularly sends reminders for vaccinations and periodic health checkups, supporting users in actively managing their health.

[0077] An example of a prompt for the generating AI model in this system would be: "Generate personalized health advice based on the user's health data. For example, if the user is habitually underhydrated, inform them of this and create advice indicating the appropriate amount of water to drink."

[0078] As described above, the system supports health maintenance tailored to individual needs by comprehensively managing and analyzing users' health information.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The terminal (sensor device) collects physiological indicators from the user.

[0082] The inputs are real-time heart rate, blood pressure, body temperature, weight, and excretory composition information. Initial processing is performed by a microcontroller within the sensor device to format the data. The output is formatted health status data, which is transmitted to a server using Bluetooth or Wi-Fi. Specifically, the sensor periodically takes measurements, accumulates the results in a data buffer, and transmits the data when a certain amount is reached.

[0083] Step 2:

[0084] The server receives data sent from the terminal and stores it in its storage device.

[0085] The input is health status data transmitted from a sensor device. The server first confirms that the data has been received, checks the integrity of the data, and filters out invalid and duplicate data. The output is the filtered health data, which is then stored in the database. Specifically, the server inserts the data into the appropriate tables and fields through database queries and associates the timestamp with the user ID.

[0086] Step 3:

[0087] The server analyzes the stored data and generates health information.

[0088] The input is user-specific health status data stored in a database. The server uses a generative AI model, such as TensorFlow, to analyze the data and perform anomaly detection and trend analysis. The output is user-specific health information, such as advice on hydration. Specifically, the process involves calling a machine learning model, inputting data into the model to obtain analysis results, and generating a report based on those results.

[0089] Step 4:

[0090] The server delivers the generated health information to the user's device.

[0091] The input is a generated health report. The server transmits the information to the user's terminal via the network. The output is health information notified to the user's terminal. Specifically, the system transmits data via a communication protocol and displays a notification on the user's smartphone app.

[0092] Step 5:

[0093] The server periodically generates and provides feedback and reminders to the user.

[0094] The input consists of analyzed health data and past feedback history. The server re-evaluates the user's health trends and generates reminders for future preventative measures and check-ups as needed. The output is a reminder notification delivered to the user's device. Specifically, a periodic timer is used to schedule the reminder generation process and send it to the user's device.

[0095] (Application Example 1)

[0096] 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."

[0097] Health management for the elderly and individuals requiring care necessitates real-time information provision and accurate advice, but systems capable of doing so are limited. Furthermore, there is a lack of means for caregivers to constantly monitor the health status of those they care for and take appropriate action. This invention aims to solve such challenges in health management and caregiving.

[0098] 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.

[0099] In this invention, the server includes means for collecting health status information of a person receiving care from sensor devices, means for transmitting the health status information to an information processing device via a communication network, and means for transmitting and notifying the health report to the information display device. This makes it possible to present the health status of the person receiving care to the caregiver in real time and to efficiently manage their health.

[0100] A "sensor device" is a device used to acquire information related to the health status of a person receiving care in real time.

[0101] "Health status information" refers to various data indicating the health status of the person receiving care, such as heart rate, blood pressure, and body temperature.

[0102] A "communication network" is the infrastructure that constitutes a system for efficiently sending and receiving data.

[0103] An "information processing device" refers to a computer system used to store and analyze collected health status information.

[0104] "Information recording medium" refers to the database or storage used by the information processing device to store health status information.

[0105] A "health report" is a report generated based on analyzed health status information, which evaluates the health status of the person receiving care and proposes appropriate measures.

[0106] An "information display device" refers to a display device used to present health reports to users, and includes smartphones and tablets.

[0107] A "computational learning model" is an algorithm used to estimate health risks based on collected health status information.

[0108] "Advice" refers to specific guidance and suggestions provided to improve the daily life of the person receiving care.

[0109] This invention is implemented by a system comprising a sensor device, an information processing device, and an information display device. The sensor device acquires health status information such as the heart rate, blood pressure, and body temperature of the person being cared for in real time. This information is transmitted to the information processing device via a communication network. The information processing device stores the health status information in an information recording medium, for example using a cloud server, and analyzes it using a computational learning model.

[0110] The server generates a health report based on the analysis results. This report includes personalized advice and predictions of health risks. The health report is sent to an information display device such as a smartphone or tablet and presented to the caregiver or the person being cared for.

[0111] For example, if a particular user has high blood pressure, the server analyzes that information and generates advice such as "reduce your salt intake." Furthermore, the generating AI model can use prompts to create more precise advice. An example of a prompt is: "Based on the heart rate and blood pressure data of person A, analyze their health status and generate effective health advice for the next week."

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The terminal acquires health information such as the care recipient's heart rate, blood pressure, and body temperature in real time through sensor devices. This information is input to the terminal as analog signals from the sensors and processed to convert it into digital data. The converted digital data is then passed on to the next step.

[0115] Step 2:

[0116] The terminal sends the converted digital data to the server via the communication network. During this transmission process, the data is properly packetized and sent to the server's specified address via the Internet Protocol. After transmission is complete, the server's receiving status is updated.

[0117] Step 3:

[0118] The server stores the received health status information in an information storage medium. A database management system (DBMS) is used to organize the received data and write it to the database as time-series data. The output of this step is a database where health data is neatly stored.

[0119] Step 4:

[0120] The server analyzes the accumulated data using a computational learning model. It receives health status data as input, and a generative AI model uses this data to execute machine learning algorithms. The analysis results in personalized health reports and health risk assessments. This process may utilize, for example, a deep learning framework.

[0121] Step 5:

[0122] The server generates specific advice using prompts based on the generated health report. In this process, the prompts are modified based on the analysis results, and the optimal advice is automatically created. The output of this step is a health report containing the advice presented to the user.

[0123] Step 6:

[0124] The server sends the generated health report to the information display device and notifies the user. The notification function uses push notifications to deliver the information to the user's information display device immediately. This process involves formatting the report content and distributing it via a message queue.

[0125] Step 7:

[0126] Users review the health reports they receive and make improvements to their daily lives based on the advice provided. This step often includes a feature that allows users to input information, such as recording daily activities and impressions. This allows the system to use the feedback for future analyses.

[0127] 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.

[0128] This invention realizes a system that comprehensively manages not only physical health but also mental health by combining user health status data with an emotion engine that recognizes emotions. This system functions in cooperation with a sensor device, emotion engine, server, and user terminal.

[0129] First, the sensor device, which acts as the terminal, measures the user's health data in real time, including heart rate, body temperature, weight, and blood pressure. Simultaneously, the emotion engine analyzes the user's voice data and text input to extract the user's emotional data. This emotional data includes emotional states such as joy, sadness, and stress. Both sets of data are transmitted to the server via the terminal.

[0130] The server receives this diverse data and stores it in a database. Machine learning models are used to comprehensively analyze health status and emotional data. The analysis evaluates the correlation between past emotional changes and health data, and predicts future health risks and mental health status.

[0131] Based on the analysis results, the server generates a personalized health report for each user. This report includes not only physical health advice but also specific advice for improving mental health in response to changes in emotions. For example, if high stress levels are identified, the report may include suggestions for relaxation techniques and recommendations for getting enough rest.

[0132] Health reports generated from the server are delivered to the user's device via the network, and the device presents the report to the user as a notification. Therefore, users can adjust their daily behaviors based on the feedback, maintaining and improving their physical and mental health.

[0133] Thus, the present invention extends conventional health management systems by utilizing an emotion engine and implements a comprehensive health management system that also takes into account the mental health of users.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The device uses sensors to measure the user's health status, collecting data such as heart rate, body temperature, weight, and blood pressure. Simultaneously, it uses an emotion engine to recognize the user's current emotional state from voice and text input, extracting emotional data such as joy, sadness, and anger. This collected data is immediately transmitted to the server.

[0137] Step 2:

[0138] The server receives health status and emotion data transmitted from the terminal. The received data is immediately stored in a database, organized and stored based on each user's identification information. This storage process includes checks to maintain data consistency and accuracy.

[0139] Step 3:

[0140] The server provides health status data and emotional data to a machine learning model, which then integrates and analyzes the data. This analysis examines the relationship between health and emotions and predicts future health risks and mental health status. As a result, each user's health profile is updated.

[0141] Step 4:

[0142] Based on the analysis results, the server generates a personalized health report for each user. The health report includes advice on physical health and suggestions for mental health based on observed emotional patterns. For example, if stress levels are high, relaxation and mindfulness practices might be recommended.

[0143] Step 5:

[0144] The health report generated by the server is sent to the user's terminal via the network. The user's terminal receives this report and notifies the user in real time. The notification includes a summary of the report and important advice.

[0145] Step 6:

[0146] Users review health reports provided by their devices. They attempt to maintain both physical and mental health by adjusting their daily routines and behaviors according to the advice provided. For example, on days when stress levels are identified as high, they might increase their rest time.

[0147] These steps allow the system to comprehensively manage the user's physical and emotional state, supporting the maintenance of health in various aspects of physical and mental well-being.

[0148] (Example 2)

[0149] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0150] Traditional health management systems have focused primarily on collecting and analyzing physical health data, making it difficult to comprehensively manage psychological health. Furthermore, there is a growing need to provide personalized advice that takes emotional states into account, thereby more accurately predicting users' health risks and improving their quality of life.

[0151] 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.

[0152] In this invention, the server includes means for performing voice or text analysis to extract the user's emotional state; means for using a machine learning model to comprehensively analyze the biometric data and emotional data and generate predictive information for each user; and means for providing the user with personalized suggestions regarding mental health improvement based on the predictive information and emotional state. This enables comprehensive health management that simultaneously considers both the user's physical and emotional state.

[0153] A "sensor device" is a general term for devices that collect a user's biometric data in real time, and includes devices that can measure data such as heart rate, body temperature, weight, and blood pressure.

[0154] "Biometric data" refers to data that serves as an indicator of a user's physical health status, and mainly includes heart rate, body temperature, weight, and blood pressure.

[0155] A "central processing unit" is a computer system that receives, analyzes, and stores data transmitted over a network in a storage device.

[0156] A "storage device" is a device used to store data received by a central processing unit for extended periods, and typically uses a database system.

[0157] "Speech or text analysis" is the process of analyzing collected speech or text data to extract emotional states, often utilizing natural language processing techniques.

[0158] "Emotional data" refers to data that indicates the user's psychological state, including emotional states such as joy, sadness, and stress.

[0159] "Predictive information" refers to information about future health risks and health conditions derived from the results of biometric and emotional data analyzed using machine learning models.

[0160] "Personalized suggestions" refer to specific, customized suggestions and advice regarding improving psychological health, based on the user's unique health and emotional state.

[0161] This invention is a system that comprehensively manages both physical and psychological health conditions, and in which a sensor device (a terminal), a server (a central processing unit), and a user terminal work together in cooperation.

[0162] The device collects the user's biometric data in real time using sensor devices. This includes data such as heart rate, body temperature, weight, and blood pressure. Common wearable devices such as smartwatches are used. This data is aggregated by the device, and at the same time, emotional data is collected through the user's voice and text input. The voice and text data is analyzed using natural language processing through an emotion engine to extract emotional states such as joy, sadness, and stress.

[0163] The collected data is sent to the server using a secure protocol. The server stores this data in storage and analyzes it using machine learning models. Specifically, database systems such as MySQL and PostgreSQL are used for data management, and machine learning libraries such as TensorFlow and PyTorch are used for analysis. Correlations with past data trends and emotional states are also evaluated. Based on the analysis results, the server generates predictive information for each user, forecasting future physical health risks and mental health conditions.

[0164] Based on predictive information generated by the server, personalized advice is generated and created as a prompt message. For example, a specific prompt message might be, "What relaxation methods would you recommend for a male office worker in his 30s to alleviate stress caused by excessive workload?"

[0165] Predictive information and advice generated by the server are sent to the user's terminal, allowing the user to incorporate them into their daily actions. In this way, the terminal, server, and user work together to enable users to comprehensively manage their physical and mental health and improve their quality of life.

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] The sensor device, acting as the terminal, acquires the user's biometric data. At this stage, heart rate, body temperature, weight, blood pressure, etc., are read from the sensor as input and temporarily stored in local memory. This biometric data is converted to a standard format to unify the data format. This conversion process facilitates data analysis in subsequent processing.

[0169] Step 2:

[0170] The device receives voice and text input from the user and analyzes this data using an emotion engine. It outputs emotional states, such as "joy" or "sadness," from the voice and text input. This analysis is performed by quantifying emotions using natural language processing techniques.

[0171] Step 3:

[0172] The collected biometric and emotional data are transferred to the server via a security protocol. This data transfer process involves encryption to maintain data confidentiality and integrity. The input data is sent to the server sequentially in packet format.

[0173] Step 4:

[0174] The server stores the received data in storage. The input biometric and emotional data are centrally managed by a database management system. During the storage process, appropriate indexes are assigned according to the database structure.

[0175] Step 5:

[0176] The server launches a machine learning model and analyzes the stored data. From the input data, the generative AI model outputs health risks and predicted mental states. During the analysis process, it identifies correlations with past data patterns and uses statistical methods to predict future risks.

[0177] Step 6:

[0178] Based on the analysis results, the server generates personalized advice for each user. The generated advice is formatted as a prompt message. For example, for a user with a high stress level, it might output a specific suggestion such as, "We recommend practicing relaxation techniques."

[0179] Step 7:

[0180] The generated advice and predictive information are delivered to the user's terminal. The user's terminal receives this information and presents it to the user through notifications and application interfaces. The user can accept the presented information and incorporate it into their daily activities.

[0181] (Application Example 2)

[0182] 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".

[0183] In recent years, advancements in information and communication technology have made it possible to collect and analyze data in real time for personal health management. However, conventional systems have focused primarily on physical health and have not provided comprehensive health management that adequately considers the user's emotions and mental health. Furthermore, there is a growing expectation for systems that can be easily integrated into daily life as smart devices and that provide intuitive and useful feedback to users.

[0184] 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.

[0185] In this invention, the server includes means for collecting biometric data of the user from sensor devices, means for analyzing emotions from acoustic input, and means for using a machine learning model to predict health risks and emotional states based on the biometric data and emotional data. This makes it possible to comprehensively manage the body and mind and provide personalized health and mental health suggestions to the user.

[0186] A "sensor device" is a device used to measure and collect a user's biometric data in real time.

[0187] "Biometric indicator data" is a general term for various data that indicate the user's physical health status, such as heart rate, body temperature, and blood pressure.

[0188] An "information processing device" is a computer system used to store and analyze data transmitted over a network.

[0189] A "storage device" is a storage medium for safely and efficiently storing collected data.

[0190] A "status report" is a report that shows the current state of the user's health and mental health based on the analyzed data.

[0191] "Audio input" refers to an input method that acquires the user's voice and surrounding sounds through sensors.

[0192] "Means of analyzing emotions" refers to a process of analyzing data obtained from acoustic input to evaluate the user's emotional state.

[0193] "Mitigation measures" refer to specific suggestions and actions aimed at improving the health and emotional state of users.

[0194] The system for implementing this invention mainly consists of a sensor device, an information processing device, a storage device, and a user terminal. The sensor device is responsible for collecting biometric data such as heart rate and body temperature. This data is transmitted to the information processing device via wireless communication, where initial processing and analysis are performed.

[0195] The information processing system stores collected data in a storage device while simultaneously analyzing the user's emotions through acoustic input. For example, a voice analysis engine using NLP (Natural Language Processing) technology extracts emotions from the user's voice. Machine learning libraries such as TensorFlow are used to evaluate the user's health and emotional state and propose mitigation measures.

[0196] The situation report generated as a result of the analysis is delivered to the user's terminal via the network and notified on the terminal. Users can read the received situation report and use it as a reference for improving their behavior in real life. For example, if the system detects that the user is experiencing stress, it may suggest a mitigation measure such as "Try taking a deep breath."

[0197] An example of a prompt message would be, "Consider the user's current health and emotional state, and suggest an appropriate relaxation method." This enables personalized health management and emotional well-being for each user.

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The sensor device collects biometric data such as heart rate and body temperature from the user. Its input is biosignals, and its output is real-time health data. The collected data is transmitted to a server using wireless communication.

[0201] Step 2:

[0202] The server receives biometric data from sensor devices as input and stores it in a storage device. Initial data processing involves filtering out error data and converting it into a format suitable for analysis. The output is organized health data.

[0203] Step 3:

[0204] The server receives the user's voice data as input and converts it into text data using a natural language processing engine. Based on this text data, it performs emotional analysis to evaluate the user's emotional state. The output is metadata related to emotions.

[0205] Step 4:

[0206] The server uses a machine learning model to perform an integrated analysis using the health data obtained in Step 2 and the emotional data obtained in Step 3 as input. Based on the data calculations, it evaluates the user's health and emotional state and predicts future health risks and emotional states. Evaluation result data is generated as output.

[0207] Step 5:

[0208] The server generates personalized status reports using evaluation data. The input is analyzed data, and the output is a personalized health report. Relaxation suggestions and behavioral guidelines are included as needed.

[0209] Step 6:

[0210] The device notifies the user of status reports received from the server via the network. The user can then adjust their daily activities based on the received health reports. Specific actions include pop-up notifications and audio alerts.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] [Second Embodiment]

[0215] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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).

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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".

[0227] This invention realizes a system that provides individually customized health guidance by aggregating and analyzing diverse health data from users. This system functions through the coordinated operation of sensor devices, a server, and user terminals.

[0228] First, the sensor device, which acts as the terminal, measures the user's health data in real time. The measured data includes heart rate, blood pressure, body temperature, weight, and information on the composition of excretions. This data is transmitted to a server via the network using wireless communication.

[0229] Next, the server stores the received health data in a database. The stored data is analyzed using a machine learning model. The results of the analysis are generated as a personalized health report for each user. For example, if the server finds through the analysis that a user's fluid intake is inadequate, it will generate advice including appropriate fluid intake levels.

[0230] Health reports generated from the server are delivered to the user's device via the network. A notification appears on the user's smartphone app prompting them to view the health report. This report includes specific advice, such as, "Drink another glass of water in the morning."

[0231] Furthermore, the server periodically analyzes users' health trends and sends reminders for vaccinations and regular health checkups to the user's device. This allows users to keep track of their daily health status and proactively work towards maintaining their health.

[0232] In this way, the present invention provides a system that can support users in their continuous health management by integrating and analyzing health data from multiple sensors.

[0233] The following describes the processing flow.

[0234] Step 1:

[0235] The device measures the user's health status in real time. Measurement items include heart rate, body temperature, weight, blood pressure, and excretory composition. Once measurement is complete, the data is transmitted from the device to a server via the network.

[0236] Step 2:

[0237] The server receives health status data sent from the terminal and stores it in a database. During this storage process, the data is managed by each user's ID and tagged with time and date.

[0238] Step 3:

[0239] The server performs analysis using machine learning models based on data in the database. The purpose of the analysis is to understand users' health trends and predict potential health risks.

[0240] Step 4:

[0241] Based on the analysis results, the server generates a personalized health report for each user. This report includes, for example, recommendations for the following day's diet, exercise, sleep, and hydration.

[0242] Step 5:

[0243] The server sends the generated health report to the user's terminal. The user's terminal receives this report and displays a notification to the user.

[0244] Step 6:

[0245] Users review reports on their devices and adjust and implement their health behaviors based on the advice provided. For example, they might decide to increase their daily water intake based on the report.

[0246] Step 7:

[0247] The server periodically analyzes data collected over the long term to understand health trends. If necessary, it delivers reminders for vaccinations and health checkups to user devices.

[0248] The above outlines the specific processing steps of this system. This provides users with an environment in which they can autonomously manage their daily health.

[0249] (Example 1)

[0250] 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."

[0251] In modern society, where individuals lead busy lives, managing one's health efficiently and effectively is not easy. Conventional health management systems only monitor individual health indicators in isolation, lacking the ability to integrate and analyze that data to provide specific health guidance. Furthermore, they lack mechanisms to automatically provide users with regular feedback and reminders, making sustainable health management difficult. To solve this problem, the development of a new system is needed.

[0252] 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.

[0253] In this invention, the server includes means for collecting health status data from sensor devices and storing it in a storage device, means for analyzing the stored data with a machine learning device and generating health information, and means for providing personalized feedback and reminders to the user based on that health information. This enables the user to understand their own health status in real time and receive specific advice.

[0254] A "sensor device" is a device that measures physiological indicators from the user's body and environment in real time and converts them into digital data.

[0255] "Health status data" refers to information that represents physiological indicators of the body, including heart rate, blood pressure, body temperature, weight, and information on the composition of excretions.

[0256] A "network" is a system that includes means of communication for sending and receiving data and information bidirectionally, and is often constructed through wireless communication technology.

[0257] An "information processing device" is a device used to analyze, store, or transmit received digital data, and often functions as a server.

[0258] A "storage device" is a device that has the function of storing digital data, and usually includes a database.

[0259] A "machine learning device" is a device that executes algorithms aimed at extracting patterns and knowledge from large amounts of data and performing predictions and classifications.

[0260] "Health information" refers to information including reports and recommendations generated from the analysis of health status data, and is useful for users' health management.

[0261] "Feedback" refers to the information and advice that a system provides to a user, used to encourage or modify the user's behavior.

[0262] A "reminder" is a means of notifying users of specific actions or events, and is used to promote regular health management.

[0263] This invention is a system for effectively supporting users' health management and consists of three main components: a sensor device, a server, and a user terminal.

[0264] The terminal (sensor device) continuously measures the user's physiological indicators, either by being attached to the body surface or carried by the user. Specifically, it collects data such as heart rate, blood pressure, body temperature, weight, and excretory substance composition, and transmits this data to a server using wireless communication technologies such as Bluetooth or Wi-Fi. This enables real-time data collection.

[0265] The server receives health status data transmitted from sensor devices. The received data is recorded in a database system. Widely used database management systems such as MySQL and MongoDB are used for this purpose. Next, the data is analyzed by a machine learning system. Machine learning frameworks that utilize generative AI models (e.g., TensorFlow and PyTorch) are used to analyze predictions and health status trends based on past data. The analysis results are generated as personalized health information. This includes content that assesses daily hydration habits and creates specific advice such as, "Drink another glass of water in the morning."

[0266] Users can receive the generated health information on their devices. Health information is notified and detailed reports can be viewed through smartphone and tablet applications. Furthermore, the server regularly sends reminders for vaccinations and periodic health checkups, supporting users in actively managing their health.

[0267] An example of a prompt for the generating AI model in this system would be: "Generate personalized health advice based on the user's health data. For example, if the user is habitually underhydrated, inform them of this and create advice indicating the appropriate amount of water to drink."

[0268] As described above, the system supports health maintenance tailored to individual needs by comprehensively managing and analyzing users' health information.

[0269] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0270] Step 1:

[0271] The terminal (sensor device) collects physiological indicators from the user.

[0272] The inputs are real-time heart rate, blood pressure, body temperature, weight, and excretory composition information. Initial processing is performed by a microcontroller within the sensor device to format the data. The output is formatted health status data, which is transmitted to a server using Bluetooth or Wi-Fi. Specifically, the sensor periodically takes measurements, accumulates the results in a data buffer, and transmits the data when a certain amount is reached.

[0273] Step 2:

[0274] The server receives data sent from the terminal and stores it in its storage device.

[0275] The input is health status data transmitted from a sensor device. The server first confirms that the data has been received, checks the integrity of the data, and filters out invalid and duplicate data. The output is the filtered health data, which is then stored in the database. Specifically, the server inserts the data into the appropriate tables and fields through database queries and associates the timestamp with the user ID.

[0276] Step 3:

[0277] The server analyzes the stored data and generates health information.

[0278] The input is user-specific health status data stored in a database. The server uses a generative AI model, such as TensorFlow, to analyze the data and perform anomaly detection and trend analysis. The output is user-specific health information, such as advice on hydration. Specifically, the process involves calling a machine learning model, inputting data into the model to obtain analysis results, and generating a report based on those results.

[0279] Step 4:

[0280] The server delivers the generated health information to the user's device.

[0281] The input is the generated health report. The server transmits information to the user terminal via the network. The output is the health information notified to the user terminal. As a specific operation, data is transmitted via a communication protocol, and a notification is displayed on the user's smartphone app.

[0282] Step 5:

[0283] The server periodically creates feedback and reminders and provides them to the user.

[0284] The input is the analyzed health data and the past feedback history. The server re-evaluates the user's health trend and generates the next preventive measure or medical examination reminder as needed. The output is the reminder notification distributed to the user's terminal. As a specific operation, the reminder generation process is scheduled using a periodic timer and transmitted to the user terminal.

[0285] (Application Example 1)

[0286] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0287] For the health management of the elderly and individuals who need care, real-time information provision and accurate advice are required, but the systems that enable this are limited. In addition, there is a lack of means for caregivers to constantly grasp the health status of care recipients and take appropriate actions. The purpose of this invention is to solve such problems in health management and care.

[0288] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0289] In this invention, the server includes means for collecting health status information of a person receiving care from sensor devices, means for transmitting the health status information to an information processing device via a communication network, and means for transmitting and notifying the health report to the information display device. This makes it possible to present the health status of the person receiving care to the caregiver in real time and to efficiently manage their health.

[0290] A "sensor device" is a device used to acquire information related to the health status of a person receiving care in real time.

[0291] "Health status information" refers to various data indicating the health status of the person receiving care, such as heart rate, blood pressure, and body temperature.

[0292] A "communication network" is the infrastructure that constitutes a system for efficiently sending and receiving data.

[0293] An "information processing device" refers to a computer system used to store and analyze collected health status information.

[0294] "Information recording medium" refers to the database or storage used by the information processing device to store health status information.

[0295] A "health report" is a report generated based on analyzed health status information, which evaluates the health status of the person receiving care and proposes appropriate measures.

[0296] An "information display device" refers to a display device used to present health reports to users, and includes smartphones and tablets.

[0297] A "computational learning model" is an algorithm used to estimate health risks based on collected health status information.

[0298] "Advice" refers to specific guidance and suggestions provided to improve the daily life of the person receiving care.

[0299] This invention is implemented by a system comprising a sensor device, an information processing device, and an information display device. The sensor device acquires health status information such as the heart rate, blood pressure, and body temperature of the person being cared for in real time. This information is transmitted to the information processing device via a communication network. The information processing device stores the health status information in an information recording medium, for example using a cloud server, and analyzes it using a computational learning model.

[0300] The server generates a health report based on the analysis results. This report includes personalized advice and predictions of health risks. The health report is sent to an information display device such as a smartphone or tablet and presented to the caregiver or the person being cared for.

[0301] For example, if a particular user has high blood pressure, the server analyzes that information and generates advice such as "reduce your salt intake." Furthermore, the generating AI model can use prompts to create more precise advice. An example of a prompt is: "Based on the heart rate and blood pressure data of person A, analyze their health status and generate effective health advice for the next week."

[0302] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0303] Step 1:

[0304] The terminal acquires health information such as the care recipient's heart rate, blood pressure, and body temperature in real time through sensor devices. This information is input to the terminal as analog signals from the sensors and processed to convert it into digital data. The converted digital data is then passed on to the next step.

[0305] Step 2:

[0306] The terminal sends the converted digital data to the server through the communication network. In this transmission process, the data is properly packetized and sent to the designated address of the server via the Internet Protocol. After the transmission is completed, the receiving status on the server side is updated.

[0307] Step 3:

[0308] The server stores the received health status information in the information recording medium. The database management system (DBMS) is used to organize the received data and write it into the database as time-series data. The output of this step is a database in which the health data is stored in an orderly manner.

[0309] Step 4:

[0310] The server analyzes the stored data using a computational learning model. It receives the health status data as input, and the generated AI model executes a machine learning algorithm using this data. As an analysis result, an individualized health report and a health risk assessment are obtained. For this process, for example, a deep learning framework may be used.

[0311] Step 5:

[0312] The server generates specific advice using the generated health report and a prompt sentence. In this process, the prompt sentence is modified based on the analysis result, and the optimal advice is automatically created. The output of this step is a health report including the advice presented to the user.

[0313] Step 6:

[0314] The server sends the generated health report to the information display device and notifies it. The notification function is delivered to the user's information display device immediately using push notifications. In this process, the formatting of the report content and the delivery in the message queue are performed.

[0315] Step 7:

[0316] Users review the health reports they receive and make improvements to their daily lives based on the advice provided. This step often includes a feature that allows users to input information, such as recording daily activities and impressions. This allows the system to use the feedback for future analyses.

[0317] 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.

[0318] This invention realizes a system that comprehensively manages not only physical health but also mental health by combining user health status data with an emotion engine that recognizes emotions. This system functions in cooperation with a sensor device, emotion engine, server, and user terminal.

[0319] First, the sensor device, which acts as the terminal, measures the user's health data in real time, including heart rate, body temperature, weight, and blood pressure. Simultaneously, the emotion engine analyzes the user's voice data and text input to extract the user's emotional data. This emotional data includes emotional states such as joy, sadness, and stress. Both sets of data are transmitted to the server via the terminal.

[0320] The server receives this diverse data and stores it in a database. Machine learning models are used to comprehensively analyze health status and emotional data. The analysis evaluates the correlation between past emotional changes and health data, and predicts future health risks and mental health status.

[0321] Based on the analysis results, the server generates a personalized health report for each user. This report includes not only physical health advice but also specific advice for improving mental health in response to changes in emotions. For example, if high stress levels are identified, the report may include suggestions for relaxation techniques and recommendations for getting enough rest.

[0322] Health reports generated from the server are delivered to the user's device via the network, and the device presents the report to the user as a notification. Therefore, users can adjust their daily behaviors based on the feedback, maintaining and improving their physical and mental health.

[0323] Thus, the present invention extends conventional health management systems by utilizing an emotion engine and implements a comprehensive health management system that also takes into account the mental health of users.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] The device uses sensors to measure the user's health status, collecting data such as heart rate, body temperature, weight, and blood pressure. Simultaneously, it uses an emotion engine to recognize the user's current emotional state from voice and text input, extracting emotional data such as joy, sadness, and anger. This collected data is immediately transmitted to the server.

[0327] Step 2:

[0328] The server receives health status and emotion data transmitted from the terminal. The received data is immediately stored in a database, organized and stored based on each user's identification information. This storage process includes checks to maintain data consistency and accuracy.

[0329] Step 3:

[0330] The server provides health status data and emotional data to a machine learning model, which then integrates and analyzes the data. This analysis examines the relationship between health and emotions and predicts future health risks and mental health status. As a result, each user's health profile is updated.

[0331] Step 4:

[0332] Based on the analysis results, the server generates a personalized health report for each user. The health report includes advice on physical health and suggestions for mental health based on observed emotional patterns. For example, if stress levels are high, relaxation and mindfulness practices might be recommended.

[0333] Step 5:

[0334] The health report generated by the server is sent to the user's terminal via the network. The user's terminal receives this report and notifies the user in real time. The notification includes a summary of the report and important advice.

[0335] Step 6:

[0336] Users review health reports provided by their devices. They attempt to maintain both physical and mental health by adjusting their daily routines and behaviors according to the advice provided. For example, on days when stress levels are identified as high, they might increase their rest time.

[0337] These steps allow the system to comprehensively manage the user's physical and emotional state, supporting the maintenance of health in various aspects of physical and mental well-being.

[0338] (Example 2)

[0339] 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".

[0340] Traditional health management systems have focused primarily on collecting and analyzing physical health data, making it difficult to comprehensively manage psychological health. Furthermore, there is a growing need to provide personalized advice that takes emotional states into account, thereby more accurately predicting users' health risks and improving their quality of life.

[0341] 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.

[0342] In this invention, the server includes means for performing voice or text analysis to extract the user's emotional state; means for using a machine learning model to comprehensively analyze the biometric data and emotional data and generate predictive information for each user; and means for providing the user with personalized suggestions regarding mental health improvement based on the predictive information and emotional state. This enables comprehensive health management that simultaneously considers both the user's physical and emotional state.

[0343] A "sensor device" is a general term for devices that collect a user's biometric data in real time, and includes devices that can measure data such as heart rate, body temperature, weight, and blood pressure.

[0344] "Biometric data" refers to data that serves as an indicator of a user's physical health status, and mainly includes heart rate, body temperature, weight, and blood pressure.

[0345] A "central processing unit" is a computer system that receives, analyzes, and stores data transmitted over a network in a storage device.

[0346] A "storage device" is a device used to store data received by a central processing unit for extended periods, and typically uses a database system.

[0347] "Speech or text analysis" is the process of analyzing collected speech or text data to extract emotional states, often utilizing natural language processing techniques.

[0348] "Emotional data" refers to data that indicates the user's psychological state, including emotional states such as joy, sadness, and stress.

[0349] "Predictive information" refers to information about future health risks and health conditions derived from the results of biometric and emotional data analyzed using machine learning models.

[0350] "Personalized suggestions" refer to specific, customized suggestions and advice regarding improving psychological health, based on the user's unique health and emotional state.

[0351] This invention is a system that comprehensively manages both physical and psychological health conditions, and in which a sensor device (a terminal), a server (a central processing unit), and a user terminal work together in cooperation.

[0352] The device collects the user's biometric data in real time using sensor devices. This includes data such as heart rate, body temperature, weight, and blood pressure. Common wearable devices such as smartwatches are used. This data is aggregated by the device, and at the same time, emotional data is collected through the user's voice and text input. The voice and text data is analyzed using natural language processing through an emotion engine to extract emotional states such as joy, sadness, and stress.

[0353] The collected data is sent to the server using a secure protocol. The server stores this data in storage and analyzes it using machine learning models. Specifically, database systems such as MySQL and PostgreSQL are used for data management, and machine learning libraries such as TensorFlow and PyTorch are used for analysis. Correlations with past data trends and emotional states are also evaluated. Based on the analysis results, the server generates predictive information for each user, forecasting future physical health risks and mental health conditions.

[0354] Based on predictive information generated by the server, personalized advice is generated and created as a prompt message. For example, a specific prompt message might be, "What relaxation methods would you recommend for a male office worker in his 30s to alleviate stress caused by excessive workload?"

[0355] Predictive information and advice generated by the server are sent to the user's terminal, allowing the user to incorporate them into their daily actions. In this way, the terminal, server, and user work together to enable users to comprehensively manage their physical and mental health and improve their quality of life.

[0356] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0357] Step 1:

[0358] The sensor device, acting as the terminal, acquires the user's biometric data. At this stage, heart rate, body temperature, weight, blood pressure, etc., are read from the sensor as input and temporarily stored in local memory. This biometric data is converted to a standard format to unify the data format. This conversion process facilitates data analysis in subsequent processing.

[0359] Step 2:

[0360] The device receives voice and text input from the user and analyzes this data using an emotion engine. It outputs emotional states, such as "joy" or "sadness," from the voice and text input. This analysis is performed by quantifying emotions using natural language processing techniques.

[0361] Step 3:

[0362] The collected biometric and emotional data are transferred to the server via a security protocol. This data transfer process involves encryption to maintain data confidentiality and integrity. The input data is sent to the server sequentially in packet format.

[0363] Step 4:

[0364] The server stores the received data in storage. The input biometric and emotional data are centrally managed by a database management system. During the storage process, appropriate indexes are assigned according to the database structure.

[0365] Step 5:

[0366] The server launches a machine learning model and analyzes the stored data. From the input data, the generative AI model outputs health risks and predicted mental states. During the analysis process, it identifies correlations with past data patterns and uses statistical methods to predict future risks.

[0367] Step 6:

[0368] Based on the analysis results, the server generates personalized advice for each user. The generated advice is formatted as a prompt message. For example, for a user with a high stress level, it might output a specific suggestion such as, "We recommend practicing relaxation techniques."

[0369] Step 7:

[0370] The generated advice and predictive information are delivered to the user's terminal. The user's terminal receives this information and presents it to the user through notifications and application interfaces. The user can accept the presented information and incorporate it into their daily activities.

[0371] (Application Example 2)

[0372] 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."

[0373] In recent years, advancements in information and communication technology have made it possible to collect and analyze data in real time for personal health management. However, conventional systems have focused primarily on physical health and have not provided comprehensive health management that adequately considers the user's emotions and mental health. Furthermore, there is a growing expectation for systems that can be easily integrated into daily life as smart devices and that provide intuitive and useful feedback to users.

[0374] 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.

[0375] In this invention, the server includes means for collecting biometric data of the user from sensor devices, means for analyzing emotions from acoustic input, and means for using a machine learning model to predict health risks and emotional states based on the biometric data and emotional data. This makes it possible to comprehensively manage the body and mind and provide personalized health and mental health suggestions to the user.

[0376] A "sensor device" is a device used to measure and collect a user's biometric data in real time.

[0377] "Biometric indicator data" is a general term for various data that indicate the user's physical health status, such as heart rate, body temperature, and blood pressure.

[0378] An "information processing device" is a computer system used to store and analyze data transmitted over a network.

[0379] A "storage device" is a storage medium for safely and efficiently storing collected data.

[0380] A "status report" is a report that shows the current state of the user's health and mental health based on the analyzed data.

[0381] "Audio input" refers to an input method that acquires the user's voice and surrounding sounds through sensors.

[0382] "Means of analyzing emotions" refers to a process of analyzing data obtained from acoustic input to evaluate the user's emotional state.

[0383] "Mitigation measures" refer to specific suggestions and actions aimed at improving the health and emotional state of users.

[0384] The system for implementing this invention mainly consists of a sensor device, an information processing device, a storage device, and a user terminal. The sensor device is responsible for collecting biometric data such as heart rate and body temperature. This data is transmitted to the information processing device via wireless communication, where initial processing and analysis are performed.

[0385] The information processing system stores collected data in a storage device while simultaneously analyzing the user's emotions through acoustic input. For example, a voice analysis engine using NLP (Natural Language Processing) technology extracts emotions from the user's voice. Machine learning libraries such as TensorFlow are used to evaluate the user's health and emotional state and propose mitigation measures.

[0386] The situation report generated as a result of the analysis is delivered to the user's terminal via the network and notified on the terminal. Users can read the received situation report and use it as a reference for improving their behavior in real life. For example, if the system detects that the user is experiencing stress, it may suggest a mitigation measure such as "Try taking a deep breath."

[0387] An example of a prompt message would be, "Consider the user's current health and emotional state, and suggest an appropriate relaxation method." This enables personalized health management and emotional well-being for each user.

[0388] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0389] Step 1:

[0390] The sensor device collects biometric data such as heart rate and body temperature from the user. Its input is biosignals, and its output is real-time health data. The collected data is transmitted to a server using wireless communication.

[0391] Step 2:

[0392] The server receives biometric data from sensor devices as input and stores it in a storage device. Initial data processing involves filtering out error data and converting it into a format suitable for analysis. The output is organized health data.

[0393] Step 3:

[0394] The server receives the user's voice data as input and converts it into text data using a natural language processing engine. Based on this text data, it performs emotional analysis to evaluate the user's emotional state. The output is metadata related to emotions.

[0395] Step 4:

[0396] The server uses a machine learning model to perform an integrated analysis using the health data obtained in Step 2 and the emotional data obtained in Step 3 as input. Based on the data calculations, it evaluates the user's health and emotional state and predicts future health risks and emotional states. Evaluation result data is generated as output.

[0397] Step 5:

[0398] The server generates personalized status reports using evaluation data. The input is analyzed data, and the output is a personalized health report. Relaxation suggestions and behavioral guidelines are included as needed.

[0399] Step 6:

[0400] The device notifies the user of status reports received from the server via the network. The user can then adjust their daily activities based on the received health reports. Specific actions include pop-up notifications and audio alerts.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] [Third Embodiment]

[0405] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0406] 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.

[0407] 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).

[0408] 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.

[0409] 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.

[0410] 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).

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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".

[0417] This invention realizes a system that provides individually customized health guidance by aggregating and analyzing diverse health data from users. This system functions through the coordinated operation of sensor devices, a server, and user terminals.

[0418] First, the sensor device, which acts as the terminal, measures the user's health data in real time. The measured data includes heart rate, blood pressure, body temperature, weight, and information on the composition of excretions. This data is transmitted to a server via the network using wireless communication.

[0419] Next, the server stores the received health data in a database. The stored data is analyzed using a machine learning model. The results of the analysis are generated as a personalized health report for each user. For example, if the server finds through the analysis that a user's fluid intake is inadequate, it will generate advice including appropriate fluid intake levels.

[0420] Health reports generated from the server are delivered to the user's device via the network. A notification appears on the user's smartphone app prompting them to view the health report. This report includes specific advice, such as, "Drink another glass of water in the morning."

[0421] Furthermore, the server periodically analyzes users' health trends and sends reminders for vaccinations and regular health checkups to the user's device. This allows users to keep track of their daily health status and proactively work towards maintaining their health.

[0422] In this way, the present invention provides a system that can support users in their continuous health management by integrating and analyzing health data from multiple sensors.

[0423] The following describes the processing flow.

[0424] Step 1:

[0425] The device measures the user's health status in real time. Measurement items include heart rate, body temperature, weight, blood pressure, and excretory composition. Once measurement is complete, the data is transmitted from the device to a server via the network.

[0426] Step 2:

[0427] The server receives health status data sent from the terminal and stores it in a database. During this storage process, the data is managed by each user's ID and tagged with time and date.

[0428] Step 3:

[0429] The server performs analysis using machine learning models based on data in the database. The purpose of the analysis is to understand users' health trends and predict potential health risks.

[0430] Step 4:

[0431] Based on the analysis results, the server generates a personalized health report for each user. This report includes, for example, recommendations for the following day's diet, exercise, sleep, and hydration.

[0432] Step 5:

[0433] The server sends the generated health report to the user's terminal. The user's terminal receives this report and displays a notification to the user.

[0434] Step 6:

[0435] Users review reports on their devices and adjust and implement their health behaviors based on the advice provided. For example, they might decide to increase their daily water intake based on the report.

[0436] Step 7:

[0437] The server periodically analyzes data collected over the long term to understand health trends. If necessary, it delivers reminders for vaccinations and health checkups to user devices.

[0438] The above outlines the specific processing steps of this system. This provides users with an environment in which they can autonomously manage their daily health.

[0439] (Example 1)

[0440] 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."

[0441] In modern society, where individuals lead busy lives, managing one's health efficiently and effectively is not easy. Conventional health management systems only monitor individual health indicators in isolation, lacking the ability to integrate and analyze that data to provide specific health guidance. Furthermore, they lack mechanisms to automatically provide users with regular feedback and reminders, making sustainable health management difficult. To solve this problem, the development of a new system is needed.

[0442] 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.

[0443] In this invention, the server includes means for collecting health status data from sensor devices and storing it in a storage device, means for analyzing the stored data with a machine learning device and generating health information, and means for providing personalized feedback and reminders to the user based on that health information. This enables the user to understand their own health status in real time and receive specific advice.

[0444] A "sensor device" is a device that measures physiological indicators from the user's body and environment in real time and converts them into digital data.

[0445] "Health status data" refers to information that represents physiological indicators of the body, including heart rate, blood pressure, body temperature, weight, and information on the composition of excretions.

[0446] A "network" is a system that includes means of communication for sending and receiving data and information bidirectionally, and is often constructed through wireless communication technology.

[0447] An "information processing device" is a device used to analyze, store, or transmit received digital data, and often functions as a server.

[0448] A "storage device" is a device that has the function of storing digital data, and usually includes a database.

[0449] A "machine learning device" is a device that executes algorithms aimed at extracting patterns and knowledge from large amounts of data and performing predictions and classifications.

[0450] "Health information" refers to information including reports and recommendations generated from the analysis of health status data, and is useful for users' health management.

[0451] "Feedback" refers to the information and advice that a system provides to a user, used to encourage or modify the user's behavior.

[0452] A "reminder" is a means of notifying users of specific actions or events, and is used to promote regular health management.

[0453] This invention is a system for effectively supporting users' health management and consists of three main components: a sensor device, a server, and a user terminal.

[0454] The terminal (sensor device) continuously measures the user's physiological indicators, either by being attached to the body surface or carried by the user. Specifically, it collects data such as heart rate, blood pressure, body temperature, weight, and excretory substance composition, and transmits this data to a server using wireless communication technologies such as Bluetooth or Wi-Fi. This enables real-time data collection.

[0455] The server receives health status data transmitted from sensor devices. The received data is recorded in a database system. Widely used database management systems such as MySQL and MongoDB are used for this purpose. Next, the data is analyzed by a machine learning system. Machine learning frameworks that utilize generative AI models (e.g., TensorFlow and PyTorch) are used to analyze predictions and health status trends based on past data. The analysis results are generated as personalized health information. This includes content that assesses daily hydration habits and creates specific advice such as, "Drink another glass of water in the morning."

[0456] Users can receive the generated health information on their devices. Health information is notified and detailed reports can be viewed through smartphone and tablet applications. Furthermore, the server regularly sends reminders for vaccinations and periodic health checkups, supporting users in actively managing their health.

[0457] An example of a prompt for the generating AI model in this system would be: "Generate personalized health advice based on the user's health data. For example, if the user is habitually underhydrated, inform them of this and create advice indicating the appropriate amount of water to drink."

[0458] As described above, the system supports health maintenance tailored to individual needs by comprehensively managing and analyzing users' health information.

[0459] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0460] Step 1:

[0461] The terminal (sensor device) collects physiological indicators from the user.

[0462] The inputs are real-time heart rate, blood pressure, body temperature, weight, and excretory composition information. Initial processing is performed by a microcontroller within the sensor device to format the data. The output is formatted health status data, which is transmitted to a server using Bluetooth or Wi-Fi. Specifically, the sensor periodically takes measurements, accumulates the results in a data buffer, and transmits the data when a certain amount is reached.

[0463] Step 2:

[0464] The server receives data sent from the terminal and stores it in its storage device.

[0465] The input is health status data transmitted from a sensor device. The server first confirms that the data has been received, checks the integrity of the data, and filters out invalid and duplicate data. The output is the filtered health data, which is then stored in the database. Specifically, the server inserts the data into the appropriate tables and fields through database queries and associates the timestamp with the user ID.

[0466] Step 3:

[0467] The server analyzes the stored data and generates health information.

[0468] The input is user-specific health status data stored in a database. The server uses a generative AI model, such as TensorFlow, to analyze the data and perform anomaly detection and trend analysis. The output is user-specific health information, such as advice on hydration. Specifically, the process involves calling a machine learning model, inputting data into the model to obtain analysis results, and generating a report based on those results.

[0469] Step 4:

[0470] The server delivers the generated health information to the user's device.

[0471] The input is a generated health report. The server transmits the information to the user's terminal via the network. The output is health information notified to the user's terminal. Specifically, the system transmits data via a communication protocol and displays a notification on the user's smartphone app.

[0472] Step 5:

[0473] The server periodically generates and provides feedback and reminders to the user.

[0474] The input consists of analyzed health data and past feedback history. The server re-evaluates the user's health trends and generates reminders for future preventative measures and check-ups as needed. The output is a reminder notification delivered to the user's device. Specifically, a periodic timer is used to schedule the reminder generation process and send it to the user's device.

[0475] (Application Example 1)

[0476] 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."

[0477] Health management for the elderly and individuals requiring care necessitates real-time information provision and accurate advice, but systems capable of doing so are limited. Furthermore, there is a lack of means for caregivers to constantly monitor the health status of those they care for and take appropriate action. This invention aims to solve such challenges in health management and caregiving.

[0478] 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.

[0479] In this invention, the server includes means for collecting health status information of a person receiving care from sensor devices, means for transmitting the health status information to an information processing device via a communication network, and means for transmitting and notifying the health report to the information display device. This makes it possible to present the health status of the person receiving care to the caregiver in real time and to efficiently manage their health.

[0480] A "sensor device" is a device used to acquire information related to the health status of a person receiving care in real time.

[0481] "Health status information" refers to various data indicating the health status of the person receiving care, such as heart rate, blood pressure, and body temperature.

[0482] A "communication network" is the infrastructure that constitutes a system for efficiently sending and receiving data.

[0483] An "information processing device" refers to a computer system used to store and analyze collected health status information.

[0484] "Information recording medium" refers to the database or storage used by the information processing device to store health status information.

[0485] A "health report" is a report generated based on analyzed health status information, which evaluates the health status of the person receiving care and proposes appropriate measures.

[0486] An "information display device" refers to a display device used to present health reports to users, and includes smartphones and tablets.

[0487] A "computational learning model" is an algorithm used to estimate health risks based on collected health status information.

[0488] "Advice" refers to specific guidance and suggestions provided to improve the daily life of the person receiving care.

[0489] This invention is implemented by a system comprising a sensor device, an information processing device, and an information display device. The sensor device acquires health status information such as the heart rate, blood pressure, and body temperature of the person being cared for in real time. This information is transmitted to the information processing device via a communication network. The information processing device stores the health status information in an information recording medium, for example using a cloud server, and analyzes it using a computational learning model.

[0490] The server generates a health report based on the analysis results. This report includes personalized advice and predictions of health risks. The health report is sent to an information display device such as a smartphone or tablet and presented to the caregiver or the person being cared for.

[0491] For example, if a particular user has high blood pressure, the server analyzes that information and generates advice such as "reduce your salt intake." Furthermore, the generating AI model can use prompts to create more precise advice. An example of a prompt is: "Based on the heart rate and blood pressure data of person A, analyze their health status and generate effective health advice for the next week."

[0492] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0493] Step 1:

[0494] The terminal acquires health information such as the care recipient's heart rate, blood pressure, and body temperature in real time through sensor devices. This information is input to the terminal as analog signals from the sensors and processed to convert it into digital data. The converted digital data is then passed on to the next step.

[0495] Step 2:

[0496] The terminal sends the converted digital data to the server via the communication network. During this transmission process, the data is properly packetized and sent to the server's specified address via the Internet Protocol. After transmission is complete, the server's receiving status is updated.

[0497] Step 3:

[0498] The server stores the received health status information in an information storage medium. A database management system (DBMS) is used to organize the received data and write it to the database as time-series data. The output of this step is a database where health data is neatly stored.

[0499] Step 4:

[0500] The server analyzes the accumulated data using a computational learning model. It receives health status data as input, and a generative AI model uses this data to execute machine learning algorithms. The analysis results in personalized health reports and health risk assessments. This process may utilize, for example, a deep learning framework.

[0501] Step 5:

[0502] The server generates specific advice using prompts based on the generated health report. In this process, the prompts are modified based on the analysis results, and the optimal advice is automatically created. The output of this step is a health report containing the advice presented to the user.

[0503] Step 6:

[0504] The server sends the generated health report to the information display device and notifies the user. The notification function uses push notifications to deliver the information to the user's information display device immediately. This process involves formatting the report content and distributing it via a message queue.

[0505] Step 7:

[0506] Users review the health reports they receive and make improvements to their daily lives based on the advice provided. This step often includes a feature that allows users to input information, such as recording daily activities and impressions. This allows the system to use the feedback for future analyses.

[0507] 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.

[0508] This invention realizes a system that comprehensively manages not only physical health but also mental health by combining user health status data with an emotion engine that recognizes emotions. This system functions in cooperation with a sensor device, emotion engine, server, and user terminal.

[0509] First, the sensor device, which acts as the terminal, measures the user's health data in real time, including heart rate, body temperature, weight, and blood pressure. Simultaneously, the emotion engine analyzes the user's voice data and text input to extract the user's emotional data. This emotional data includes emotional states such as joy, sadness, and stress. Both sets of data are transmitted to the server via the terminal.

[0510] The server receives this diverse data and stores it in a database. Machine learning models are used to comprehensively analyze health status and emotional data. The analysis evaluates the correlation between past emotional changes and health data, and predicts future health risks and mental health status.

[0511] Based on the analysis results, the server generates a personalized health report for each user. This report includes not only physical health advice but also specific advice for improving mental health in response to changes in emotions. For example, if high stress levels are identified, the report may include suggestions for relaxation techniques and recommendations for getting enough rest.

[0512] Health reports generated from the server are delivered to the user's device via the network, and the device presents the report to the user as a notification. Therefore, users can adjust their daily behaviors based on the feedback, maintaining and improving their physical and mental health.

[0513] Thus, the present invention extends conventional health management systems by utilizing an emotion engine and implements a comprehensive health management system that also takes into account the mental health of users.

[0514] The following describes the processing flow.

[0515] Step 1:

[0516] The device uses sensors to measure the user's health status, collecting data such as heart rate, body temperature, weight, and blood pressure. Simultaneously, it uses an emotion engine to recognize the user's current emotional state from voice and text input, extracting emotional data such as joy, sadness, and anger. This collected data is immediately transmitted to the server.

[0517] Step 2:

[0518] The server receives health status and emotion data transmitted from the terminal. The received data is immediately stored in a database, organized and stored based on each user's identification information. This storage process includes checks to maintain data consistency and accuracy.

[0519] Step 3:

[0520] The server provides health status data and emotional data to a machine learning model, which then integrates and analyzes the data. This analysis examines the relationship between health and emotions and predicts future health risks and mental health status. As a result, each user's health profile is updated.

[0521] Step 4:

[0522] Based on the analysis results, the server generates a personalized health report for each user. The health report includes advice on physical health and suggestions for mental health based on observed emotional patterns. For example, if stress levels are high, relaxation and mindfulness practices might be recommended.

[0523] Step 5:

[0524] The health report generated by the server is sent to the user's terminal via the network. The user's terminal receives this report and notifies the user in real time. The notification includes a summary of the report and important advice.

[0525] Step 6:

[0526] Users review health reports provided by their devices. They attempt to maintain both physical and mental health by adjusting their daily routines and behaviors according to the advice provided. For example, on days when stress levels are identified as high, they might increase their rest time.

[0527] These steps allow the system to comprehensively manage the user's physical and emotional state, supporting the maintenance of health in various aspects of physical and mental well-being.

[0528] (Example 2)

[0529] 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."

[0530] Traditional health management systems have focused primarily on collecting and analyzing physical health data, making it difficult to comprehensively manage psychological health. Furthermore, there is a growing need to provide personalized advice that takes emotional states into account, thereby more accurately predicting users' health risks and improving their quality of life.

[0531] 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.

[0532] In this invention, the server includes means for performing voice or text analysis to extract the user's emotional state; means for using a machine learning model to comprehensively analyze the biometric data and emotional data and generate predictive information for each user; and means for providing the user with personalized suggestions regarding mental health improvement based on the predictive information and emotional state. This enables comprehensive health management that simultaneously considers both the user's physical and emotional state.

[0533] A "sensor device" is a general term for devices that collect a user's biometric data in real time, and includes devices that can measure data such as heart rate, body temperature, weight, and blood pressure.

[0534] "Biometric data" refers to data that serves as an indicator of a user's physical health status, and mainly includes heart rate, body temperature, weight, and blood pressure.

[0535] A "central processing unit" is a computer system that receives, analyzes, and stores data transmitted over a network in a storage device.

[0536] A "storage device" is a device used to store data received by a central processing unit for extended periods, and typically uses a database system.

[0537] "Speech or text analysis" is the process of analyzing collected speech or text data to extract emotional states, often utilizing natural language processing techniques.

[0538] "Emotional data" refers to data that indicates the user's psychological state, including emotional states such as joy, sadness, and stress.

[0539] "Predictive information" refers to information about future health risks and health conditions derived from the results of biometric and emotional data analyzed using machine learning models.

[0540] "Personalized suggestions" refer to specific, customized suggestions and advice regarding improving psychological health, based on the user's unique health and emotional state.

[0541] This invention is a system that comprehensively manages both physical and psychological health conditions, and in which a sensor device (a terminal), a server (a central processing unit), and a user terminal work together in cooperation.

[0542] The device collects the user's biometric data in real time using sensor devices. This includes data such as heart rate, body temperature, weight, and blood pressure. Common wearable devices such as smartwatches are used. This data is aggregated by the device, and at the same time, emotional data is collected through the user's voice and text input. The voice and text data is analyzed using natural language processing through an emotion engine to extract emotional states such as joy, sadness, and stress.

[0543] The collected data is sent to the server using a secure protocol. The server stores this data in storage and analyzes it using machine learning models. Specifically, database systems such as MySQL and PostgreSQL are used for data management, and machine learning libraries such as TensorFlow and PyTorch are used for analysis. Correlations with past data trends and emotional states are also evaluated. Based on the analysis results, the server generates predictive information for each user, forecasting future physical health risks and mental health conditions.

[0544] Based on predictive information generated by the server, personalized advice is generated and created as a prompt message. For example, a specific prompt message might be, "What relaxation methods would you recommend for a male office worker in his 30s to alleviate stress caused by excessive workload?"

[0545] Predictive information and advice generated by the server are sent to the user's terminal, allowing the user to incorporate them into their daily actions. In this way, the terminal, server, and user work together to enable users to comprehensively manage their physical and mental health and improve their quality of life.

[0546] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0547] Step 1:

[0548] The sensor device, acting as the terminal, acquires the user's biometric data. At this stage, heart rate, body temperature, weight, blood pressure, etc., are read from the sensor as input and temporarily stored in local memory. This biometric data is converted to a standard format to unify the data format. This conversion process facilitates data analysis in subsequent processing.

[0549] Step 2:

[0550] The device receives voice and text input from the user and analyzes this data using an emotion engine. It outputs emotional states, such as "joy" or "sadness," from the voice and text input. This analysis is performed by quantifying emotions using natural language processing techniques.

[0551] Step 3:

[0552] The collected biometric and emotional data are transferred to the server via a security protocol. This data transfer process involves encryption to maintain data confidentiality and integrity. The input data is sent to the server sequentially in packet format.

[0553] Step 4:

[0554] The server stores the received data in storage. The input biometric and emotional data are centrally managed by a database management system. During the storage process, appropriate indexes are assigned according to the database structure.

[0555] Step 5:

[0556] The server launches a machine learning model and analyzes the stored data. From the input data, the generative AI model outputs health risks and predicted mental states. During the analysis process, it identifies correlations with past data patterns and uses statistical methods to predict future risks.

[0557] Step 6:

[0558] Based on the analysis results, the server generates personalized advice for each user. The generated advice is formatted as a prompt message. For example, for a user with a high stress level, it might output a specific suggestion such as, "We recommend practicing relaxation techniques."

[0559] Step 7:

[0560] The generated advice and predictive information are delivered to the user's terminal. The user's terminal receives this information and presents it to the user through notifications and application interfaces. The user can accept the presented information and incorporate it into their daily activities.

[0561] (Application Example 2)

[0562] 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."

[0563] In recent years, advancements in information and communication technology have made it possible to collect and analyze data in real time for personal health management. However, conventional systems have focused primarily on physical health and have not provided comprehensive health management that adequately considers the user's emotions and mental health. Furthermore, there is a growing expectation for systems that can be easily integrated into daily life as smart devices and that provide intuitive and useful feedback to users.

[0564] 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.

[0565] In this invention, the server includes means for collecting biometric data of the user from sensor devices, means for analyzing emotions from acoustic input, and means for using a machine learning model to predict health risks and emotional states based on the biometric data and emotional data. This makes it possible to comprehensively manage the body and mind and provide personalized health and mental health suggestions to the user.

[0566] A "sensor device" is a device used to measure and collect a user's biometric data in real time.

[0567] "Biometric indicator data" is a general term for various data that indicate the user's physical health status, such as heart rate, body temperature, and blood pressure.

[0568] An "information processing device" is a computer system used to store and analyze data transmitted over a network.

[0569] A "storage device" is a storage medium for safely and efficiently storing collected data.

[0570] A "status report" is a report that shows the current state of the user's health and mental health based on the analyzed data.

[0571] "Audio input" refers to an input method that acquires the user's voice and surrounding sounds through sensors.

[0572] "Means of analyzing emotions" refers to a process of analyzing data obtained from acoustic input to evaluate the user's emotional state.

[0573] "Mitigation measures" refer to specific suggestions and actions aimed at improving the health and emotional state of users.

[0574] The system for implementing this invention mainly consists of a sensor device, an information processing device, a storage device, and a user terminal. The sensor device is responsible for collecting biometric data such as heart rate and body temperature. This data is transmitted to the information processing device via wireless communication, where initial processing and analysis are performed.

[0575] The information processing system stores collected data in a storage device while simultaneously analyzing the user's emotions through acoustic input. For example, a voice analysis engine using NLP (Natural Language Processing) technology extracts emotions from the user's voice. Machine learning libraries such as TensorFlow are used to evaluate the user's health and emotional state and propose mitigation measures.

[0576] The situation report generated as a result of the analysis is delivered to the user's terminal via the network and notified on the terminal. Users can read the received situation report and use it as a reference for improving their behavior in real life. For example, if the system detects that the user is experiencing stress, it may suggest a mitigation measure such as "Try taking a deep breath."

[0577] An example of a prompt message would be, "Consider the user's current health and emotional state, and suggest an appropriate relaxation method." This enables personalized health management and emotional well-being for each user.

[0578] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0579] Step 1:

[0580] The sensor device collects biometric data such as heart rate and body temperature from the user. Its input is biosignals, and its output is real-time health data. The collected data is transmitted to a server using wireless communication.

[0581] Step 2:

[0582] The server receives biometric data from sensor devices as input and stores it in a storage device. Initial data processing involves filtering out error data and converting it into a format suitable for analysis. The output is organized health data.

[0583] Step 3:

[0584] The server receives the user's voice data as input and converts it into text data using a natural language processing engine. Based on this text data, it performs emotional analysis to evaluate the user's emotional state. The output is metadata related to emotions.

[0585] Step 4:

[0586] The server uses a machine learning model to perform an integrated analysis using the health data obtained in Step 2 and the emotional data obtained in Step 3 as input. Based on the data calculations, it evaluates the user's health and emotional state and predicts future health risks and emotional states. Evaluation result data is generated as output.

[0587] Step 5:

[0588] The server generates personalized status reports using evaluation data. The input is analyzed data, and the output is a personalized health report. Relaxation suggestions and behavioral guidelines are included as needed.

[0589] Step 6:

[0590] The device notifies the user of status reports received from the server via the network. The user can then adjust their daily activities based on the received health reports. Specific actions include pop-up notifications and audio alerts.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] [Fourth Embodiment]

[0595] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0596] 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.

[0597] 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).

[0598] 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.

[0599] 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.

[0600] 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).

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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.

[0607] 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".

[0608] This invention realizes a system that provides individually customized health guidance by aggregating and analyzing diverse health data from users. This system functions through the coordinated operation of sensor devices, a server, and user terminals.

[0609] First, the sensor device, which acts as the terminal, measures the user's health data in real time. The measured data includes heart rate, blood pressure, body temperature, weight, and information on the composition of excretions. This data is transmitted to a server via the network using wireless communication.

[0610] Next, the server stores the received health data in a database. The stored data is analyzed using a machine learning model. The results of the analysis are generated as a personalized health report for each user. For example, if the server finds through the analysis that a user's fluid intake is inadequate, it will generate advice including appropriate fluid intake levels.

[0611] Health reports generated from the server are delivered to the user's device via the network. A notification appears on the user's smartphone app prompting them to view the health report. This report includes specific advice, such as, "Drink another glass of water in the morning."

[0612] Furthermore, the server periodically analyzes users' health trends and sends reminders for vaccinations and regular health checkups to the user's device. This allows users to keep track of their daily health status and proactively work towards maintaining their health.

[0613] In this way, the present invention provides a system that can support users in their continuous health management by integrating and analyzing health data from multiple sensors.

[0614] The following describes the processing flow.

[0615] Step 1:

[0616] The device measures the user's health status in real time. Measurement items include heart rate, body temperature, weight, blood pressure, and excretory composition. Once measurement is complete, the data is transmitted from the device to a server via the network.

[0617] Step 2:

[0618] The server receives health status data sent from the terminal and stores it in a database. During this storage process, the data is managed by each user's ID and tagged with time and date.

[0619] Step 3:

[0620] The server performs analysis using machine learning models based on data in the database. The purpose of the analysis is to understand users' health trends and predict potential health risks.

[0621] Step 4:

[0622] Based on the analysis results, the server generates a personalized health report for each user. This report includes, for example, recommendations for the following day's diet, exercise, sleep, and hydration.

[0623] Step 5:

[0624] The server sends the generated health report to the user's terminal. The user's terminal receives this report and displays a notification to the user.

[0625] Step 6:

[0626] Users review reports on their devices and adjust and implement their health behaviors based on the advice provided. For example, they might decide to increase their daily water intake based on the report.

[0627] Step 7:

[0628] The server periodically analyzes data collected over the long term to understand health trends. If necessary, it delivers reminders for vaccinations and health checkups to user devices.

[0629] The above outlines the specific processing steps of this system. This provides users with an environment in which they can autonomously manage their daily health.

[0630] (Example 1)

[0631] 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".

[0632] In modern society, where individuals lead busy lives, managing one's health efficiently and effectively is not easy. Conventional health management systems only monitor individual health indicators in isolation, lacking the ability to integrate and analyze that data to provide specific health guidance. Furthermore, they lack mechanisms to automatically provide users with regular feedback and reminders, making sustainable health management difficult. To solve this problem, the development of a new system is needed.

[0633] 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.

[0634] In this invention, the server includes means for collecting health status data from sensor devices and storing it in a storage device, means for analyzing the stored data with a machine learning device and generating health information, and means for providing personalized feedback and reminders to the user based on that health information. This enables the user to understand their own health status in real time and receive specific advice.

[0635] A "sensor device" is a device that measures physiological indicators from the user's body and environment in real time and converts them into digital data.

[0636] "Health status data" refers to information that represents physiological indicators of the body, including heart rate, blood pressure, body temperature, weight, and information on the composition of excretions.

[0637] A "network" is a system that includes means of communication for sending and receiving data and information bidirectionally, and is often constructed through wireless communication technology.

[0638] An "information processing device" is a device used to analyze, store, or transmit received digital data, and often functions as a server.

[0639] A "storage device" is a device that has the function of storing digital data, and usually includes a database.

[0640] A "machine learning device" is a device that executes algorithms aimed at extracting patterns and knowledge from large amounts of data and performing predictions and classifications.

[0641] "Health information" refers to information including reports and recommendations generated from the analysis of health status data, and is useful for users' health management.

[0642] "Feedback" refers to the information and advice that a system provides to a user, used to encourage or modify the user's behavior.

[0643] A "reminder" is a means of notifying users of specific actions or events, and is used to promote regular health management.

[0644] This invention is a system for effectively supporting users' health management and consists of three main components: a sensor device, a server, and a user terminal.

[0645] The terminal (sensor device) continuously measures the user's physiological indicators, either by being attached to the body surface or carried by the user. Specifically, it collects data such as heart rate, blood pressure, body temperature, weight, and excretory substance composition, and transmits this data to a server using wireless communication technologies such as Bluetooth or Wi-Fi. This enables real-time data collection.

[0646] The server receives health status data transmitted from sensor devices. The received data is recorded in a database system. Widely used database management systems such as MySQL and MongoDB are used for this purpose. Next, the data is analyzed by a machine learning system. Machine learning frameworks that utilize generative AI models (e.g., TensorFlow and PyTorch) are used to analyze predictions and health status trends based on past data. The analysis results are generated as personalized health information. This includes content that assesses daily hydration habits and creates specific advice such as, "Drink another glass of water in the morning."

[0647] Users can receive the generated health information on their devices. Health information is notified and detailed reports can be viewed through smartphone and tablet applications. Furthermore, the server regularly sends reminders for vaccinations and periodic health checkups, supporting users in actively managing their health.

[0648] An example of a prompt for the generating AI model in this system would be: "Generate personalized health advice based on the user's health data. For example, if the user is habitually underhydrated, inform them of this and create advice indicating the appropriate amount of water to drink."

[0649] As described above, the system supports health maintenance tailored to individual needs by comprehensively managing and analyzing users' health information.

[0650] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0651] Step 1:

[0652] The terminal (sensor device) collects physiological indicators from the user.

[0653] The inputs are real-time heart rate, blood pressure, body temperature, weight, and excretory composition information. Initial processing is performed by a microcontroller within the sensor device to format the data. The output is formatted health status data, which is transmitted to a server using Bluetooth or Wi-Fi. Specifically, the sensor periodically takes measurements, accumulates the results in a data buffer, and transmits the data when a certain amount is reached.

[0654] Step 2:

[0655] The server receives data sent from the terminal and stores it in its storage device.

[0656] The input is health status data transmitted from a sensor device. The server first confirms that the data has been received, checks the integrity of the data, and filters out invalid and duplicate data. The output is the filtered health data, which is then stored in the database. Specifically, the server inserts the data into the appropriate tables and fields through database queries and associates the timestamp with the user ID.

[0657] Step 3:

[0658] The server analyzes the stored data and generates health information.

[0659] The input is user-specific health status data stored in a database. The server uses a generative AI model, such as TensorFlow, to analyze the data and perform anomaly detection and trend analysis. The output is user-specific health information, such as advice on hydration. Specifically, the process involves calling a machine learning model, inputting data into the model to obtain analysis results, and generating a report based on those results.

[0660] Step 4:

[0661] The server delivers the generated health information to the user's device.

[0662] The input is a generated health report. The server transmits the information to the user's terminal via the network. The output is health information notified to the user's terminal. Specifically, the system transmits data via a communication protocol and displays a notification on the user's smartphone app.

[0663] Step 5:

[0664] The server periodically generates and provides feedback and reminders to the user.

[0665] The input consists of analyzed health data and past feedback history. The server re-evaluates the user's health trends and generates reminders for future preventative measures and check-ups as needed. The output is a reminder notification delivered to the user's device. Specifically, a periodic timer is used to schedule the reminder generation process and send it to the user's device.

[0666] (Application Example 1)

[0667] 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".

[0668] Health management for the elderly and individuals requiring care necessitates real-time information provision and accurate advice, but systems capable of doing so are limited. Furthermore, there is a lack of means for caregivers to constantly monitor the health status of those they care for and take appropriate action. This invention aims to solve such challenges in health management and caregiving.

[0669] 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.

[0670] In this invention, the server includes means for collecting health status information of a person receiving care from sensor devices, means for transmitting the health status information to an information processing device via a communication network, and means for transmitting and notifying the health report to the information display device. This makes it possible to present the health status of the person receiving care to the caregiver in real time and to efficiently manage their health.

[0671] A "sensor device" is a device used to acquire information related to the health status of a person receiving care in real time.

[0672] "Health status information" refers to various data indicating the health status of the person receiving care, such as heart rate, blood pressure, and body temperature.

[0673] A "communication network" is the infrastructure that constitutes a system for efficiently sending and receiving data.

[0674] An "information processing device" refers to a computer system used to store and analyze collected health status information.

[0675] "Information recording medium" refers to the database or storage used by the information processing device to store health status information.

[0676] A "health report" is a report generated based on analyzed health status information, which evaluates the health status of the person receiving care and proposes appropriate measures.

[0677] An "information display device" refers to a display device used to present health reports to users, and includes smartphones and tablets.

[0678] A "computational learning model" is an algorithm used to estimate health risks based on collected health status information.

[0679] "Advice" refers to specific guidance and suggestions provided to improve the daily life of the person receiving care.

[0680] This invention is implemented by a system comprising a sensor device, an information processing device, and an information display device. The sensor device acquires health status information such as the heart rate, blood pressure, and body temperature of the person being cared for in real time. This information is transmitted to the information processing device via a communication network. The information processing device stores the health status information in an information recording medium, for example using a cloud server, and analyzes it using a computational learning model.

[0681] The server generates a health report based on the analysis results. This report includes personalized advice and predictions of health risks. The health report is sent to an information display device such as a smartphone or tablet and presented to the caregiver or the person being cared for.

[0682] For example, if a particular user has high blood pressure, the server analyzes that information and generates advice such as "reduce your salt intake." Furthermore, the generating AI model can use prompts to create more precise advice. An example of a prompt is: "Based on the heart rate and blood pressure data of person A, analyze their health status and generate effective health advice for the next week."

[0683] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0684] Step 1:

[0685] The terminal acquires health information such as the care recipient's heart rate, blood pressure, and body temperature in real time through sensor devices. This information is input to the terminal as analog signals from the sensors and processed to convert it into digital data. The converted digital data is then passed on to the next step.

[0686] Step 2:

[0687] The terminal sends the converted digital data to the server via the communication network. During this transmission process, the data is properly packetized and sent to the server's specified address via the Internet Protocol. After transmission is complete, the server's receiving status is updated.

[0688] Step 3:

[0689] The server stores the received health status information in an information storage medium. A database management system (DBMS) is used to organize the received data and write it to the database as time-series data. The output of this step is a database where health data is neatly stored.

[0690] Step 4:

[0691] The server analyzes the accumulated data using a computational learning model. It receives health status data as input, and a generative AI model uses this data to execute machine learning algorithms. The analysis results in personalized health reports and health risk assessments. This process may utilize, for example, a deep learning framework.

[0692] Step 5:

[0693] The server generates specific advice using prompts based on the generated health report. In this process, the prompts are modified based on the analysis results, and the optimal advice is automatically created. The output of this step is a health report containing the advice presented to the user.

[0694] Step 6:

[0695] The server sends the generated health report to the information display device and notifies the user. The notification function uses push notifications to deliver the information to the user's information display device immediately. This process involves formatting the report content and distributing it via a message queue.

[0696] Step 7:

[0697] Users review the health reports they receive and make improvements to their daily lives based on the advice provided. This step often includes a feature that allows users to input information, such as recording daily activities and impressions. This allows the system to use the feedback for future analyses.

[0698] 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.

[0699] This invention realizes a system that comprehensively manages not only physical health but also mental health by combining user health status data with an emotion engine that recognizes emotions. This system functions in cooperation with a sensor device, emotion engine, server, and user terminal.

[0700] First, the sensor device, which acts as the terminal, measures the user's health data in real time, including heart rate, body temperature, weight, and blood pressure. Simultaneously, the emotion engine analyzes the user's voice data and text input to extract the user's emotional data. This emotional data includes emotional states such as joy, sadness, and stress. Both sets of data are transmitted to the server via the terminal.

[0701] The server receives this diverse data and stores it in a database. Machine learning models are used to comprehensively analyze health status and emotional data. The analysis evaluates the correlation between past emotional changes and health data, and predicts future health risks and mental health status.

[0702] Based on the analysis results, the server generates a personalized health report for each user. This report includes not only physical health advice but also specific advice for improving mental health in response to changes in emotions. For example, if high stress levels are identified, the report may include suggestions for relaxation techniques and recommendations for getting enough rest.

[0703] Health reports generated from the server are delivered to the user's device via the network, and the device presents the report to the user as a notification. Therefore, users can adjust their daily behaviors based on the feedback, maintaining and improving their physical and mental health.

[0704] Thus, the present invention extends conventional health management systems by utilizing an emotion engine and implements a comprehensive health management system that also takes into account the mental health of users.

[0705] The following describes the processing flow.

[0706] Step 1:

[0707] The device uses sensors to measure the user's health status, collecting data such as heart rate, body temperature, weight, and blood pressure. Simultaneously, it uses an emotion engine to recognize the user's current emotional state from voice and text input, extracting emotional data such as joy, sadness, and anger. This collected data is immediately transmitted to the server.

[0708] Step 2:

[0709] The server receives health status and emotion data transmitted from the terminal. The received data is immediately stored in a database, organized and stored based on each user's identification information. This storage process includes checks to maintain data consistency and accuracy.

[0710] Step 3:

[0711] The server provides health status data and emotional data to a machine learning model, which then integrates and analyzes the data. This analysis examines the relationship between health and emotions and predicts future health risks and mental health status. As a result, each user's health profile is updated.

[0712] Step 4:

[0713] Based on the analysis results, the server generates a personalized health report for each user. The health report includes advice on physical health and suggestions for mental health based on observed emotional patterns. For example, if stress levels are high, relaxation and mindfulness practices might be recommended.

[0714] Step 5:

[0715] The health report generated by the server is sent to the user's terminal via the network. The user's terminal receives this report and notifies the user in real time. The notification includes a summary of the report and important advice.

[0716] Step 6:

[0717] Users review health reports provided by their devices. They attempt to maintain both physical and mental health by adjusting their daily routines and behaviors according to the advice provided. For example, on days when stress levels are identified as high, they might increase their rest time.

[0718] These steps allow the system to comprehensively manage the user's physical and emotional state, supporting the maintenance of health in various aspects of physical and mental well-being.

[0719] (Example 2)

[0720] 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".

[0721] Traditional health management systems have focused primarily on collecting and analyzing physical health data, making it difficult to comprehensively manage psychological health. Furthermore, there is a growing need to provide personalized advice that takes emotional states into account, thereby more accurately predicting users' health risks and improving their quality of life.

[0722] 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.

[0723] In this invention, the server includes means for performing voice or text analysis to extract the user's emotional state; means for using a machine learning model to comprehensively analyze the biometric data and emotional data and generate predictive information for each user; and means for providing the user with personalized suggestions regarding mental health improvement based on the predictive information and emotional state. This enables comprehensive health management that simultaneously considers both the user's physical and emotional state.

[0724] A "sensor device" is a general term for devices that collect a user's biometric data in real time, and includes devices that can measure data such as heart rate, body temperature, weight, and blood pressure.

[0725] "Biometric data" refers to data that serves as an indicator of a user's physical health status, and mainly includes heart rate, body temperature, weight, and blood pressure.

[0726] A "central processing unit" is a computer system that receives, analyzes, and stores data transmitted over a network in a storage device.

[0727] A "storage device" is a device used to store data received by a central processing unit for extended periods, and typically uses a database system.

[0728] "Speech or text analysis" is the process of analyzing collected speech or text data to extract emotional states, often utilizing natural language processing techniques.

[0729] "Emotional data" refers to data that indicates the user's psychological state, including emotional states such as joy, sadness, and stress.

[0730] "Predictive information" refers to information about future health risks and health conditions derived from the results of biometric and emotional data analyzed using machine learning models.

[0731] "Personalized suggestions" refer to specific, customized suggestions and advice regarding improving psychological health, based on the user's unique health and emotional state.

[0732] This invention is a system that comprehensively manages both physical and psychological health conditions, and in which a sensor device (a terminal), a server (a central processing unit), and a user terminal work together in cooperation.

[0733] The device collects the user's biometric data in real time using sensor devices. This includes data such as heart rate, body temperature, weight, and blood pressure. Common wearable devices such as smartwatches are used. This data is aggregated by the device, and at the same time, emotional data is collected through the user's voice and text input. The voice and text data is analyzed using natural language processing through an emotion engine to extract emotional states such as joy, sadness, and stress.

[0734] The collected data is sent to the server using a secure protocol. The server stores this data in storage and analyzes it using machine learning models. Specifically, database systems such as MySQL and PostgreSQL are used for data management, and machine learning libraries such as TensorFlow and PyTorch are used for analysis. Correlations with past data trends and emotional states are also evaluated. Based on the analysis results, the server generates predictive information for each user, forecasting future physical health risks and mental health conditions.

[0735] Based on predictive information generated by the server, personalized advice is generated and created as a prompt message. For example, a specific prompt message might be, "What relaxation methods would you recommend for a male office worker in his 30s to alleviate stress caused by excessive workload?"

[0736] Predictive information and advice generated by the server are sent to the user's terminal, allowing the user to incorporate them into their daily actions. In this way, the terminal, server, and user work together to enable users to comprehensively manage their physical and mental health and improve their quality of life.

[0737] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0738] Step 1:

[0739] The sensor device, acting as the terminal, acquires the user's biometric data. At this stage, heart rate, body temperature, weight, blood pressure, etc., are read from the sensor as input and temporarily stored in local memory. This biometric data is converted to a standard format to unify the data format. This conversion process facilitates data analysis in subsequent processing.

[0740] Step 2:

[0741] The device receives voice and text input from the user and analyzes this data using an emotion engine. It outputs emotional states, such as "joy" or "sadness," from the voice and text input. This analysis is performed by quantifying emotions using natural language processing techniques.

[0742] Step 3:

[0743] The collected biometric and emotional data are transferred to the server via a security protocol. This data transfer process involves encryption to maintain data confidentiality and integrity. The input data is sent to the server sequentially in packet format.

[0744] Step 4:

[0745] The server stores the received data in storage. The input biometric and emotional data are centrally managed by a database management system. During the storage process, appropriate indexes are assigned according to the database structure.

[0746] Step 5:

[0747] The server launches a machine learning model and analyzes the stored data. From the input data, the generative AI model outputs health risks and predicted mental states. During the analysis process, it identifies correlations with past data patterns and uses statistical methods to predict future risks.

[0748] Step 6:

[0749] Based on the analysis results, the server generates personalized advice for each user. The generated advice is formatted as a prompt message. For example, for a user with a high stress level, it might output a specific suggestion such as, "We recommend practicing relaxation techniques."

[0750] Step 7:

[0751] The generated advice and predictive information are delivered to the user's terminal. The user's terminal receives this information and presents it to the user through notifications and application interfaces. The user can accept the presented information and incorporate it into their daily activities.

[0752] (Application Example 2)

[0753] 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".

[0754] In recent years, advancements in information and communication technology have made it possible to collect and analyze data in real time for personal health management. However, conventional systems have focused primarily on physical health and have not provided comprehensive health management that adequately considers the user's emotions and mental health. Furthermore, there is a growing expectation for systems that can be easily integrated into daily life as smart devices and that provide intuitive and useful feedback to users.

[0755] 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.

[0756] In this invention, the server includes means for collecting biometric data of the user from sensor devices, means for analyzing emotions from acoustic input, and means for using a machine learning model to predict health risks and emotional states based on the biometric data and emotional data. This makes it possible to comprehensively manage the body and mind and provide personalized health and mental health suggestions to the user.

[0757] A "sensor device" is a device used to measure and collect a user's biometric data in real time.

[0758] "Biometric indicator data" is a general term for various data that indicate the user's physical health status, such as heart rate, body temperature, and blood pressure.

[0759] An "information processing device" is a computer system used to store and analyze data transmitted over a network.

[0760] A "storage device" is a storage medium for safely and efficiently storing collected data.

[0761] A "status report" is a report that shows the current state of the user's health and mental health based on the analyzed data.

[0762] "Audio input" refers to an input method that acquires the user's voice and surrounding sounds through sensors.

[0763] "Means of analyzing emotions" refers to a process of analyzing data obtained from acoustic input to evaluate the user's emotional state.

[0764] "Mitigation measures" refer to specific suggestions and actions aimed at improving the health and emotional state of users.

[0765] The system for implementing this invention mainly consists of a sensor device, an information processing device, a storage device, and a user terminal. The sensor device is responsible for collecting biometric data such as heart rate and body temperature. This data is transmitted to the information processing device via wireless communication, where initial processing and analysis are performed.

[0766] The information processing system stores collected data in a storage device while simultaneously analyzing the user's emotions through acoustic input. For example, a voice analysis engine using NLP (Natural Language Processing) technology extracts emotions from the user's voice. Machine learning libraries such as TensorFlow are used to evaluate the user's health and emotional state and propose mitigation measures.

[0767] The situation report generated as a result of the analysis is delivered to the user's terminal via the network and notified on the terminal. Users can read the received situation report and use it as a reference for improving their behavior in real life. For example, if the system detects that the user is experiencing stress, it may suggest a mitigation measure such as "Try taking a deep breath."

[0768] An example of a prompt message would be, "Consider the user's current health and emotional state, and suggest an appropriate relaxation method." This enables personalized health management and emotional well-being for each user.

[0769] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0770] Step 1:

[0771] The sensor device collects biometric data such as heart rate and body temperature from the user. Its input is biosignals, and its output is real-time health data. The collected data is transmitted to a server using wireless communication.

[0772] Step 2:

[0773] The server receives biometric data from sensor devices as input and stores it in a storage device. Initial data processing involves filtering out error data and converting it into a format suitable for analysis. The output is organized health data.

[0774] Step 3:

[0775] The server receives the user's voice data as input and converts it into text data using a natural language processing engine. Based on this text data, it performs emotional analysis to evaluate the user's emotional state. The output is metadata related to emotions.

[0776] Step 4:

[0777] The server uses a machine learning model to perform an integrated analysis using the health data obtained in Step 2 and the emotional data obtained in Step 3 as input. Based on the data calculations, it evaluates the user's health and emotional state and predicts future health risks and emotional states. Evaluation result data is generated as output.

[0778] Step 5:

[0779] The server generates personalized status reports using evaluation data. The input is analyzed data, and the output is a personalized health report. Relaxation suggestions and behavioral guidelines are included as needed.

[0780] Step 6:

[0781] The device notifies the user of status reports received from the server via the network. The user can then adjust their daily activities based on the received health reports. Specific actions include pop-up notifications and audio alerts.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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."

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] The following is further disclosed regarding the embodiments described above.

[0804] (Claim 1)

[0805] A means of collecting user health status data from a sensor device,

[0806] Means for transmitting the aforementioned health status data to a server via a network,

[0807] The server provides means for storing the aforementioned health status data in a database,

[0808] A means of analyzing stored data and generating health reports for each user,

[0809] A means for delivering and notifying the user of the aforementioned health report,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, further comprising means of using a machine learning model to predict health risks based on the aforementioned health status data.

[0813] (Claim 3)

[0814] The system according to claim 1, comprising means for providing users with personalized advice regarding diet, exercise, sleep, and hydration based on the aforementioned health report.

[0815] "Example 1"

[0816] (Claim 1)

[0817] A means of collecting user health status data from a sensor device,

[0818] Means for transmitting the aforementioned health status data to an information processing device via a network,

[0819] An information processing device includes means for storing the health status data in a storage device,

[0820] A means of analyzing stored data and generating health information for each user,

[0821] A means for distributing and notifying the user terminal of the aforementioned health information,

[0822] A means of providing feedback and reminders to users based on the aforementioned health information,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, further comprising means for using a machine learning device to evaluate trends in health status based on the aforementioned health status data.

[0826] (Claim 3)

[0827] The system according to claim 1, comprising means for providing users with personalized information regarding lifestyle management based on the aforementioned health information.

[0828] "Application Example 1"

[0829] (Claim 1)

[0830] A means of collecting health status information of the person receiving care from sensor devices,

[0831] Means for transmitting the aforementioned health status information to an information processing device via a communication network,

[0832] An information processing device includes means for storing the health status information in an information recording medium,

[0833] A means of analyzing accumulated information and generating health reports for each person receiving care,

[0834] Means for transmitting and notifying the aforementioned health report to an information display device,

[0835] A means of presenting real-time health status to caregivers,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, comprising means for using a computational learning model to estimate health risks based on the aforementioned health status information.

[0839] (Claim 3)

[0840] The system according to claim 1, comprising means for providing the care recipient with personalized advice regarding nutrition, physical exercise, rest, and fluid intake based on the aforementioned health report.

[0841] "Example 2 of combining an emotion engine"

[0842] (Claim 1)

[0843] A means of collecting user biometric data from a sensor device,

[0844] Means for transmitting multiple types of data, including the aforementioned biological data, to a central processing unit via a network,

[0845] A means for storing the aforementioned multiple types of data in a storage device in a central processing unit,

[0846] A means of extracting the user's emotional state by performing voice or text analysis,

[0847] A means for integrating and analyzing the aforementioned biometric data and emotional data using a machine learning model to generate predictive information for each user,

[0848] A means of providing users with personalized suggestions regarding the improvement of their mental health based on the aforementioned predictive information and emotional state,

[0849] A means for transmitting and displaying the aforementioned predictive information and suggestions to the user's terminal,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, comprising analytical means for evaluating the correlation between biometric data and emotional data and predicting the user's health risk.

[0853] (Claim 3)

[0854] The system according to claim 1, which includes suggestions for improving psychological stability for the user based on the aforementioned predictive information.

[0855] "Application example 2 when combining with an emotional engine"

[0856] (Claim 1)

[0857] A means for collecting user biometric data from a sensor device,

[0858] Means for transmitting the aforementioned biometric data to an information processing device via a network,

[0859] An information processing device includes means for storing the biometric data in a storage device,

[0860] A means of analyzing stored data and generating status reports for each user,

[0861] A means for distributing and notifying the user terminal of the aforementioned status report,

[0862] A means of analyzing emotions from acoustic input,

[0863] A means of providing mitigation measures to users based on acoustic analysis results,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, further comprising means of using a machine learning model to predict health risks and emotional states based on the aforementioned biometric data and emotional data.

[0867] (Claim 3)

[0868] The system according to claim 1, comprising means for providing the user with personalized instructions regarding exercise, rest, and stress reduction based on the aforementioned status report. [Explanation of symbols]

[0869] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting user health status data from a sensor device, Means for transmitting the aforementioned health status data to a server via a network, The server provides means for storing the aforementioned health status data in a database, A means of analyzing stored data and generating health reports for each user, A means for delivering and notifying the user of the aforementioned health report, A system that includes this.

2. The system according to claim 1, further comprising means for using a machine learning model to predict health risks based on the aforementioned health status data.

3. The system according to claim 1, comprising means for providing the user with personalized advice regarding diet, exercise, sleep, and hydration based on the aforementioned health report.

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