system

Abstract

Provide a system. 【Solution means】 Means for receiving biometric information input from a user and storing the information in a storage medium; Processing means for analyzing the stored biometric information to evaluate the user's health condition; Means for generating individualized health management guidance based on the health condition; Means for automatically transmitting to a medical facility when an abnormality is detected; Means via an electronic device for notifying the user of the health management guidance; Means for connecting to an electronic device via a non-public network to provide the guidance content; Means for automatically sending a notification to a caregiver or family member when an abnormality is detected; A system including the above.

Description

Technical Field

[0004] , ,

[0005] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] Contrary to the increasing awareness of modern health management, it is still difficult for individual users to accurately grasp their own health conditions and take appropriate actions. This problem is caused by the complexity of health information management and the delay in response to medical institutions, resulting in a serious problem of increasing health risks. In addition, the lack of personalized health advice that enables individual response hinders users from taking effective health measures.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides a system that receives health information entered by a user, stores it in a database, and then evaluates the user's health status in real time through data analysis. This system generates personalized health management advice based on the analysis results and notifies the user via their terminal. Furthermore, if an abnormality is detected in the health information, the system aims to reduce the user's health risks by quickly and automatically contacting a medical institution. In addition, by using data encryption means, the security of the information is ensured, and the system strengthens its ability to provide personalized support by receiving feedback from the user and using it to improve the quality of subsequent advice.

[0006] A "user" refers to an entity that inputs health information and lifestyle data and receives health management advice.

[0007] "Health information" refers to data provided by the user that relates to their physical condition and daily life, including weight, exercise level, diet, and sleep duration.

[0008] A "database" refers to a storage device or system for securely and systematically storing health information received from users.

[0009] An "analysis algorithm" is a computational method used to evaluate health information received from a user using machine learning and statistical techniques, and to diagnose the user's health status.

[0010] "Health management advice" refers to messages containing suggestions and improvement measures based on the user's health status, as determined by an analysis algorithm.

[0011] "Anomaly detection" is a function that identifies unusual data that deviates from normal health conditions or health risks requiring immediate attention, during the process of analyzing health information provided by users.

[0012] "Medical institutions" refers to hospitals, clinics, or other healthcare providers that are expected to be contacted in order to respond to the user after an anomaly has been detected.

[0013] A "terminal" refers to an electronic device or computer used by a user to input health information and receive generated health management advice.

[0014] "Data encryption" refers to a technology that transforms data to enable secure communication and storage of user information, thereby preventing unauthorized access. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 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 Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), and the like.

[0019] 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.

[0020] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention relates to a system that collects and analyzes health information and lifestyle data from users and generates health management advice based on the results. This system is implemented using a server and terminals and aims to personalize advice according to each individual's health condition by utilizing the information provided by the user.

[0037] Server Processing

[0038] The server receives health information transmitted from the user via their device and securely stores it in a database. The stored data is analyzed using AI algorithms to assess the user's health status. This analysis utilizes statistical models and machine learning techniques to identify risks and areas for improvement related to the user.

[0039] Based on the analysis results, the server generates personalized health management advice for each user. This advice can include specific suggestions regarding diet, exercise, and sleep, tailored to the user's lifestyle. Furthermore, if any abnormalities are detected in the health data, the system automatically contacts registered medical institutions to encourage prompt action.

[0040] Terminal processing

[0041] The device provides an interface for users to input their health information. Users input daily data such as weight, exercise level, diet, and sleep duration, and the device sends this information to the server. When health management advice or notifications of abnormalities are sent from the server, the device displays this information to the user in real time.

[0042] User actions

[0043] Users input their daily health information through their device and review the advice generated by the analysis. For example, users who receive advice regarding their weight or exercise habits can use this information to plan their diet and exercise. If an abnormality is detected, they can check the notification displayed on their device and take action to contact a medical institution promptly if necessary.

[0044] Specific example

[0045] Specifically, suppose a user enters their weight into their device every morning, and the server receives and analyzes this data, detecting a rapid weight gain over the past few days. Based on this information, the server generates advice recommending "paying attention to your diet and reducing salt intake," and notifies the user of this advice. The user can then review their daily eating habits and take actions to maintain their health by following this advice.

[0046] This invention is expected to enable users to proactively manage their health at the appropriate time, significantly contributing to the reduction of health risks and the improvement of quality of life.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] Users input health information such as weight, exercise level, diet, and sleep duration into the device. This input is done via the app's interface or a wearable device.

[0050] Step 2:

[0051] The device encrypts the health information entered by the user and sends it to the server via a secure communication protocol.

[0052] Step 3:

[0053] The server receives health information sent from the terminal, organizes it for each user, and stores it in a database. The accuracy and integrity of the data are verified during storage.

[0054] Step 4:

[0055] The server analyzes the stored health information using AI algorithms. The analysis utilizes machine learning models to assess the user's health status and potential risks.

[0056] Step 5:

[0057] The server generates personalized health management advice based on the analysis results. This advice may include specific suggestions for diet and exercise.

[0058] Step 6:

[0059] The server sends the generated advice to the terminal. At the same time, if an anomaly is detected, it automatically contacts the registered medical institution.

[0060] Step 7:

[0061] The device displays health management advice and abnormality notifications sent from the server to the user. Notifications can be sent via push notifications or email.

[0062] Step 8:

[0063] Users can review the advice from their device and incorporate it into their lives as needed. They can also send feedback on the advice to the server via their device.

[0064] (Example 1)

[0065] 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."

[0066] In modern times, health management is increasingly important, but many people lack the means to effectively utilize their health information and accurately understand their health status. Conventional systems struggle to provide personalized health management advice to each user, and often fail to respond quickly enough when abnormalities are detected. There is a need for a system that can solve these problems and enable users to proactively manage their own health.

[0067] 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.

[0068] In this invention, the server includes means for receiving health information entered by the user and storing the information in a storage device, calculation means for analyzing the stored health information and evaluating the user's health status, and means for generating personalized health management advice based on the health status. As a result, the user can receive personalized, specific, and practical health management advice based on their health information and perform quick and appropriate health management.

[0069] "Health-related information" refers to data collected from users that indicates their health status, such as weight, exercise level, diet, and sleep duration.

[0070] A "storage device" is a computer system or storage system for securely storing and managing received data.

[0071] "Computational means" refers to machine learning models and algorithms used to analyze stored health information and evaluate the user's health status.

[0072] "Health management advice" refers to specific guidance on how to improve the user's health and mitigate risks, generated based on the analysis results.

[0073] The "automatic notification system" is a system that automatically notifies pre-registered medical institutions and other relevant parties in real time when an abnormality is detected.

[0074] "Means of using data encryption" refers to technologies or methods that encrypt and protect data in order to securely send and receive user health information over the internet.

[0075] "Machine learning techniques" are a type of artificial intelligence technology used to enable computers to learn patterns and relationships from data and output analytical results.

[0076] "Means of outputting using natural language processing" refers to computer technology that conveys generated health management advice in language that is easy for the user to understand.

[0077] A "generative AI model" refers to an artificial intelligence system that generates personalized content based on user input data, and is used in applications such as natural language generation.

[0078] This invention is a system for effectively managing users' health information. The system is built around a server and terminals, and has the function of analyzing health information obtained from users and providing personalized health management advice.

[0079] The device provides an interface for receiving health information that users input on a daily basis. Users can input data such as weight, exercise levels, diet, and sleep duration into the device. The device is responsible for organizing this information and securely transmitting it to the server. Data encryption technology is used to protect user privacy during data transmission.

[0080] The server receives data transmitted from the terminal and securely stores it in a storage device. The stored data is analyzed using AI algorithms with programming languages ​​such as Python and R. Machine learning techniques are employed to evaluate the user's health status from the data and identify risks and areas for improvement. Based on this analysis, the server generates specific health management advice for the user. In this process, generative AI models and natural language processing technologies are used to provide the user with easy-to-understand and actionable advice.

[0081] As a concrete example, suppose a user enters their weight into their device every morning, and the server receives this data and detects a rapid weight gain through analysis. In this case, the server generates advice such as "reducing salt intake is recommended" and notifies the user's device in real time. In this way, the user can incorporate the advice into their daily life and use it to maintain or improve their health.

[0082] Examples of prompt messages include, "Please tell me how to input user health data and generate health management advice," and "Please show me an example of advice generated when rapid weight gain is detected."

[0083] This system enables users to efficiently utilize their own health information and receive specific advice that helps improve their health, thereby realizing truly personalized health management.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The device provides an interface for receiving health-related information from the user as input. Specifically, the user manually enters data such as weight, exercise level, diet, and sleep duration into input fields. The entered data is then organized in digital format. At this stage, the output is that the user's health information is stored as digital data on the device.

[0087] Step 2:

[0088] The device performs the process of sending the user's health information to the server. To ensure data security, this operation is encrypted. Specifically, the device sends encrypted health data as data packets to the server via Wi-Fi or a mobile network. The output is securely encrypted health information reaching the server.

[0089] Step 3:

[0090] The server receives health-related information from terminals as input. The received data is stored in a storage device and prepared for analysis. Specifically, a database management system is used to systematically organize the data and build the foundation for subsequent analysis processes. The output of this stage is structured health data stored in the database.

[0091] Step 4:

[0092] The server analyzes stored health data. It receives data stored in a storage device as input and outputs a health status assessment for each user. Specifically, machine learning algorithms using Python or R are executed to identify user health risks and areas for improvement. The data calculations performed here include statistical analysis, pattern recognition, and the execution of predictive models.

[0093] Step 5:

[0094] The server generates health management advice based on the analysis results. The input is the evaluation results of the user's health status analyzed by the server. As output, personalized health management advice is generated. Specifically, the generating AI model uses natural language processing to construct health management advice in a format that is easy for the user to understand. The advice generated through this process includes specific improvement measures regarding diet, exercise, and lifestyle habits.

[0095] Step 6:

[0096] Health management advice generated from the server is sent to the user's device. Encryption is used in this transmission process to enhance data security. As an output, the advice is notified to the user's device in real time. Specifically, the advice is delivered to the user via application notifications or email, and the user can check it and work on improving their daily life.

[0097] (Application Example 1)

[0098] 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."

[0099] There is a need to build a system that enables effective health management and rapid response by providing appropriate health management guidance based on individual health information in real time to the elderly and people requiring care, and by promptly notifying caregivers, family members, and medical facilities when abnormalities occur. However, conventional systems have problems such as difficulty in providing individualized advice and making rapid contact in emergencies, as well as difficulty in handling information securely.

[0100] 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.

[0101] In this invention, the server includes means for receiving biometric information input by a user and storing it in a storage medium, processing means for analyzing the stored biometric information and evaluating the user's health status, and means for generating personalized health management guidance based on the user's health status. This enables the provision of personalized health management guidance based on individual biometric information to the user in real time, and prompt notification to caregivers, family members, and medical facilities if an abnormality is detected.

[0102] "Biometric information" refers to specific numerical values ​​and indicators that show the health status of the person who has been deactivated, such as the user's weight, blood pressure, and body temperature.

[0103] A "storage medium" refers to a device or means for securely storing received digital information, and includes databases.

[0104] "Processing means" refers to a function that includes a digital processor for analyzing stored digital information and performing necessary calculations and decisions.

[0105] "Health management guidance" refers to specific advice and plans for maintaining or improving health, tailored to each individual user, based on analysis results.

[0106] A "care support worker" is a person who directly assists a person in their daily life through voice and actions to help them with their daily activities.

[0107] A "medical facility" is a facility that provides medical services, such as a hospital or clinic, and is equipped to support caregivers and family members when an abnormality is detected.

[0108] "Data concealment technology" refers to a technical method of encrypting digital information to protect it from misuse and unauthorized access.

[0109] The system for realizing this invention includes a terminal for users to input daily health information, a server for securely storing and analyzing the input data, and a communication network for providing health management guidance based on the analysis results.

[0110] The server receives biometric information transmitted by the user and stores it in storage. User privacy is protected by encrypting the information using data concealment technology. For example, standard encryption techniques such as AES (Advanced Encryption Standard) are often used for data encryption. The stored information is analyzed using AI algorithms to evaluate the user's health status. Specifically, machine learning models using TENSORFLOW® or PyTorch are used. This model extracts risks and areas for improvement, and generates personalized health management guidance.

[0111] The devices used by users are mobile devices such as smartphones and smart glasses, and they provide an interface that makes it easy for users to input their daily health information. In addition, health management guidance and notifications about abnormalities generated through these devices are provided to the user in real time. For example, if a rapid weight gain is detected, the user will be notified with recommendations to review their diet and reduce their salt intake.

[0112] Based on the information and behavior registered by the user, the analysis results are notified to caregivers and family members as needed. Furthermore, if a serious abnormality is detected, the server automatically notifies medical facilities. This allows users to detect abnormalities early and take appropriate medical action quickly.

[0113] Examples of prompts for a generative AI model include the following:

[0114] "Detect abnormal patterns from the user's health data over the past week and generate personalized health advice. If abnormalities are detected, suggest a low-sodium diet and exercise to reduce stress."

[0115] Thus, the present invention is an important means of supporting personalized health management in real time by utilizing users' health information, and of providing a safer and more comfortable living environment for the elderly and people who require care.

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The user inputs biometric information into the device. The user inputs daily health information such as weight, blood pressure, and body temperature through the interface of a smartphone or smart glasses. This input data is temporarily stored within the device and prepared for transmission to the server.

[0119] Step 2:

[0120] The device sends biometric information to the server. The device encrypts the collected biometric information using data concealment technology and securely transmits it to the server. At this stage, the input is the user's biometric information, and the output is encrypted data.

[0121] Step 3:

[0122] The server receives data and stores it in storage. The server receives encrypted data and stores it in secure storage. This storage makes the data available for subsequent analysis. The input is encrypted biometric information, and the output is biometric information in a database.

[0123] Step 4:

[0124] The server analyzes biometric information and assesses the user's health status. The server inputs the stored data into an AI algorithm to perform analysis to evaluate the user's health. This analysis involves comparing the data with past data and performing pattern recognition to identify risks and areas for improvement. The input is stored biometric information, and the output is the analysis results.

[0125] Step 5:

[0126] The server generates personalized health management guidance. Based on the analysis results, the server generates health management guidance tailored to each individual user. This guidance includes specific suggestions regarding diet, exercise, and sleep. The input is the analysis results, and the output is the health management guidance.

[0127] Step 6:

[0128] The server generates health management guidance and sends it to the terminal. The server sends the generated guidance to the terminal using data encryption technology. On the terminal, the guidance is decrypted and displayed to the user. The input is health management guidance, and the output is the guidance content displayed on the terminal.

[0129] Step 7:

[0130] The user receives the provided health management guidance. The user reviews the guidance displayed on the device and adjusts their daily health management based on the guidance. The user may also provide feedback or enter additional data. The input is the guidance content displayed on the device, and the output is the user's behavioral changes and feedback.

[0131] Step 8:

[0132] The server receives feedback from users and analyzes it for generating future lessons. The server receives feedback and additional data provided by users and incorporates the feedback information into the AI ​​model. This process aims to improve the quality of future lessons. The input is user feedback, and the output is an updated model and training data for generating future lessons.

[0133] 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.

[0134] This invention is a health management system that takes into account not only the user's health information but also their emotional state. This system comprises a server, terminals, and an emotion engine, and aims to provide highly personalized health management advice by analyzing multifaceted data from the user.

[0135] Server Processing

[0136] The server receives health information and emotional data transmitted from the terminal and stores it in a database. The stored data is analyzed using an AI algorithm to perform a comprehensive health assessment that includes not only physical health but also emotional state. Based on this comprehensive assessment, advice is generated that takes the user's mental health into consideration.

[0137] The server generates customized health management advice based on health information and emotional data. This advice can include coping strategies and encouraging messages tailored to the user's emotional situation. Furthermore, if an abnormality is detected, the server automatically contacts a medical institution to prompt the user for immediate action.

[0138] Terminal processing

[0139] The device provides an interface for capturing health information and user emotions. Users can register emotional data through voice input and facial expression analysis, along with their daily health data. The emotion engine processes this data and visualizes the user's current mental state.

[0140] The device visually displays advice and anomaly notifications sent from the server to the user. This allows the user to comprehensively understand their health status and take necessary measures.

[0141] User actions

[0142] Users input health information and emotional states through their devices to check their overall health status. Users who receive health management advice can then decide how to apply it to their daily lives. For example, if they are assessed as experiencing high levels of emotional stress, they might be advised to try breathing exercises to relax.

[0143] Specific example

[0144] Specifically, suppose a user weighs themselves in the morning, enters their food intake into the device, and simultaneously uses the device's camera to check their emotional state. The emotion engine analyzes the user's facial expressions, and if it detects signs of stress, the server generates advice such as, "Try to increase your exercise this week," and notifies the user of this message on the device. The user then follows this advice and decides to attend a yoga class on the weekend.

[0145] Thus, the present invention enables users to perform comprehensive health management that takes into account not only physical care but also mental health, and is expected to help them maintain a high quality of life (QOL).

[0146] The following describes the processing flow.

[0147] Step 1:

[0148] Users input health information (weight, exercise level, diet, etc.) along with emotional data through voice and facial expressions into the device. This information is easily collected using the device's app.

[0149] Step 2:

[0150] The device processes health information and emotional data entered by the user in real time and identifies the user's emotional state through an emotion engine. This process evaluates the user's mental state.

[0151] Step 3:

[0152] The device transmits health information and emotional data to the server using a secure protocol. The data is encrypted during this process to maintain confidentiality.

[0153] Step 4:

[0154] The server receives data sent from the terminal and stores it in the database. The stored data is managed on a per-user basis and used later for analysis.

[0155] Step 5:

[0156] The server uses AI algorithms to analyze stored health and emotional data to assess the user's physical and mental health. This assessment identifies personalized health risks and suggested improvements.

[0157] Step 6:

[0158] The server generates personalized health management advice based on the analysis results. This advice may include exercise and relaxation methods that take into account the user's emotional state.

[0159] Step 7:

[0160] The server sends the generated advice to the terminal, which then notifies the user. The notification is displayed in a visualized format, allowing the user to receive timely feedback.

[0161] Step 8:

[0162] Users review health management advice provided by their devices and adjust their daily lives accordingly. They aim to maintain and improve their health by incorporating specific actions that address their emotional state (e.g., meditation or taking walks).

[0163] Step 9:

[0164] The emotion data, regularly updated by the emotion engine, is continuously analyzed by the server for long-term health assessment and used to provide useful feedback to the user.

[0165] (Example 2)

[0166] 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".

[0167] In modern society, mental health is just as important as physical health when it comes to managing one's own health. However, current health management systems primarily provide assessments and advice based only on physical data, making it difficult to provide comprehensive health management that takes mental health into account. Furthermore, these systems leave the ability to respond quickly when an abnormality is detected to the user's discretion, which may result in inadequate and timely contact with medical institutions.

[0168] 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.

[0169] In this invention, the server includes means for receiving and storing biometric information and emotional state data in a storage device, information processing means for analyzing the stored data and performing a comprehensive health assessment, and means for generating personalized health management suggestions based on the analysis results. This enables comprehensive health management that takes into account both physical and mental health conditions.

[0170] "Biometric information" refers to data that indicates the user's physical health status, and includes weight, diet, exercise history, etc.

[0171] "Emotional state data" refers to data that indicates the user's mental health status, and includes emotional information obtained from the user's facial expressions and voice.

[0172] A "storage device" is an information storage device that includes a database for safely and efficiently storing collected data.

[0173] "Information processing means" refers to software and hardware components used to analyze stored data and comprehensively evaluate the user's health status.

[0174] "Health management suggestions" refer to personalized advice and guidelines for health improvement that are generated based on the user's health information and emotional state.

[0175] A "display device" refers to a monitor or screen used to provide information to a user visually.

[0176] "Information encryption" refers to a technology that transforms and protects digital data to ensure the security of information during transmission and reception.

[0177] This invention is a system that collects and analyzes a user's biometric information and emotional state data to generate personalized health management suggestions. Specific embodiments are described below.

[0178] The server uses a powerful database management system to receive and store data. For example, the database can be MySQL® or MongoDB. AI algorithms are introduced to analyze the stored data, and machine learning libraries such as TensorFlow and PyTorch are utilized. These technologies support a comprehensive health assessment of biometric and emotional state data.

[0179] The terminal collects information from the user and transmits it to the server. The terminal incorporates sensors such as a camera and microphone, and uses OpenCV and other library technologies to perform facial expression analysis and voice data analysis. This allows for an efficient understanding of the user's emotional state.

[0180] Users can regularly input their weight and meals into the device. It's also easy to record their emotional state using the device's camera. The data collected by the user is sent to the server periodically or each time the user inputs data via a secure communication protocol.

[0181] A concrete example is a user weighing themselves in the morning, entering their meal details into a device, and recording their emotions at that time through facial expression analysis using the device's camera. Based on this data, the server comprehensively evaluates the user's health status and generates advice such as, "Try to increase your exercise this week," which is then sent to the device. The user can then decide to take part in a yoga class on the weekend based on this advice.

[0182] An example of a prompt generated by a generative AI model is the requirement, "Generate personalized health advice considering the user's health information and emotional state." Using this prompt, the AI ​​model generates appropriate health management suggestions.

[0183] Thus, this invention realizes a system that comprehensively manages the user's physical and mental health and has the effect of improving their quality of life.

[0184] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0185] Step 1:

[0186] The user inputs biometric information (weight, diet, etc.) and emotional state into the device. The input emotional state is analyzed in real time using the device's camera. The input data is processed as primary data indicating the user's health status.

[0187] Step 2:

[0188] The device transmits collected health information and emotional state data to the server. At this point, the data is securely transferred to the server using a secure protocol such as HTTPS. The input is the user's biometric and emotional data, and the output is the secure transmission of data to the server.

[0189] Step 3:

[0190] The server stores the received data in a storage device. The stored information is organized by user and systematized for later analysis. The input is health and emotional data from the terminal, and the output is data stored in the database for preliminary analysis.

[0191] Step 4:

[0192] The server analyzes the stored data using AI algorithms. Using TensorFlow and PyTorch, the data is input into a health assessment model, which then performs a comprehensive evaluation of the user's health status. The input is user data stored in the database, and the output is the health status evaluation result based on the analysis.

[0193] Step 5:

[0194] The server generates personalized health management suggestions based on the analysis results. In this step, a generative AI model is used to create specific advice, such as "Let's increase our exercise this week." The input is the result of the overall evaluation, and the output is the health management suggestions for the user.

[0195] Step 6:

[0196] The server sends the generated health management suggestions to the terminal. This process is also carried out using a secure protocol. The input is the generated suggestions, and the output is the suggestion notification to the terminal.

[0197] Step 7:

[0198] The terminal displays received health management suggestions to the user. The user visually reviews the provided advice and decides how to apply it to their daily life. The input is the health management suggestions from the server, and the output is the user's acceptance of the suggestions and decision on an action plan.

[0199] Through this series of processes, the system provides users with comprehensive health management.

[0200] (Application Example 2)

[0201] 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".

[0202] Health management often focuses heavily on the physical aspect, and mental health management is frequently neglected. Furthermore, many systems lack mechanisms to improve the accuracy of advice based on user feedback. Security issues also exist in the transmission and reception of health information. This invention aims to solve these problems and provide a system for comprehensively managing a user's physical and mental health.

[0203] 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.

[0204] In this invention, the server includes means for receiving health-related information input by the user and storing the information in a data collection device, calculation means for analyzing the stored health-related information and evaluating the user's health status, and means for generating personalized health management recommendations based on the health status. This enables comprehensive health management that takes into account the user's emotional state as well as their physical health.

[0205] "Health-related information" refers to data that indicates the user's physical health status, including information such as weight and heart rate.

[0206] A "data collection device" is a storage device used to store data collected from users.

[0207] A "computational device" is a device that has the function of analyzing and evaluating the user's health status based on collected data.

[0208] "Personalized health management recommendations" refer to advice that outlines management policies based on the user's individual health condition.

[0209] A "medical facility" is a medical professional institution that should be contacted when necessary based on the results of health monitoring.

[0210] "Display means" refers to a device for visually presenting information to a user, and includes displays and the like.

[0211] "Emotional state" refers to data that indicates the user's mental and emotional state.

[0212] A "natural language processing device" is a processing device that communicates with users using natural language.

[0213] "Lifestyle data" refers to information about a user's habits and behaviors related to their daily life.

[0214] "Data encryption" is a technology used to ensure the security of data being transmitted or received.

[0215] The system implementing this invention mainly consists of a user terminal, a data collection device, a server, and a home care robot in the application example. The user inputs health-related information via the user terminal, and this information is stored in the data collection device. The server has advanced computing means to analyze this health-related information and the user's emotional state. For example, it receives the user's health information (weight, heart rate, etc.) and facial expression data acquired by cameras mounted on the terminal or robot in real time to understand the emotional state.

[0216] Based on the received data, the server uses a generative AI model to comprehensively evaluate the user's health status and generate personalized health management recommendations. These recommendations include physical health management policies as well as mental health recommendations tailored to the user's emotional state. Communication with the user is conducted through display means using advanced natural language processing.

[0217] For example, if a user voice-inputs "I'm a little tired" in the morning, their emotional state is determined to be one of increased stress. Based on this information, the server displays a message on the home care robot or the user's device saying, "How about taking a walk today to relax?" This encourages the user to take actions appropriate to their condition.

[0218] An example of a prompt message is: "Based on a recent health assessment, generate stress-reducing advice. The emotional state has been detected as (sad). As an example, suggest relaxation methods that can be visualized to reduce stress." The advice generated through such prompt messages is designed not only to support the user's health but also to help improve their quality of life.

[0219] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0220] Step 1:

[0221] Users input health-related information and emotional states through the device. Specifically, users input their weight and heart rate, and capture their facial expressions with the device's camera. This input data is initially processed on the device and then sent to a data collection device. The input consists of health-related biometric data and facial images, while the output is data converted from these into an appropriate data format.

[0222] Step 2:

[0223] The terminal transmits collected health-related information and emotional states to the server. The server stores this data in a data aggregation device and accumulates it in a database for later analysis. The input is the data transmitted from the terminal, and the output is the data in the format stored in the database.

[0224] Step 3:

[0225] The server analyzes stored health-related information and emotional states. A generative AI model is used to evaluate the user's physical and emotional state. This analysis process involves data processing, including calculating the mean and deviation of numerical data, and extracting features from facial image data. The input is data read from a database, and the output is the evaluation result.

[0226] Step 4:

[0227] The server generates personalized health management recommendations based on the analysis results. These recommendations include consideration of the user's physical and emotional state. The generative AI model uses natural language generation in this step to create easily understandable advice. The input is the analysis results, and the output is advice expressed in natural language.

[0228] Step 5:

[0229] The server sends the generated health management recommendations to the terminal and home care robot. The terminal or robot presents this to the user visually or audibly. This display includes visual elements and audio messages. The input is the advice data from the server, and the output is the presentation to the user.

[0230] Step 6:

[0231] The user reviews the health management recommendations they receive and decides on an action based on them. For example, if they receive the advice, "Take a walk today to reduce stress," the user chooses to go for a walk. In this step, the input is the advice from the server, and the output is the user's action choice.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] [Second Embodiment]

[0236] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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).

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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".

[0248] This invention relates to a system that collects and analyzes health information and lifestyle data from users and generates health management advice based on the results. This system is implemented using a server and terminals and aims to personalize advice according to each individual's health condition by utilizing the information provided by the user.

[0249] Server Processing

[0250] The server receives health information transmitted from the user via their device and securely stores it in a database. The stored data is analyzed using AI algorithms to assess the user's health status. This analysis utilizes statistical models and machine learning techniques to identify risks and areas for improvement related to the user.

[0251] Based on the analysis results, the server generates personalized health management advice for each user. This advice can include specific suggestions regarding diet, exercise, and sleep, tailored to the user's lifestyle. Furthermore, if any abnormalities are detected in the health data, the system automatically contacts registered medical institutions to encourage prompt action.

[0252] Terminal processing

[0253] The device provides an interface for users to input their health information. Users input daily data such as weight, exercise level, diet, and sleep duration, and the device sends this information to the server. When health management advice or notifications of abnormalities are sent from the server, the device displays this information to the user in real time.

[0254] User actions

[0255] Users input their daily health information through their device and review the advice generated by the analysis. For example, users who receive advice regarding their weight or exercise habits can use this information to plan their diet and exercise. If an abnormality is detected, they can check the notification displayed on their device and take action to contact a medical institution promptly if necessary.

[0256] Specific example

[0257] Specifically, suppose a user enters their weight into their device every morning, and the server receives and analyzes this data, detecting a rapid weight gain over the past few days. Based on this information, the server generates advice recommending "paying attention to your diet and reducing salt intake," and notifies the user of this advice. The user can then review their daily eating habits and take actions to maintain their health by following this advice.

[0258] This invention is expected to enable users to proactively manage their health at the appropriate time, significantly contributing to the reduction of health risks and the improvement of quality of life.

[0259] The following describes the processing flow.

[0260] Step 1:

[0261] Users input health information such as weight, exercise level, diet, and sleep duration into the device. This input is done via the app's interface or a wearable device.

[0262] Step 2:

[0263] The device encrypts the health information entered by the user and sends it to the server via a secure communication protocol.

[0264] Step 3:

[0265] The server receives health information sent from the terminal, organizes it for each user, and stores it in a database. The accuracy and integrity of the data are verified during storage.

[0266] Step 4:

[0267] The server analyzes the stored health information using AI algorithms. The analysis utilizes machine learning models to assess the user's health status and potential risks.

[0268] Step 5:

[0269] The server generates personalized health management advice based on the analysis results. This advice may include specific suggestions for diet and exercise.

[0270] Step 6:

[0271] The server sends the generated advice to the terminal. At the same time, if an anomaly is detected, it automatically contacts the registered medical institution.

[0272] Step 7:

[0273] The device displays health management advice and abnormality notifications sent from the server to the user. Notifications can be sent via push notifications or email.

[0274] Step 8:

[0275] Users can review the advice from their device and incorporate it into their lives as needed. They can also send feedback on the advice to the server via their device.

[0276] (Example 1)

[0277] 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."

[0278] In modern times, health management is increasingly important, but many people lack the means to effectively utilize their health information and accurately understand their health status. Conventional systems struggle to provide personalized health management advice to each user, and often fail to respond quickly enough when abnormalities are detected. There is a need for a system that can solve these problems and enable users to proactively manage their own health.

[0279] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Example 1 is realized by the following means.

[0280] In this invention, the server includes means for receiving health-related information input from a user and storing the information in a storage device, calculation means for analyzing the stored health-related information to evaluate the user's health status, and means for generating advice regarding personalized health management based on the health status. As a result, the user can receive personalized, specific, and practical health management advice based on their own health information and can perform rapid and appropriate health management.

[0281] "Health-related information" refers to data indicating the health status such as weight, amount of exercise, dietary content, and sleep time collected from the user.

[0282] "Storage device" refers to a computer system or storage system for safely storing and managing the received data.

[0283] "Calculation means" refers to machine learning models or algorithms used to analyze the stored health-related information and evaluate the user's health status.

[0284] "Advice regarding health management" refers to specific guidance regarding methods for improving the user's health and risk reduction measures generated based on the analysis results.

[0285] "Means for automatically contacting" refers to a system for automatically notifying in real time a pre-registered medical institution or the like when an abnormality is detected.

[0286] "Means using data encryption" refers to a technology or method for encrypting and protecting data in order to safely receive and transmit the user's health-related information over the Internet.

[0287] "Machine learning techniques" are a type of artificial intelligence technology used to enable computers to learn patterns and relationships from data and output analytical results.

[0288] "Means of outputting using natural language processing" refers to computer technology that conveys generated health management advice in language that is easy for the user to understand.

[0289] A "generative AI model" refers to an artificial intelligence system that generates personalized content based on user input data, and is used in applications such as natural language generation.

[0290] This invention is a system for effectively managing users' health information. The system is built around a server and terminals, and has the function of analyzing health information obtained from users and providing personalized health management advice.

[0291] The device provides an interface for receiving health information that users input on a daily basis. Users can input data such as weight, exercise levels, diet, and sleep duration into the device. The device is responsible for organizing this information and securely transmitting it to the server. Data encryption technology is used to protect user privacy during data transmission.

[0292] The server receives data transmitted from the terminal and securely stores it in a storage device. The stored data is analyzed using AI algorithms with programming languages ​​such as Python and R. Machine learning techniques are employed to evaluate the user's health status from the data and identify risks and areas for improvement. Based on this analysis, the server generates specific health management advice for the user. In this process, generative AI models and natural language processing technologies are used to provide the user with easy-to-understand and actionable advice.

[0293] As a concrete example, suppose a user enters their weight into their device every morning, and the server receives this data and detects a rapid weight gain through analysis. In this case, the server generates advice such as "reducing salt intake is recommended" and notifies the user's device in real time. In this way, the user can incorporate the advice into their daily life and use it to maintain or improve their health.

[0294] Examples of prompt messages include, "Please tell me how to input user health data and generate health management advice," and "Please show me an example of advice generated when rapid weight gain is detected."

[0295] This system enables users to efficiently utilize their own health information and receive specific advice that helps improve their health, thereby realizing truly personalized health management.

[0296] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0297] Step 1:

[0298] The device provides an interface for receiving health-related information from the user as input. Specifically, the user manually enters data such as weight, exercise level, diet, and sleep duration into input fields. The entered data is then organized in digital format. At this stage, the output is that the user's health information is stored as digital data on the device.

[0299] Step 2:

[0300] The device performs the process of sending the user's health information to the server. To ensure data security, this operation is encrypted. Specifically, the device sends encrypted health data as data packets to the server via Wi-Fi or a mobile network. The output is securely encrypted health information reaching the server.

[0301] Step 3:

[0302] The server receives the health-related information received from the terminal as input. The received data is stored in the storage device and prepared for analysis. Specifically, the database management system is used to systematically organize the data and build the foundation for subsequent analysis processes. The output at this stage is the structured health data stored in the database.

[0303] Step 4:

[0304] The server analyzes the stored health data. The data stored in the storage device is received as input, and the evaluation result of the health status for each user is obtained as output. As a specific operation, machine learning algorithms using Python or R are executed to identify the health risks and improvement points of the user. The data operations performed here include statistical analysis, pattern recognition, and execution of prediction models.

[0305] Step 5:

[0306] The server generates health management advice based on the analysis results. The input is the evaluation result of the health status of the user analyzed by the server. As output, personalized health management advice is generated. As a specific operation, the generation AI model uses natural language processing to construct advice on health management in a form that is easy for the user to understand. The advice generated by this process includes specific improvement measures regarding diet, exercise, and lifestyle habits.

[0307] Step 6:

[0308] Health management advice generated from the server is sent to the user's device. Encryption is used in this transmission process to enhance data security. As an output, the advice is notified to the user's device in real time. Specifically, the advice is delivered to the user via application notifications or email, and the user can check it and work on improving their daily life.

[0309] (Application Example 1)

[0310] 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."

[0311] There is a need to build a system that enables effective health management and rapid response by providing appropriate health management guidance based on individual health information in real time to the elderly and people requiring care, and by promptly notifying caregivers, family members, and medical facilities when abnormalities occur. However, conventional systems have problems such as difficulty in providing individualized advice and making rapid contact in emergencies, as well as difficulty in handling information securely.

[0312] 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.

[0313] In this invention, the server includes means for receiving biometric information input by a user and storing it in a storage medium, processing means for analyzing the stored biometric information and evaluating the user's health status, and means for generating personalized health management guidance based on the user's health status. This enables the provision of personalized health management guidance based on individual biometric information to the user in real time, and prompt notification to caregivers, family members, and medical facilities if an abnormality is detected.

[0314] "Biometric information" refers to specific numerical values ​​and indicators that show the health status of the person who has been deactivated, such as the user's weight, blood pressure, and body temperature.

[0315] A "storage medium" refers to a device or means for securely storing received digital information, and includes databases.

[0316] "Processing means" refers to a function that includes a digital processor for analyzing stored digital information and performing necessary calculations and decisions.

[0317] "Health management guidance" refers to specific advice and plans for maintaining or improving health, tailored to each individual user, based on analysis results.

[0318] A "care support worker" is a person who directly assists a person in their daily life through voice and actions to help them with their daily activities.

[0319] A "medical facility" is a facility that provides medical services, such as a hospital or clinic, and is equipped to support caregivers and family members when an abnormality is detected.

[0320] "Data concealment technology" refers to a technical method of encrypting digital information to protect it from misuse and unauthorized access.

[0321] The system for realizing this invention includes a terminal for users to input daily health information, a server for securely storing and analyzing the input data, and a communication network for providing health management guidance based on the analysis results.

[0322] The server receives biometric information transmitted by the user and stores it in storage. User privacy is protected by encrypting the information using data concealment technology. For example, standard encryption techniques such as AES (Advanced Encryption Standard) are often used for data encryption. The stored information is analyzed using AI algorithms to evaluate the user's health status. Specifically, machine learning models using TensorFlow or PyTorch are used. This model extracts risks and areas for improvement, and generates personalized health management guidance.

[0323] The devices used by users are mobile devices such as smartphones and smart glasses, and they provide an interface that makes it easy for users to input their daily health information. In addition, health management guidance and notifications about abnormalities generated through these devices are provided to the user in real time. For example, if a rapid weight gain is detected, the user will be notified with recommendations to review their diet and reduce their salt intake.

[0324] Based on the information and behavior registered by the user, the analysis results are notified to caregivers and family members as needed. Furthermore, if a serious abnormality is detected, the server automatically notifies medical facilities. This allows users to detect abnormalities early and take appropriate medical action quickly.

[0325] Examples of prompts for a generative AI model include the following:

[0326] "Detect abnormal patterns from the user's health data over the past week and generate personalized health advice. If abnormalities are detected, suggest a low-sodium diet and exercise to reduce stress."

[0327] Thus, the present invention is an important means of supporting personalized health management in real time by utilizing users' health information, and of providing a safer and more comfortable living environment for the elderly and people who require care.

[0328] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0329] Step 1:

[0330] The user inputs biometric information into the device. The user inputs daily health information such as weight, blood pressure, and body temperature through the interface of a smartphone or smart glasses. This input data is temporarily stored within the device and prepared for transmission to the server.

[0331] Step 2:

[0332] The device sends biometric information to the server. The device encrypts the collected biometric information using data concealment technology and securely transmits it to the server. At this stage, the input is the user's biometric information, and the output is encrypted data.

[0333] Step 3:

[0334] The server receives data and stores it in storage. The server receives encrypted data and stores it in secure storage. This storage makes the data available for subsequent analysis. The input is encrypted biometric information, and the output is biometric information in a database.

[0335] Step 4:

[0336] The server analyzes biometric information and assesses the user's health status. The server inputs the stored data into an AI algorithm to perform analysis to evaluate the user's health. This analysis involves comparing the data with past data and performing pattern recognition to identify risks and areas for improvement. The input is stored biometric information, and the output is the analysis results.

[0337] Step 5:

[0338] The server generates personalized health management guidance. Based on the analysis results, the server generates health management guidance tailored to each individual user. This guidance includes specific suggestions regarding diet, exercise, and sleep. The input is the analysis results, and the output is the health management guidance.

[0339] Step 6:

[0340] The server generates health management guidance and sends it to the terminal. The server sends the generated guidance to the terminal using data encryption technology. On the terminal, the guidance is decrypted and displayed to the user. The input is health management guidance, and the output is the guidance content displayed on the terminal.

[0341] Step 7:

[0342] The user receives the provided health management guidance. The user reviews the guidance displayed on the device and adjusts their daily health management based on the guidance. The user may also provide feedback or enter additional data. The input is the guidance content displayed on the device, and the output is the user's behavioral changes and feedback.

[0343] Step 8:

[0344] The server receives feedback from users and analyzes it for generating future lessons. The server receives feedback and additional data provided by users and incorporates the feedback information into the AI ​​model. This process aims to improve the quality of future lessons. The input is user feedback, and the output is an updated model and training data for generating future lessons.

[0345] 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.

[0346] This invention is a health management system that takes into account not only the user's health information but also their emotional state. This system comprises a server, terminals, and an emotion engine, and aims to provide highly personalized health management advice by analyzing multifaceted data from the user.

[0347] Server Processing

[0348] The server receives health information and emotional data transmitted from the terminal and stores it in a database. The stored data is analyzed using an AI algorithm to perform a comprehensive health assessment that includes not only physical health but also emotional state. Based on this comprehensive assessment, advice is generated that takes the user's mental health into consideration.

[0349] The server generates customized health management advice based on health information and emotional data. This advice can include coping strategies and encouraging messages tailored to the user's emotional situation. Furthermore, if an abnormality is detected, the server automatically contacts a medical institution to prompt the user for immediate action.

[0350] Terminal processing

[0351] The device provides an interface for capturing health information and user emotions. Users can register emotional data through voice input and facial expression analysis, along with their daily health data. The emotion engine processes this data and visualizes the user's current mental state.

[0352] The device visually displays advice and anomaly notifications sent from the server to the user. This allows the user to comprehensively understand their health status and take necessary measures.

[0353] User actions

[0354] Users input health information and emotional states through their devices to check their overall health status. Users who receive health management advice can then decide how to apply it to their daily lives. For example, if they are assessed as experiencing high levels of emotional stress, they might be advised to try breathing exercises to relax.

[0355] Specific example

[0356] Specifically, suppose a user weighs themselves in the morning, enters their food intake into the device, and simultaneously uses the device's camera to check their emotional state. The emotion engine analyzes the user's facial expressions, and if it detects signs of stress, the server generates advice such as, "Try to increase your exercise this week," and notifies the user of this message on the device. The user then follows this advice and decides to attend a yoga class on the weekend.

[0357] Thus, the present invention enables users to perform comprehensive health management that takes into account not only physical care but also mental health, and is expected to help them maintain a high quality of life (QOL).

[0358] The following describes the processing flow.

[0359] Step 1:

[0360] Users input health information (weight, exercise level, diet, etc.) along with emotional data through voice and facial expressions into the device. This information is easily collected using the device's app.

[0361] Step 2:

[0362] The device processes health information and emotional data entered by the user in real time and identifies the user's emotional state through an emotion engine. This process evaluates the user's mental state.

[0363] Step 3:

[0364] The device transmits health information and emotional data to the server using a secure protocol. The data is encrypted during this process to maintain confidentiality.

[0365] Step 4:

[0366] The server receives data sent from the terminal and stores it in the database. The stored data is managed on a per-user basis and used later for analysis.

[0367] Step 5:

[0368] The server uses AI algorithms to analyze stored health and emotional data to assess the user's physical and mental health. This assessment identifies personalized health risks and suggested improvements.

[0369] Step 6:

[0370] The server generates personalized health management advice based on the analysis results. This advice may include exercise and relaxation methods that take into account the user's emotional state.

[0371] Step 7:

[0372] The server sends the generated advice to the terminal, which then notifies the user. The notification is displayed in a visualized format, allowing the user to receive timely feedback.

[0373] Step 8:

[0374] Users review health management advice provided by their devices and adjust their daily lives accordingly. They aim to maintain and improve their health by incorporating specific actions that address their emotional state (e.g., meditation or taking walks).

[0375] Step 9:

[0376] The emotion data, regularly updated by the emotion engine, is continuously analyzed by the server for long-term health assessment and used to provide useful feedback to the user.

[0377] (Example 2)

[0378] 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".

[0379] In modern society, mental health is just as important as physical health when it comes to managing one's own health. However, current health management systems primarily provide assessments and advice based only on physical data, making it difficult to provide comprehensive health management that takes mental health into account. Furthermore, these systems leave the ability to respond quickly when an abnormality is detected to the user's discretion, which may result in inadequate and timely contact with medical institutions.

[0380] 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.

[0381] In this invention, the server includes means for receiving and storing biometric information and emotional state data in a storage device, information processing means for analyzing the stored data and performing a comprehensive health assessment, and means for generating personalized health management suggestions based on the analysis results. This enables comprehensive health management that takes into account both physical and mental health conditions.

[0382] "Biometric information" refers to data that indicates the user's physical health status, and includes weight, diet, exercise history, etc.

[0383] "Emotional state data" refers to data that indicates the user's mental health status, and includes emotional information obtained from the user's facial expressions and voice.

[0384] A "storage device" is an information storage device that includes a database for safely and efficiently storing collected data.

[0385] "Information processing means" refers to software and hardware components used to analyze stored data and comprehensively evaluate the user's health status.

[0386] "Health management suggestions" refer to personalized advice and guidelines for health improvement that are generated based on the user's health information and emotional state.

[0387] A "display device" refers to a monitor or screen used to provide information to a user visually.

[0388] "Information encryption" refers to a technology that transforms and protects digital data to ensure the security of information during transmission and reception.

[0389] This invention is a system that collects and analyzes a user's biometric information and emotional state data to generate personalized health management suggestions. Specific embodiments are described below.

[0390] The server uses a powerful database management system to receive and store data. For example, the database can be MySQL or MongoDB. AI algorithms are introduced to analyze the stored data, and machine learning libraries such as TensorFlow and PyTorch are utilized. These technologies support a comprehensive health assessment of biometric and emotional state data.

[0391] The terminal collects information from the user and transmits it to the server. The terminal incorporates sensors such as a camera and microphone, and uses OpenCV and other library technologies to perform facial expression analysis and voice data analysis. This allows for an efficient understanding of the user's emotional state.

[0392] Users can regularly input their weight and meals into the device. It's also easy to record their emotional state using the device's camera. The data collected by the user is sent to the server periodically or each time the user inputs data via a secure communication protocol.

[0393] A concrete example is a user weighing themselves in the morning, entering their meal details into a device, and recording their emotions at that time through facial expression analysis using the device's camera. Based on this data, the server comprehensively evaluates the user's health status and generates advice such as, "Try to increase your exercise this week," which is then sent to the device. The user can then decide to take part in a yoga class on the weekend based on this advice.

[0394] An example of a prompt generated by a generative AI model is the requirement, "Generate personalized health advice considering the user's health information and emotional state." Using this prompt, the AI ​​model generates appropriate health management suggestions.

[0395] Thus, this invention realizes a system that comprehensively manages the user's physical and mental health and has the effect of improving their quality of life.

[0396] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0397] Step 1:

[0398] The user inputs biometric information (weight, diet, etc.) and emotional state into the device. The input emotional state is analyzed in real time using the device's camera. The input data is processed as primary data indicating the user's health status.

[0399] Step 2:

[0400] The device transmits collected health information and emotional state data to the server. At this point, the data is securely transferred to the server using a secure protocol such as HTTPS. The input is the user's biometric and emotional data, and the output is the secure transmission of data to the server.

[0401] Step 3:

[0402] The server stores the received data in a storage device. The stored information is organized by user and systematized for later analysis. The input is health and emotional data from the terminal, and the output is data stored in the database for preliminary analysis.

[0403] Step 4:

[0404] The server analyzes the stored data using AI algorithms. Using TensorFlow and PyTorch, the data is input into a health assessment model, which then performs a comprehensive evaluation of the user's health status. The input is user data stored in the database, and the output is the health status evaluation result based on the analysis.

[0405] Step 5:

[0406] The server generates personalized health management suggestions based on the analysis results. In this step, a generative AI model is used to create specific advice, such as "Let's increase our exercise this week." The input is the result of the overall evaluation, and the output is the health management suggestions for the user.

[0407] Step 6:

[0408] The server sends the generated health management suggestions to the terminal. This process is also carried out using a secure protocol. The input is the generated suggestions, and the output is the suggestion notification to the terminal.

[0409] Step 7:

[0410] The terminal displays received health management suggestions to the user. The user visually reviews the provided advice and decides how to apply it to their daily life. The input is the health management suggestions from the server, and the output is the user's acceptance of the suggestions and decision on an action plan.

[0411] Through this series of processes, the system provides users with comprehensive health management.

[0412] (Application Example 2)

[0413] 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."

[0414] Health management often focuses heavily on the physical aspect, and mental health management is frequently neglected. Furthermore, many systems lack mechanisms to improve the accuracy of advice based on user feedback. Security issues also exist in the transmission and reception of health information. This invention aims to solve these problems and provide a system for comprehensively managing a user's physical and mental health.

[0415] 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.

[0416] In this invention, the server includes means for receiving health-related information input by the user and storing the information in a data collection device, calculation means for analyzing the stored health-related information and evaluating the user's health status, and means for generating personalized health management recommendations based on the health status. This enables comprehensive health management that takes into account the user's emotional state as well as their physical health.

[0417] "Health-related information" refers to data that indicates the user's physical health status, including information such as weight and heart rate.

[0418] A "data collection device" is a storage device used to store data collected from users.

[0419] A "computational device" is a device that has the function of analyzing and evaluating the user's health status based on collected data.

[0420] "Personalized health management recommendations" refer to advice that outlines management policies based on the user's individual health condition.

[0421] A "medical facility" is a medical professional institution that should be contacted when necessary based on the results of health monitoring.

[0422] "Display means" refers to a device for visually presenting information to a user, and includes displays and the like.

[0423] "Emotional state" refers to data that indicates the user's mental and emotional state.

[0424] A "natural language processing device" is a processing device that communicates with users using natural language.

[0425] "Lifestyle data" refers to information about a user's habits and behaviors related to their daily life.

[0426] "Data encryption" is a technology used to ensure the security of data being transmitted or received.

[0427] The system implementing this invention mainly consists of a user terminal, a data collection device, a server, and a home care robot in the application example. The user inputs health-related information via the user terminal, and this information is stored in the data collection device. The server has advanced computing means to analyze this health-related information and the user's emotional state. For example, it receives the user's health information (weight, heart rate, etc.) and facial expression data acquired by cameras mounted on the terminal or robot in real time to understand the emotional state.

[0428] Based on the received data, the server uses a generative AI model to comprehensively evaluate the user's health status and generate personalized health management recommendations. These recommendations include physical health management policies as well as mental health recommendations tailored to the user's emotional state. Communication with the user is conducted through display means using advanced natural language processing.

[0429] For example, if a user voice-inputs "I'm a little tired" in the morning, their emotional state is determined to be one of increased stress. Based on this information, the server displays a message on the home care robot or the user's device saying, "How about taking a walk today to relax?" This encourages the user to take actions appropriate to their condition.

[0430] An example of a prompt message is: "Based on a recent health assessment, generate stress-reducing advice. The emotional state has been detected as (sad). As an example, suggest relaxation methods that can be visualized to reduce stress." The advice generated through such prompt messages is designed not only to support the user's health but also to help improve their quality of life.

[0431] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0432] Step 1:

[0433] Users input health-related information and emotional states through the device. Specifically, users input their weight and heart rate, and capture their facial expressions with the device's camera. This input data is initially processed on the device and then sent to a data collection device. The input consists of health-related biometric data and facial images, while the output is data converted from these into an appropriate data format.

[0434] Step 2:

[0435] The terminal transmits collected health-related information and emotional states to the server. The server stores this data in a data aggregation device and accumulates it in a database for later analysis. The input is the data transmitted from the terminal, and the output is the data in the format stored in the database.

[0436] Step 3:

[0437] The server analyzes stored health-related information and emotional states. A generative AI model is used to evaluate the user's physical and emotional state. This analysis process involves data processing, including calculating the mean and deviation of numerical data, and extracting features from facial image data. The input is data read from a database, and the output is the evaluation result.

[0438] Step 4:

[0439] The server generates personalized health management recommendations based on the analysis results. These recommendations include consideration of the user's physical and emotional state. The generative AI model uses natural language generation in this step to create easily understandable advice. The input is the analysis results, and the output is advice expressed in natural language.

[0440] Step 5:

[0441] The server sends the generated health management recommendations to the terminal and home care robot. The terminal or robot presents this to the user visually or audibly. This display includes visual elements and audio messages. The input is the advice data from the server, and the output is the presentation to the user.

[0442] Step 6:

[0443] The user reviews the health management recommendations they receive and decides on an action based on them. For example, if they receive the advice, "Take a walk today to reduce stress," the user chooses to go for a walk. In this step, the input is the advice from the server, and the output is the user's action choice.

[0444] 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.

[0445] 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.

[0446] 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.

[0447] [Third Embodiment]

[0448] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0449] 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.

[0450] 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).

[0451] 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.

[0452] 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.

[0453] 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).

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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".

[0460] This invention relates to a system that collects and analyzes health information and lifestyle data from users and generates health management advice based on the results. This system is implemented using a server and terminals and aims to personalize advice according to each individual's health condition by utilizing the information provided by the user.

[0461] Server Processing

[0462] The server receives health information transmitted from the user via their device and securely stores it in a database. The stored data is analyzed using AI algorithms to assess the user's health status. This analysis utilizes statistical models and machine learning techniques to identify risks and areas for improvement related to the user.

[0463] Based on the analysis results, the server generates personalized health management advice for each user. This advice can include specific suggestions regarding diet, exercise, and sleep, tailored to the user's lifestyle. Furthermore, if any abnormalities are detected in the health data, the system automatically contacts registered medical institutions to encourage prompt action.

[0464] Terminal processing

[0465] The device provides an interface for users to input their health information. Users input daily data such as weight, exercise level, diet, and sleep duration, and the device sends this information to the server. When health management advice or notifications of abnormalities are sent from the server, the device displays this information to the user in real time.

[0466] User actions

[0467] Users input their daily health information through their device and review the advice generated by the analysis. For example, users who receive advice regarding their weight or exercise habits can use this information to plan their diet and exercise. If an abnormality is detected, they can check the notification displayed on their device and take action to contact a medical institution promptly if necessary.

[0468] Specific example

[0469] Specifically, suppose a user enters their weight into their device every morning, and the server receives and analyzes this data, detecting a rapid weight gain over the past few days. Based on this information, the server generates advice recommending "paying attention to your diet and reducing salt intake," and notifies the user of this advice. The user can then review their daily eating habits and take actions to maintain their health by following this advice.

[0470] This invention is expected to enable users to proactively manage their health at the appropriate time, significantly contributing to the reduction of health risks and the improvement of quality of life.

[0471] The following describes the processing flow.

[0472] Step 1:

[0473] Users input health information such as weight, exercise level, diet, and sleep duration into the device. This input is done via the app's interface or a wearable device.

[0474] Step 2:

[0475] The device encrypts the health information entered by the user and sends it to the server via a secure communication protocol.

[0476] Step 3:

[0477] The server receives health information sent from the terminal, organizes it for each user, and stores it in a database. The accuracy and integrity of the data are verified during storage.

[0478] Step 4:

[0479] The server analyzes the stored health information using AI algorithms. The analysis utilizes machine learning models to assess the user's health status and potential risks.

[0480] Step 5:

[0481] The server generates personalized health management advice based on the analysis results. This advice may include specific suggestions for diet and exercise.

[0482] Step 6:

[0483] The server sends the generated advice to the terminal. At the same time, if an anomaly is detected, it automatically contacts the registered medical institution.

[0484] Step 7:

[0485] The device displays health management advice and abnormality notifications sent from the server to the user. Notifications can be sent via push notifications or email.

[0486] Step 8:

[0487] Users can review the advice from their device and incorporate it into their lives as needed. They can also send feedback on the advice to the server via their device.

[0488] (Example 1)

[0489] 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."

[0490] In modern times, health management is increasingly important, but many people lack the means to effectively utilize their health information and accurately understand their health status. Conventional systems struggle to provide personalized health management advice to each user, and often fail to respond quickly enough when abnormalities are detected. There is a need for a system that can solve these problems and enable users to proactively manage their own health.

[0491] 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.

[0492] In this invention, the server includes means for receiving health information entered by the user and storing the information in a storage device, calculation means for analyzing the stored health information and evaluating the user's health status, and means for generating personalized health management advice based on the health status. As a result, the user can receive personalized, specific, and practical health management advice based on their health information and perform quick and appropriate health management.

[0493] "Health-related information" refers to data collected from users that indicates their health status, such as weight, exercise level, diet, and sleep duration.

[0494] A "storage device" is a computer system or storage system for securely storing and managing received data.

[0495] "Computational means" refers to machine learning models and algorithms used to analyze stored health information and evaluate the user's health status.

[0496] "Health management advice" refers to specific guidance on how to improve the user's health and mitigate risks, generated based on the analysis results.

[0497] The "automatic notification system" is a system that automatically notifies pre-registered medical institutions and other relevant parties in real time when an abnormality is detected.

[0498] "Means of using data encryption" refers to technologies or methods that encrypt and protect data in order to securely send and receive user health information over the internet.

[0499] "Machine learning techniques" are a type of artificial intelligence technology used to enable computers to learn patterns and relationships from data and output analytical results.

[0500] "Means of outputting using natural language processing" refers to computer technology that conveys generated health management advice in language that is easy for the user to understand.

[0501] A "generative AI model" refers to an artificial intelligence system that generates personalized content based on user input data, and is used in applications such as natural language generation.

[0502] This invention is a system for effectively managing users' health information. The system is built around a server and terminals, and has the function of analyzing health information obtained from users and providing personalized health management advice.

[0503] The device provides an interface for receiving health information that users input on a daily basis. Users can input data such as weight, exercise levels, diet, and sleep duration into the device. The device is responsible for organizing this information and securely transmitting it to the server. Data encryption technology is used to protect user privacy during data transmission.

[0504] The server receives data transmitted from the terminal and securely stores it in a storage device. The stored data is analyzed using AI algorithms with programming languages ​​such as Python and R. Machine learning techniques are employed to evaluate the user's health status from the data and identify risks and areas for improvement. Based on this analysis, the server generates specific health management advice for the user. In this process, generative AI models and natural language processing technologies are used to provide the user with easy-to-understand and actionable advice.

[0505] As a concrete example, suppose a user enters their weight into their device every morning, and the server receives this data and detects a rapid weight gain through analysis. In this case, the server generates advice such as "reducing salt intake is recommended" and notifies the user's device in real time. In this way, the user can incorporate the advice into their daily life and use it to maintain or improve their health.

[0506] Examples of prompt messages include, "Please tell me how to input user health data and generate health management advice," and "Please show me an example of advice generated when rapid weight gain is detected."

[0507] This system enables users to efficiently utilize their own health information and receive specific advice that helps improve their health, thereby realizing truly personalized health management.

[0508] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0509] Step 1:

[0510] The device provides an interface for receiving health-related information from the user as input. Specifically, the user manually enters data such as weight, exercise level, diet, and sleep duration into input fields. The entered data is then organized in digital format. At this stage, the output is that the user's health information is stored as digital data on the device.

[0511] Step 2:

[0512] The device performs the process of sending the user's health information to the server. To ensure data security, this operation is encrypted. Specifically, the device sends encrypted health data as data packets to the server via Wi-Fi or a mobile network. The output is securely encrypted health information reaching the server.

[0513] Step 3:

[0514] The server receives health-related information from terminals as input. The received data is stored in a storage device and prepared for analysis. Specifically, a database management system is used to systematically organize the data and build the foundation for subsequent analysis processes. The output of this stage is structured health data stored in the database.

[0515] Step 4:

[0516] The server analyzes stored health data. It receives data stored in a storage device as input and outputs a health status assessment for each user. Specifically, machine learning algorithms using Python or R are executed to identify user health risks and areas for improvement. The data calculations performed here include statistical analysis, pattern recognition, and the execution of predictive models.

[0517] Step 5:

[0518] The server generates health management advice based on the analysis results. The input is the evaluation results of the user's health status analyzed by the server. As output, personalized health management advice is generated. Specifically, the generating AI model uses natural language processing to construct health management advice in a format that is easy for the user to understand. The advice generated through this process includes specific improvement measures regarding diet, exercise, and lifestyle habits.

[0519] Step 6:

[0520] Health management advice generated from the server is sent to the user's device. Encryption is used in this transmission process to enhance data security. As an output, the advice is notified to the user's device in real time. Specifically, the advice is delivered to the user via application notifications or email, and the user can check it and work on improving their daily life.

[0521] (Application Example 1)

[0522] 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."

[0523] There is a need to build a system that enables effective health management and rapid response by providing appropriate health management guidance based on individual health information in real time to the elderly and people requiring care, and by promptly notifying caregivers, family members, and medical facilities when abnormalities occur. However, conventional systems have problems such as difficulty in providing individualized advice and making rapid contact in emergencies, as well as difficulty in handling information securely.

[0524] 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.

[0525] In this invention, the server includes means for receiving biometric information input by a user and storing it in a storage medium, processing means for analyzing the stored biometric information and evaluating the user's health status, and means for generating personalized health management guidance based on the user's health status. This enables the provision of personalized health management guidance based on individual biometric information to the user in real time, and prompt notification to caregivers, family members, and medical facilities if an abnormality is detected.

[0526] "Biometric information" refers to specific numerical values ​​and indicators that show the health status of the person who has been deactivated, such as the user's weight, blood pressure, and body temperature.

[0527] A "storage medium" refers to a device or means for securely storing received digital information, and includes databases.

[0528] "Processing means" refers to a function that includes a digital processor for analyzing stored digital information and performing necessary calculations and decisions.

[0529] "Health management guidance" refers to specific advice and plans for maintaining or improving health, tailored to each individual user, based on analysis results.

[0530] A "care support worker" is a person who directly assists a person in their daily life through voice and actions to help them with their daily activities.

[0531] A "medical facility" is a facility that provides medical services, such as a hospital or clinic, and is equipped to support caregivers and family members when an abnormality is detected.

[0532] "Data concealment technology" refers to a technical method of encrypting digital information to protect it from misuse and unauthorized access.

[0533] The system for realizing this invention includes a terminal for users to input daily health information, a server for securely storing and analyzing the input data, and a communication network for providing health management guidance based on the analysis results.

[0534] The server receives biometric information transmitted by the user and stores it in storage. User privacy is protected by encrypting the information using data concealment technology. For example, standard encryption techniques such as AES (Advanced Encryption Standard) are often used for data encryption. The stored information is analyzed using AI algorithms to evaluate the user's health status. Specifically, machine learning models using TensorFlow or PyTorch are used. This model extracts risks and areas for improvement, and generates personalized health management guidance.

[0535] The devices used by users are mobile devices such as smartphones and smart glasses, and they provide an interface that makes it easy for users to input their daily health information. In addition, health management guidance and notifications about abnormalities generated through these devices are provided to the user in real time. For example, if a rapid weight gain is detected, the user will be notified with recommendations to review their diet and reduce their salt intake.

[0536] Based on the information and behavior registered by the user, the analysis results are notified to caregivers and family members as needed. Furthermore, if a serious abnormality is detected, the server automatically notifies medical facilities. This allows users to detect abnormalities early and take appropriate medical action quickly.

[0537] Examples of prompts for a generative AI model include the following:

[0538] "Detect abnormal patterns from the user's health data over the past week and generate personalized health advice. If abnormalities are detected, suggest a low-sodium diet and exercise to reduce stress."

[0539] Thus, the present invention is an important means of supporting personalized health management in real time by utilizing users' health information, and of providing a safer and more comfortable living environment for the elderly and people who require care.

[0540] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0541] Step 1:

[0542] The user inputs biometric information into the device. The user inputs daily health information such as weight, blood pressure, and body temperature through the interface of a smartphone or smart glasses. This input data is temporarily stored within the device and prepared for transmission to the server.

[0543] Step 2:

[0544] The device sends biometric information to the server. The device encrypts the collected biometric information using data concealment technology and securely transmits it to the server. At this stage, the input is the user's biometric information, and the output is encrypted data.

[0545] Step 3:

[0546] The server receives data and stores it in storage. The server receives encrypted data and stores it in secure storage. This storage makes the data available for subsequent analysis. The input is encrypted biometric information, and the output is biometric information in a database.

[0547] Step 4:

[0548] The server analyzes biometric information and assesses the user's health status. The server inputs the stored data into an AI algorithm to perform analysis to evaluate the user's health. This analysis involves comparing the data with past data and performing pattern recognition to identify risks and areas for improvement. The input is stored biometric information, and the output is the analysis results.

[0549] Step 5:

[0550] The server generates personalized health management guidance. Based on the analysis results, the server generates health management guidance tailored to each individual user. This guidance includes specific suggestions regarding diet, exercise, and sleep. The input is the analysis results, and the output is the health management guidance.

[0551] Step 6:

[0552] The server generates health management guidance and sends it to the terminal. The server sends the generated guidance to the terminal using data encryption technology. On the terminal, the guidance is decrypted and displayed to the user. The input is health management guidance, and the output is the guidance content displayed on the terminal.

[0553] Step 7:

[0554] The user receives the provided health management guidance. The user reviews the guidance displayed on the device and adjusts their daily health management based on the guidance. The user may also provide feedback or enter additional data. The input is the guidance content displayed on the device, and the output is the user's behavioral changes and feedback.

[0555] Step 8:

[0556] The server receives feedback from users and analyzes it for generating future lessons. The server receives feedback and additional data provided by users and incorporates the feedback information into the AI ​​model. This process aims to improve the quality of future lessons. The input is user feedback, and the output is an updated model and training data for generating future lessons.

[0557] 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.

[0558] This invention is a health management system that takes into account not only the user's health information but also their emotional state. This system comprises a server, terminals, and an emotion engine, and aims to provide highly personalized health management advice by analyzing multifaceted data from the user.

[0559] Server Processing

[0560] The server receives health information and emotional data transmitted from the terminal and stores it in a database. The stored data is analyzed using an AI algorithm to perform a comprehensive health assessment that includes not only physical health but also emotional state. Based on this comprehensive assessment, advice is generated that takes the user's mental health into consideration.

[0561] The server generates customized health management advice based on health information and emotional data. This advice can include coping strategies and encouraging messages tailored to the user's emotional situation. Furthermore, if an abnormality is detected, the server automatically contacts a medical institution to prompt the user for immediate action.

[0562] Terminal processing

[0563] The device provides an interface for capturing health information and user emotions. Users can register emotional data through voice input and facial expression analysis, along with their daily health data. The emotion engine processes this data and visualizes the user's current mental state.

[0564] The device visually displays advice and anomaly notifications sent from the server to the user. This allows the user to comprehensively understand their health status and take necessary measures.

[0565] User actions

[0566] Users input health information and emotional states through their devices to check their overall health status. Users who receive health management advice can then decide how to apply it to their daily lives. For example, if they are assessed as experiencing high levels of emotional stress, they might be advised to try breathing exercises to relax.

[0567] Specific example

[0568] Specifically, suppose a user weighs themselves in the morning, enters their food intake into the device, and simultaneously uses the device's camera to check their emotional state. The emotion engine analyzes the user's facial expressions, and if it detects signs of stress, the server generates advice such as, "Try to increase your exercise this week," and notifies the user of this message on the device. The user then follows this advice and decides to attend a yoga class on the weekend.

[0569] Thus, the present invention enables users to perform comprehensive health management that takes into account not only physical care but also mental health, and is expected to help them maintain a high quality of life (QOL).

[0570] The following describes the processing flow.

[0571] Step 1:

[0572] Users input health information (weight, exercise level, diet, etc.) along with emotional data through voice and facial expressions into the device. This information is easily collected using the device's app.

[0573] Step 2:

[0574] The device processes health information and emotional data entered by the user in real time and identifies the user's emotional state through an emotion engine. This process evaluates the user's mental state.

[0575] Step 3:

[0576] The device transmits health information and emotional data to the server using a secure protocol. The data is encrypted during this process to maintain confidentiality.

[0577] Step 4:

[0578] The server receives data sent from the terminal and stores it in the database. The stored data is managed on a per-user basis and used later for analysis.

[0579] Step 5:

[0580] The server uses AI algorithms to analyze stored health and emotional data to assess the user's physical and mental health. This assessment identifies personalized health risks and suggested improvements.

[0581] Step 6:

[0582] The server generates personalized health management advice based on the analysis results. This advice may include exercise and relaxation methods that take into account the user's emotional state.

[0583] Step 7:

[0584] The server sends the generated advice to the terminal, which then notifies the user. The notification is displayed in a visualized format, allowing the user to receive timely feedback.

[0585] Step 8:

[0586] Users review health management advice provided by their devices and adjust their daily lives accordingly. They aim to maintain and improve their health by incorporating specific actions that address their emotional state (e.g., meditation or taking walks).

[0587] Step 9:

[0588] The emotion data, regularly updated by the emotion engine, is continuously analyzed by the server for long-term health assessment and used to provide useful feedback to the user.

[0589] (Example 2)

[0590] 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."

[0591] In modern society, mental health is just as important as physical health when it comes to managing one's own health. However, current health management systems primarily provide assessments and advice based only on physical data, making it difficult to provide comprehensive health management that takes mental health into account. Furthermore, these systems leave the ability to respond quickly when an abnormality is detected to the user's discretion, which may result in inadequate and timely contact with medical institutions.

[0592] 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.

[0593] In this invention, the server includes means for receiving and storing biometric information and emotional state data in a storage device, information processing means for analyzing the stored data and performing a comprehensive health assessment, and means for generating personalized health management suggestions based on the analysis results. This enables comprehensive health management that takes into account both physical and mental health conditions.

[0594] "Biometric information" refers to data that indicates the user's physical health status, and includes weight, diet, exercise history, etc.

[0595] "Emotional state data" refers to data that indicates the user's mental health status, and includes emotional information obtained from the user's facial expressions and voice.

[0596] A "storage device" is an information storage device that includes a database for safely and efficiently storing collected data.

[0597] "Information processing means" refers to software and hardware components used to analyze stored data and comprehensively evaluate the user's health status.

[0598] "Health management suggestions" refer to personalized advice and guidelines for health improvement that are generated based on the user's health information and emotional state.

[0599] A "display device" refers to a monitor or screen used to provide information to a user visually.

[0600] "Information encryption" refers to a technology that transforms and protects digital data to ensure the security of information during transmission and reception.

[0601] This invention is a system that collects and analyzes a user's biometric information and emotional state data to generate personalized health management suggestions. Specific embodiments are described below.

[0602] The server uses a powerful database management system to receive and store data. For example, the database can be MySQL or MongoDB. AI algorithms are introduced to analyze the stored data, and machine learning libraries such as TensorFlow and PyTorch are utilized. These technologies support a comprehensive health assessment of biometric and emotional state data.

[0603] The terminal collects information from the user and transmits it to the server. The terminal incorporates sensors such as a camera and microphone, and uses OpenCV and other library technologies to perform facial expression analysis and voice data analysis. This allows for an efficient understanding of the user's emotional state.

[0604] Users can regularly input their weight and meals into the device. It's also easy to record their emotional state using the device's camera. The data collected by the user is sent to the server periodically or each time the user inputs data via a secure communication protocol.

[0605] A concrete example is a user weighing themselves in the morning, entering their meal details into a device, and recording their emotions at that time through facial expression analysis using the device's camera. Based on this data, the server comprehensively evaluates the user's health status and generates advice such as, "Try to increase your exercise this week," which is then sent to the device. The user can then decide to take part in a yoga class on the weekend based on this advice.

[0606] An example of a prompt generated by a generative AI model is the requirement, "Generate personalized health advice considering the user's health information and emotional state." Using this prompt, the AI ​​model generates appropriate health management suggestions.

[0607] Thus, this invention realizes a system that comprehensively manages the user's physical and mental health and has the effect of improving their quality of life.

[0608] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0609] Step 1:

[0610] The user inputs biometric information (weight, diet, etc.) and emotional state into the device. The input emotional state is analyzed in real time using the device's camera. The input data is processed as primary data indicating the user's health status.

[0611] Step 2:

[0612] The device transmits collected health information and emotional state data to the server. At this point, the data is securely transferred to the server using a secure protocol such as HTTPS. The input is the user's biometric and emotional data, and the output is the secure transmission of data to the server.

[0613] Step 3:

[0614] The server stores the received data in a storage device. The stored information is organized by user and systematized for later analysis. The input is health and emotional data from the terminal, and the output is data stored in the database for preliminary analysis.

[0615] Step 4:

[0616] The server analyzes the stored data using AI algorithms. Using TensorFlow and PyTorch, the data is input into a health assessment model, which then performs a comprehensive evaluation of the user's health status. The input is user data stored in the database, and the output is the health status evaluation result based on the analysis.

[0617] Step 5:

[0618] The server generates personalized health management suggestions based on the analysis results. In this step, a generative AI model is used to create specific advice, such as "Let's increase our exercise this week." The input is the result of the overall evaluation, and the output is the health management suggestions for the user.

[0619] Step 6:

[0620] The server sends the generated health management suggestions to the terminal. This process is also carried out using a secure protocol. The input is the generated suggestions, and the output is the suggestion notification to the terminal.

[0621] Step 7:

[0622] The terminal displays received health management suggestions to the user. The user visually reviews the provided advice and decides how to apply it to their daily life. The input is the health management suggestions from the server, and the output is the user's acceptance of the suggestions and decision on an action plan.

[0623] Through this series of processes, the system provides users with comprehensive health management.

[0624] (Application Example 2)

[0625] 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."

[0626] Health management often focuses heavily on the physical aspect, and mental health management is frequently neglected. Furthermore, many systems lack mechanisms to improve the accuracy of advice based on user feedback. Security issues also exist in the transmission and reception of health information. This invention aims to solve these problems and provide a system for comprehensively managing a user's physical and mental health.

[0627] 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.

[0628] In this invention, the server includes means for receiving health-related information input by the user and storing the information in a data collection device, calculation means for analyzing the stored health-related information and evaluating the user's health status, and means for generating personalized health management recommendations based on the health status. This enables comprehensive health management that takes into account the user's emotional state as well as their physical health.

[0629] "Health-related information" refers to data that indicates the user's physical health status, including information such as weight and heart rate.

[0630] A "data collection device" is a storage device used to store data collected from users.

[0631] A "computational device" is a device that has the function of analyzing and evaluating the user's health status based on collected data.

[0632] "Personalized health management recommendations" refer to advice that outlines management policies based on the user's individual health condition.

[0633] A "medical facility" is a medical professional institution that should be contacted when necessary based on the results of health monitoring.

[0634] "Display means" refers to a device for visually presenting information to a user, and includes displays and the like.

[0635] "Emotional state" refers to data that indicates the user's mental and emotional state.

[0636] A "natural language processing device" is a processing device that communicates with users using natural language.

[0637] "Lifestyle data" refers to information about a user's habits and behaviors related to their daily life.

[0638] "Data encryption" is a technology used to ensure the security of data being transmitted or received.

[0639] The system implementing this invention mainly consists of a user terminal, a data collection device, a server, and a home care robot in the application example. The user inputs health-related information via the user terminal, and this information is stored in the data collection device. The server has advanced computing means to analyze this health-related information and the user's emotional state. For example, it receives the user's health information (weight, heart rate, etc.) and facial expression data acquired by cameras mounted on the terminal or robot in real time to understand the emotional state.

[0640] Based on the received data, the server uses a generative AI model to comprehensively evaluate the user's health status and generate personalized health management recommendations. These recommendations include physical health management policies as well as mental health recommendations tailored to the user's emotional state. Communication with the user is conducted through display means using advanced natural language processing.

[0641] For example, if a user voice-inputs "I'm a little tired" in the morning, their emotional state is determined to be one of increased stress. Based on this information, the server displays a message on the home care robot or the user's device saying, "How about taking a walk today to relax?" This encourages the user to take actions appropriate to their condition.

[0642] An example of a prompt message is: "Based on a recent health assessment, generate stress-reducing advice. The emotional state has been detected as (sad). As an example, suggest relaxation methods that can be visualized to reduce stress." The advice generated through such prompt messages is designed not only to support the user's health but also to help improve their quality of life.

[0643] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0644] Step 1:

[0645] Users input health-related information and emotional states through the device. Specifically, users input their weight and heart rate, and capture their facial expressions with the device's camera. This input data is initially processed on the device and then sent to a data collection device. The input consists of health-related biometric data and facial images, while the output is data converted from these into an appropriate data format.

[0646] Step 2:

[0647] The terminal transmits collected health-related information and emotional states to the server. The server stores this data in a data aggregation device and accumulates it in a database for later analysis. The input is the data transmitted from the terminal, and the output is the data in the format stored in the database.

[0648] Step 3:

[0649] The server analyzes stored health-related information and emotional states. A generative AI model is used to evaluate the user's physical and emotional state. This analysis process involves data processing, including calculating the mean and deviation of numerical data, and extracting features from facial image data. The input is data read from a database, and the output is the evaluation result.

[0650] Step 4:

[0651] The server generates personalized health management recommendations based on the analysis results. These recommendations include consideration of the user's physical and emotional state. The generative AI model uses natural language generation in this step to create easily understandable advice. The input is the analysis results, and the output is advice expressed in natural language.

[0652] Step 5:

[0653] The server sends the generated health management recommendations to the terminal and home care robot. The terminal or robot presents this to the user visually or audibly. This display includes visual elements and audio messages. The input is the advice data from the server, and the output is the presentation to the user.

[0654] Step 6:

[0655] The user reviews the health management recommendations they receive and decides on an action based on them. For example, if they receive the advice, "Take a walk today to reduce stress," the user chooses to go for a walk. In this step, the input is the advice from the server, and the output is the user's action choice.

[0656] 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.

[0657] 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.

[0658] 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.

[0659] [Fourth Embodiment]

[0660] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0661] 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.

[0662] 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).

[0663] 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.

[0664] 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.

[0665] 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).

[0666] 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.

[0667] 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.

[0668] 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.

[0669] 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.

[0670] 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.

[0671] 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.

[0672] 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".

[0673] This invention relates to a system that collects and analyzes health information and lifestyle data from users and generates health management advice based on the results. This system is implemented using a server and terminals and aims to personalize advice according to each individual's health condition by utilizing the information provided by the user.

[0674] Server Processing

[0675] The server receives health information transmitted from the user via their device and securely stores it in a database. The stored data is analyzed using AI algorithms to assess the user's health status. This analysis utilizes statistical models and machine learning techniques to identify risks and areas for improvement related to the user.

[0676] Based on the analysis results, the server generates personalized health management advice for each user. This advice can include specific suggestions regarding diet, exercise, and sleep, tailored to the user's lifestyle. Furthermore, if any abnormalities are detected in the health data, the system automatically contacts registered medical institutions to encourage prompt action.

[0677] Terminal processing

[0678] The device provides an interface for users to input their health information. Users input daily data such as weight, exercise level, diet, and sleep duration, and the device sends this information to the server. When health management advice or notifications of abnormalities are sent from the server, the device displays this information to the user in real time.

[0679] User actions

[0680] Users input their daily health information through their device and review the advice generated by the analysis. For example, users who receive advice regarding their weight or exercise habits can use this information to plan their diet and exercise. If an abnormality is detected, they can check the notification displayed on their device and take action to contact a medical institution promptly if necessary.

[0681] Specific example

[0682] Specifically, suppose a user enters their weight into their device every morning, and the server receives and analyzes this data, detecting a rapid weight gain over the past few days. Based on this information, the server generates advice recommending "paying attention to your diet and reducing salt intake," and notifies the user of this advice. The user can then review their daily eating habits and take actions to maintain their health by following this advice.

[0683] This invention is expected to enable users to proactively manage their health at the appropriate time, significantly contributing to the reduction of health risks and the improvement of quality of life.

[0684] The following describes the processing flow.

[0685] Step 1:

[0686] Users input health information such as weight, exercise level, diet, and sleep duration into the device. This input is done via the app's interface or a wearable device.

[0687] Step 2:

[0688] The device encrypts the health information entered by the user and sends it to the server via a secure communication protocol.

[0689] Step 3:

[0690] The server receives health information sent from the terminal, organizes it for each user, and stores it in a database. The accuracy and integrity of the data are verified during storage.

[0691] Step 4:

[0692] The server analyzes the stored health information using AI algorithms. The analysis utilizes machine learning models to assess the user's health status and potential risks.

[0693] Step 5:

[0694] The server generates personalized health management advice based on the analysis results. This advice may include specific suggestions for diet and exercise.

[0695] Step 6:

[0696] The server sends the generated advice to the terminal. At the same time, if an anomaly is detected, it automatically contacts the registered medical institution.

[0697] Step 7:

[0698] The device displays health management advice and abnormality notifications sent from the server to the user. Notifications can be sent via push notifications or email.

[0699] Step 8:

[0700] Users can review the advice from their device and incorporate it into their lives as needed. They can also send feedback on the advice to the server via their device.

[0701] (Example 1)

[0702] 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".

[0703] In modern times, health management is increasingly important, but many people lack the means to effectively utilize their health information and accurately understand their health status. Conventional systems struggle to provide personalized health management advice to each user, and often fail to respond quickly enough when abnormalities are detected. There is a need for a system that can solve these problems and enable users to proactively manage their own health.

[0704] 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.

[0705] In this invention, the server includes means for receiving health information entered by the user and storing the information in a storage device, calculation means for analyzing the stored health information and evaluating the user's health status, and means for generating personalized health management advice based on the health status. As a result, the user can receive personalized, specific, and practical health management advice based on their health information and perform quick and appropriate health management.

[0706] "Health-related information" refers to data collected from users that indicates their health status, such as weight, exercise level, diet, and sleep duration.

[0707] A "storage device" is a computer system or storage system for securely storing and managing received data.

[0708] "Computational means" refers to machine learning models and algorithms used to analyze stored health information and evaluate the user's health status.

[0709] "Health management advice" refers to specific guidance on how to improve the user's health and mitigate risks, generated based on the analysis results.

[0710] The "automatic notification system" is a system that automatically notifies pre-registered medical institutions and other relevant parties in real time when an abnormality is detected.

[0711] "Means of using data encryption" refers to technologies or methods that encrypt and protect data in order to securely send and receive user health information over the internet.

[0712] "Machine learning techniques" are a type of artificial intelligence technology used to enable computers to learn patterns and relationships from data and output analytical results.

[0713] "Means of outputting using natural language processing" refers to computer technology that conveys generated health management advice in language that is easy for the user to understand.

[0714] A "generative AI model" refers to an artificial intelligence system that generates personalized content based on user input data, and is used in applications such as natural language generation.

[0715] This invention is a system for effectively managing users' health information. The system is built around a server and terminals, and has the function of analyzing health information obtained from users and providing personalized health management advice.

[0716] The device provides an interface for receiving health information that users input on a daily basis. Users can input data such as weight, exercise levels, diet, and sleep duration into the device. The device is responsible for organizing this information and securely transmitting it to the server. Data encryption technology is used to protect user privacy during data transmission.

[0717] The server receives data transmitted from the terminal and securely stores it in a storage device. The stored data is analyzed using AI algorithms with programming languages ​​such as Python and R. Machine learning techniques are employed to evaluate the user's health status from the data and identify risks and areas for improvement. Based on this analysis, the server generates specific health management advice for the user. In this process, generative AI models and natural language processing technologies are used to provide the user with easy-to-understand and actionable advice.

[0718] As a concrete example, suppose a user enters their weight into their device every morning, and the server receives this data and detects a rapid weight gain through analysis. In this case, the server generates advice such as "reducing salt intake is recommended" and notifies the user's device in real time. In this way, the user can incorporate the advice into their daily life and use it to maintain or improve their health.

[0719] Examples of prompt messages include, "Please tell me how to input user health data and generate health management advice," and "Please show me an example of advice generated when rapid weight gain is detected."

[0720] This system enables users to efficiently utilize their own health information and receive specific advice that helps improve their health, thereby realizing truly personalized health management.

[0721] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0722] Step 1:

[0723] The device provides an interface for receiving health-related information from the user as input. Specifically, the user manually enters data such as weight, exercise level, diet, and sleep duration into input fields. The entered data is then organized in digital format. At this stage, the output is that the user's health information is stored as digital data on the device.

[0724] Step 2:

[0725] The device performs the process of sending the user's health information to the server. To ensure data security, this operation is encrypted. Specifically, the device sends encrypted health data as data packets to the server via Wi-Fi or a mobile network. The output is securely encrypted health information reaching the server.

[0726] Step 3:

[0727] The server receives health-related information from terminals as input. The received data is stored in a storage device and prepared for analysis. Specifically, a database management system is used to systematically organize the data and build the foundation for subsequent analysis processes. The output of this stage is structured health data stored in the database.

[0728] Step 4:

[0729] The server analyzes stored health data. It receives data stored in a storage device as input and outputs a health status assessment for each user. Specifically, machine learning algorithms using Python or R are executed to identify user health risks and areas for improvement. The data calculations performed here include statistical analysis, pattern recognition, and the execution of predictive models.

[0730] Step 5:

[0731] The server generates health management advice based on the analysis results. The input is the evaluation results of the user's health status analyzed by the server. As output, personalized health management advice is generated. Specifically, the generating AI model uses natural language processing to construct health management advice in a format that is easy for the user to understand. The advice generated through this process includes specific improvement measures regarding diet, exercise, and lifestyle habits.

[0732] Step 6:

[0733] Health management advice generated from the server is sent to the user's device. Encryption is used in this transmission process to enhance data security. As an output, the advice is notified to the user's device in real time. Specifically, the advice is delivered to the user via application notifications or email, and the user can check it and work on improving their daily life.

[0734] (Application Example 1)

[0735] 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".

[0736] There is a need to build a system that enables effective health management and rapid response by providing appropriate health management guidance based on individual health information in real time to the elderly and people requiring care, and by promptly notifying caregivers, family members, and medical facilities when abnormalities occur. However, conventional systems have problems such as difficulty in providing individualized advice and making rapid contact in emergencies, as well as difficulty in handling information securely.

[0737] 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.

[0738] In this invention, the server includes means for receiving biometric information input by a user and storing it in a storage medium, processing means for analyzing the stored biometric information and evaluating the user's health status, and means for generating personalized health management guidance based on the user's health status. This enables the provision of personalized health management guidance based on individual biometric information to the user in real time, and prompt notification to caregivers, family members, and medical facilities if an abnormality is detected.

[0739] "Biometric information" refers to specific numerical values ​​and indicators that show the health status of the person who has been deactivated, such as the user's weight, blood pressure, and body temperature.

[0740] A "storage medium" refers to a device or means for securely storing received digital information, and includes databases.

[0741] "Processing means" refers to a function that includes a digital processor for analyzing stored digital information and performing necessary calculations and decisions.

[0742] "Health management guidance" refers to specific advice and plans for maintaining or improving health, tailored to each individual user, based on analysis results.

[0743] A "care support worker" is a person who directly assists a person in their daily life through voice and actions to help them with their daily activities.

[0744] A "medical facility" is a facility that provides medical services, such as a hospital or clinic, and is equipped to support caregivers and family members when an abnormality is detected.

[0745] "Data concealment technology" refers to a technical method of encrypting digital information to protect it from misuse and unauthorized access.

[0746] The system for realizing this invention includes a terminal for users to input daily health information, a server for securely storing and analyzing the input data, and a communication network for providing health management guidance based on the analysis results.

[0747] The server receives biometric information transmitted by the user and stores it in storage. User privacy is protected by encrypting the information using data concealment technology. For example, standard encryption techniques such as AES (Advanced Encryption Standard) are often used for data encryption. The stored information is analyzed using AI algorithms to evaluate the user's health status. Specifically, machine learning models using TensorFlow or PyTorch are used. This model extracts risks and areas for improvement, and generates personalized health management guidance.

[0748] The devices used by users are mobile devices such as smartphones and smart glasses, and they provide an interface that makes it easy for users to input their daily health information. In addition, health management guidance and notifications about abnormalities generated through these devices are provided to the user in real time. For example, if a rapid weight gain is detected, the user will be notified with recommendations to review their diet and reduce their salt intake.

[0749] Based on the information and behavior registered by the user, the analysis results are notified to caregivers and family members as needed. Furthermore, if a serious abnormality is detected, the server automatically notifies medical facilities. This allows users to detect abnormalities early and take appropriate medical action quickly.

[0750] Examples of prompts for a generative AI model include the following:

[0751] "Detect abnormal patterns from the user's health data over the past week and generate personalized health advice. If abnormalities are detected, suggest a low-sodium diet and exercise to reduce stress."

[0752] Thus, the present invention is an important means of supporting personalized health management in real time by utilizing users' health information, and of providing a safer and more comfortable living environment for the elderly and people who require care.

[0753] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0754] Step 1:

[0755] The user inputs biometric information into the device. The user inputs daily health information such as weight, blood pressure, and body temperature through the interface of a smartphone or smart glasses. This input data is temporarily stored within the device and prepared for transmission to the server.

[0756] Step 2:

[0757] The device sends biometric information to the server. The device encrypts the collected biometric information using data concealment technology and securely transmits it to the server. At this stage, the input is the user's biometric information, and the output is encrypted data.

[0758] Step 3:

[0759] The server receives data and stores it in storage. The server receives encrypted data and stores it in secure storage. This storage makes the data available for subsequent analysis. The input is encrypted biometric information, and the output is biometric information in a database.

[0760] Step 4:

[0761] The server analyzes biometric information and assesses the user's health status. The server inputs the stored data into an AI algorithm to perform analysis to evaluate the user's health. This analysis involves comparing the data with past data and performing pattern recognition to identify risks and areas for improvement. The input is stored biometric information, and the output is the analysis results.

[0762] Step 5:

[0763] The server generates personalized health management guidance. Based on the analysis results, the server generates health management guidance tailored to each individual user. This guidance includes specific suggestions regarding diet, exercise, and sleep. The input is the analysis results, and the output is the health management guidance.

[0764] Step 6:

[0765] The server generates health management guidance and sends it to the terminal. The server sends the generated guidance to the terminal using data encryption technology. On the terminal, the guidance is decrypted and displayed to the user. The input is health management guidance, and the output is the guidance content displayed on the terminal.

[0766] Step 7:

[0767] The user receives the provided health management guidance. The user reviews the guidance displayed on the device and adjusts their daily health management based on the guidance. The user may also provide feedback or enter additional data. The input is the guidance content displayed on the device, and the output is the user's behavioral changes and feedback.

[0768] Step 8:

[0769] The server receives feedback from users and analyzes it for generating future lessons. The server receives feedback and additional data provided by users and incorporates the feedback information into the AI ​​model. This process aims to improve the quality of future lessons. The input is user feedback, and the output is an updated model and training data for generating future lessons.

[0770] 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.

[0771] This invention is a health management system that takes into account not only the user's health information but also their emotional state. This system comprises a server, terminals, and an emotion engine, and aims to provide highly personalized health management advice by analyzing multifaceted data from the user.

[0772] Server Processing

[0773] The server receives health information and emotional data transmitted from the terminal and stores it in a database. The stored data is analyzed using an AI algorithm to perform a comprehensive health assessment that includes not only physical health but also emotional state. Based on this comprehensive assessment, advice is generated that takes the user's mental health into consideration.

[0774] The server generates customized health management advice based on health information and emotional data. This advice can include coping strategies and encouraging messages tailored to the user's emotional situation. Furthermore, if an abnormality is detected, the server automatically contacts a medical institution to prompt the user for immediate action.

[0775] Terminal processing

[0776] The device provides an interface for capturing health information and user emotions. Users can register emotional data through voice input and facial expression analysis, along with their daily health data. The emotion engine processes this data and visualizes the user's current mental state.

[0777] The device visually displays advice and anomaly notifications sent from the server to the user. This allows the user to comprehensively understand their health status and take necessary measures.

[0778] User actions

[0779] Users input health information and emotional states through their devices to check their overall health status. Users who receive health management advice can then decide how to apply it to their daily lives. For example, if they are assessed as experiencing high levels of emotional stress, they might be advised to try breathing exercises to relax.

[0780] Specific example

[0781] Specifically, suppose a user weighs themselves in the morning, enters their food intake into the device, and simultaneously uses the device's camera to check their emotional state. The emotion engine analyzes the user's facial expressions, and if it detects signs of stress, the server generates advice such as, "Try to increase your exercise this week," and notifies the user of this message on the device. The user then follows this advice and decides to attend a yoga class on the weekend.

[0782] Thus, the present invention enables users to perform comprehensive health management that takes into account not only physical care but also mental health, and is expected to help them maintain a high quality of life (QOL).

[0783] The following describes the processing flow.

[0784] Step 1:

[0785] Users input health information (weight, exercise level, diet, etc.) along with emotional data through voice and facial expressions into the device. This information is easily collected using the device's app.

[0786] Step 2:

[0787] The device processes health information and emotional data entered by the user in real time and identifies the user's emotional state through an emotion engine. This process evaluates the user's mental state.

[0788] Step 3:

[0789] The device transmits health information and emotional data to the server using a secure protocol. The data is encrypted during this process to maintain confidentiality.

[0790] Step 4:

[0791] The server receives data sent from the terminal and stores it in the database. The stored data is managed on a per-user basis and used later for analysis.

[0792] Step 5:

[0793] The server uses AI algorithms to analyze stored health and emotional data to assess the user's physical and mental health. This assessment identifies personalized health risks and suggested improvements.

[0794] Step 6:

[0795] The server generates personalized health management advice based on the analysis results. This advice may include exercise and relaxation methods that take into account the user's emotional state.

[0796] Step 7:

[0797] The server sends the generated advice to the terminal, which then notifies the user. The notification is displayed in a visualized format, allowing the user to receive timely feedback.

[0798] Step 8:

[0799] Users review health management advice provided by their devices and adjust their daily lives accordingly. They aim to maintain and improve their health by incorporating specific actions that address their emotional state (e.g., meditation or taking walks).

[0800] Step 9:

[0801] The emotion data, regularly updated by the emotion engine, is continuously analyzed by the server for long-term health assessment and used to provide useful feedback to the user.

[0802] (Example 2)

[0803] 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".

[0804] In modern society, mental health is just as important as physical health when it comes to managing one's own health. However, current health management systems primarily provide assessments and advice based only on physical data, making it difficult to provide comprehensive health management that takes mental health into account. Furthermore, these systems leave the ability to respond quickly when an abnormality is detected to the user's discretion, which may result in inadequate and timely contact with medical institutions.

[0805] 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.

[0806] In this invention, the server includes means for receiving and storing biometric information and emotional state data in a storage device, information processing means for analyzing the stored data and performing a comprehensive health assessment, and means for generating personalized health management suggestions based on the analysis results. This enables comprehensive health management that takes into account both physical and mental health conditions.

[0807] "Biometric information" refers to data that indicates the user's physical health status, and includes weight, diet, exercise history, etc.

[0808] "Emotional state data" refers to data that indicates the user's mental health status, and includes emotional information obtained from the user's facial expressions and voice.

[0809] A "storage device" is an information storage device that includes a database for safely and efficiently storing collected data.

[0810] "Information processing means" refers to software and hardware components used to analyze stored data and comprehensively evaluate the user's health status.

[0811] "Health management suggestions" refer to personalized advice and guidelines for health improvement that are generated based on the user's health information and emotional state.

[0812] A "display device" refers to a monitor or screen used to provide information to a user visually.

[0813] "Information encryption" refers to a technology that transforms and protects digital data to ensure the security of information during transmission and reception.

[0814] This invention is a system that collects and analyzes a user's biometric information and emotional state data to generate personalized health management suggestions. Specific embodiments are described below.

[0815] The server uses a powerful database management system to receive and store data. For example, the database can be MySQL or MongoDB. AI algorithms are introduced to analyze the stored data, and machine learning libraries such as TensorFlow and PyTorch are utilized. These technologies support a comprehensive health assessment of biometric and emotional state data.

[0816] The terminal collects information from the user and transmits it to the server. The terminal incorporates sensors such as a camera and microphone, and uses OpenCV and other library technologies to perform facial expression analysis and voice data analysis. This allows for an efficient understanding of the user's emotional state.

[0817] Users can regularly input their weight and meals into the device. It's also easy to record their emotional state using the device's camera. The data collected by the user is sent to the server periodically or each time the user inputs data via a secure communication protocol.

[0818] A concrete example is a user weighing themselves in the morning, entering their meal details into a device, and recording their emotions at that time through facial expression analysis using the device's camera. Based on this data, the server comprehensively evaluates the user's health status and generates advice such as, "Try to increase your exercise this week," which is then sent to the device. The user can then decide to take part in a yoga class on the weekend based on this advice.

[0819] An example of a prompt generated by a generative AI model is the requirement, "Generate personalized health advice considering the user's health information and emotional state." Using this prompt, the AI ​​model generates appropriate health management suggestions.

[0820] Thus, this invention realizes a system that comprehensively manages the user's physical and mental health and has the effect of improving their quality of life.

[0821] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0822] Step 1:

[0823] The user inputs biometric information (weight, diet, etc.) and emotional state into the device. The input emotional state is analyzed in real time using the device's camera. The input data is processed as primary data indicating the user's health status.

[0824] Step 2:

[0825] The device transmits collected health information and emotional state data to the server. At this point, the data is securely transferred to the server using a secure protocol such as HTTPS. The input is the user's biometric and emotional data, and the output is the secure transmission of data to the server.

[0826] Step 3:

[0827] The server stores the received data in a storage device. The stored information is organized by user and systematized for later analysis. The input is health and emotional data from the terminal, and the output is data stored in the database for preliminary analysis.

[0828] Step 4:

[0829] The server analyzes the stored data using AI algorithms. Using TensorFlow and PyTorch, the data is input into a health assessment model, which then performs a comprehensive evaluation of the user's health status. The input is user data stored in the database, and the output is the health status evaluation result based on the analysis.

[0830] Step 5:

[0831] The server generates personalized health management suggestions based on the analysis results. In this step, a generative AI model is used to create specific advice, such as "Let's increase our exercise this week." The input is the result of the overall evaluation, and the output is the health management suggestions for the user.

[0832] Step 6:

[0833] The server sends the generated health management suggestions to the terminal. This process is also carried out using a secure protocol. The input is the generated suggestions, and the output is the suggestion notification to the terminal.

[0834] Step 7:

[0835] The terminal displays received health management suggestions to the user. The user visually reviews the provided advice and decides how to apply it to their daily life. The input is the health management suggestions from the server, and the output is the user's acceptance of the suggestions and decision on an action plan.

[0836] Through this series of processes, the system provides users with comprehensive health management.

[0837] (Application Example 2)

[0838] 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".

[0839] Health management often focuses heavily on the physical aspect, and mental health management is frequently neglected. Furthermore, many systems lack mechanisms to improve the accuracy of advice based on user feedback. Security issues also exist in the transmission and reception of health information. This invention aims to solve these problems and provide a system for comprehensively managing a user's physical and mental health.

[0840] 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.

[0841] In this invention, the server includes means for receiving health-related information input by the user and storing the information in a data collection device, calculation means for analyzing the stored health-related information and evaluating the user's health status, and means for generating personalized health management recommendations based on the health status. This enables comprehensive health management that takes into account the user's emotional state as well as their physical health.

[0842] "Health-related information" refers to data that indicates the user's physical health status, including information such as weight and heart rate.

[0843] A "data collection device" is a storage device used to store data collected from users.

[0844] A "computational device" is a device that has the function of analyzing and evaluating the user's health status based on collected data.

[0845] "Personalized health management recommendations" refer to advice that outlines management policies based on the user's individual health condition.

[0846] A "medical facility" is a medical professional institution that should be contacted when necessary based on the results of health monitoring.

[0847] "Display means" refers to a device for visually presenting information to a user, and includes displays and the like.

[0848] "Emotional state" refers to data that indicates the user's mental and emotional state.

[0849] A "natural language processing device" is a processing device that communicates with users using natural language.

[0850] "Lifestyle data" refers to information about a user's habits and behaviors related to their daily life.

[0851] "Data encryption" is a technology used to ensure the security of data being transmitted or received.

[0852] The system implementing this invention mainly consists of a user terminal, a data collection device, a server, and a home care robot in the application example. The user inputs health-related information via the user terminal, and this information is stored in the data collection device. The server has advanced computing means to analyze this health-related information and the user's emotional state. For example, it receives the user's health information (weight, heart rate, etc.) and facial expression data acquired by cameras mounted on the terminal or robot in real time to understand the emotional state.

[0853] Based on the received data, the server uses a generative AI model to comprehensively evaluate the user's health status and generate personalized health management recommendations. These recommendations include physical health management policies as well as mental health recommendations tailored to the user's emotional state. Communication with the user is conducted through display means using advanced natural language processing.

[0854] For example, if a user voice-inputs "I'm a little tired" in the morning, their emotional state is determined to be one of increased stress. Based on this information, the server displays a message on the home care robot or the user's device saying, "How about taking a walk today to relax?" This encourages the user to take actions appropriate to their condition.

[0855] An example of a prompt message is: "Based on a recent health assessment, generate stress-reducing advice. The emotional state has been detected as (sad). As an example, suggest relaxation methods that can be visualized to reduce stress." The advice generated through such prompt messages is designed not only to support the user's health but also to help improve their quality of life.

[0856] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0857] Step 1:

[0858] Users input health-related information and emotional states through the device. Specifically, users input their weight and heart rate, and capture their facial expressions with the device's camera. This input data is initially processed on the device and then sent to a data collection device. The input consists of health-related biometric data and facial images, while the output is data converted from these into an appropriate data format.

[0859] Step 2:

[0860] The terminal transmits collected health-related information and emotional states to the server. The server stores this data in a data aggregation device and accumulates it in a database for later analysis. The input is the data transmitted from the terminal, and the output is the data in the format stored in the database.

[0861] Step 3:

[0862] The server analyzes stored health-related information and emotional states. A generative AI model is used to evaluate the user's physical and emotional state. This analysis process involves data processing, including calculating the mean and deviation of numerical data, and extracting features from facial image data. The input is data read from a database, and the output is the evaluation result.

[0863] Step 4:

[0864] The server generates personalized health management recommendations based on the analysis results. These recommendations include consideration of the user's physical and emotional state. The generative AI model uses natural language generation in this step to create easily understandable advice. The input is the analysis results, and the output is advice expressed in natural language.

[0865] Step 5:

[0866] The server sends the generated health management recommendations to the terminal and home care robot. The terminal or robot presents this to the user visually or audibly. This display includes visual elements and audio messages. The input is the advice data from the server, and the output is the presentation to the user.

[0867] Step 6:

[0868] The user reviews the health management recommendations they receive and decides on an action based on them. For example, if they receive the advice, "Take a walk today to reduce stress," the user chooses to go for a walk. In this step, the input is the advice from the server, and the output is the user's action choice.

[0869] 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.

[0870] 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.

[0871] 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 robot 414.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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."

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] The following is further disclosed regarding the embodiments described above.

[0891] (Claim 1)

[0892] A means for receiving health information entered by a user and storing that information in a database,

[0893] An algorithmic means for analyzing stored health information to evaluate the user's health status,

[0894] A means of generating personalized health management advice based on health status,

[0895] A means of automatically contacting a medical institution if an abnormality is detected,

[0896] A means of notifying users of health management advice via a terminal,

[0897] A system that includes this.

[0898] (Claim 2)

[0899] The system according to claim 1, further comprising means for receiving user-provided feedback and analyzing it to improve the quality of future health management advice.

[0900] (Claim 3)

[0901] The system according to claim 1, further comprising means for using data encryption to securely receive and transmit user health information and lifestyle data.

[0902] "Example 1"

[0903] (Claim 1)

[0904] A means for receiving health information entered by a user and storing said information in a storage device,

[0905] A computational means for analyzing stored health information and evaluating the user's health status,

[0906] A means for generating personalized health management advice based on health status,

[0907] A means of automatically contacting medical institutions if an abnormality is detected,

[0908] A means of notifying users in real time of health management advice via a terminal,

[0909] A method of using data encryption to securely transmit information entered by a user,

[0910] An analytical method incorporating machine learning techniques to identify risks and areas for improvement from structured data,

[0911] A means of outputting generated health management advice in a format easily understandable to the user using natural language processing,

[0912] A system that includes this.

[0913] (Claim 2)

[0914] The system according to claim 1, further comprising means for receiving user-provided feedback and analyzing it to improve the quality of future health management advice.

[0915] (Claim 3)

[0916] The system according to claim 1, further comprising means for creating personalized health management advice using a generative AI model.

[0917] "Application Example 1"

[0918] (Claim 1)

[0919] A means for receiving biometric information entered by a user and storing said information on a storage medium,

[0920] A processing means for analyzing stored biometric information to evaluate the user's health status,

[0921] A means of generating personalized health management guidance based on health status,

[0922] A means of automatically transmitting information to medical facilities when an abnormality is detected,

[0923] A means of notifying users of health management guidance via electronic devices,

[0924] Means of connecting to electronic devices via a private network in order to provide instructional content,

[0925] A means of automatically sending notifications to caregivers and family members when an anomaly is detected,

[0926] A system that includes this.

[0927] (Claim 2)

[0928] The system according to claim 1, further comprising means for receiving information provided by a user and analyzing it to improve the quality of future health management guidance.

[0929] (Claim 3)

[0930] The system according to claim 1, further comprising means for using data concealment technology to securely receive and transmit user biometric information and lifestyle data.

[0931] "Example 2 of combining an emotion engine"

[0932] (Claim 1)

[0933] A means for receiving biometric information and emotional state data entered by a user and storing the data in a storage device,

[0934] Information processing means for analyzing stored biometric information and emotional state data to perform a comprehensive health assessment of the user,

[0935] A means for generating personalized health management suggestions that take into account the user's physical and mental health based on the analysis results,

[0936] A means of automatically communicating with a medical institution when an abnormality is detected,

[0937] A means of notifying the user of health management suggestions via a display device,

[0938] A system that includes this.

[0939] (Claim 2)

[0940] The system according to claim 1, further comprising means for receiving user-provided response information and analyzing it to improve the quality of future health management suggestions.

[0941] (Claim 3)

[0942] The system according to claim 1, further comprising means for utilizing information encryption to securely receive and transmit user biometric information and lifestyle data.

[0943] "Application example 2 when combining with an emotional engine"

[0944] (Claim 1)

[0945] A means for receiving health-related information entered by a user and storing said information in a data collection device,

[0946] A computational means for analyzing stored health-related information to evaluate the user's health status,

[0947] A means for generating personalized health management recommendations based on health status,

[0948] A means of automatically contacting medical facilities if an abnormality is detected,

[0949] A means of displaying health management recommendations to the user,

[0950] A means for detecting the user's emotional state and generating additional mental health recommendations based on it,

[0951] A natural language processing method for interacting with users in natural language,

[0952] A system that includes this.

[0953] (Claim 2)

[0954] The system according to claim 1, further comprising means for receiving user-provided feedback and analyzing it to improve the accuracy of future health management recommendations.

[0955] (Claim 3)

[0956] The system according to claim 1, further comprising means for using data encryption to securely receive and transmit user health-related information and lifestyle data. [Explanation of Symbols]

[0957] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving biometric information entered by a user and storing said information on a storage medium, A processing means for analyzing stored biometric information to evaluate the user's health status, A means of generating personalized health management guidance based on health status, A means of automatically transmitting information to medical facilities when an abnormality is detected, A means of notifying users of health management guidance via electronic devices, Means of connecting to electronic devices via a private network in order to provide instructional content, A means of automatically sending notifications to caregivers and family members when an anomaly is detected, A system that includes this.

2. The system according to claim 1, further comprising means for receiving information provided by a user and analyzing it to improve the quality of future health management guidance.

3. The system according to claim 1, further comprising means for using data concealment technology to securely receive and transmit user biometric information and lifestyle data.

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