system

Abstract

We provide the system. [Solution] Data collection methods for obtaining individual health information, A data integration means for aggregating acquired health information and storing it in a storage device, An analytical means for analyzing aggregated data and generating health assessments and risk diagnoses, A notification system to communicate the generated evaluation results to the user and, if necessary, to send warnings to external supporters. 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 persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional health management systems have the problem that since they are limited to individual data provision, it is difficult for users to comprehensively grasp their health status and take appropriate measures. In addition, the method of integrally utilizing the acquired data is insufficient, and there are limitations in making highly accurate diagnoses and proposals from individual health information.

Means for Solving the Problems

[0005] This invention provides a system that includes means for acquiring individual health information and for integrating and storing this data in a database. Furthermore, it includes analytical means that perform highly accurate health assessments and risk diagnoses using machine learning algorithms based on the integrated data. It also includes notification means for quickly and concisely notifying users of the generated assessment results, thereby enabling users to comprehensively understand their health status and make appropriate health management and medical treatment choices.

[0006] "Data acquisition means" refers to a function for collecting individual health information of users.

[0007] A "data integration tool" is a function that consolidates and organizes multiple pieces of acquired health information into a single database for storage.

[0008] "Analysis tools" refer to functions that use machine learning algorithms to perform health assessments and risk diagnoses based on integrated health information.

[0009] "Notification means" refers to a communication function for notifying users of the generated health assessment results and risk diagnosis.

[0010] A "machine learning algorithm" is a mathematical method for analyzing large amounts of data, learning patterns, and making highly accurate predictions and diagnoses.

[0011] "Health assessment" is a process of comprehensively evaluating a user's health status based on acquired health information.

[0012] "Risk assessment" is a diagnostic process that predicts the likelihood of disease based on integrated health information.

[0013] A "database" is a digital storage system for accumulating acquired and integrated health information and maintaining it in a format that can be accessed as needed. [Brief explanation of the drawing]

[0014] [Figure 1]It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. <00000z7> [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It 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 an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

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

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

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is an AI-based health management system that comprehensively evaluates the user's health status and provides risk assessments and health advice. In particular, this system has the function of comprehensively acquiring and managing individual health information and providing specific diagnoses and recommendations through AI analysis.

[0036] The system operates as follows: First, the terminal collects various health data from the user. This includes information on daily diet, exercise, heart rate, body temperature, and lifestyle habits. The terminal organizes this data and sends it to the server via a security protocol.

[0037] The server stores data received from the terminal in a database and integrates various data to create an overall health profile of the user. This integrated data is then passed to an AI analysis module, which uses machine learning algorithms to perform a detailed assessment and risk diagnosis. The AI ​​module compares historical and current data to identify disease risks and lifestyle habits that need improvement.

[0038] The analysis results are sent from the server to the terminal. The terminal receives these results and displays them in a user-friendly format. As a specific example of its use, if the analysis results indicate a high risk of heart disease, the system will advise reducing salt intake and recommend scheduling regular health checkups.

[0039] Furthermore, users can not only improve their lifestyle habits by following these suggestions, but by providing feedback, the system can integrate new data and provide even more accurate diagnoses and advice. This cycle allows for continuous support of users' health management.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Users input health data related to their daily lives into the device. Specifically, they record information such as their diet, exercise level, heart rate, body temperature, and sleep duration.

[0043] Step 2:

[0044] The device organizes the collected data and prepares it to be securely sent to the server using encryption technology.

[0045] Step 3:

[0046] The server receives the health data, stores it in a database, and integrates related data. This includes existing health checkup results and past lifestyle data.

[0047] Step 4:

[0048] The server feeds the integrated data into an AI analysis module, which uses machine learning algorithms to perform health assessments and risk diagnoses. The model analyzes data patterns to identify the user's health status and disease risk.

[0049] Step 5:

[0050] The server generates analysis results and creates specific health advice and risk assessments. These results are tailored to the individual user.

[0051] Step 6:

[0052] The server sends the generated results to the terminal and notifies the user.

[0053] Step 7:

[0054] The device displays acquired advice and diagnostic results to the user. This information is provided to the user in a format that is easy to view and understand.

[0055] Step 8:

[0056] The cycle restarts when the user improves their lifestyle based on the advice provided and records new data. Continuous data provision and feedback enable the system to provide even more accurate diagnoses and advice.

[0057] (Example 1)

[0058] 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."

[0059] Traditional health management systems have struggled to provide a multifaceted assessment of individual health conditions and personalized health advice through continuous data updates. In particular, a lack of highly accurate risk assessment through integrated data analysis and the ability to incorporate user feedback have made it difficult to address individual needs.

[0060] 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.

[0061] In this invention, the server includes information gathering means for collecting personal health-related data, data storage means for integrating and storing the collected data, and AI analysis means for analyzing the integrated data using AI. This enables not only a comprehensive evaluation of individual health conditions and highly accurate risk diagnosis, but also continuous health management that reflects user feedback.

[0062] "Information gathering means" refers to methods or devices for obtaining health-related data from individuals, including data entered by users and data collected by sensors.

[0063] "Organizational methods" refer to the processes or functions for structuring collected health-related data and preparing it in an analyzable format.

[0064] "Communication means" refers to a method or technique for transmitting organized data using security technology to another device or server.

[0065] "Data storage means" refers to a database or storage medium used to store received health-related data, making it easily accessible and analyzable.

[0066] "AI analysis tools" refer to processes or modules that analyze integrated health-related data using advanced machine learning algorithms to generate health assessments and risk diagnoses.

[0067] "Notification means" refers to a function or process that presents generated health assessment results and diagnoses to users and provides information in an easily understandable format.

[0068] "Feedback integration means" refers to a function or mechanism that incorporates user opinions and feedback into the system and reflects them in health-related data, thereby improving the accuracy of results and providing personalized advice.

[0069] "Encryption technology" refers to a technique used to enhance data security by transforming transmitted information using specific algorithms and preventing unauthorized access.

[0070] This invention is a system for health management purposes and consists of multiple processes, including the collection of health-related data by a terminal, the storage and analysis of the data by a server, and the notification of results to the user.

[0071] The terminal serves as an interface for collecting personal health information. This includes smartphones, fitness devices, and wearable sensors, which collect data from users regarding vital signs, lifestyle, and diet. The terminal organizes this data, verifies for any missing information, and then encrypts it using security technology before transmitting it to the server.

[0072] The server stores health-related data received from terminals in a database and creates a unified health profile based on this data. Next, an AI analysis module analyzes this data and performs a detailed health assessment and risk diagnosis using machine learning algorithms. Specifically, it predicts future health risks based on a large amount of historical data and suggests areas for lifestyle improvement. At this time, the server uses generative AI models such as random forests and neural networks to achieve highly accurate analysis.

[0073] The analysis results are sent back to the device in an easy-to-understand format. The device visualizes the received information and displays advice to help the user understand it. For example, if the device determines that the user is at high risk of heart disease, it will send a notification recommending things like limiting salt intake or getting regular health checkups.

[0074] Users can review their lifestyle habits based on health guidance received through their devices and provide feedback to the system. This feedback is integrated into the server and used for subsequent analysis. In this way, the system can continuously and efficiently manage the user's health.

[0075] As a concrete example, suppose a user takes photos of their daily meals and uploads them to the app, along with their exercise data measured by a fitness device, and sends this data to the server. Based on this data, the server performs a health assessment, and the AI ​​diagnoses the risks. Based on the diagnosis, the system notifies the user's device with specific health advice. An example of a prompt to be entered into the generative AI model used in this case would be, "Analyze the user's diet and exercise data and generate advice to minimize health risks."

[0076] This invention enables personalized health management by combining the integration of diverse data sources with AI analysis, thereby supporting users in maintaining and improving their health.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The device collects health-related data from the user. This data includes information on diet, exercise, heart rate, body temperature, and lifestyle. The device receives this data through an input interface and also automatically acquires data from sensor devices. Specific actions include the user taking photos of meals with their smartphone and uploading them to the app, or wearing a fitness band to record exercise levels.

[0080] Step 2:

[0081] The terminal organizes the collected data and converts it into a digital format. The input here is the raw data obtained in step 1, and the output is structured data organized by category. Specifically, the data is compiled into a unified format by extracting text data from photographs using character recognition technology, for example.

[0082] Step 3:

[0083] The terminal protects the organized data using encryption methods and sends it to the server. The input is the structured data formed in step 2, and the output is encrypted data packets. During this process, data confidentiality is maintained while transferring the data to the server using methods such as TLS.

[0084] Step 4:

[0085] The server receives data sent from the terminal and stores it in a database. The input here is encrypted data packets, and the output is the user's health profile stored in the database. The server first decrypts the received data and then registers it in the database.

[0086] Step 5:

[0087] The server passes the stored data to an AI analysis module for analysis. The input is integrated health data, and the output is a health assessment and risk diagnosis result. Specifically, it uses a generative AI model to analyze the data with machine learning algorithms and identify risks by comparing them with past data.

[0088] Step 6:

[0089] The server compiles the analysis results and sends them to the terminal, notifying the user of the results. The input is the analysis results obtained in step 5, and the output is a user-friendly formatted health report. The server retrieves the necessary information, formats it for display, and then transfers it to the terminal.

[0090] Step 7:

[0091] Users review the health assessments and risk diagnoses received on their devices and improve their lifestyle habits as needed. Furthermore, users can provide their feedback and results to the system by entering them into their devices. The input here is user feedback, and the output is updated data used for the next analysis. This is specifically done through simple questionnaires and free-response fields within the app.

[0092] These steps enable the system to manage the user's daily health and continuously provide personalized advice.

[0093] (Application Example 1)

[0094] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0095] For elderly people to live with peace of mind, it is necessary to continuously monitor their health and respond quickly when abnormalities are detected. However, there is currently a lack of mechanisms to effectively carry out this in daily life. Furthermore, the collection and analysis of health data requires appropriate technology, as it demands both the protection of personal information and highly accurate diagnosis.

[0096] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0097] In this invention, the server includes data collection means for acquiring individual health information, data integration means for aggregating the acquired health information and storing it in a storage device, and analysis means for analyzing the aggregated data and generating health assessments and risk diagnoses. This makes it possible to monitor the health status of elderly people in real time and notify appropriate caregivers as needed.

[0098] "Data collection means" refers to a device or method for acquiring health-related information of healthy individuals or elderly people using various sensors and devices.

[0099] "Data integration means" refers to a device or method for integrating, organizing, and storing collected health-related information in a single storage device.

[0100] "Analysis means" refers to a device or method that performs health assessments and risk diagnoses using machine learning or other analytical techniques based on integrated data.

[0101] "Notification means" refers to a device or method that presents the generated health assessment or diagnostic results to the user and, if necessary, sends a warning to a third party or supporter.

[0102] "Cryptographic technology" is a method of protecting information used to safeguard the security and privacy of data communications.

[0103] This invention is a system for users to monitor the health status of elderly individuals and other persons and to detect abnormalities early. This system consists of a terminal for collecting health data, a server for analysis, and a terminal for notifying the user of the results.

[0104] First, the device is equipped with various sensors to acquire everyday health data such as heart rate, body temperature, and steps taken. Wearable devices and smartphones are examples of this. These devices collect data via Bluetooth communication and transmit that data to a server using a secure protocol.

[0105] The server stores the received data in a cloud environment and organizes it using data integration tools. The aggregated data is then passed to an analysis tool that runs machine learning algorithms, which is used for health assessment and risk diagnosis. AI models developed using programming languages ​​such as Python are typically used.

[0106] The analysis results are sent back from the server to the terminal. The terminal, through an application using React Native, displays the results to the user in an easy-to-understand manner and has the functionality to send warnings to external supporters via the Twilio API as needed.

[0107] For example, if an elderly person's heart rate suddenly increases while they are out for a walk, the system can detect this increase and quickly send a notification to their caregiver stating, "Your heart rate has increased abnormally. Immediate attention is required."

[0108] An example of a prompt for a generative AI model is, "What is a way to predict trends based on health data and prepare for abnormal situations?"

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The device collects health information. Specifically, it acquires heart rate, body temperature, and step count using sensors built into wearable devices and smartphones. The acquired data is temporarily stored within the device.

[0112] Input: Biometric data from sensors.

[0113] Output: Temporarily stored biometric data.

[0114] Step 2:

[0115] The device transmits temporarily stored data to the server via a security protocol. This is done using Bluetooth or Wi-Fi communication. The data is encrypted and transmitted through a secure channel.

[0116] Input: Temporarily stored biometric data.

[0117] Output: Encrypted data sent to the server.

[0118] Step 3:

[0119] The server decodes the received biometric data and stores it in a database in the cloud. Afterward, data integration tools are used to organize and integrate all the data.

[0120] Input: Encrypted data from the device.

[0121] Output: Integrated data stored in the database.

[0122] Step 4:

[0123] The server passes the integrated data to the AI ​​analysis module. The analysis module uses machine learning algorithms to perform health assessments and risk diagnoses. Through a generative AI model, it extracts outliers and risk factors from the data.

[0124] Input: Integrated data stored in the database.

[0125] Output: Results of health assessment and risk diagnosis.

[0126] Step 5:

[0127] The server sends the analysis results to the terminal. The transmitted data is encrypted again, ensuring the security of the communication.

[0128] Input: Results of health assessment and risk diagnosis.

[0129] Output: Encryption evaluation results sent to the terminal.

[0130] Step 6:

[0131] The terminal decodes the received analysis results and presents them to the user in a visualized format via the application. Furthermore, it generates notifications and sends alerts to external support personnel when anomalies are detected, as needed.

[0132] Input: Decoded evaluation results.

[0133] Output: Health assessments and external alerts displayed in the user interface.

[0134] 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.

[0135] This invention is a system that supports the comprehensive health management of users, and in addition to evaluating health status, it also has an emotion analysis function. The system collects health information from the user, integrates and analyzes it, and provides health risk assessment and individual advice. However, this invention can further identify the user's emotional state using an "emotion engine" and provide comprehensive advice that also takes those emotions into account.

[0136] First, users use their devices to input health data related to their daily lives. This includes physiological data such as diet, exercise, heart rate, and body temperature, as well as information on lifestyle and sleep habits. This data is securely transmitted to the server.

[0137] Next, an emotion engine operates on the device, analyzing the user's facial expressions and voice input to generate emotion data. This emotion data, along with health information, is sent to a server for integration.

[0138] The server stores integrated health and emotional data in a database, which is then analyzed by an AI analysis module. This analysis utilizes machine learning techniques to assess the user's health status, emotional tendencies, and risks. Emotional data is used specifically to evaluate stress levels and anxiety levels.

[0139] Based on the analysis, the server generates customized health advice that takes emotional states into account. For example, if the emotional engine detects a high-stress state, the system suggests relaxation methods and, if necessary, refers the user to a specialist. Exercise and dietary advice is also adjusted to the user's current emotional state.

[0140] Ultimately, the device notifies the user of these pieces of advice and displays them in an easy-to-understand format. This helps users maintain and improve a balanced lifestyle in terms of both physical and emotional well-being. This cycle is repeated through continuous data provision and feedback, and the system provides health management optimized for the user.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] Users input data about their daily lives using specific applications or devices. This includes information such as diet, exercise levels, heart rate, body temperature, and sleep patterns.

[0144] Step 2:

[0145] The device encrypts the collected data and prepares it for secure transmission to the server. At this stage, data integrity and privacy are maintained.

[0146] Step 3:

[0147] The device uses an emotion engine to analyze the user's facial expressions and voice data. The emotion engine specifically utilizes the camera and microphone to perform facial recognition and voice analysis to identify the user's emotional state.

[0148] Step 4:

[0149] The device sends emotional data generated by the emotion engine to the server along with health data.

[0150] Step 5:

[0151] The server integrates the received health and emotional data and stores it in a database. The integrated data is then organized along with the user's personal history for use in analysis.

[0152] Step 6:

[0153] The server feeds the integrated data into an AI analysis module, which uses machine learning algorithms to perform a health assessment and risk diagnosis for the user. Emotional data is also considered, and the effects of stress and anxiety are reflected in the assessment.

[0154] Step 7:

[0155] The server generates customized health advice and emotion-based relaxation and improvement strategies based on the analysis results. This advice is tailored to best suit the user's current emotional state.

[0156] Step 8:

[0157] The server sends the generated results to the terminal and notifies the user.

[0158] Step 9:

[0159] The device displays advice and diagnostic results to the user. The advice is provided in a visual and easy-to-understand format, and is designed to be easily incorporated into daily life.

[0160] Step 10:

[0161] The system is continuously improved as users act on the advice provided and update their daily data. This feedback loop ensures that the system continues to provide users with optimized advice and diagnoses.

[0162] (Example 2)

[0163] 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".

[0164] Traditional health management systems perform health assessments and risk diagnoses based on users' physiological information, but they lack assessments that take emotional states into account. This makes it difficult to provide appropriate advice on users' overall health and lifestyle. Furthermore, there is a need to securely manage this data and provide personalized advice to each user.

[0165] 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.

[0166] In this invention, the server includes data acquisition means for obtaining individual physiological information, data integration means for integrating the acquired physiological information and emotional state and storing it in a memory device, and analysis means for analyzing the integrated data and generating health assessments, emotional tendencies, and risk diagnoses. This enables health assessments and advice that comprehensively consider the user's physiological information and emotional state.

[0167] "Physiological information" refers to information related to the user's physical condition, such as heart rate, body temperature, exercise level, and sleep quality.

[0168] "Emotional state" refers to the psychological and emotional tendencies and reaction states analyzed from the user's facial expressions, voice, etc.

[0169] "Data acquisition means" refers to devices and methods for collecting physiological information and emotional states from users, and includes sensors and user interfaces.

[0170] "Data integration means" refers to processes and systems for centrally managing acquired physiological information and emotional states and storing them in a memory device.

[0171] "Analysis means" refers to methods and techniques used to analyze integrated physiological information and emotional states to generate health assessments, emotional tendencies, and risk diagnoses.

[0172] "Communication encryption technology" refers to encryption methods used to prevent unauthorized access and information leakage during the data transmission process.

[0173] An "emotion analysis engine" refers to software or algorithms used to analyze emotional states from voice and facial expression data.

[0174] This invention is a system that supports the comprehensive health management of users, providing customized health advice by combining physiological information and emotional state.

[0175] First, users input physiological information related to their daily lives using their smartphones or wearable devices. This input data includes information such as heart rate, body temperature, exercise level, and sleep quality. This data is securely transmitted to the server via the device. During the communication process, the data is encrypted using communication encryption technology, protecting the user's privacy.

[0176] Next, the device's emotion analysis engine runs to analyze the user's emotional state. Specifically, it acquires facial expression data through the camera and analyzes voice tone from voice input. This analysis processes the data using, for example, facial recognition software and voice analysis libraries. The generated emotion data is then sent back to the server.

[0177] The server centrally integrates received physiological information and emotional states and stores them in a database. The stored data is analyzed by an AI analysis module to generate user health assessments, emotional tendencies, and risk diagnoses. This analysis utilizes machine learning techniques, such as TENSORFLOW® and other analysis libraries.

[0178] Based on the analysis, the server generates customized health advice. This advice is adjusted to take emotional states into account and suggests user-specific methods for maintaining and improving health. For example, if the emotional analysis indicates a high-stress state, relaxation methods or consultation with a specialist will be suggested.

[0179] Ultimately, the device notifies the user of the generated advice, displaying it in an easy-to-understand format. This allows the user to gain a comprehensive understanding of their health status and take appropriate action.

[0180] As a concrete example of its use, a prompt such as, "I'm a 35-year-old woman who's been experiencing persistent insomnia lately. I'd like some relaxation techniques and sleep advice," is input into the AI ​​model. Based on this prompt, the system provides advice tailored to the user's needs.

[0181] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0182] Step 1:

[0183] Users input physiological information using smartphones or wearable devices. This data includes heart rate, body temperature, activity level, and sleep quality. Once this data is entered, the device transmits it to the server in encrypted form via a secure communication protocol such as HTTPS. In this process, the input is physiological information provided by the user, and the output is encrypted data.

[0184] Step 2:

[0185] On the device, an emotion analysis engine operates, generating emotion data using the user's camera and microphone. Specifically, image analysis algorithms are used to acquire facial expression data and identify specific emotions, and audio data is analyzed to infer emotions from tone of voice. The input is camera video and audio data, and the output is the identified emotional state. The generated emotion data is then encrypted again and sent to the server.

[0186] Step 3:

[0187] The server integrates the received physiological and emotional data and stores it in a database. A NoSQL database, for example, is used for this purpose, enabling efficient storage and management of large amounts of data. The input consists of encrypted physiological and emotional data, while the output is an integrated data record.

[0188] Step 4:

[0189] An AI analysis module on the server runs and analyzes the integrated data. Here, machine learning algorithms are used to assess the user's health status, emotional tendencies, and health risks. Libraries such as TensorFlow are utilized for this analysis. The input is integrated physiological and emotional data, and the output is the results of the health assessment, emotional tendency, and risk diagnosis.

[0190] Step 5:

[0191] Based on the analysis results, the server uses a generative AI model to generate customized health advice that takes into account the user's emotional state. For example, if high stress is detected, advice including relaxation methods and referrals to specialists will be generated. The input is the analysis results, and the output is customized health advice.

[0192] Step 6:

[0193] The device notifies the user of the generated advice and displays it in an easy-to-understand format. The user reviews the advice received through the notification from the device and incorporates it into their daily life. The input is the generated health advice, and the output is the notification provided to the user.

[0194] (Application Example 2)

[0195] 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".

[0196] Traditional health management systems have focused primarily on managing users' physical health, but have lacked adequate support that takes emotional states into account. Therefore, it has been difficult to address health risks that fluctuate with users' emotional states and to provide appropriate advice in response to changes in their emotions. In this context, there is a need for a system that effectively analyzes emotional states and integrates them into health management.

[0197] 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.

[0198] In this invention, the server includes data acquisition means for obtaining individual health information and emotional information, data integration means for integrating the acquired health information and emotional information and storing it in an information storage device, and analysis means for analyzing the integrated data and generating health assessments, emotional assessments, and risk diagnoses. This enables comprehensive health management that takes emotional states into account.

[0199] "Health information" refers to physiological data and lifestyle information related to individual users. Specifically, it includes data such as diet, exercise, heart rate, body temperature, and sleep habits.

[0200] "Emotional information" refers to data that quantifies or categorizes a user's emotional state. This includes emotional indicators obtained through voice tone and facial expression analysis.

[0201] "Data acquisition means" refers to technical methods and devices for collecting health information and emotional information, including sensors and user interfaces.

[0202] "Data integration means" refers to technology that centrally manages acquired health and emotional information and stores it in an information storage device. This utilizes database technology and cloud storage.

[0203] "Analysis tools" refer to technical processes and devices for analyzing integrated data to generate health assessments, sentiment assessments, and risk diagnoses. This includes machine learning algorithms and data analysis software.

[0204] "Notification methods" refer to technologies used to communicate generated evaluation results and advice to users. These utilize smartphone apps and voice assistants.

[0205] "Information storage device" refers to a device or system used for the long-term storage and management of data. This includes hard disks and cloud storage services.

[0206] A "machine learning algorithm" is a type of artificial intelligence technology applied to data analysis, referring to a method that automatically finds patterns in data and builds predictive models. Various statistical methods are used in the processing.

[0207] "Encryption technology" refers to the transformation process performed to protect data and the methods used to prevent unauthorized access. Representative methods include public-key cryptography and symmetric-key cryptography.

[0208] This invention is a system that comprehensively manages the user's health and emotional state and provides customized health advice. The system functions by collecting data using terminals such as smartphones and smart glasses, and analyzing it on a server in the cloud.

[0209] At the heart of the system is a data acquisition mechanism that collects health and emotional information from users through their devices. Health information includes data such as diet, exercise, heart rate, body temperature, and sleep quality, which are acquired through manual input or sensors on smart devices. Emotional information is collected by analyzing the user's facial expressions and voice using the device's camera and microphone.

[0210] The collected data is securely encrypted and transmitted to a cloud server, where it is stored in an information storage device via a data integration mechanism. On the cloud server, an analysis mechanism using machine learning algorithms analyzes the data to assess the user's health status, emotional state, and risk. Based on the analysis results, the server generates personalized health advice and sends it to the user's device via a notification mechanism.

[0211] For example, if the emotional engine detects a high stress level, the server will provide advice on relaxation methods, specific exercises, and dietary habits. This may include music recommendations or instructions for yoga and meditation. Notifications are delivered to the user visually or audibly via smartphone or smart glasses.

[0212] Examples of prompts for a generative AI model include: "List exercises and relaxation methods that your emotional engine can suggest to reduce the stress that elderly people experience in their daily lives. Also, explain how these can be helpful in caregiving support." This prompt enables the AI ​​to generate specific suggestions for the user.

[0213] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0214] Step 1:

[0215] Users input health and emotional information using a smartphone or smart glasses. Health information includes data such as diet, exercise, heart rate, body temperature, and sleep patterns. Emotional information is obtained through facial recognition and voice analysis via the device. At this stage, the input data is temporarily stored on the device.

[0216] Step 2:

[0217] The device encrypts the acquired health and emotional information and transmits it to the server using a secure communication protocol. Input data is sent through a secure data tunnel to prevent data leakage during transmission. The output here is encrypted data.

[0218] Step 3:

[0219] The server decodes the received data and stores it in the information storage device using data integration means. The synchronized and organized data is saved in individual database entries for each user. After this, the integrated data is sent for subsequent analysis processing.

[0220] Step 4:

[0221] The analysis tools on the server process the integrated data. Using machine learning algorithms and an emotion analysis engine, it generates health assessments, emotion assessments, and risk diagnoses. Here, the AI ​​model identifies health status and emotional tendencies and predicts risk factors such as high stress levels. The analysis results are output as evaluation results.

[0222] Step 5:

[0223] The server creates personalized health advice for the user based on the generated evaluation results. It takes into account the user's emotional state and includes, as needed, suggestions for relaxation methods, exercise, and dietary improvements. A generative AI model guides the generation of detailed advice.

[0224] Step 6:

[0225] The server sends the generated health advice to the device. The content is delivered to the user via a secure communication protocol through a notification system. The device receives the notification and displays it to the user audibly or visually. The user can review the advice received through the device and take action based on it.

[0226] 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.

[0227] Data generation model 58 is a type of 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.

[0228] 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.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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).

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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".

[0242] This invention is an AI-based health management system that comprehensively evaluates the user's health status and provides risk assessments and health advice. In particular, this system has the function of comprehensively acquiring and managing individual health information and providing specific diagnoses and recommendations through AI analysis.

[0243] The system operates as follows: First, the terminal collects various health data from the user. This includes information on daily diet, exercise, heart rate, body temperature, and lifestyle habits. The terminal organizes this data and sends it to the server via a security protocol.

[0244] The server stores data received from the terminal in a database and integrates various data to create an overall health profile of the user. This integrated data is then passed to an AI analysis module, which uses machine learning algorithms to perform a detailed assessment and risk diagnosis. The AI ​​module compares historical and current data to identify disease risks and lifestyle habits that need improvement.

[0245] The analysis results are sent from the server to the terminal. The terminal receives these results and displays them in a user-friendly format. As a specific example of its use, if the analysis results indicate a high risk of heart disease, the system will advise reducing salt intake and recommend scheduling regular health checkups.

[0246] Furthermore, users can not only improve their lifestyle habits by following these suggestions, but by providing feedback, the system can integrate new data and provide even more accurate diagnoses and advice. This cycle allows for continuous support of users' health management.

[0247] The following describes the processing flow.

[0248] Step 1:

[0249] Users input health data related to their daily lives into the device. Specifically, they record information such as their diet, exercise level, heart rate, body temperature, and sleep duration.

[0250] Step 2:

[0251] The device organizes the collected data and prepares it to be securely sent to the server using encryption technology.

[0252] Step 3:

[0253] The server receives the health data, stores it in a database, and integrates related data. This includes existing health checkup results and past lifestyle data.

[0254] Step 4:

[0255] The server feeds the integrated data into an AI analysis module, which uses machine learning algorithms to perform health assessments and risk diagnoses. The model analyzes data patterns to identify the user's health status and disease risk.

[0256] Step 5:

[0257] The server generates analysis results and creates specific health advice and risk assessments. These results are tailored to the individual user.

[0258] Step 6:

[0259] The server sends the generated results to the terminal and notifies the user.

[0260] Step 7:

[0261] The device displays acquired advice and diagnostic results to the user. This information is provided to the user in a format that is easy to view and understand.

[0262] Step 8:

[0263] The cycle restarts when the user improves their lifestyle based on the advice provided and records new data. Continuous data provision and feedback enable the system to provide even more accurate diagnoses and advice.

[0264] (Example 1)

[0265] 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."

[0266] Traditional health management systems have struggled to provide a multifaceted assessment of individual health conditions and personalized health advice through continuous data updates. In particular, a lack of highly accurate risk assessment through integrated data analysis and the ability to incorporate user feedback have made it difficult to address individual needs.

[0267] 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.

[0268] In this invention, the server includes information gathering means for collecting personal health-related data, data storage means for integrating and storing the collected data, and AI analysis means for analyzing the integrated data using AI. This enables not only a comprehensive evaluation of individual health conditions and highly accurate risk diagnosis, but also continuous health management that reflects user feedback.

[0269] "Information gathering means" refers to methods or devices for obtaining health-related data from individuals, including data entered by users and data collected by sensors.

[0270] "Organizational methods" refer to the processes or functions for structuring collected health-related data and preparing it in an analyzable format.

[0271] "Communication means" refers to a method or technique for transmitting organized data using security technology to another device or server.

[0272] "Data storage means" refers to a database or storage medium used to store received health-related data, making it easily accessible and analyzable.

[0273] "AI analysis tools" refer to processes or modules that analyze integrated health-related data using advanced machine learning algorithms to generate health assessments and risk diagnoses.

[0274] "Notification means" refers to a function or process that presents generated health assessment results and diagnoses to users and provides information in an easily understandable format.

[0275] "Feedback integration means" refers to a function or mechanism that incorporates user opinions and feedback into the system and reflects them in health-related data, thereby improving the accuracy of results and providing personalized advice.

[0276] "Encryption technology" refers to a technique used to enhance data security by transforming transmitted information using specific algorithms and preventing unauthorized access.

[0277] This invention is a system for health management purposes and consists of multiple processes, including the collection of health-related data by a terminal, the storage and analysis of the data by a server, and the notification of results to the user.

[0278] The terminal serves as an interface for collecting personal health information. This includes smartphones, fitness devices, and wearable sensors, which collect data from users regarding vital signs, lifestyle, and diet. The terminal organizes this data, verifies for any missing information, and then encrypts it using security technology before transmitting it to the server.

[0279] The server stores health-related data received from terminals in a database and creates a unified health profile based on this data. Next, an AI analysis module analyzes this data and performs a detailed health assessment and risk diagnosis using machine learning algorithms. Specifically, it predicts future health risks based on a large amount of historical data and suggests areas for lifestyle improvement. At this time, the server uses generative AI models such as random forests and neural networks to achieve highly accurate analysis.

[0280] The analysis results are sent back to the terminal again to be presented in a user-friendly format. The terminal visualizes the received information and displays advice to make it easier for the user to understand. For example, if it is determined that the risk of heart disease is high, notifications recommending salt intake restrictions and regular health checks are issued.

[0281] The user can review their lifestyle according to the health guidance received through the terminal and provide feedback to the system. This feedback is integrated into the server and used for subsequent analysis. In this way, the system can continuously and efficiently manage the user's health.

[0282] As a specific example, for instance, assume the user takes pictures of their daily diet and uploads them to the app, and sends them to the server together with the amount of exercise measured by a fitness device. Based on this data, the server conducts a health assessment, and the AI diagnoses the risks. Based on the diagnosis results, the system notifies the user terminal with specific health advice. At this time, an example of inputting a prompt sentence into the generative AI model used is "Analyze the user's diet and exercise data and generate advice to minimize health risks."

[0283] In this invention, by combining the integration of various data sources and AI analysis, it is possible to achieve personalized health management and support the maintenance and improvement of the users' health.

[0284] The flow of the specific process in Example 1 will be described using FIG. 11.

[0285] Step 1:

[0286] The terminal collects health-related data from the user. The inputs here are data related to diet content, exercise information, heart rate, body temperature, and lifestyle. The terminal receives these data through an input interface and also performs automatic data acquisition from sensor devices. Specific operations include the user taking a photo of their meal with a smartphone and uploading it to an app, or wearing a fitness band to record the amount of exercise.

[0287] Step 2:

[0288] The terminal sorts out the collected data and converts it into a digital format. The input here is the raw data obtained in Step 1, and the output is structured data sorted by category. Specifically, text data is extracted from photos using character recognition technology, etc., to organize the data into a unified format.

[0289] Step 3:

[0290] The terminal protects the sorted data using encryption techniques and sends it to the server. The input is the structured data formed in Step 2, and the output is encrypted data packets. In this process, operations such as transferring the data to the server while maintaining the confidentiality of the data using TLS, etc., are performed.

[0291] Step 4:

[0292] The server receives the data sent from the terminal and stores it in the database. The input here is the encrypted data packets, and the output is the user's health profile stored in the database. The server first decrypts the received data and then registers it in the database.

[0293] Step 5:

[0294] The server passes the stored data to an AI analysis module for analysis. The input is integrated health data, and the output is a health assessment and risk diagnosis result. Specifically, it uses a generative AI model to analyze the data with machine learning algorithms and identify risks by comparing them with past data.

[0295] Step 6:

[0296] The server compiles the analysis results and sends them to the terminal, notifying the user of the results. The input is the analysis results obtained in step 5, and the output is a user-friendly formatted health report. The server retrieves the necessary information, formats it for display, and then transfers it to the terminal.

[0297] Step 7:

[0298] Users review the health assessments and risk diagnoses received on their devices and improve their lifestyle habits as needed. Furthermore, users can provide their feedback and results to the system by entering them into their devices. The input here is user feedback, and the output is updated data used for the next analysis. This is specifically done through simple questionnaires and free-response fields within the app.

[0299] These steps enable the system to manage the user's daily health and continuously provide personalized advice.

[0300] (Application Example 1)

[0301] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0302] In order for the elderly to live with peace of mind, it is necessary to continuously monitor their health status and respond promptly when abnormalities are detected. However, the current situation is that there is a lack of an effective mechanism to do this in daily life. In addition, for the collection and analysis of health data, appropriate technology is required because high-precision diagnosis is required along with the protection of personal information.

[0303] 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.

[0304] In this invention, the server includes a data collection means for acquiring individual health information, a data integration means for aggregating the acquired health information and storing it in a storage device, and an analysis means for analyzing the aggregated data to generate a health assessment and a risk diagnosis. As a result, it becomes possible to monitor the health status of the elderly in real time and notify appropriate supporters as needed.

[0305] The "data collection means" is a device or method for acquiring health-related information of healthy or elderly people using various sensors and devices.

[0306] The "data integration means" is a device or method for integrating the collected health-related information into one storage device, organizing it, and storing it.

[0307] The "analysis means" is a device or method for performing a health assessment and a risk diagnosis using machine learning or other analysis methods based on the integrated data.

[0308] The "notification means" is a device or method for presenting the generated health assessment and diagnosis results to the user and sending a warning to a third party or a supporter as needed.

[0309] The "encryption technology" is a method for protecting information used to protect the security and privacy of data communication.

[0310] This invention is a system for users to monitor the health status of elderly individuals and other persons and to detect abnormalities early. This system consists of a terminal for collecting health data, a server for analysis, and a terminal for notifying the user of the results.

[0311] First, the device is equipped with various sensors to acquire everyday health data such as heart rate, body temperature, and steps taken. Wearable devices and smartphones are examples of this. These devices collect data via Bluetooth communication and transmit that data to a server using a secure protocol.

[0312] The server stores the received data in a cloud environment and organizes it using data integration tools. The aggregated data is then passed to an analysis tool that runs machine learning algorithms, which is used for health assessment and risk diagnosis. AI models developed using programming languages ​​such as Python are typically used.

[0313] The analysis results are sent back from the server to the terminal. The terminal, through an application using React Native, displays the results to the user in an easy-to-understand manner and has the functionality to send warnings to external supporters via the Twilio API as needed.

[0314] For example, if an elderly person's heart rate suddenly increases while they are out for a walk, the system can detect this increase and quickly send a notification to their caregiver stating, "Your heart rate has increased abnormally. Immediate attention is required."

[0315] An example of a prompt for a generative AI model is, "What is a way to predict trends based on health data and prepare for abnormal situations?"

[0316] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0317] Step 1:

[0318] The device collects health information. Specifically, it acquires heart rate, body temperature, and step count using sensors built into wearable devices and smartphones. The acquired data is temporarily stored within the device.

[0319] Input: Biometric data from sensors.

[0320] Output: Temporarily stored biometric data.

[0321] Step 2:

[0322] The device transmits temporarily stored data to the server via a security protocol. This is done using Bluetooth or Wi-Fi communication. The data is encrypted and transmitted through a secure channel.

[0323] Input: Temporarily stored biometric data.

[0324] Output: Encrypted data sent to the server.

[0325] Step 3:

[0326] The server decodes the received biometric data and stores it in a database in the cloud. Afterward, data integration tools are used to organize and integrate all the data.

[0327] Input: Encrypted data from the device.

[0328] Output: Integrated data stored in the database.

[0329] Step 4:

[0330] The server passes the integrated data to the AI ​​analysis module. The analysis module uses machine learning algorithms to perform health assessments and risk diagnoses. Through a generative AI model, it extracts outliers and risk factors from the data.

[0331] Input: Integrated data stored in the database.

[0332] Output: Results of health assessment and risk diagnosis.

[0333] Step 5:

[0334] The server sends the analysis results to the terminal. The transmitted data is encrypted again, ensuring the security of the communication.

[0335] Input: Results of health assessment and risk diagnosis.

[0336] Output: Encryption evaluation results sent to the terminal.

[0337] Step 6:

[0338] The terminal decodes the received analysis results and presents them to the user in a visualized format via the application. Furthermore, it generates notifications and sends alerts to external support personnel when anomalies are detected, as needed.

[0339] Input: Decoded evaluation results.

[0340] Output: Health assessments and external alerts displayed in the user interface.

[0341] 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.

[0342] This invention is a system that supports the comprehensive health management of users, and in addition to evaluating health status, it also has an emotion analysis function. The system collects health information from the user, integrates and analyzes it, and provides health risk assessment and individual advice. However, this invention can further identify the user's emotional state using an "emotion engine" and provide comprehensive advice that also takes those emotions into account.

[0343] First, users use their devices to input health data related to their daily lives. This includes physiological data such as diet, exercise, heart rate, and body temperature, as well as information on lifestyle and sleep habits. This data is securely transmitted to the server.

[0344] Next, an emotion engine operates on the device, analyzing the user's facial expressions and voice input to generate emotion data. This emotion data, along with health information, is sent to a server for integration.

[0345] The server stores integrated health and emotional data in a database, which is then analyzed by an AI analysis module. This analysis utilizes machine learning techniques to assess the user's health status, emotional tendencies, and risks. Emotional data is used specifically to evaluate stress levels and anxiety levels.

[0346] Based on the analysis, the server generates customized health advice that takes emotional states into account. For example, if the emotional engine detects a high-stress state, the system suggests relaxation methods and, if necessary, refers the user to a specialist. Exercise and dietary advice is also adjusted to the user's current emotional state.

[0347] Ultimately, the device notifies the user of these pieces of advice and displays them in an easy-to-understand format. This helps users maintain and improve a balanced lifestyle in terms of both physical and emotional well-being. This cycle is repeated through continuous data provision and feedback, and the system provides health management optimized for the user.

[0348] The following describes the processing flow.

[0349] Step 1:

[0350] Users input data about their daily lives using specific applications or devices. This includes information such as diet, exercise levels, heart rate, body temperature, and sleep patterns.

[0351] Step 2:

[0352] The device encrypts the collected data and prepares it for secure transmission to the server. At this stage, data integrity and privacy are maintained.

[0353] Step 3:

[0354] The device uses an emotion engine to analyze the user's facial expressions and voice data. The emotion engine specifically utilizes the camera and microphone to perform facial recognition and voice analysis to identify the user's emotional state.

[0355] Step 4:

[0356] The device sends emotional data generated by the emotion engine to the server along with health data.

[0357] Step 5:

[0358] The server integrates the received health and emotional data and stores it in a database. The integrated data is then organized along with the user's personal history for use in analysis.

[0359] Step 6:

[0360] The server feeds the integrated data into an AI analysis module, which uses machine learning algorithms to perform a health assessment and risk diagnosis for the user. Emotional data is also considered, and the effects of stress and anxiety are reflected in the assessment.

[0361] Step 7:

[0362] The server generates customized health advice and emotion-based relaxation and improvement strategies based on the analysis results. This advice is tailored to best suit the user's current emotional state.

[0363] Step 8:

[0364] The server sends the generated results to the terminal and notifies the user.

[0365] Step 9:

[0366] The device displays advice and diagnostic results to the user. The advice is provided in a visual and easy-to-understand format, and is designed to be easily incorporated into daily life.

[0367] Step 10:

[0368] The system is continuously improved as users act on the advice provided and update their daily data. This feedback loop ensures that the system continues to provide users with optimized advice and diagnoses.

[0369] (Example 2)

[0370] 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".

[0371] Traditional health management systems perform health assessments and risk diagnoses based on users' physiological information, but they lack assessments that take emotional states into account. This makes it difficult to provide appropriate advice on users' overall health and lifestyle. Furthermore, there is a need to securely manage this data and provide personalized advice to each user.

[0372] 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.

[0373] In this invention, the server includes data acquisition means for obtaining individual physiological information, data integration means for integrating the acquired physiological information and emotional state and storing it in a memory device, and analysis means for analyzing the integrated data and generating health assessments, emotional tendencies, and risk diagnoses. This enables health assessments and advice that comprehensively consider the user's physiological information and emotional state.

[0374] "Physiological information" refers to information related to the user's physical condition, such as heart rate, body temperature, exercise level, and sleep quality.

[0375] "Emotional state" refers to the psychological and emotional tendencies and reaction states analyzed from the user's facial expressions, voice, etc.

[0376] "Data acquisition means" refers to devices and methods for collecting physiological information and emotional states from users, and includes sensors and user interfaces.

[0377] "Data integration means" refers to processes and systems for centrally managing acquired physiological information and emotional states and storing them in a memory device.

[0378] "Analysis means" refers to methods and techniques used to analyze integrated physiological information and emotional states to generate health assessments, emotional tendencies, and risk diagnoses.

[0379] "Communication encryption technology" refers to encryption methods used to prevent unauthorized access and information leakage during the data transmission process.

[0380] An "emotion analysis engine" refers to software or algorithms used to analyze emotional states from voice and facial expression data.

[0381] This invention is a system that supports the comprehensive health management of users, providing customized health advice by combining physiological information and emotional state.

[0382] First, users input physiological information related to their daily lives using their smartphones or wearable devices. This input data includes information such as heart rate, body temperature, exercise level, and sleep quality. This data is securely transmitted to the server via the device. During the communication process, the data is encrypted using communication encryption technology, protecting the user's privacy.

[0383] Next, the device's emotion analysis engine runs to analyze the user's emotional state. Specifically, it acquires facial expression data through the camera and analyzes voice tone from voice input. This analysis processes the data using, for example, facial recognition software and voice analysis libraries. The generated emotion data is then sent back to the server.

[0384] The server centrally integrates received physiological information and emotional states and stores them in a database. The stored data is then analyzed by an AI analysis module to generate user health assessments, emotional tendencies, and risk diagnoses. This analysis utilizes machine learning techniques, such as TensorFlow and other analysis libraries.

[0385] Based on the analysis, the server generates customized health advice. This advice is adjusted to take emotional states into account and suggests user-specific methods for maintaining and improving health. For example, if the emotional analysis indicates a high-stress state, relaxation methods or consultation with a specialist will be suggested.

[0386] Ultimately, the device notifies the user of the generated advice, displaying it in an easy-to-understand format. This allows the user to gain a comprehensive understanding of their health status and take appropriate action.

[0387] As a concrete example of its use, a prompt such as, "I'm a 35-year-old woman who's been experiencing persistent insomnia lately. I'd like some relaxation techniques and sleep advice," is input into the AI ​​model. Based on this prompt, the system provides advice tailored to the user's needs.

[0388] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0389] Step 1:

[0390] Users input physiological information using smartphones or wearable devices. This data includes heart rate, body temperature, activity level, and sleep quality. Once this data is entered, the device transmits it to the server in encrypted form via a secure communication protocol such as HTTPS. In this process, the input is physiological information provided by the user, and the output is encrypted data.

[0391] Step 2:

[0392] On the device, an emotion analysis engine operates, generating emotion data using the user's camera and microphone. Specifically, image analysis algorithms are used to acquire facial expression data and identify specific emotions, and audio data is analyzed to infer emotions from tone of voice. The input is camera video and audio data, and the output is the identified emotional state. The generated emotion data is then encrypted again and sent to the server.

[0393] Step 3:

[0394] The server integrates the received physiological and emotional data and stores it in a database. A NoSQL database, for example, is used for this purpose, enabling efficient storage and management of large amounts of data. The input consists of encrypted physiological and emotional data, while the output is an integrated data record.

[0395] Step 4:

[0396] An AI analysis module on the server runs and analyzes the integrated data. Here, machine learning algorithms are used to assess the user's health status, emotional tendencies, and health risks. Libraries such as TensorFlow are utilized for this analysis. The input is integrated physiological and emotional data, and the output is the results of the health assessment, emotional tendency, and risk diagnosis.

[0397] Step 5:

[0398] Based on the analysis results, the server uses a generative AI model to generate customized health advice that takes into account the user's emotional state. For example, if high stress is detected, advice including relaxation methods and referrals to specialists will be generated. The input is the analysis results, and the output is customized health advice.

[0399] Step 6:

[0400] The device notifies the user of the generated advice and displays it in an easy-to-understand format. The user reviews the advice received through the notification from the device and incorporates it into their daily life. The input is the generated health advice, and the output is the notification provided to the user.

[0401] (Application Example 2)

[0402] 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."

[0403] Traditional health management systems have focused primarily on managing users' physical health, but have lacked adequate support that takes emotional states into account. Therefore, it has been difficult to address health risks that fluctuate with users' emotional states and to provide appropriate advice in response to changes in their emotions. In this context, there is a need for a system that effectively analyzes emotional states and integrates them into health management.

[0404] 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.

[0405] In this invention, the server includes data acquisition means for obtaining individual health information and emotional information, data integration means for integrating the acquired health information and emotional information and storing it in an information storage device, and analysis means for analyzing the integrated data and generating health assessments, emotional assessments, and risk diagnoses. This enables comprehensive health management that takes emotional states into account.

[0406] "Health information" refers to physiological data and lifestyle information related to individual users. Specifically, it includes data such as diet, exercise, heart rate, body temperature, and sleep habits.

[0407] "Emotional information" refers to data that quantifies or categorizes a user's emotional state. This includes emotional indicators obtained through voice tone and facial expression analysis.

[0408] "Data acquisition means" refers to technical methods and devices for collecting health information and emotional information, including sensors and user interfaces.

[0409] "Data integration means" refers to technology that centrally manages acquired health and emotional information and stores it in an information storage device. This utilizes database technology and cloud storage.

[0410] "Analysis tools" refer to technical processes and devices for analyzing integrated data to generate health assessments, sentiment assessments, and risk diagnoses. This includes machine learning algorithms and data analysis software.

[0411] "Notification methods" refer to technologies used to communicate generated evaluation results and advice to users. These utilize smartphone apps and voice assistants.

[0412] "Information storage device" refers to a device or system used for the long-term storage and management of data. This includes hard disks and cloud storage services.

[0413] A "machine learning algorithm" is a type of artificial intelligence technology applied to data analysis, referring to a method that automatically finds patterns in data and builds predictive models. Various statistical methods are used in the processing.

[0414] "Encryption technology" refers to the transformation process performed to protect data and the methods used to prevent unauthorized access. Representative methods include public-key cryptography and symmetric-key cryptography.

[0415] This invention is a system that comprehensively manages the user's health and emotional state and provides customized health advice. The system functions by collecting data using terminals such as smartphones and smart glasses, and analyzing it on a server in the cloud.

[0416] At the heart of the system is a data acquisition mechanism that collects health and emotional information from users through their devices. Health information includes data such as diet, exercise, heart rate, body temperature, and sleep quality, which are acquired through manual input or sensors on smart devices. Emotional information is collected by analyzing the user's facial expressions and voice using the device's camera and microphone.

[0417] The collected data is securely encrypted and transmitted to a cloud server, where it is stored in an information storage device via a data integration mechanism. On the cloud server, an analysis mechanism using machine learning algorithms analyzes the data to assess the user's health status, emotional state, and risk. Based on the analysis results, the server generates personalized health advice and sends it to the user's device via a notification mechanism.

[0418] For example, if the emotional engine detects a high stress level, the server will provide advice on relaxation methods, specific exercises, and dietary habits. This may include music recommendations or instructions for yoga and meditation. Notifications are delivered to the user visually or audibly via smartphone or smart glasses.

[0419] Examples of prompts for a generative AI model include: "List exercises and relaxation methods that your emotional engine can suggest to reduce the stress that elderly people experience in their daily lives. Also, explain how these can be helpful in caregiving support." This prompt enables the AI ​​to generate specific suggestions for the user.

[0420] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0421] Step 1:

[0422] Users input health and emotional information using a smartphone or smart glasses. Health information includes data such as diet, exercise, heart rate, body temperature, and sleep patterns. Emotional information is obtained through facial recognition and voice analysis via the device. At this stage, the input data is temporarily stored on the device.

[0423] Step 2:

[0424] The device encrypts the acquired health and emotional information and transmits it to the server using a secure communication protocol. Input data is sent through a secure data tunnel to prevent data leakage during transmission. The output here is encrypted data.

[0425] Step 3:

[0426] The server decodes the received data and stores it in the information storage device using data integration means. The synchronized and organized data is saved in individual database entries for each user. After this, the integrated data is sent for subsequent analysis processing.

[0427] Step 4:

[0428] The analysis tools on the server process the integrated data. Using machine learning algorithms and an emotion analysis engine, it generates health assessments, emotion assessments, and risk diagnoses. Here, the AI ​​model identifies health status and emotional tendencies and predicts risk factors such as high stress levels. The analysis results are output as evaluation results.

[0429] Step 5:

[0430] The server creates personalized health advice for the user based on the generated evaluation results. It takes into account the user's emotional state and includes, as needed, suggestions for relaxation methods, exercise, and dietary improvements. A generative AI model guides the generation of detailed advice.

[0431] Step 6:

[0432] The server sends the generated health advice to the device. The content is delivered to the user via a secure communication protocol through a notification system. The device receives the notification and displays it to the user audibly or visually. The user can review the advice received through the device and take action based on it.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] [Third Embodiment]

[0437] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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".

[0449] This invention is an AI-based health management system that comprehensively evaluates the user's health status and provides risk assessments and health advice. In particular, this system has the function of comprehensively acquiring and managing individual health information and providing specific diagnoses and recommendations through AI analysis.

[0450] The system operates as follows: First, the terminal collects various health data from the user. This includes information on daily diet, exercise, heart rate, body temperature, and lifestyle habits. The terminal organizes this data and sends it to the server via a security protocol.

[0451] The server stores data received from the terminal in a database and integrates various data to create an overall health profile of the user. This integrated data is then passed to an AI analysis module, which uses machine learning algorithms to perform a detailed assessment and risk diagnosis. The AI ​​module compares historical and current data to identify disease risks and lifestyle habits that need improvement.

[0452] The analysis results are sent from the server to the terminal. The terminal receives these results and displays them in a user-friendly format. As a specific example of its use, if the analysis results indicate a high risk of heart disease, the system will advise reducing salt intake and recommend scheduling regular health checkups.

[0453] Furthermore, users can not only improve their lifestyle habits by following these suggestions, but by providing feedback, the system can integrate new data and provide even more accurate diagnoses and advice. This cycle allows for continuous support of users' health management.

[0454] The following describes the processing flow.

[0455] Step 1:

[0456] Users input health data related to their daily lives into the device. Specifically, they record information such as their diet, exercise level, heart rate, body temperature, and sleep duration.

[0457] Step 2:

[0458] The device organizes the collected data and prepares it to be securely sent to the server using encryption technology.

[0459] Step 3:

[0460] The server receives the health data, stores it in a database, and integrates related data. This includes existing health checkup results and past lifestyle data.

[0461] Step 4:

[0462] The server feeds the integrated data into an AI analysis module, which uses machine learning algorithms to perform health assessments and risk diagnoses. The model analyzes data patterns to identify the user's health status and disease risk.

[0463] Step 5:

[0464] The server generates analysis results and creates specific health advice and risk assessments. These results are tailored to the individual user.

[0465] Step 6:

[0466] The server sends the generated results to the terminal and notifies the user.

[0467] Step 7:

[0468] The device displays acquired advice and diagnostic results to the user. This information is provided to the user in a format that is easy to view and understand.

[0469] Step 8:

[0470] The cycle restarts when the user improves their lifestyle based on the advice provided and records new data. Continuous data provision and feedback enable the system to provide even more accurate diagnoses and advice.

[0471] (Example 1)

[0472] 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."

[0473] Traditional health management systems have struggled to provide a multifaceted assessment of individual health conditions and personalized health advice through continuous data updates. In particular, a lack of highly accurate risk assessment through integrated data analysis and the ability to incorporate user feedback have made it difficult to address individual needs.

[0474] 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.

[0475] In this invention, the server includes information gathering means for collecting personal health-related data, data storage means for integrating and storing the collected data, and AI analysis means for analyzing the integrated data using AI. This enables not only a comprehensive evaluation of individual health conditions and highly accurate risk diagnosis, but also continuous health management that reflects user feedback.

[0476] "Information gathering means" refers to methods or devices for obtaining health-related data from individuals, including data entered by users and data collected by sensors.

[0477] "Organizational methods" refer to the processes or functions for structuring collected health-related data and preparing it in an analyzable format.

[0478] "Communication means" refers to a method or technique for transmitting organized data using security technology to another device or server.

[0479] "Data storage means" refers to a database or storage medium used to store received health-related data, making it easily accessible and analyzable.

[0480] "AI analysis tools" refer to processes or modules that analyze integrated health-related data using advanced machine learning algorithms to generate health assessments and risk diagnoses.

[0481] "Notification means" refers to a function or process that presents generated health assessment results and diagnoses to users and provides information in an easily understandable format.

[0482] "Feedback integration means" refers to a function or mechanism that incorporates user opinions and feedback into the system and reflects them in health-related data, thereby improving the accuracy of results and providing personalized advice.

[0483] "Encryption technology" refers to a technique used to enhance data security by transforming transmitted information using specific algorithms and preventing unauthorized access.

[0484] This invention is a system for health management purposes and consists of multiple processes, including the collection of health-related data by a terminal, the storage and analysis of the data by a server, and the notification of results to the user.

[0485] The terminal serves as an interface for collecting personal health information. This includes smartphones, fitness devices, and wearable sensors, which collect data from users regarding vital signs, lifestyle, and diet. The terminal organizes this data, verifies for any missing information, and then encrypts it using security technology before transmitting it to the server.

[0486] The server stores health-related data received from terminals in a database and creates a unified health profile based on this data. Next, an AI analysis module analyzes this data and performs a detailed health assessment and risk diagnosis using machine learning algorithms. Specifically, it predicts future health risks based on a large amount of historical data and suggests areas for lifestyle improvement. At this time, the server uses generative AI models such as random forests and neural networks to achieve highly accurate analysis.

[0487] The analysis results are sent back to the device in an easy-to-understand format. The device visualizes the received information and displays advice to help the user understand it. For example, if the device determines that the user is at high risk of heart disease, it will send a notification recommending things like limiting salt intake or getting regular health checkups.

[0488] Users can review their lifestyle habits based on health guidance received through their devices and provide feedback to the system. This feedback is integrated into the server and used for subsequent analysis. In this way, the system can continuously and efficiently manage the user's health.

[0489] As a concrete example, suppose a user takes photos of their daily meals and uploads them to the app, along with their exercise data measured by a fitness device, and sends this data to the server. Based on this data, the server performs a health assessment, and the AI ​​diagnoses the risks. Based on the diagnosis, the system notifies the user's device with specific health advice. An example of a prompt to be entered into the generative AI model used in this case would be, "Analyze the user's diet and exercise data and generate advice to minimize health risks."

[0490] This invention enables personalized health management by combining the integration of diverse data sources with AI analysis, thereby supporting users in maintaining and improving their health.

[0491] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0492] Step 1:

[0493] The device collects health-related data from the user. This data includes information on diet, exercise, heart rate, body temperature, and lifestyle. The device receives this data through an input interface and also automatically acquires data from sensor devices. Specific actions include the user taking photos of meals with their smartphone and uploading them to the app, or wearing a fitness band to record exercise levels.

[0494] Step 2:

[0495] The terminal organizes the collected data and converts it into a digital format. The input here is the raw data obtained in step 1, and the output is structured data organized by category. Specifically, the data is compiled into a unified format by extracting text data from photographs using character recognition technology, for example.

[0496] Step 3:

[0497] The terminal protects the organized data using encryption methods and sends it to the server. The input is the structured data formed in step 2, and the output is encrypted data packets. During this process, data confidentiality is maintained while transferring the data to the server using methods such as TLS.

[0498] Step 4:

[0499] The server receives data sent from the terminal and stores it in a database. The input here is encrypted data packets, and the output is the user's health profile stored in the database. The server first decrypts the received data and then registers it in the database.

[0500] Step 5:

[0501] The server passes the stored data to an AI analysis module for analysis. The input is integrated health data, and the output is a health assessment and risk diagnosis result. Specifically, it uses a generative AI model to analyze the data with machine learning algorithms and identify risks by comparing them with past data.

[0502] Step 6:

[0503] The server compiles the analysis results and sends them to the terminal, notifying the user of the results. The input is the analysis results obtained in step 5, and the output is a user-friendly formatted health report. The server retrieves the necessary information, formats it for display, and then transfers it to the terminal.

[0504] Step 7:

[0505] Users review the health assessments and risk diagnoses received on their devices and improve their lifestyle habits as needed. Furthermore, users can provide their feedback and results to the system by entering them into their devices. The input here is user feedback, and the output is updated data used for the next analysis. This is specifically done through simple questionnaires and free-response fields within the app.

[0506] These steps enable the system to manage the user's daily health and continuously provide personalized advice.

[0507] (Application Example 1)

[0508] 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."

[0509] For elderly people to live with peace of mind, it is necessary to continuously monitor their health and respond quickly when abnormalities are detected. However, there is currently a lack of mechanisms to effectively carry out this in daily life. Furthermore, the collection and analysis of health data requires appropriate technology, as it demands both the protection of personal information and highly accurate diagnosis.

[0510] 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.

[0511] In this invention, the server includes data collection means for acquiring individual health information, data integration means for aggregating the acquired health information and storing it in a storage device, and analysis means for analyzing the aggregated data and generating health assessments and risk diagnoses. This makes it possible to monitor the health status of elderly people in real time and notify appropriate caregivers as needed.

[0512] "Data collection means" refers to a device or method for acquiring health-related information of healthy individuals or elderly people using various sensors and devices.

[0513] "Data integration means" refers to a device or method for integrating, organizing, and storing collected health-related information in a single storage device.

[0514] "Analysis means" refers to a device or method that performs health assessments and risk diagnoses using machine learning or other analytical techniques based on integrated data.

[0515] "Notification means" refers to a device or method that presents the generated health assessment or diagnostic results to the user and, if necessary, sends a warning to a third party or supporter.

[0516] "Cryptographic technology" is a method of protecting information used to safeguard the security and privacy of data communications.

[0517] This invention is a system for users to monitor the health status of elderly individuals and other persons and to detect abnormalities early. This system consists of a terminal for collecting health data, a server for analysis, and a terminal for notifying the user of the results.

[0518] First, the device is equipped with various sensors to acquire everyday health data such as heart rate, body temperature, and steps taken. Wearable devices and smartphones are examples of this. These devices collect data via Bluetooth communication and transmit that data to a server using a secure protocol.

[0519] The server stores the received data in a cloud environment and organizes it using data integration tools. The aggregated data is then passed to an analysis tool that runs machine learning algorithms, which is used for health assessment and risk diagnosis. AI models developed using programming languages ​​such as Python are typically used.

[0520] The analysis results are sent back from the server to the terminal. The terminal, through an application using React Native, displays the results to the user in an easy-to-understand manner and has the functionality to send warnings to external supporters via the Twilio API as needed.

[0521] For example, if an elderly person's heart rate suddenly increases while they are out for a walk, the system can detect this increase and quickly send a notification to their caregiver stating, "Your heart rate has increased abnormally. Immediate attention is required."

[0522] An example of a prompt for a generative AI model is, "What is a way to predict trends based on health data and prepare for abnormal situations?"

[0523] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0524] Step 1:

[0525] The device collects health information. Specifically, it acquires heart rate, body temperature, and step count using sensors built into wearable devices and smartphones. The acquired data is temporarily stored within the device.

[0526] Input: Biometric data from sensors.

[0527] Output: Temporarily stored biometric data.

[0528] Step 2:

[0529] The device transmits temporarily stored data to the server via a security protocol. This is done using Bluetooth or Wi-Fi communication. The data is encrypted and transmitted through a secure channel.

[0530] Input: Temporarily stored biometric data.

[0531] Output: Encrypted data sent to the server.

[0532] Step 3:

[0533] The server decodes the received biometric data and stores it in a database in the cloud. Afterward, data integration tools are used to organize and integrate all the data.

[0534] Input: Encrypted data from the device.

[0535] Output: Integrated data stored in the database.

[0536] Step 4:

[0537] The server passes the integrated data to the AI ​​analysis module. The analysis module uses machine learning algorithms to perform health assessments and risk diagnoses. Through a generative AI model, it extracts outliers and risk factors from the data.

[0538] Input: Integrated data stored in the database.

[0539] Output: Results of health assessment and risk diagnosis.

[0540] Step 5:

[0541] The server sends the analysis results to the terminal. The transmitted data is encrypted again, ensuring the security of the communication.

[0542] Input: Results of health assessment and risk diagnosis.

[0543] Output: Encryption evaluation results sent to the terminal.

[0544] Step 6:

[0545] The terminal decodes the received analysis results and presents them to the user in a visualized format via the application. Furthermore, it generates notifications and sends alerts to external support personnel when anomalies are detected, as needed.

[0546] Input: Decoded evaluation results.

[0547] Output: Health assessments and external alerts displayed in the user interface.

[0548] 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.

[0549] This invention is a system that supports the comprehensive health management of users, and in addition to evaluating health status, it also has an emotion analysis function. The system collects health information from the user, integrates and analyzes it, and provides health risk assessment and individual advice. However, this invention can further identify the user's emotional state using an "emotion engine" and provide comprehensive advice that also takes those emotions into account.

[0550] First, users use their devices to input health data related to their daily lives. This includes physiological data such as diet, exercise, heart rate, and body temperature, as well as information on lifestyle and sleep habits. This data is securely transmitted to the server.

[0551] Next, an emotion engine operates on the device, analyzing the user's facial expressions and voice input to generate emotion data. This emotion data, along with health information, is sent to a server for integration.

[0552] The server stores integrated health and emotional data in a database, which is then analyzed by an AI analysis module. This analysis utilizes machine learning techniques to assess the user's health status, emotional tendencies, and risks. Emotional data is used specifically to evaluate stress levels and anxiety levels.

[0553] Based on the analysis, the server generates customized health advice that takes emotional states into account. For example, if the emotional engine detects a high-stress state, the system suggests relaxation methods and, if necessary, refers the user to a specialist. Exercise and dietary advice is also adjusted to the user's current emotional state.

[0554] Ultimately, the device notifies the user of these pieces of advice and displays them in an easy-to-understand format. This helps users maintain and improve a balanced lifestyle in terms of both physical and emotional well-being. This cycle is repeated through continuous data provision and feedback, and the system provides health management optimized for the user.

[0555] The following describes the processing flow.

[0556] Step 1:

[0557] Users input data about their daily lives using specific applications or devices. This includes information such as diet, exercise levels, heart rate, body temperature, and sleep patterns.

[0558] Step 2:

[0559] The device encrypts the collected data and prepares it for secure transmission to the server. At this stage, data integrity and privacy are maintained.

[0560] Step 3:

[0561] The device uses an emotion engine to analyze the user's facial expressions and voice data. The emotion engine specifically utilizes the camera and microphone to perform facial recognition and voice analysis to identify the user's emotional state.

[0562] Step 4:

[0563] The device sends emotional data generated by the emotion engine to the server along with health data.

[0564] Step 5:

[0565] The server integrates the received health and emotional data and stores it in a database. The integrated data is then organized along with the user's personal history for use in analysis.

[0566] Step 6:

[0567] The server feeds the integrated data into an AI analysis module, which uses machine learning algorithms to perform a health assessment and risk diagnosis for the user. Emotional data is also considered, and the effects of stress and anxiety are reflected in the assessment.

[0568] Step 7:

[0569] The server generates customized health advice and emotion-based relaxation and improvement strategies based on the analysis results. This advice is tailored to best suit the user's current emotional state.

[0570] Step 8:

[0571] The server sends the generated results to the terminal and notifies the user.

[0572] Step 9:

[0573] The device displays advice and diagnostic results to the user. The advice is provided in a visual and easy-to-understand format, and is designed to be easily incorporated into daily life.

[0574] Step 10:

[0575] The system is continuously improved as users act on the advice provided and update their daily data. This feedback loop ensures that the system continues to provide users with optimized advice and diagnoses.

[0576] (Example 2)

[0577] 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."

[0578] Traditional health management systems perform health assessments and risk diagnoses based on users' physiological information, but they lack assessments that take emotional states into account. This makes it difficult to provide appropriate advice on users' overall health and lifestyle. Furthermore, there is a need to securely manage this data and provide personalized advice to each user.

[0579] 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.

[0580] In this invention, the server includes data acquisition means for obtaining individual physiological information, data integration means for integrating the acquired physiological information and emotional state and storing it in a memory device, and analysis means for analyzing the integrated data and generating health assessments, emotional tendencies, and risk diagnoses. This enables health assessments and advice that comprehensively consider the user's physiological information and emotional state.

[0581] "Physiological information" refers to information related to the user's physical condition, such as heart rate, body temperature, exercise level, and sleep quality.

[0582] "Emotional state" refers to the psychological and emotional tendencies and reaction states analyzed from the user's facial expressions, voice, etc.

[0583] "Data acquisition means" refers to devices and methods for collecting physiological information and emotional states from users, and includes sensors and user interfaces.

[0584] "Data integration means" refers to processes and systems for centrally managing acquired physiological information and emotional states and storing them in a memory device.

[0585] "Analysis means" refers to methods and techniques used to analyze integrated physiological information and emotional states to generate health assessments, emotional tendencies, and risk diagnoses.

[0586] "Communication encryption technology" refers to encryption methods used to prevent unauthorized access and information leakage during the data transmission process.

[0587] An "emotion analysis engine" refers to software or algorithms used to analyze emotional states from voice and facial expression data.

[0588] This invention is a system that supports the comprehensive health management of users, providing customized health advice by combining physiological information and emotional state.

[0589] First, users input physiological information related to their daily lives using their smartphones or wearable devices. This input data includes information such as heart rate, body temperature, exercise level, and sleep quality. This data is securely transmitted to the server via the device. During the communication process, the data is encrypted using communication encryption technology, protecting the user's privacy.

[0590] Next, the device's emotion analysis engine runs to analyze the user's emotional state. Specifically, it acquires facial expression data through the camera and analyzes voice tone from voice input. This analysis processes the data using, for example, facial recognition software and voice analysis libraries. The generated emotion data is then sent back to the server.

[0591] The server centrally integrates received physiological information and emotional states and stores them in a database. The stored data is then analyzed by an AI analysis module to generate user health assessments, emotional tendencies, and risk diagnoses. This analysis utilizes machine learning techniques, such as TensorFlow and other analysis libraries.

[0592] Based on the analysis, the server generates customized health advice. This advice is adjusted to take emotional states into account and suggests user-specific methods for maintaining and improving health. For example, if the emotional analysis indicates a high-stress state, relaxation methods or consultation with a specialist will be suggested.

[0593] Ultimately, the device notifies the user of the generated advice, displaying it in an easy-to-understand format. This allows the user to gain a comprehensive understanding of their health status and take appropriate action.

[0594] As a concrete example of its use, a prompt such as, "I'm a 35-year-old woman who's been experiencing persistent insomnia lately. I'd like some relaxation techniques and sleep advice," is input into the AI ​​model. Based on this prompt, the system provides advice tailored to the user's needs.

[0595] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0596] Step 1:

[0597] Users input physiological information using smartphones or wearable devices. This data includes heart rate, body temperature, activity level, and sleep quality. Once this data is entered, the device transmits it to the server in encrypted form via a secure communication protocol such as HTTPS. In this process, the input is physiological information provided by the user, and the output is encrypted data.

[0598] Step 2:

[0599] On the device, an emotion analysis engine operates, generating emotion data using the user's camera and microphone. Specifically, image analysis algorithms are used to acquire facial expression data and identify specific emotions, and audio data is analyzed to infer emotions from tone of voice. The input is camera video and audio data, and the output is the identified emotional state. The generated emotion data is then encrypted again and sent to the server.

[0600] Step 3:

[0601] The server integrates the received physiological and emotional data and stores it in a database. A NoSQL database, for example, is used for this purpose, enabling efficient storage and management of large amounts of data. The input consists of encrypted physiological and emotional data, while the output is an integrated data record.

[0602] Step 4:

[0603] An AI analysis module on the server runs and analyzes the integrated data. Here, machine learning algorithms are used to assess the user's health status, emotional tendencies, and health risks. Libraries such as TensorFlow are utilized for this analysis. The input is integrated physiological and emotional data, and the output is the results of the health assessment, emotional tendency, and risk diagnosis.

[0604] Step 5:

[0605] Based on the analysis results, the server uses a generative AI model to generate customized health advice that takes into account the user's emotional state. For example, if high stress is detected, advice including relaxation methods and referrals to specialists will be generated. The input is the analysis results, and the output is customized health advice.

[0606] Step 6:

[0607] The device notifies the user of the generated advice and displays it in an easy-to-understand format. The user reviews the advice received through the notification from the device and incorporates it into their daily life. The input is the generated health advice, and the output is the notification provided to the user.

[0608] (Application Example 2)

[0609] 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."

[0610] Traditional health management systems have focused primarily on managing users' physical health, but have lacked adequate support that takes emotional states into account. Therefore, it has been difficult to address health risks that fluctuate with users' emotional states and to provide appropriate advice in response to changes in their emotions. In this context, there is a need for a system that effectively analyzes emotional states and integrates them into health management.

[0611] 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.

[0612] In this invention, the server includes data acquisition means for obtaining individual health information and emotional information, data integration means for integrating the acquired health information and emotional information and storing it in an information storage device, and analysis means for analyzing the integrated data and generating health assessments, emotional assessments, and risk diagnoses. This enables comprehensive health management that takes emotional states into account.

[0613] "Health information" refers to physiological data and lifestyle information related to individual users. Specifically, it includes data such as diet, exercise, heart rate, body temperature, and sleep habits.

[0614] "Emotional information" refers to data that quantifies or categorizes a user's emotional state. This includes emotional indicators obtained through voice tone and facial expression analysis.

[0615] "Data acquisition means" refers to technical methods and devices for collecting health information and emotional information, including sensors and user interfaces.

[0616] "Data integration means" refers to technology that centrally manages acquired health and emotional information and stores it in an information storage device. This utilizes database technology and cloud storage.

[0617] "Analysis tools" refer to technical processes and devices for analyzing integrated data to generate health assessments, sentiment assessments, and risk diagnoses. This includes machine learning algorithms and data analysis software.

[0618] "Notification methods" refer to technologies used to communicate generated evaluation results and advice to users. These utilize smartphone apps and voice assistants.

[0619] "Information storage device" refers to a device or system used for the long-term storage and management of data. This includes hard disks and cloud storage services.

[0620] A "machine learning algorithm" is a type of artificial intelligence technology applied to data analysis, referring to a method that automatically finds patterns in data and builds predictive models. Various statistical methods are used in the processing.

[0621] "Encryption technology" refers to the transformation process performed to protect data and the methods used to prevent unauthorized access. Representative methods include public-key cryptography and symmetric-key cryptography.

[0622] This invention is a system that comprehensively manages the user's health and emotional state and provides customized health advice. The system functions by collecting data using terminals such as smartphones and smart glasses, and analyzing it on a server in the cloud.

[0623] At the heart of the system is a data acquisition mechanism that collects health and emotional information from users through their devices. Health information includes data such as diet, exercise, heart rate, body temperature, and sleep quality, which are acquired through manual input or sensors on smart devices. Emotional information is collected by analyzing the user's facial expressions and voice using the device's camera and microphone.

[0624] The collected data is securely encrypted and transmitted to a cloud server, where it is stored in an information storage device via a data integration mechanism. On the cloud server, an analysis mechanism using machine learning algorithms analyzes the data to assess the user's health status, emotional state, and risk. Based on the analysis results, the server generates personalized health advice and sends it to the user's device via a notification mechanism.

[0625] For example, if the emotional engine detects a high stress level, the server will provide advice on relaxation methods, specific exercises, and dietary habits. This may include music recommendations or instructions for yoga and meditation. Notifications are delivered to the user visually or audibly via smartphone or smart glasses.

[0626] Examples of prompts for a generative AI model include: "List exercises and relaxation methods that your emotional engine can suggest to reduce the stress that elderly people experience in their daily lives. Also, explain how these can be helpful in caregiving support." This prompt enables the AI ​​to generate specific suggestions for the user.

[0627] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0628] Step 1:

[0629] Users input health and emotional information using a smartphone or smart glasses. Health information includes data such as diet, exercise, heart rate, body temperature, and sleep patterns. Emotional information is obtained through facial recognition and voice analysis via the device. At this stage, the input data is temporarily stored on the device.

[0630] Step 2:

[0631] The device encrypts the acquired health and emotional information and transmits it to the server using a secure communication protocol. Input data is sent through a secure data tunnel to prevent data leakage during transmission. The output here is encrypted data.

[0632] Step 3:

[0633] The server decodes the received data and stores it in the information storage device using data integration means. The synchronized and organized data is saved in individual database entries for each user. After this, the integrated data is sent for subsequent analysis processing.

[0634] Step 4:

[0635] The analysis tools on the server process the integrated data. Using machine learning algorithms and an emotion analysis engine, it generates health assessments, emotion assessments, and risk diagnoses. Here, the AI ​​model identifies health status and emotional tendencies and predicts risk factors such as high stress levels. The analysis results are output as evaluation results.

[0636] Step 5:

[0637] The server creates personalized health advice for the user based on the generated evaluation results. It takes into account the user's emotional state and includes, as needed, suggestions for relaxation methods, exercise, and dietary improvements. A generative AI model guides the generation of detailed advice.

[0638] Step 6:

[0639] The server sends the generated health advice to the device. The content is delivered to the user via a secure communication protocol through a notification system. The device receives the notification and displays it to the user audibly or visually. The user can review the advice received through the device and take action based on it.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] [Fourth Embodiment]

[0644] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0645] 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.

[0646] 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).

[0647] 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.

[0648] 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.

[0649] 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).

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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.

[0655] 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.

[0656] 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".

[0657] This invention is an AI-based health management system that comprehensively evaluates the user's health status and provides risk assessments and health advice. In particular, this system has the function of comprehensively acquiring and managing individual health information and providing specific diagnoses and recommendations through AI analysis.

[0658] The system operates as follows: First, the terminal collects various health data from the user. This includes information on daily diet, exercise, heart rate, body temperature, and lifestyle habits. The terminal organizes this data and sends it to the server via a security protocol.

[0659] The server stores data received from the terminal in a database and integrates various data to create an overall health profile of the user. This integrated data is then passed to an AI analysis module, which uses machine learning algorithms to perform a detailed assessment and risk diagnosis. The AI ​​module compares historical and current data to identify disease risks and lifestyle habits that need improvement.

[0660] The analysis results are sent from the server to the terminal. The terminal receives these results and displays them in a user-friendly format. As a specific example of its use, if the analysis results indicate a high risk of heart disease, the system will advise reducing salt intake and recommend scheduling regular health checkups.

[0661] Furthermore, users can not only improve their lifestyle habits by following these suggestions, but by providing feedback, the system can integrate new data and provide even more accurate diagnoses and advice. This cycle allows for continuous support of users' health management.

[0662] The following describes the processing flow.

[0663] Step 1:

[0664] Users input health data related to their daily lives into the device. Specifically, they record information such as their diet, exercise level, heart rate, body temperature, and sleep duration.

[0665] Step 2:

[0666] The device organizes the collected data and prepares it to be securely sent to the server using encryption technology.

[0667] Step 3:

[0668] The server receives the health data, stores it in a database, and integrates related data. This includes existing health checkup results and past lifestyle data.

[0669] Step 4:

[0670] The server feeds the integrated data into an AI analysis module, which uses machine learning algorithms to perform health assessments and risk diagnoses. The model analyzes data patterns to identify the user's health status and disease risk.

[0671] Step 5:

[0672] The server generates analysis results and creates specific health advice and risk assessments. These results are tailored to the individual user.

[0673] Step 6:

[0674] The server sends the generated results to the terminal and notifies the user.

[0675] Step 7:

[0676] The device displays acquired advice and diagnostic results to the user. This information is provided to the user in a format that is easy to view and understand.

[0677] Step 8:

[0678] The cycle restarts when the user improves their lifestyle based on the advice provided and records new data. Continuous data provision and feedback enable the system to provide even more accurate diagnoses and advice.

[0679] (Example 1)

[0680] 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".

[0681] Traditional health management systems have struggled to provide a multifaceted assessment of individual health conditions and personalized health advice through continuous data updates. In particular, a lack of highly accurate risk assessment through integrated data analysis and the ability to incorporate user feedback have made it difficult to address individual needs.

[0682] 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.

[0683] In this invention, the server includes information gathering means for collecting personal health-related data, data storage means for integrating and storing the collected data, and AI analysis means for analyzing the integrated data using AI. This enables not only a comprehensive evaluation of individual health conditions and highly accurate risk diagnosis, but also continuous health management that reflects user feedback.

[0684] "Information gathering means" refers to methods or devices for obtaining health-related data from individuals, including data entered by users and data collected by sensors.

[0685] "Organizational methods" refer to the processes or functions for structuring collected health-related data and preparing it in an analyzable format.

[0686] "Communication means" refers to a method or technique for transmitting organized data using security technology to another device or server.

[0687] "Data storage means" refers to a database or storage medium used to store received health-related data, making it easily accessible and analyzable.

[0688] "AI analysis tools" refer to processes or modules that analyze integrated health-related data using advanced machine learning algorithms to generate health assessments and risk diagnoses.

[0689] "Notification means" refers to a function or process that presents generated health assessment results and diagnoses to users and provides information in an easily understandable format.

[0690] "Feedback integration means" refers to a function or mechanism that incorporates user opinions and feedback into the system and reflects them in health-related data to improve the accuracy of results and provide personalized advice.

[0691] "Encryption technology" refers to a technique used to enhance data security by transforming transmitted information using specific algorithms and preventing unauthorized access.

[0692] This invention is a system for health management purposes and consists of multiple processes, including the collection of health-related data by a terminal, the storage and analysis of the data by a server, and the notification of results to the user.

[0693] The terminal serves as an interface for collecting personal health information. This includes smartphones, fitness devices, and wearable sensors, which collect data from users regarding vital signs, lifestyle, and diet. The terminal organizes this data, verifies for any missing information, and then encrypts it using security technology before transmitting it to the server.

[0694] The server stores health-related data received from terminals in a database and creates a unified health profile based on this data. Next, an AI analysis module analyzes this data and performs a detailed health assessment and risk diagnosis using machine learning algorithms. Specifically, it predicts future health risks based on a large amount of historical data and suggests areas for lifestyle improvement. At this time, the server uses generative AI models such as random forests and neural networks to achieve highly accurate analysis.

[0695] The analysis results are sent back to the device in an easy-to-understand format. The device visualizes the received information and displays advice to help the user understand it. For example, if the device determines that the user is at high risk of heart disease, it will send a notification recommending things like limiting salt intake or getting regular health checkups.

[0696] Users can review their lifestyle habits based on health guidance received through their devices and provide feedback to the system. This feedback is integrated into the server and used for subsequent analysis. In this way, the system can continuously and efficiently manage the user's health.

[0697] As a concrete example, suppose a user takes photos of their daily meals and uploads them to the app, along with their exercise data measured by a fitness device, and sends this data to the server. Based on this data, the server performs a health assessment, and the AI ​​diagnoses the risks. Based on the diagnosis, the system notifies the user's device with specific health advice. An example of a prompt to be entered into the generative AI model used in this case would be, "Analyze the user's diet and exercise data and generate advice to minimize health risks."

[0698] This invention enables personalized health management by combining the integration of diverse data sources with AI analysis, thereby supporting users in maintaining and improving their health.

[0699] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0700] Step 1:

[0701] The device collects health-related data from the user. This data includes information on diet, exercise, heart rate, body temperature, and lifestyle. The device receives this data through an input interface and also automatically acquires data from sensor devices. Specific actions include the user taking photos of meals with their smartphone and uploading them to the app, or wearing a fitness band to record exercise levels.

[0702] Step 2:

[0703] The terminal organizes the collected data and converts it into a digital format. The input here is the raw data obtained in step 1, and the output is structured data organized by category. Specifically, the data is compiled into a unified format by extracting text data from photographs using character recognition technology, for example.

[0704] Step 3:

[0705] The terminal protects the organized data using encryption methods and sends it to the server. The input is the structured data formed in step 2, and the output is an encrypted data packet. During this process, data confidentiality is maintained while transferring the data to the server using methods such as TLS.

[0706] Step 4:

[0707] The server receives data sent from the terminal and stores it in a database. The input here is encrypted data packets, and the output is the user's health profile stored in the database. The server first decrypts the received data and then registers it in the database.

[0708] Step 5:

[0709] The server passes the stored data to an AI analysis module for analysis. The input is integrated health data, and the output is a health assessment and risk diagnosis result. Specifically, it uses a generative AI model to analyze the data with machine learning algorithms and identify risks by comparing them with past data.

[0710] Step 6:

[0711] The server compiles the analysis results and sends them to the terminal, notifying the user of the results. The input is the analysis results obtained in step 5, and the output is a user-friendly formatted health report. The server retrieves the necessary information, formats it for display, and then transfers it to the terminal.

[0712] Step 7:

[0713] Users review the health assessments and risk diagnoses received on their devices and improve their lifestyle habits as needed. Furthermore, users can provide their feedback and results to the system by entering them into their devices. The input here is user feedback, and the output is updated data used for the next analysis. This is specifically done through simple questionnaires and free-response fields within the app.

[0714] These steps enable the system to manage the user's daily health and continuously provide personalized advice.

[0715] (Application Example 1)

[0716] 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".

[0717] For elderly people to live with peace of mind, it is necessary to continuously monitor their health and respond quickly when abnormalities are detected. However, there is currently a lack of mechanisms to effectively carry out this in daily life. Furthermore, the collection and analysis of health data requires appropriate technology, as it demands both the protection of personal information and highly accurate diagnosis.

[0718] 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.

[0719] In this invention, the server includes data collection means for acquiring individual health information, data integration means for aggregating the acquired health information and storing it in a storage device, and analysis means for analyzing the aggregated data and generating health assessments and risk diagnoses. This makes it possible to monitor the health status of elderly people in real time and notify appropriate caregivers as needed.

[0720] "Data collection means" refers to a device or method for acquiring health-related information of healthy individuals or elderly people using various sensors and devices.

[0721] "Data integration means" refers to a device or method for integrating, organizing, and storing collected health-related information in a single storage device.

[0722] "Analysis means" refers to a device or method that performs health assessments and risk diagnoses using machine learning or other analytical techniques based on integrated data.

[0723] "Notification means" refers to a device or method that presents the generated health assessment or diagnostic results to the user and, if necessary, sends a warning to a third party or supporter.

[0724] "Cryptographic technology" is a method of protecting information used to safeguard the security and privacy of data communications.

[0725] This invention is a system for users to monitor the health status of elderly individuals and other persons and to detect abnormalities early. This system consists of a terminal for collecting health data, a server for analysis, and a terminal for notifying the user of the results.

[0726] First, the device is equipped with various sensors to acquire everyday health data such as heart rate, body temperature, and steps taken. Wearable devices and smartphones are examples of this. These devices collect data via Bluetooth communication and transmit that data to a server using a secure protocol.

[0727] The server stores the received data in a cloud environment and organizes it using data integration tools. The aggregated data is then passed to an analysis tool that runs machine learning algorithms, which is used for health assessment and risk diagnosis. AI models developed using programming languages ​​such as Python are typically used.

[0728] The analysis results are sent back from the server to the terminal. The terminal, through an application using React Native, displays the results to the user in an easy-to-understand manner and has the functionality to send warnings to external supporters via the Twilio API as needed.

[0729] For example, if an elderly person's heart rate suddenly increases while they are out for a walk, the system can detect this increase and quickly send a notification to their caregiver stating, "Your heart rate has increased abnormally. Immediate attention is required."

[0730] An example of a prompt for a generative AI model is, "What is a way to predict trends based on health data and prepare for abnormal situations?"

[0731] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0732] Step 1:

[0733] The device collects health information. Specifically, it acquires heart rate, body temperature, and step count using sensors built into the wearable device or smartphone. The acquired data is temporarily stored within the device.

[0734] Input: Biometric data from sensors.

[0735] Output: Temporarily stored biometric data.

[0736] Step 2:

[0737] The device transmits temporarily stored data to the server via a security protocol. This is done using Bluetooth or Wi-Fi communication. The data is encrypted and transmitted through a secure channel.

[0738] Input: Temporarily stored biometric data.

[0739] Output: Encrypted data sent to the server.

[0740] Step 3:

[0741] The server decodes the received biometric data and stores it in a database in the cloud. Afterward, data integration tools are used to organize and integrate all the data.

[0742] Input: Encrypted data from the device.

[0743] Output: Integrated data stored in the database.

[0744] Step 4:

[0745] The server passes the integrated data to the AI ​​analysis module. The analysis module uses machine learning algorithms to perform health assessments and risk diagnoses. Through a generative AI model, it extracts outliers and risk factors from the data.

[0746] Input: Integrated data stored in the database.

[0747] Output: Results of health assessment and risk diagnosis.

[0748] Step 5:

[0749] The server sends the analysis results to the terminal. The transmitted data is encrypted again, ensuring the security of the communication.

[0750] Input: Results of health assessment and risk diagnosis.

[0751] Output: Encryption evaluation results sent to the terminal.

[0752] Step 6:

[0753] The terminal decodes the received analysis results and presents them to the user in a visualized format via the application. Furthermore, it generates notifications and sends alerts to external support personnel when anomalies are detected, as needed.

[0754] Input: Decoded evaluation results.

[0755] Output: Health assessments and external alerts displayed in the user interface.

[0756] 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.

[0757] This invention is a system that supports the comprehensive health management of users, and in addition to evaluating health status, it also has an emotion analysis function. The system collects health information from the user, integrates and analyzes it, and provides health risk assessment and individual advice. However, this invention can further identify the user's emotional state using an "emotion engine" and provide comprehensive advice that also takes those emotions into account.

[0758] First, users use their devices to input health data related to their daily lives. This includes physiological data such as diet, exercise, heart rate, and body temperature, as well as information on lifestyle and sleep habits. This data is securely transmitted to the server.

[0759] Next, an emotion engine operates on the device, analyzing the user's facial expressions and voice input to generate emotion data. This emotion data, along with health information, is sent to a server for integration.

[0760] The server stores integrated health and emotional data in a database, which is then analyzed by an AI analysis module. This analysis utilizes machine learning techniques to assess the user's health status, emotional tendencies, and risks. Emotional data is used specifically to evaluate stress levels and anxiety levels.

[0761] Based on the analysis, the server generates customized health advice that takes emotional states into account. For example, if the emotional engine detects a high-stress state, the system suggests relaxation methods and, if necessary, refers the user to a specialist. Exercise and dietary advice is also adjusted to the user's current emotional state.

[0762] Ultimately, the device notifies the user of these pieces of advice and displays them in an easy-to-understand format. This helps users maintain and improve a balanced lifestyle in terms of both physical and emotional well-being. This cycle is repeated through continuous data provision and feedback, and the system provides health management optimized for the user.

[0763] The following describes the processing flow.

[0764] Step 1:

[0765] Users input data about their daily lives using specific applications or devices. This includes information such as diet, exercise levels, heart rate, body temperature, and sleep patterns.

[0766] Step 2:

[0767] The device encrypts the collected data and prepares it for secure transmission to the server. At this stage, data integrity and privacy are maintained.

[0768] Step 3:

[0769] The device uses an emotion engine to analyze the user's facial expressions and voice data. The emotion engine specifically utilizes the camera and microphone to perform facial recognition and voice analysis to identify the user's emotional state.

[0770] Step 4:

[0771] The device sends emotional data generated by the emotion engine to the server along with health data.

[0772] Step 5:

[0773] The server integrates the received health and emotional data and stores it in a database. The integrated data is then organized along with the user's personal history for use in analysis.

[0774] Step 6:

[0775] The server feeds the integrated data into an AI analysis module, which uses machine learning algorithms to perform a health assessment and risk diagnosis for the user. Emotional data is also considered, and the effects of stress and anxiety are reflected in the assessment.

[0776] Step 7:

[0777] The server generates customized health advice and emotion-based relaxation and improvement strategies based on the analysis results. This advice is tailored to best suit the user's current emotional state.

[0778] Step 8:

[0779] The server sends the generated results to the terminal and notifies the user.

[0780] Step 9:

[0781] The device displays advice and diagnostic results to the user. The advice is provided in a visual and easy-to-understand format, and is designed to be easily incorporated into daily life.

[0782] Step 10:

[0783] The system is continuously improved as users act on the advice provided and update their daily data. This feedback loop ensures that the system continues to provide users with optimized advice and diagnoses.

[0784] (Example 2)

[0785] 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".

[0786] Traditional health management systems perform health assessments and risk diagnoses based on users' physiological information, but they lack assessments that take emotional states into account. This makes it difficult to provide appropriate advice on users' overall health and lifestyle. Furthermore, there is a need to securely manage this data and provide personalized advice to each user.

[0787] 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.

[0788] In this invention, the server includes data acquisition means for obtaining individual physiological information, data integration means for integrating the acquired physiological information and emotional state and storing it in a memory device, and analysis means for analyzing the integrated data and generating health assessments, emotional tendencies, and risk diagnoses. This enables health assessments and advice that comprehensively consider the user's physiological information and emotional state.

[0789] "Physiological information" refers to information related to the user's physical condition, such as heart rate, body temperature, exercise level, and sleep quality.

[0790] "Emotional state" refers to the psychological and emotional tendencies and reaction states analyzed from the user's facial expressions, voice, etc.

[0791] "Data acquisition means" refers to devices and methods for collecting physiological information and emotional states from users, and includes sensors and user interfaces.

[0792] "Data integration means" refers to processes and systems for centrally managing acquired physiological information and emotional states and storing them in a memory device.

[0793] "Analysis means" refers to methods and techniques used to analyze integrated physiological information and emotional states to generate health assessments, emotional tendencies, and risk diagnoses.

[0794] "Communication encryption technology" refers to encryption methods used to prevent unauthorized access and information leakage during the data transmission process.

[0795] An "emotion analysis engine" refers to software or algorithms used to analyze emotional states from voice and facial expression data.

[0796] This invention is a system that supports the comprehensive health management of users, providing customized health advice by combining physiological information and emotional state.

[0797] First, users input physiological information related to their daily lives using their smartphones or wearable devices. This input data includes information such as heart rate, body temperature, exercise level, and sleep quality. This data is securely transmitted to the server via the device. During the communication process, the data is encrypted using communication encryption technology, protecting the user's privacy.

[0798] Next, the device's emotion analysis engine runs to analyze the user's emotional state. Specifically, it acquires facial expression data through the camera and analyzes voice tone from voice input. This analysis processes the data using, for example, facial recognition software and voice analysis libraries. The generated emotion data is then sent back to the server.

[0799] The server centrally integrates received physiological information and emotional states and stores them in a database. The stored data is then analyzed by an AI analysis module to generate user health assessments, emotional tendencies, and risk diagnoses. This analysis utilizes machine learning techniques, such as TensorFlow and other analysis libraries.

[0800] Based on the analysis, the server generates customized health advice. This advice is adjusted to take emotional states into account and suggests user-specific methods for maintaining and improving health. For example, if the emotional analysis indicates a high-stress state, relaxation methods or consultation with a specialist will be suggested.

[0801] Ultimately, the device notifies the user of the generated advice, displaying it in an easy-to-understand format. This allows the user to gain a comprehensive understanding of their health status and take appropriate action.

[0802] As a concrete example of its use, a prompt such as, "I'm a 35-year-old woman who's been experiencing persistent insomnia lately. I'd like some relaxation techniques and sleep advice," is input into the AI ​​model. Based on this prompt, the system provides advice tailored to the user's needs.

[0803] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0804] Step 1:

[0805] Users input physiological information using smartphones or wearable devices. This data includes heart rate, body temperature, activity level, and sleep quality. Once this data is entered, the device transmits it to the server in encrypted form via a secure communication protocol such as HTTPS. In this process, the input is physiological information provided by the user, and the output is encrypted data.

[0806] Step 2:

[0807] On the device, an emotion analysis engine operates, generating emotion data using the user's camera and microphone. Specifically, image analysis algorithms are used to acquire facial expression data and identify specific emotions, and audio data is analyzed to infer emotions from tone of voice. The input is camera video and audio data, and the output is the identified emotional state. The generated emotion data is then encrypted again and sent to the server.

[0808] Step 3:

[0809] The server integrates the received physiological and emotional data and stores it in a database. A NoSQL database, for example, is used for this purpose, enabling efficient storage and management of large amounts of data. The input consists of encrypted physiological and emotional data, while the output is an integrated data record.

[0810] Step 4:

[0811] An AI analysis module on the server runs and analyzes the integrated data. Here, machine learning algorithms are used to assess the user's health status, emotional tendencies, and health risks. Libraries such as TensorFlow are utilized for this analysis. The input is integrated physiological and emotional data, and the output is the results of the health assessment, emotional tendency, and risk diagnosis.

[0812] Step 5:

[0813] Based on the analysis results, the server uses a generative AI model to generate customized health advice that takes into account the user's emotional state. For example, if high stress is detected, advice including relaxation methods and referrals to specialists will be generated. The input is the analysis results, and the output is customized health advice.

[0814] Step 6:

[0815] The device notifies the user of the generated advice and displays it in an easy-to-understand format. The user reviews the advice received through the notification from the device and incorporates it into their daily life. The input is the generated health advice, and the output is the notification provided to the user.

[0816] (Application Example 2)

[0817] 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".

[0818] Traditional health management systems have focused primarily on managing users' physical health, but have lacked adequate support that takes emotional states into account. Therefore, it has been difficult to address health risks that fluctuate with users' emotional states and to provide appropriate advice in response to changes in their emotions. In this context, there is a need for a system that effectively analyzes emotional states and integrates them into health management.

[0819] 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.

[0820] In this invention, the server includes data acquisition means for obtaining individual health information and emotional information, data integration means for integrating the acquired health information and emotional information and storing it in an information storage device, and analysis means for analyzing the integrated data and generating health assessments, emotional assessments, and risk diagnoses. This enables comprehensive health management that takes emotional states into account.

[0821] "Health information" refers to physiological data and lifestyle information related to individual users. Specifically, it includes data such as diet, exercise, heart rate, body temperature, and sleep habits.

[0822] "Emotional information" refers to data that quantifies or categorizes a user's emotional state. This includes emotional indicators obtained through voice tone and facial expression analysis.

[0823] "Data acquisition means" refers to technical methods and devices for collecting health information and emotional information, including sensors and user interfaces.

[0824] "Data integration means" refers to technology that centrally manages acquired health and emotional information and stores it in an information storage device. This utilizes database technology and cloud storage.

[0825] "Analysis tools" refer to technical processes and devices for analyzing integrated data to generate health assessments, sentiment assessments, and risk diagnoses. This includes machine learning algorithms and data analysis software.

[0826] "Notification methods" refer to technologies used to communicate generated evaluation results and advice to users. These utilize smartphone apps and voice assistants.

[0827] "Information storage device" refers to a device or system used for the long-term storage and management of data. This includes hard disks and cloud storage services.

[0828] A "machine learning algorithm" is a type of artificial intelligence technology applied to data analysis, referring to a method that automatically finds patterns in data and builds predictive models. Various statistical methods are used in the processing.

[0829] "Encryption technology" refers to the transformation process performed to protect data and the methods used to prevent unauthorized access. Representative methods include public-key cryptography and symmetric-key cryptography.

[0830] This invention is a system that comprehensively manages the user's health and emotional state and provides customized health advice. The system functions by collecting data using terminals such as smartphones and smart glasses, and analyzing it on a server in the cloud.

[0831] At the heart of the system is a data acquisition mechanism that collects health and emotional information from users through their devices. Health information includes data such as diet, exercise, heart rate, body temperature, and sleep quality, which are acquired through manual input or sensors on smart devices. Emotional information is collected by analyzing the user's facial expressions and voice using the device's camera and microphone.

[0832] The collected data is securely encrypted and transmitted to a cloud server, where it is stored in an information storage device via a data integration mechanism. On the cloud server, an analysis mechanism using machine learning algorithms analyzes the data to assess the user's health status, emotional state, and risk. Based on the analysis results, the server generates personalized health advice and sends it to the user's device via a notification mechanism.

[0833] For example, if the emotional engine detects a high stress level, the server will provide advice on relaxation methods, specific exercises, and dietary habits. This may include music recommendations or instructions for yoga and meditation. Notifications are delivered to the user visually or audibly via smartphone or smart glasses.

[0834] Examples of prompts for a generative AI model include: "List exercises and relaxation methods that your emotional engine can suggest to reduce the stress that elderly people experience in their daily lives. Also, explain how these can be helpful in caregiving support." This prompt enables the AI ​​to generate specific suggestions for the user.

[0835] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0836] Step 1:

[0837] Users input health and emotional information using a smartphone or smart glasses. Health information includes data such as diet, exercise, heart rate, body temperature, and sleep patterns. Emotional information is obtained through facial recognition and voice analysis via the device. At this stage, the input data is temporarily stored on the device.

[0838] Step 2:

[0839] The device encrypts the acquired health and emotional information and transmits it to the server using a secure communication protocol. Input data is sent through a secure data tunnel to prevent data leakage during transmission. The output here is encrypted data.

[0840] Step 3:

[0841] The server decodes the received data and stores it in the information storage device using data integration means. The synchronized and organized data is saved in individual database entries for each user. After this, the integrated data is sent for subsequent analysis processing.

[0842] Step 4:

[0843] The analysis tools on the server process the integrated data. Using machine learning algorithms and an emotion analysis engine, it generates health assessments, emotion assessments, and risk diagnoses. Here, the AI ​​model identifies health status and emotional tendencies and predicts risk factors such as high stress levels. The analysis results are output as evaluation results.

[0844] Step 5:

[0845] The server creates personalized health advice for the user based on the generated evaluation results. It takes into account the user's emotional state and includes, as needed, suggestions for relaxation methods, exercise, and dietary improvements. A generative AI model guides the generation of detailed advice.

[0846] Step 6:

[0847] The server sends the generated health advice to the device. The content is delivered to the user via a secure communication protocol through a notification system. The device receives the notification and displays it to the user audibly or visually. The user can review the advice received through the device and take action based on it.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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."

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] The following is further disclosed regarding the embodiments described above.

[0870] (Claim 1)

[0871] A means of acquiring data to obtain individual health information,

[0872] A data integration method for integrating acquired health information and storing it in a database,

[0873] Analytical means for analyzing integrated data and generating health assessments and risk diagnoses,

[0874] A notification means for communicating the generated evaluation results to the user,

[0875] A system that includes this.

[0876] (Claim 2)

[0877] The system according to claim 1, which uses a machine learning algorithm for analyzing information.

[0878] (Claim 3)

[0879] The system according to claim 1, which uses encryption technology to communicate data.

[0880] "Example 1"

[0881] (Claim 1)

[0882] Information gathering methods for collecting personal health-related data,

[0883] A means of organizing collected health-related data,

[0884] A means of communication for communicating organized data using a security protocol,

[0885] A data storage means for storing and integrating received data,

[0886] AI analysis tools for analyzing integrated data and generating health assessments and risk diagnoses,

[0887] A notification means for displaying the generated evaluation results to the user,

[0888] A feedback integration mechanism for receiving user feedback and reflecting it in the data,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, which uses a machine learning algorithm for analyzing health-related data.

[0892] (Claim 3)

[0893] The system according to claim 1, comprising communication means that use encryption technology to protect data.

[0894] "Application Example 1"

[0895] (Claim 1)

[0896] Data collection methods for obtaining individual health information,

[0897] A data integration means for aggregating acquired health information and storing it in a storage device,

[0898] An analytical means for analyzing aggregated data and generating health assessments and risk diagnoses,

[0899] A notification system to communicate the generated evaluation results to the user and, if necessary, to send warnings to external supporters.

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, which uses machine learning techniques to analyze information and automatically sends a warning when an anomaly is detected.

[0903] (Claim 3)

[0904] The system according to claim 1, which uses cryptographic techniques to communicate data and, in addition, executes an algorithm to generate an appropriate response.

[0905] "Example 2 of combining an emotion engine"

[0906] (Claim 1)

[0907] A data acquisition method for obtaining individual physiological information,

[0908] A data integration means for integrating acquired physiological information and emotional states and storing them in a memory device,

[0909] An analytical means for analyzing integrated data and generating health assessments, emotional tendencies, and risk diagnoses,

[0910] A notification mechanism for communicating the generated evaluation results and customized advice to the user,

[0911] A system that includes this.

[0912] (Claim 2)

[0913] The system according to claim 1, which uses machine learning techniques to analyze information and further has an emotion analysis engine for analyzing emotional states.

[0914] (Claim 3)

[0915] The system according to claim 1, which uses communication encryption technology to transmit data.

[0916] "Application example 2 when combining with an emotional engine"

[0917] (Claim 1)

[0918] A means for acquiring data to obtain individual health information and emotional information,

[0919] A data integration means for integrating acquired health information and emotional information and storing it in an information storage device,

[0920] Analytical means for analyzing integrated data and generating health assessments, emotional assessments, and risk diagnoses,

[0921] A notification mechanism for communicating the generated evaluation results to the user in a customized format,

[0922] A system that includes this.

[0923] (Claim 2)

[0924] The system according to claim 1, which uses a machine learning algorithm for analysis and generates health advice that takes emotional data into consideration.

[0925] (Claim 3)

[0926] The system according to claim 1, which uses encryption technology to communicate data and transmits it securely. [Explanation of symbols]

[0927] 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. Data collection methods for obtaining individual health information, A data integration means for aggregating acquired health information and storing it in a storage device, An analytical means for analyzing aggregated data and generating health assessments and risk diagnoses, A notification system to communicate the generated evaluation results to the user and, if necessary, to send warnings to external supporters. A system that includes this.

2. The system according to claim 1, which uses machine learning techniques to analyze information and automatically sends a warning when an anomaly is detected.

3. The system according to claim 1, which uses cryptographic techniques to communicate data and also executes an algorithm to generate an appropriate response.

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