system

Abstract

We provide the system. [Solution] A means of receiving personal health data, An analytical means for analyzing collected health data and evaluating the user's health status, A generation means for generating personalized health management advice based on analysis results, A notification method that notifies the user's device of the generated advice, A healthcare system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] In current medical services, the lack of medical access, especially for remote areas and the elderly, is a major issue. It is difficult for these target individuals to receive regular health check-ups and appropriate health management, and as a result, there is a risk of delayed early detection of diseases. Also, the lack of more effective preventive measures based on individual health conditions and lifestyles is an issue.

Means for Solving the Problems

[0005] This invention provides a healthcare system comprising means for collecting personal health data, means for analyzing the collected data to evaluate health status, means for generating personalized health management advice based on the analysis results, and means for notifying the user of that advice. This system allows users to receive appropriate health management based on the latest health information from the comfort of their homes. Furthermore, by linking with medical institution databases, real-time information updates are possible, and by using AI models to perform detailed health status analysis as needed, highly accurate preventative measures tailored to individual needs are provided.

[0006] "Health data" refers to information about an individual's physical condition, including heart rate, body temperature, activity level, and sleep patterns.

[0007] "Analysis methods" refer to processes and technologies used to analyze collected health data and evaluate the health status and risks of individual users.

[0008] "Generation method" refers to a mechanism for creating personalized health management advice based on analyzed data and customizing that advice for the user.

[0009] "Notification means" refers to methods and technologies for informing the user's device of the generated health management advice.

[0010] A "healthcare system" refers to a comprehensive system that includes a series of processes and technologies for collecting, analyzing, generating, and notifying health data.

[0011] A "medical institution database" refers to a collection of information managed by medical institutions, including the latest medical information, treatment guidelines, and disease prevention information.

[0012] An "AI model" refers to an algorithm or learning model that uses artificial intelligence to analyze data and evaluate the user's health status. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] 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.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] The healthcare system described in this patent effectively collects and analyzes individual health data to provide users with appropriate health management advice. This system consists of a server, terminals, and users.

[0035] The server receives health data from the user. For example, heart rate and activity levels recorded by the user wearing a wearable device are sent to the server via smartphone. This data is stored in a database on the server and then analyzed by a designated AI model.

[0036] In the analysis, the server assesses the user's health status. An AI model assists in this process, detecting anomalies and assessing risks. This identifies health risks specific to each user. For example, if the heart rate exceeds the normal range, it suggests an increase in stress levels.

[0037] The server uses the analysis results to generate personalized health management advice. This requires up-to-date medical information, so it accesses databases of medical institutions to obtain that information. For example, it can determine the need for vaccination based on the influenza season.

[0038] The generated advice is notified to the device. The device provides information to the user in the form of a smartphone or tablet. The notification includes suggestions for improving lifestyle habits and recommendations for regular health checkups. This serves as an opportunity for the user to take action on their daily health management.

[0039] As a concrete example, consider a case where user A regularly records their heart rate and activity level through a device. If the server analyzes this data and finds a recent decrease in activity and an increase in heart rate, it determines that stress management is necessary. Therefore, the server suggests stretching and relaxation methods and notifies user A through the device. In this way, specific advice tailored to individual health needs is provided.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server receives the user's health data. The system works by transmitting data from the user's wearable device via a smartphone and storing it in the server's database. This data includes heart rate, body temperature, and activity level.

[0043] Step 2:

[0044] The server processes the accumulated data through an AI model to analyze the user's health status. Through regular data analysis, it understands the user's daily health patterns and detects unexpected changes or anomalies. This analysis identifies specific health risks.

[0045] Step 3:

[0046] Based on the analysis results, the server generates personalized health management advice. To do this, the server accesses databases of medical institutions to obtain the latest medical information. This ensures that the advice reflects current medical guidelines and disease information.

[0047] Step 4:

[0048] The device notifies the user of health management advice received from the server. This notification is displayed as a message or alert in the smartphone app. Specific action plans and health management suggestions are then provided.

[0049] Step 5:

[0050] Based on notifications from their devices, users incorporate the provided health management advice into their daily lives. For example, they might review their lifestyle habits or implement recommended exercise programs. Furthermore, as needed, users can send feedback to the server via their devices, contributing to the system's analysis accuracy by providing additional data.

[0051] (Example 1)

[0052] 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."

[0053] In recent years, the importance of individuals managing their health in a more precise and personalized way has increased. However, conventional healthcare systems struggle to effectively monitor the dynamic health status of individual users and provide timely and appropriate health management advice. Solving this challenge is crucial to contributing to health maintenance and disease prevention.

[0054] 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.

[0055] In this invention, the server includes receiving means for collecting personal health information, analysis means for analyzing the collected health information and evaluating the user's health status, notification means for notifying the user's device of the generated advice, and feedback receiving means for collecting the user's responses from the device and using them for future analysis. This makes it possible to analyze the dynamic health status of the user in real time and to quickly provide personalized health management advice.

[0056] "Personal health information" refers to biometric data and activity data related to the user's physical condition, including heart rate and activity level.

[0057] "Receiving means" refers to the function that allows a server to acquire health information transmitted from wearable devices or terminals.

[0058] "Analysis means" refers to the process of evaluating the user's health status based on received health information, and specifically includes the function of analyzing data using an AI model.

[0059] "Generating means" refers to the function that generates personalized health management advice for each user based on the analyzed results.

[0060] "Notification means" refers to the function that sends generated health management advice to the user's device to inform the user.

[0061] "Means for receiving feedback" refers to a function that collects responses from users and utilizes them for future analysis.

[0062] A "generative AI model" refers to an artificial intelligence model used to analyze health information, and includes functions for detecting anomalies and assessing risks.

[0063] A "medical institution's information repository" refers to a database where the latest medical information is stored, and it is used to retrieve necessary medical information.

[0064] This invention is a system for supporting personal health management, consisting of a server, a terminal, and a user. The server is equipped with a receiving means to receive health information from a wearable device worn by the user. The received information includes heart rate and activity level. The terminal takes the form of a smartphone or tablet and is responsible for collecting data from the wearable device and transmitting it to the server.

[0065] The server has analytical capabilities to analyze the collected health information. This analysis uses a generative AI model to assess the user's health status. The AI ​​model detects anomalies and assesses risks, providing insights into the user's health. For example, if the heart rate exceeds the normal range, it may suggest that stress levels may be increasing.

[0066] Based on the analysis results, the server is equipped with a generation mechanism to generate personalized health management advice for the user. This advice generation takes into account the latest medical information. Therefore, the server accesses the information storage of medical institutions to obtain the necessary information. For example, it can provide vaccination recommendations depending on the influenza season.

[0067] The generated health management advice is sent from the server to the terminal. The terminal has a means of notifying the user and conveys the advice to the user. This advice includes suggestions for improving lifestyle habits and recommendations for regular health checkups. The user receives the notification and takes daily health management actions based on it.

[0068] As a concrete example, consider a case where user A records their heart rate and activity level through a wearable device. This data is sent to a server and analyzed by an AI model. If the analysis reveals a recent decrease in activity level and an increase in heart rate, the server provides stress management advice. This advice may include suggestions for stretching and relaxation techniques, and this information is sent to user A's device.

[0069] A concrete example of a prompt message for the generating AI model is, "Based on the analysis of user A's recent heart rate and activity data, generate appropriate health management advice." This will provide advice tailored to the user's health condition.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The user wears a wearable device to collect health information such as heart rate and activity level. This information is recorded in real time by the device. The input is biometric data related to the user's physical activity. The output is health data stored in the wearable device.

[0073] Step 2:

[0074] The terminal receives data from wearable devices via Bluetooth or Wi-Fi. The input is health data transmitted from the wearable device. The terminal formats this data and prepares it for transmission to the server. The output is the prepared set of health data, which is stored for further processing.

[0075] Step 3:

[0076] The server receives health data transmitted from the terminal. This data is stored in a database on the server. The input is the data transmitted from the terminal. Storing it in the database allows for historical data accumulation, which is useful for later analysis. The output is the health data as it is stored in the database.

[0077] Step 4:

[0078] The server analyzes data using a generative AI model. The input is health data stored in a database. The AI ​​model analyzes the data to detect anomalies and assess risks. For example, it analyzes heart rate trends and detects abnormal increases. This provides insights for evaluating the user's health status. The output is the analysis result, which is information indicating the user's health status.

[0079] Step 5:

[0080] The server generates personalized health management advice based on the analysis results. The inputs are the AI ​​analysis results and the latest medical information. The server accesses the information repository of healthcare institutions to retrieve relevant medical information and supplement the advice. For example, it might make recommendations about stretching or relaxation. The output is the health management advice provided to the user.

[0081] Step 6:

[0082] The server sends the generated advice to the terminal. The input is the generated health management advice. The terminal receives this information and displays it on the screen or provides alerts to notify the user. The output is the advice information the user receives through the terminal.

[0083] Step 7:

[0084] Users adjust their lifestyles based on the advice provided. Input is health management advice received via the device. Users can provide feedback via the device. Output is user feedback, which will be used for future analysis.

[0085] (Application Example 1)

[0086] 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."

[0087] In modern society, there is a need to effectively manage individual health information and provide appropriate health advice in real time. However, existing systems struggle to provide personalized health management information in real time. Furthermore, the introduction of advice that reflects the latest medical information and in a form that can be easily used at home is lagging behind. Therefore, the challenge is to realize a system that efficiently and in real time monitors an individual's health status and provides optimal health advice.

[0088] 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.

[0089] In this invention, the server includes a receiving device for collecting personal health information, an analysis device for analyzing the collected health information and evaluating the user's health status, a generating device for generating personalized health management information based on the analysis results, a notification device for notifying the user's device of the generated information, and a robot for monitoring the user's condition in real time and providing appropriate advice. This makes it possible to grasp an individual's health status in real time and easily obtain specific and effective health management information based on the latest medical information within the home.

[0090] A "receiving device for collecting personal health information" refers to a device that acquires health-related data from wearable devices and sensors.

[0091] An "analysis device" is a system used to evaluate and analyze a user's health status based on acquired health information.

[0092] A "generator" is a device that creates individually customized health management information based on the analysis results.

[0093] A "notification device" refers to a terminal or interface used to inform users of generated health management information.

[0094] A "robot" is an automated device that monitors the user's health status in real time within the home and provides appropriate health advice.

[0095] An "artificial intelligence model" is an algorithm or technology that analyzes acquired health information and identifies patterns to evaluate fluctuations in health status.

[0096] "Personalized health management information" refers to information that provides recommendations for maintaining and improving health, tailored to each user's health condition and lifestyle.

[0097] The system for implementing this invention combines multiple devices and technologies to effectively manage personal health information.

[0098] First, the user wears a wearable device to collect daily health information in real time. The receiving device uses the Bluetooth communication protocol to automatically transmit this data to a server. The collected data includes heart rate and daily activity levels.

[0099] After receiving the data, the server uses analysis software such as Python and TENSORFLOW® to analyze the health information using artificial intelligence models. This makes it possible to identify patterns and abnormalities related to the user's health status.

[0100] Next, the server generates personalized health management information based on the analysis results. During this process, API access to external medical information databases is performed, incorporating the latest medical information to improve the accuracy of user advice. This information is generated by a generator and provided to the user's notification device.

[0101] A specific example of its use is that if the robot detects that a user is experiencing excessive stress, it might provide voice advice such as, "Your heart rate has recently increased. We recommend you take three minutes of deep breathing." In this way, users can monitor their health in their daily lives and take necessary actions.

[0102] The generating AI model is used to suggest lifestyle improvements based on health information and is instructed by an example prompt: "Generate lifestyle improvement advice based on the user's health data. Provide relaxation methods to cope with recent increases in heart rate." This prompt allows the AI ​​model to generate specific advice on stress reduction methods and daily habits.

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The user wears a wearable device to collect health information. The wearable device measures heart rate and activity level, and transmits this data to the terminal in real time via Bluetooth communication. The input is raw data about the user's physical condition, and the output is formatted data transmitted to the terminal via Bluetooth.

[0106] Step 2:

[0107] The terminal transfers the received raw data to the server. The server receives the health information sent from the terminal and stores it in a database. The input in this step is formatted health information data, and the output is data in an available format such as JSON stored in the database.

[0108] Step 3:

[0109] The server analyzes the stored data using Python and TensorFlow. The server initiates the analysis, using a generated AI model to detect anomalies and perform trend analysis. The input is user health information stored in a database, and the output is diagnosed data and identified health risk information.

[0110] Step 4:

[0111] The server generates personalized health management information using a generation device based on the analysis results. It obtains the latest medical information from an external API and creates advice that takes this information into account. The input is the analyzed health information and the latest information obtained from the medical database, and the output is personalized, specific health management advice for the user.

[0112] Step 5:

[0113] The generated health management advice is sent to the terminal via a notification device, and the terminal uses a robot to present the information to the user. In this process, the analyzed information is presented as audio and visual messages. The input is the generated advice information, and the output is a display or audio output as a notification intended for the user to receive and understand.

[0114] Step 6:

[0115] The user receives advice from the robot and implements it in their daily life. Specifically, the robot guides the user through recommended actions, and the user follows these instructions to manage their health. The input is the advice from the robot, and the output is the user's improved actions.

[0116] 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.

[0117] The healthcare system described in this patent collects and analyzes individual health and emotional data to provide users with appropriate health management advice. Specifically, it centers on the interaction between the server, terminal, and user.

[0118] The server receives user health and emotional data. Through wearable devices and smartphones worn by the user, it acquires physiological data such as heart rate, body temperature, and activity levels, as well as emotional data derived from voice data and behavioral patterns. The server stores this data in a database.

[0119] In the analysis, the server uses an AI model to analyze health data and evaluate the health status of individual users. Emotional data is also analyzed using speech recognition technology and machine learning models to understand the user's emotions. As a result, a comprehensive health profile is formed that combines the user's physical and emotional state.

[0120] The server generates personalized health management advice based on the analysis results. This generation method designs advice considering not only the user's physical condition but also their emotional state. For example, it generates advice that includes suggestions for relaxation methods for users who are feeling stressed. It also links with a database of medical institutions to reflect the latest medical information.

[0121] The device notifies the user of health management advice received from the server. Through a smartphone or tablet app, the user is presented with specific action plans to incorporate into their daily life and health suggestions tailored to their emotional state.

[0122] As a concrete example, consider a case where user B regularly wears a wearable device and records their emotional state in a smartphone app. The server acquires heart rate data and emotional data from voice from the device and analyzes them with an AI model. If the system determines that user B's stress level has recently increased and their activity level has decreased, it provides a notification via the device suggesting yoga or meditation to help them relax. In this way, specific advice based on the user's health and emotional state is realized.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] Users record health data such as heart rate, activity levels, and voice input, as well as emotional data, using wearable devices and smartphones. This data is transferred from the device to the smartphone via Bluetooth or Wi-Fi.

[0126] Step 2:

[0127] The device sends received health and emotional data to the server. This transmission uses a secure communication method and is updated regularly in real time.

[0128] Step 3:

[0129] The server stores the received data in a database and begins analyzing the health status using an AI model. It analyzes changes in heart rate and activity levels from the health data, and analyzes emotional states based on voice patterns and behavioral data from the emotional data.

[0130] Step 4:

[0131] Based on the analysis results, the server comprehensively evaluates the user's health and emotional state. For example, if an increase in stress levels or a decrease in activity levels is detected, this will be taken into consideration.

[0132] Step 5:

[0133] The server generates personalized health management advice based on the user's physical and emotional state. This advice includes programs for relaxation and instructions to encourage positive activity. It also accesses a database of healthcare institutions to incorporate the latest medical information into the advice.

[0134] Step 6:

[0135] The device notifies the user of advice received from the server. The app provides the user with detailed action plans and suggestions for improving lifestyle habits, encouraging them to take action based on these suggestions.

[0136] Step 7:

[0137] Users receive advice from their devices and adjust their lifestyles as needed. They provide feedback on their actions through the app and send additional data to the server. This data is used for future analysis.

[0138] (Example 2)

[0139] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0140] In modern society, comprehensively managing an individual's health and mental state is becoming increasingly important. However, conventional systems treat physiological and emotional information separately, resulting in insufficient comprehensive health management. In particular, providing personalized health advice that takes emotional states into account has been difficult.

[0141] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0142] In this invention, the server includes means for acquiring personal physiological and emotional information, means for storing the acquired physiological and emotional information, and means for analyzing the physiological and emotional state using the stored information. This makes it possible to provide health advice optimized for the user.

[0143] "Physiological information" refers to information that indicates an individual's physical condition, such as heart rate, body temperature, and activity level.

[0144] "Emotional information" refers to information that indicates an individual's emotional state, extracted from audio data and behavioral patterns.

[0145] "Means of acquisition" refers to devices and processes for collecting physiological and emotional information from individuals.

[0146] "Storage means" refers to a mechanism for storing acquired physiological and emotional information in a database or storage device.

[0147] "Analysis means" refers to methods and techniques used to evaluate an individual's physiological and emotional state using acquired and stored information.

[0148] "Generation method" refers to the process of creating individually tailored health advice based on the analysis results.

[0149] "Notification means" refers to methods or devices for transmitting generated health advice to the user's information terminal.

[0150] A "medical institution's information repository" is a database or information system where the latest medical information is stored and accessible.

[0151] A "machine learning model" refers to an algorithm or framework used for data analysis; it is a mathematical model that learns patterns from data and makes predictions.

[0152] The system for implementing this invention is realized through interaction between a server, a user's terminal, and the user themselves. The user wears a wearable device or smartphone to collect physiological information. These devices acquire physiological information such as heart rate, body temperature, and activity level in real time, and further extract emotional information from voice data using speech recognition technology.

[0153] The device continuously transmits acquired physiological and emotional information to the server. The server organizes and stores this information in a database. The server also uses machine learning models on the stored information to evaluate the user's health and emotional state. Specifically, by running the AI ​​model, the health state of each user is quantified and their emotional state is analyzed.

[0154] To generate health advice optimized for the user, the server uses a generating AI model based on data provided by the AI ​​model. This generating AI model creates advice aimed at improving lifestyle habits and providing mental support, based on the user's analysis results. An example of this prompt is, "Please suggest a specific action plan that addresses the user's changing health condition."

[0155] As a concrete example, suppose a user wears a wearable device daily. This device records heart rate variability and sends it to a server along with audio recordings from a smartphone. The server analyzes the data and, if it determines that the user has been experiencing stress recently, suggests yoga exercises to promote relaxation. This notification appears on the user's device, allowing them to learn specific steps to improve their health.

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] Users collect physiological and emotional information using wearable devices and smartphones. Specifically, the wearable device measures heart rate, body temperature, and activity level, while the smartphone acquires voice data to collect emotional information. Input data includes heart rate, body temperature, activity level, and voice data, and the output is a set of these data.

[0159] Step 2:

[0160] The device sends collected physiological and emotional information to the server. Specifically, the device packets the measurement data and sends a request to the server's API endpoint. The input data is a dataset sent from the wearable device and smartphone, and the output is the data sent to the server.

[0161] Step 3:

[0162] The server stores and organizes the received data in a database. Specifically, physiological and emotional information is written to user-specific database tables and organized along a timeline. The input is data sent from the terminal, and the output is the information stored in the database.

[0163] Step 4:

[0164] The server uses stored data to perform analysis with an AI model. Specifically, it applies machine learning algorithms to evaluate and quantify the user's health and emotional state. The input is information obtained from the database, and the output is the health and emotional state quantified from the analysis results.

[0165] Step 5:

[0166] Using a generative AI model, the server generates health advice optimized for the user. During this process, it takes the analysis results as input and executes the prompt message, "Please propose a specific action plan that addresses changes in the user's health condition." The output is the generated health advice.

[0167] Step 6:

[0168] The device receives advice generated from the server and notifies the user. For example, a smartphone app displays a pop-up notification for the user to review. The input is health advice from the server, and the output is a specific action plan displayed on the user's screen.

[0169] (Application Example 2)

[0170] 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".

[0171] In modern urban areas, maintaining individual health management and emotional balance is crucial, but there is a lack of systems to effectively support this. In particular, there is a need for methods to simultaneously collect individual health and emotional data and provide appropriate health management and advice tailored to their emotional state based on this data. Furthermore, effectively utilizing this data to coordinate with urban medical resources and relaxation facilities to provide individualized support remains challenging.

[0172] 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.

[0173] In this invention, the server includes receiving means for collecting individual health and emotional data, analyzing means for analyzing the collected health and emotional data and evaluating the health and emotional state of individual users, and generating means for generating personalized health management and emotional advice based on the analysis results. This makes it possible to effectively support the balance of health and emotional well-being for individual citizens in urban areas.

[0174] "Personal health data" refers to numerical values ​​and indicators that show the physical condition of individual users, such as heart rate, activity level, and body temperature.

[0175] "Emotional data" refers to information that indicates a user's mental state and emotional tendencies, obtained by analyzing their voice and behavioral patterns.

[0176] "Receiving means" refers to a system or device used to collect an individual's health data and emotional data.

[0177] "Analysis means" refers to a method or apparatus used to evaluate a user's health and emotional state using collected health data and emotional data.

[0178] A "generation method" is a system or device that constructs personalized health management advice and recommendations tailored to emotional states based on analyzed data.

[0179] "Notification means" refers to a method or device for promptly and appropriately conveying generated advice to the user's information processing terminal.

[0180] "Information processing equipment" refers to electronic devices such as mobile terminals and computers that users use on a daily basis.

[0181] A "system" is a collection of devices or methods in which individual components work together to achieve a specific purpose.

[0182] The system for implementing this invention mainly consists of a server, a terminal, and a wearable device or information processing device used by the user. The program is implemented through a series of processes that automatically perform everything from data collection to analysis and notification.

[0183] The server receives personal health and emotional data from wearable devices and smartphones worn by users. Specifically, health data includes heart rate, body temperature, and activity levels, while emotional data includes voice data and emotional states based on behavioral patterns. The data is stored on a cloud server and used for analysis. Hardware used includes Apple Watches and typical smartphones, and cloud computing technology is applied for data processing.

[0184] As an analysis method, the server uses Python to run machine learning models and evaluate health and emotional data. This process utilizes AI frameworks such as TensorFlow and PyTorch. Based on the analysis results, the system comprehensively evaluates the user's health and emotional state and generates appropriate health management advice. In particular, the health advice is personalized based on the individual's health condition and emotions.

[0185] The device itself is responsible for quickly notifying users of the generated advice on their smartphones. This notification includes specific action plans tailored to the user's current situation and suggestions for activities utilizing local healthcare resources.

[0186] As a concrete example, suppose a user inputs data for one month, and the analysis results indicate an increase in stress levels. In this case, the system would provide a notification recommending a yoga session at a relaxation facility in the city. Another example of a prompt for the generating AI model is, "Generate appropriate health management advice based on the user's health and emotional data from the past week."

[0187] This system will enable individual users to effectively manage their own health and emotional state, supporting a healthy lifestyle within urban environments.

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] A wearable device and smartphone worn by the user periodically measure personal health data (e.g., heart rate, body temperature, activity level) and emotional data (e.g., emotional state from voice), and transmit this data to a server. This data is collected, and the server prepares to store this input data in a database. The collected data is stored as information necessary for subsequent analysis.

[0191] Step 2:

[0192] The server retrieves health and emotional data stored in a database. The input data also includes historical measurement data. The server uses this data as input to perform data analysis using a machine learning model (e.g., a model using TensorFlow). Specifically, it evaluates the user's current health status and emotional tendencies from the data. This analysis outputs indicators related to the user's health and emotional state.

[0193] Step 3:

[0194] Based on the analysis results, the server generates health advice. This generation process takes the analyzed indicators as input and outputs health recommendations and advice tailored to the user's emotional state. This process utilizes a pre-configured rule-based system and a generative AI model. The generated advice includes specific action plans and suggestions for utilizing medical resources to provide to the user.

[0195] Step 4:

[0196] The server sends the generated advice to the terminal, which then notifies the user's smartphone. Here, the generated advice is taken as input and output in an appropriate format for display on the user's screen. This notification is presented in real time for the user to review.

[0197] Step 5:

[0198] Users review the notifications they receive and take action based on the presented health and emotional care plans. As users take action, a feedback loop is formed throughout the system, influencing subsequent data acquisition.

[0199] Through the processes described above, the system continues to support the user's health and emotional management.

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

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

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

[0203] [Second Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0216] The healthcare system described in this patent effectively collects and analyzes individual health data to provide users with appropriate health management advice. This system consists of a server, terminals, and users.

[0217] The server receives health data from the user. For example, heart rate and activity levels recorded by the user wearing a wearable device are sent to the server via smartphone. This data is stored in a database on the server and then analyzed by a designated AI model.

[0218] In the analysis, the server assesses the user's health status. An AI model assists in this process, detecting anomalies and assessing risks. This identifies health risks specific to each user. For example, if the heart rate exceeds the normal range, it suggests an increase in stress levels.

[0219] The server uses the analysis results to generate personalized health management advice. This requires up-to-date medical information, so it accesses databases of medical institutions to obtain that information. For example, it can determine the need for vaccination based on the influenza season.

[0220] The generated advice is notified to the device. The device provides information to the user in the form of a smartphone or tablet. The notification includes suggestions for improving lifestyle habits and recommendations for regular health checkups. This serves as an opportunity for the user to take action on their daily health management.

[0221] As a concrete example, consider a case where user A regularly records their heart rate and activity level through a device. If the server analyzes this data and finds a recent decrease in activity and an increase in heart rate, it determines that stress management is necessary. Therefore, the server suggests stretching and relaxation methods and notifies user A through the device. In this way, specific advice tailored to individual health needs is provided.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The server receives the user's health data. The system works by transmitting data from the user's wearable device via a smartphone and storing it in the server's database. This data includes heart rate, body temperature, and activity level.

[0225] Step 2:

[0226] The server processes the accumulated data through an AI model to analyze the user's health status. Through regular data analysis, it understands the user's daily health patterns and detects unexpected changes or anomalies. This analysis identifies specific health risks.

[0227] Step 3:

[0228] Based on the analysis results, the server generates personalized health management advice. To do this, the server accesses databases of medical institutions to obtain the latest medical information. This ensures that the advice reflects current medical guidelines and disease information.

[0229] Step 4:

[0230] The device notifies the user of health management advice received from the server. This notification is displayed as a message or alert in the smartphone app. Specific action plans and health management suggestions are then provided.

[0231] Step 5:

[0232] Based on notifications from their devices, users incorporate the provided health management advice into their daily lives. For example, they might review their lifestyle habits or implement recommended exercise programs. Furthermore, as needed, users can send feedback to the server via their devices, contributing to the system's analysis accuracy by providing additional data.

[0233] (Example 1)

[0234] 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."

[0235] In recent years, the importance of individuals managing their health in a more precise and personalized way has increased. However, conventional healthcare systems struggle to effectively monitor the dynamic health status of individual users and provide timely and appropriate health management advice. Solving this challenge is crucial to contributing to health maintenance and disease prevention.

[0236] 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.

[0237] In this invention, the server includes receiving means for collecting personal health information, analysis means for analyzing the collected health information and evaluating the user's health status, notification means for notifying the user's device of the generated advice, and feedback receiving means for collecting the user's responses from the device and using them for future analysis. This makes it possible to analyze the dynamic health status of the user in real time and to quickly provide personalized health management advice.

[0238] "Personal health information" refers to biometric data and activity data related to the user's physical condition, including heart rate and activity level.

[0239] "Receiving means" refers to the function that allows a server to acquire health information transmitted from wearable devices or terminals.

[0240] "Analysis means" refers to the process of evaluating the user's health status based on received health information, and specifically includes the function of analyzing data using an AI model.

[0241] "Generating means" refers to the function that generates personalized health management advice for each user based on the analyzed results.

[0242] "Notification means" refers to the function that sends generated health management advice to the user's device to inform the user.

[0243] "Means for receiving feedback" refers to a function that collects responses from users and utilizes them for future analysis.

[0244] A "generative AI model" refers to an artificial intelligence model used to analyze health information, and includes functions for detecting anomalies and assessing risks.

[0245] A "medical institution's information repository" refers to a database where the latest medical information is stored, and it is used to retrieve necessary medical information.

[0246] This invention is a system for supporting personal health management, consisting of a server, a terminal, and a user. The server is equipped with a receiving means to receive health information from a wearable device worn by the user. The received information includes heart rate and activity level. The terminal takes the form of a smartphone or tablet and is responsible for collecting data from the wearable device and transmitting it to the server.

[0247] The server has analytical capabilities to analyze the collected health information. This analysis uses a generative AI model to assess the user's health status. The AI ​​model detects anomalies and assesses risks, providing insights into the user's health. For example, if the heart rate exceeds the normal range, it may suggest that stress levels may be increasing.

[0248] Based on the analysis results, the server is equipped with a generation mechanism to generate personalized health management advice for the user. This advice generation takes into account the latest medical information. Therefore, the server accesses the information storage of medical institutions to obtain the necessary information. For example, it can provide vaccination recommendations depending on the influenza season.

[0249] The generated health management advice is sent from the server to the terminal. The terminal has a means of notifying the user and conveys the advice to the user. This advice includes suggestions for improving lifestyle habits and recommendations for regular health checkups. The user receives the notification and takes daily health management actions based on it.

[0250] As a concrete example, consider a case where user A records their heart rate and activity level through a wearable device. This data is sent to a server and analyzed by an AI model. If the analysis reveals a recent decrease in activity level and an increase in heart rate, the server provides stress management advice. This advice may include suggestions for stretching and relaxation techniques, and this information is sent to user A's device.

[0251] A concrete example of a prompt message for the generating AI model is, "Based on the analysis of user A's recent heart rate and activity data, generate appropriate health management advice." This will provide advice tailored to the user's health condition.

[0252] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0253] Step 1:

[0254] The user wears a wearable device to collect health information such as heart rate and activity level. This information is recorded in real time by the device. The input is biometric data related to the user's physical activity. The output is health data stored in the wearable device.

[0255] Step 2:

[0256] The terminal receives data from wearable devices via Bluetooth or Wi-Fi. The input is health data transmitted from the wearable device. The terminal formats this data and prepares it for transmission to the server. The output is the prepared set of health data, which is stored for further processing.

[0257] Step 3:

[0258] The server receives health data transmitted from the terminal. This data is stored in a database on the server. The input is the data transmitted from the terminal. Storing it in the database allows for historical data accumulation, which is useful for later analysis. The output is the health data as it is stored in the database.

[0259] Step 4:

[0260] The server analyzes data using a generative AI model. The input is health data stored in a database. The AI ​​model analyzes the data to detect anomalies and assess risks. For example, it analyzes heart rate trends and detects abnormal increases. This provides insights for evaluating the user's health status. The output is the analysis result, which is information indicating the user's health status.

[0261] Step 5:

[0262] The server generates personalized health management advice based on the analysis results. The inputs are the AI ​​analysis results and the latest medical information. The server accesses the information repository of healthcare institutions to retrieve relevant medical information and supplement the advice. For example, it might make recommendations about stretching or relaxation. The output is the health management advice provided to the user.

[0263] Step 6:

[0264] The server sends the generated advice to the terminal. The input is the generated health management advice. The terminal receives this information and displays it on the screen or provides alerts to notify the user. The output is the advice information the user receives through the terminal.

[0265] Step 7:

[0266] Users adjust their lifestyles based on the advice provided. Input is health management advice received via the device. Users can provide feedback via the device. Output is user feedback, which will be used for future analysis.

[0267] (Application Example 1)

[0268] 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."

[0269] In modern society, there is a need to effectively manage individual health information and provide appropriate health advice in real time. However, existing systems struggle to provide personalized health management information in real time. Furthermore, the introduction of advice that reflects the latest medical information and in a form that can be easily used at home is lagging behind. Therefore, the challenge is to realize a system that efficiently and in real time monitors an individual's health status and provides optimal health advice.

[0270] 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.

[0271] In this invention, the server includes a receiving device for collecting personal health information, an analysis device for analyzing the collected health information and evaluating the user's health status, a generating device for generating personalized health management information based on the analysis results, a notification device for notifying the user's device of the generated information, and a robot for monitoring the user's condition in real time and providing appropriate advice. This makes it possible to grasp an individual's health status in real time and easily obtain specific and effective health management information based on the latest medical information within the home.

[0272] A "receiving device for collecting personal health information" refers to a device that acquires health-related data from wearable devices and sensors.

[0273] An "analysis device" is a system used to evaluate and analyze a user's health status based on acquired health information.

[0274] A "generator" is a device that creates individually customized health management information based on the analysis results.

[0275] A "notification device" refers to a terminal or interface used to inform users of generated health management information.

[0276] A "robot" is an automated device that monitors the user's health status in real time within the home and provides appropriate health advice.

[0277] An "artificial intelligence model" is an algorithm or technology that analyzes acquired health information and identifies patterns to evaluate fluctuations in health status.

[0278] "Personalized health management information" refers to information that provides recommendations for maintaining and improving health, tailored to each user's health condition and lifestyle.

[0279] The system for implementing this invention combines multiple devices and technologies to effectively manage personal health information.

[0280] First, the user wears a wearable device to collect daily health information in real time. The receiving device uses the Bluetooth communication protocol to automatically transmit this data to a server. The collected data includes heart rate and daily activity levels.

[0281] After receiving the data, the server uses analysis software such as Python or TensorFlow to analyze the health information using artificial intelligence models. This makes it possible to identify patterns and anomalies related to the user's health status.

[0282] Subsequently, based on the analysis results, the server generates health management information optimized for each user. In this process, API access to an external medical information database is performed, and by incorporating the latest medical information, the accuracy of advice provided to users is improved. This information is created by the generation device and provided to the user's notification device.

[0283] As a specific example of use, when a user is determined to have excessive stress, the robot provides voice advice such as "An increase in heart rate has been observed recently. It is recommended to perform deep breathing for 3 minutes." In this way, users can monitor their health status in daily life and take necessary actions.

[0284] The generation AI model is used to propose improvements to lifestyle habits based on health information and is instructed by an example of a prompt sentence such as "Based on the user's health data, generate advice on improving lifestyle habits. Provide relaxation methods to address the recent increase in heart rate." With this prompt, the AI model can generate specific advice on stress relief methods and daily habits.

[0285] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0286] Step 1:

[0287] The user wears a wearable device to collect health information. The wearable device measures the heart rate and activity level and transmits the data to the terminal in real-time via Bluetooth communication. The input is raw data regarding the user's physical condition, and the output is formatted data transmitted to the terminal via Bluetooth.

[0288] Step 2:

[0289] The terminal transfers the received raw data to the server. The server receives the health information sent from the terminal and stores it in a database. The input in this step is formatted health information data, and the output is data in an available format such as JSON stored in the database.

[0290] Step 3:

[0291] The server analyzes the stored data using Python and TensorFlow. The server initiates the analysis, using a generated AI model to detect anomalies and perform trend analysis. The input is user health information stored in a database, and the output is diagnosed data and identified health risk information.

[0292] Step 4:

[0293] The server generates personalized health management information using a generation device based on the analysis results. It obtains the latest medical information from an external API and creates advice that takes this information into account. The input is the analyzed health information and the latest information obtained from the medical database, and the output is personalized, specific health management advice for the user.

[0294] Step 5:

[0295] The generated health management advice is sent to the terminal via a notification device, and the terminal uses a robot to present the information to the user. In this process, the analyzed information is presented as audio and visual messages. The input is the generated advice information, and the output is a display or audio output as a notification intended for the user to receive and understand.

[0296] Step 6:

[0297] The user receives advice from the robot and implements it in their daily life. Specifically, the robot guides the user through recommended actions, and the user follows these instructions to manage their health. The input is the advice from the robot, and the output is the user's improved actions.

[0298] 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.

[0299] The healthcare system described in this patent collects and analyzes individual health and emotional data to provide users with appropriate health management advice. Specifically, it centers on the interaction between the server, terminal, and user.

[0300] The server receives user health and emotional data. Through wearable devices and smartphones worn by the user, it acquires physiological data such as heart rate, body temperature, and activity levels, as well as emotional data derived from voice data and behavioral patterns. The server stores this data in a database.

[0301] In the analysis, the server uses an AI model to analyze health data and evaluate the health status of individual users. Emotional data is also analyzed using speech recognition technology and machine learning models to understand the user's emotions. As a result, a comprehensive health profile is formed that combines the user's physical and emotional state.

[0302] The server generates personalized health management advice based on the analysis results. This generation method designs advice considering not only the user's physical condition but also their emotional state. For example, it generates advice that includes suggestions for relaxation methods for users who are feeling stressed. It also links with a database of medical institutions to reflect the latest medical information.

[0303] The terminal notifies the user of the health management advice received from the server. Through apps on smartphones or tablets, specific action plans to be incorporated into daily life and health suggestions tailored to the user's emotions are presented to the user.

[0304] As a specific example, consider the case where User B regularly wears a wearable device and records their emotional state in a smartphone app. The server acquires heart rate data from the device and emotional data from the voice, and analyzes them using an AI model. As a result, if it is determined that User B's stress level has recently increased and their activity level has decreased, a notification suggesting yoga or meditation to help with relaxation is provided via the terminal. In this way, specific advice based on the user's health and emotional states is realized.

[0305] The following explains the processing flow.

[0306] Step 1:

[0307] The user uses a wearable device and a smartphone to record health data and emotional data such as heart rate, activity level, and voice input. These data are transferred from the device to the smartphone via Bluetooth or Wi-Fi.

[0308] Step 2:

[0309] The terminal sends the received health data and emotional data to the server. This transmission is performed using a secure communication method and is updated regularly in real time.

[0310] Step 3:

[0311] The server stores the received data in a database and starts analyzing the health state using an AI model. From the health data, changes in heart rate and activity level are analyzed, and from the emotional data, the emotional state is analyzed based on voice patterns and behavioral data.

[0312] Step 4:

[0313] Based on the analysis results, the server comprehensively evaluates the user's health and emotional state. For example, if an increase in stress levels or a decrease in activity levels is detected, this will be taken into consideration.

[0314] Step 5:

[0315] The server generates personalized health management advice based on the user's physical and emotional state. This advice includes programs for relaxation and instructions to encourage positive activity. It also accesses a database of healthcare institutions to incorporate the latest medical information into the advice.

[0316] Step 6:

[0317] The device notifies the user of advice received from the server. The app provides the user with detailed action plans and suggestions for improving lifestyle habits, encouraging them to take action based on these suggestions.

[0318] Step 7:

[0319] Users receive advice from their devices and adjust their lifestyles as needed. They provide feedback on their actions through the app and send additional data to the server. This data is used for future analysis.

[0320] (Example 2)

[0321] 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".

[0322] In modern society, comprehensively managing an individual's health and mental state is becoming increasingly important. However, conventional systems treat physiological and emotional information separately, resulting in insufficient comprehensive health management. In particular, providing personalized health advice that takes emotional states into account has been difficult.

[0323] 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.

[0324] In this invention, the server includes means for acquiring personal physiological and emotional information, means for storing the acquired physiological and emotional information, and means for analyzing the physiological and emotional state using the stored information. This makes it possible to provide health advice optimized for the user.

[0325] "Physiological information" refers to information that indicates an individual's physical condition, such as heart rate, body temperature, and activity level.

[0326] "Emotional information" refers to information that indicates an individual's emotional state, extracted from audio data and behavioral patterns.

[0327] "Means of acquisition" refers to devices and processes for collecting physiological and emotional information from individuals.

[0328] "Storage means" refers to a mechanism for storing acquired physiological and emotional information in a database or storage device.

[0329] "Analysis means" refers to methods and techniques used to evaluate an individual's physiological and emotional state using acquired and stored information.

[0330] "Generation method" refers to the process of creating individually tailored health advice based on the analysis results.

[0331] "Notification means" refers to methods or devices for transmitting generated health advice to the user's information terminal.

[0332] A "medical institution's information repository" is a database or information system where the latest medical information is stored and accessible.

[0333] A "machine learning model" refers to an algorithm or framework used for data analysis; it is a mathematical model that learns patterns from data and makes predictions.

[0334] The system for implementing this invention is realized through interaction between a server, a user's terminal, and the user themselves. The user wears a wearable device or smartphone to collect physiological information. These devices acquire physiological information such as heart rate, body temperature, and activity level in real time, and further extract emotional information from voice data using speech recognition technology.

[0335] The device continuously transmits acquired physiological and emotional information to the server. The server organizes and stores this information in a database. The server also uses machine learning models on the stored information to evaluate the user's health and emotional state. Specifically, by running the AI ​​model, the health state of each user is quantified and their emotional state is analyzed.

[0336] To generate health advice optimized for the user, the server uses a generating AI model based on data provided by the AI ​​model. This generating AI model creates advice aimed at improving lifestyle habits and providing mental support, based on the user's analysis results. An example of this prompt is, "Please suggest a specific action plan that addresses the user's changing health condition."

[0337] As a concrete example, suppose a user wears a wearable device daily. This device records heart rate variability and sends it to a server along with audio recordings from a smartphone. The server analyzes the data and, if it determines that the user has been experiencing stress recently, suggests yoga exercises to promote relaxation. This notification appears on the user's device, allowing them to learn specific steps to improve their health.

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

[0339] Step 1:

[0340] Users collect physiological and emotional information using wearable devices and smartphones. Specifically, the wearable device measures heart rate, body temperature, and activity level, while the smartphone acquires voice data to collect emotional information. Input data includes heart rate, body temperature, activity level, and voice data, and the output is a set of these data.

[0341] Step 2:

[0342] The device sends collected physiological and emotional information to the server. Specifically, the device packets the measurement data and sends a request to the server's API endpoint. The input data is a dataset sent from the wearable device and smartphone, and the output is the data sent to the server.

[0343] Step 3:

[0344] The server stores and organizes the received data in a database. Specifically, physiological and emotional information is written to user-specific database tables and organized along a timeline. The input is data sent from the terminal, and the output is the information stored in the database.

[0345] Step 4:

[0346] The server uses stored data to perform analysis with an AI model. Specifically, it applies machine learning algorithms to evaluate and quantify the user's health and emotional state. The input is information obtained from the database, and the output is the health and emotional state quantified from the analysis results.

[0347] Step 5:

[0348] Using a generative AI model, the server generates health advice optimized for the user. During this process, it takes the analysis results as input and executes the prompt message, "Please propose a specific action plan that addresses changes in the user's health condition." The output is the generated health advice.

[0349] Step 6:

[0350] The device receives advice generated from the server and notifies the user. For example, a smartphone app displays a pop-up notification for the user to review. The input is health advice from the server, and the output is a specific action plan displayed on the user's screen.

[0351] (Application Example 2)

[0352] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0353] In modern urban areas, maintaining individual health management and emotional balance is crucial, but there is a lack of systems to effectively support this. In particular, there is a need for methods to simultaneously collect individual health and emotional data and provide appropriate health management and advice tailored to their emotional state based on this data. Furthermore, effectively utilizing this data to coordinate with urban medical resources and relaxation facilities to provide individualized support remains challenging.

[0354] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0355] In this invention, the server includes receiving means for collecting individual health and emotional data, analyzing means for analyzing the collected health and emotional data and evaluating the health and emotional state of individual users, and generating means for generating personalized health management and emotional advice based on the analysis results. This makes it possible to effectively support the balance of health and emotional well-being for individual citizens in urban areas.

[0356] "Personal health data" refers to numerical values ​​and indicators that show the physical condition of individual users, such as heart rate, activity level, and body temperature.

[0357] "Emotional data" refers to information that indicates a user's mental state and emotional tendencies, obtained by analyzing their voice and behavioral patterns.

[0358] "Receiving means" refers to a system or device used to collect an individual's health data and emotional data.

[0359] "Analysis means" refers to a method or apparatus used to evaluate a user's health and emotional state using collected health data and emotional data.

[0360] A "generation method" is a system or device that constructs personalized health management advice and recommendations tailored to emotional states based on analyzed data.

[0361] "Notification means" refers to a method or device for promptly and appropriately conveying generated advice to the user's information processing terminal.

[0362] "Information processing equipment" refers to electronic devices such as mobile terminals and computers that users use on a daily basis.

[0363] A "system" is a collection of devices or methods in which individual components work together to achieve a specific purpose.

[0364] The system for implementing this invention mainly consists of a server, a terminal, and a wearable device or information processing device used by the user. The program is implemented through a series of processes that automatically perform everything from data collection to analysis and notification.

[0365] The server receives personal health and emotional data from wearable devices and smartphones worn by users. Specifically, health data includes heart rate, body temperature, and activity levels, while emotional data includes voice data and emotional states based on behavioral patterns. The data is stored on a cloud server and used for analysis. Hardware used includes Apple Watches and typical smartphones, and cloud computing technology is applied for data processing.

[0366] As an analysis method, the server uses Python to run machine learning models and evaluate health and emotional data. This process utilizes AI frameworks such as TensorFlow and PyTorch. Based on the analysis results, the system comprehensively evaluates the user's health and emotional state and generates appropriate health management advice. In particular, the health advice is personalized based on the individual's health condition and emotions.

[0367] The device itself is responsible for quickly notifying users of the generated advice on their smartphones. This notification includes specific action plans tailored to the user's current situation and suggestions for activities utilizing local healthcare resources.

[0368] As a concrete example, suppose a user inputs data for one month, and the analysis results indicate an increase in stress levels. In this case, the system would provide a notification recommending a yoga session at a relaxation facility in the city. Another example of a prompt for the generating AI model is, "Generate appropriate health management advice based on the user's health and emotional data from the past week."

[0369] This system will enable individual users to effectively manage their own health and emotional state, supporting a healthy lifestyle within urban environments.

[0370] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0371] Step 1:

[0372] A wearable device and smartphone worn by the user periodically measure personal health data (e.g., heart rate, body temperature, activity level) and emotional data (e.g., emotional state from voice), and transmit this data to a server. This data is collected, and the server prepares to store this input data in a database. The collected data is stored as information necessary for subsequent analysis.

[0373] Step 2:

[0374] The server retrieves health and emotional data stored in a database. The input data also includes historical measurement data. The server uses this data as input to perform data analysis using a machine learning model (e.g., a model using TensorFlow). Specifically, it evaluates the user's current health status and emotional tendencies from the data. This analysis outputs indicators related to the user's health and emotional state.

[0375] Step 3:

[0376] Based on the analysis results, the server generates health advice. This generation process takes the analyzed indicators as input and outputs health recommendations and advice tailored to the user's emotional state. This process utilizes a pre-configured rule-based system and a generative AI model. The generated advice includes specific action plans and suggestions for utilizing medical resources to provide to the user.

[0377] Step 4:

[0378] The server sends the generated advice to the terminal, which then notifies the user's smartphone. Here, the generated advice is taken as input and output in an appropriate format for display on the user's screen. This notification is presented in real time for the user to review.

[0379] Step 5:

[0380] Users review the notifications they receive and take action based on the presented health and emotional care plans. As users take action, a feedback loop is formed throughout the system, influencing subsequent data acquisition.

[0381] Through the processes described above, the system continues to support the user's health and emotional management.

[0382] 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.

[0383] 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.

[0384] 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.

[0385] [Third Embodiment]

[0386] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0387] 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.

[0388] 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).

[0389] 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.

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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".

[0398] The healthcare system described in this patent effectively collects and analyzes individual health data to provide users with appropriate health management advice. This system consists of a server, terminals, and users.

[0399] The server receives health data from the user. For example, heart rate and activity levels recorded by the user wearing a wearable device are sent to the server via smartphone. This data is stored in a database on the server and then analyzed by a designated AI model.

[0400] In the analysis, the server assesses the user's health status. An AI model assists in this process, detecting anomalies and assessing risks. This identifies health risks specific to each user. For example, if the heart rate exceeds the normal range, it suggests an increase in stress levels.

[0401] The server uses the analysis results to generate personalized health management advice. This requires up-to-date medical information, so it accesses databases of medical institutions to obtain that information. For example, it can determine the need for vaccination based on the influenza season.

[0402] The generated advice is notified to the device. The device provides information to the user in the form of a smartphone or tablet. The notification includes suggestions for improving lifestyle habits and recommendations for regular health checkups. This serves as an opportunity for the user to take action on their daily health management.

[0403] As a concrete example, consider a case where user A regularly records their heart rate and activity level through a device. If the server analyzes this data and finds a recent decrease in activity and an increase in heart rate, it determines that stress management is necessary. Therefore, the server suggests stretching and relaxation methods and notifies user A through the device. In this way, specific advice tailored to individual health needs is provided.

[0404] The following describes the processing flow.

[0405] Step 1:

[0406] The server receives the user's health data. The system works by transmitting data from the user's wearable device via a smartphone and storing it in the server's database. This data includes heart rate, body temperature, and activity level.

[0407] Step 2:

[0408] The server processes the accumulated data through an AI model to analyze the user's health status. Through regular data analysis, it understands the user's daily health patterns and detects unexpected changes or anomalies. This analysis identifies specific health risks.

[0409] Step 3:

[0410] Based on the analysis results, the server generates personalized health management advice. To do this, the server accesses databases of medical institutions to obtain the latest medical information. This ensures that the advice reflects current medical guidelines and disease information.

[0411] Step 4:

[0412] The device notifies the user of health management advice received from the server. This notification is displayed as a message or alert in the smartphone app. Specific action plans and health management suggestions are then provided.

[0413] Step 5:

[0414] Based on notifications from their devices, users incorporate the provided health management advice into their daily lives. For example, they might review their lifestyle habits or implement recommended exercise programs. Furthermore, as needed, users can send feedback to the server via their devices, contributing to the system's analysis accuracy by providing additional data.

[0415] (Example 1)

[0416] 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."

[0417] In recent years, the importance of individuals managing their health in a more precise and personalized way has increased. However, conventional healthcare systems struggle to effectively monitor the dynamic health status of individual users and provide timely and appropriate health management advice. Solving this challenge is crucial to contributing to health maintenance and disease prevention.

[0418] 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.

[0419] In this invention, the server includes receiving means for collecting personal health information, analysis means for analyzing the collected health information and evaluating the user's health status, notification means for notifying the user's device of the generated advice, and feedback receiving means for collecting the user's responses from the device and using them for future analysis. This makes it possible to analyze the dynamic health status of the user in real time and to quickly provide personalized health management advice.

[0420] "Personal health information" refers to biometric data and activity data related to the user's physical condition, including heart rate and activity level.

[0421] "Receiving means" refers to the function that allows a server to acquire health information transmitted from wearable devices or terminals.

[0422] "Analysis means" refers to the process of evaluating the user's health status based on received health information, and specifically includes the function of analyzing data using an AI model.

[0423] "Generating means" refers to the function that generates personalized health management advice for each user based on the analyzed results.

[0424] "Notification means" refers to the function that sends generated health management advice to the user's device to inform the user.

[0425] "Means for receiving feedback" refers to a function that collects responses from users and utilizes them for future analysis.

[0426] A "generative AI model" refers to an artificial intelligence model used to analyze health information, and includes functions for detecting anomalies and assessing risks.

[0427] A "medical institution's information repository" refers to a database where the latest medical information is stored, and it is used to retrieve necessary medical information.

[0428] This invention is a system for supporting personal health management, consisting of a server, a terminal, and a user. The server is equipped with a receiving means to receive health information from a wearable device worn by the user. The received information includes heart rate and activity level. The terminal takes the form of a smartphone or tablet and is responsible for collecting data from the wearable device and transmitting it to the server.

[0429] The server has analytical capabilities to analyze the collected health information. This analysis uses a generative AI model to assess the user's health status. The AI ​​model detects anomalies and assesses risks, providing insights into the user's health. For example, if the heart rate exceeds the normal range, it may suggest that stress levels may be increasing.

[0430] Based on the analysis results, the server is equipped with a generation mechanism to generate personalized health management advice for the user. This advice generation takes into account the latest medical information. Therefore, the server accesses the information storage of medical institutions to obtain the necessary information. For example, it can provide vaccination recommendations depending on the influenza season.

[0431] The generated health management advice is sent from the server to the terminal. The terminal has a means of notifying the user and conveys the advice to the user. This advice includes suggestions for improving lifestyle habits and recommendations for regular health checkups. The user receives the notification and takes daily health management actions based on it.

[0432] As a concrete example, consider a case where user A records their heart rate and activity level through a wearable device. This data is sent to a server and analyzed by an AI model. If the analysis reveals a recent decrease in activity level and an increase in heart rate, the server provides stress management advice. This advice may include suggestions for stretching and relaxation techniques, and this information is sent to user A's device.

[0433] A concrete example of a prompt message for the generating AI model is, "Based on the analysis of user A's recent heart rate and activity data, generate appropriate health management advice." This will provide advice tailored to the user's health condition.

[0434] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0435] Step 1:

[0436] The user wears a wearable device to collect health information such as heart rate and activity level. This information is recorded in real time by the device. The input is biometric data related to the user's physical activity. The output is health data stored in the wearable device.

[0437] Step 2:

[0438] The terminal receives data from wearable devices via Bluetooth or Wi-Fi. The input is health data transmitted from the wearable device. The terminal formats this data and prepares it for transmission to the server. The output is the prepared set of health data, which is stored for further processing.

[0439] Step 3:

[0440] The server receives health data transmitted from the terminal. This data is stored in a database on the server. The input is the data transmitted from the terminal. Storing it in the database allows for historical data accumulation, which is useful for later analysis. The output is the health data as it is stored in the database.

[0441] Step 4:

[0442] The server analyzes data using a generative AI model. The input is health data stored in a database. The AI ​​model analyzes the data to detect anomalies and assess risks. For example, it analyzes heart rate trends and detects abnormal increases. This provides insights for evaluating the user's health status. The output is the analysis result, which is information indicating the user's health status.

[0443] Step 5:

[0444] The server generates personalized health management advice based on the analysis results. The inputs are the AI ​​analysis results and the latest medical information. The server accesses the information repository of healthcare institutions to retrieve relevant medical information and supplement the advice. For example, it might make recommendations about stretching or relaxation. The output is the health management advice provided to the user.

[0445] Step 6:

[0446] The server sends the generated advice to the terminal. The input is the generated health management advice. The terminal receives this information and displays it on the screen or provides alerts to notify the user. The output is the advice information the user receives through the terminal.

[0447] Step 7:

[0448] Users adjust their lifestyles based on the advice provided. Input is health management advice received via the device. Users can provide feedback via the device. Output is user feedback, which will be used for future analysis.

[0449] (Application Example 1)

[0450] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0451] In modern society, there is a need to effectively manage individual health information and provide appropriate health advice in real time. However, existing systems struggle to provide personalized health management information in real time. Furthermore, the introduction of advice that reflects the latest medical information and in a form that can be easily used at home is lagging behind. Therefore, the challenge is to realize a system that efficiently and in real time monitors an individual's health status and provides optimal health advice.

[0452] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0453] In this invention, the server includes a receiving device for collecting personal health information, an analysis device for analyzing the collected health information and evaluating the user's health status, a generating device for generating personalized health management information based on the analysis results, a notification device for notifying the user's device of the generated information, and a robot for monitoring the user's condition in real time and providing appropriate advice. This makes it possible to grasp an individual's health status in real time and easily obtain specific and effective health management information based on the latest medical information within the home.

[0454] A "receiving device for collecting personal health information" refers to a device that acquires health-related data from wearable devices and sensors.

[0455] An "analysis device" is a system used to evaluate and analyze a user's health status based on acquired health information.

[0456] A "generator" is a device that creates individually customized health management information based on the analysis results.

[0457] A "notification device" refers to a terminal or interface used to inform users of generated health management information.

[0458] A "robot" is an automated device that monitors the user's health status in real time within the home and provides appropriate health advice.

[0459] An "artificial intelligence model" is an algorithm or technology that analyzes acquired health information and identifies patterns to evaluate fluctuations in health status.

[0460] "Personalized health management information" refers to information that provides recommendations for maintaining and improving health, tailored to each user's health condition and lifestyle.

[0461] The system for implementing this invention combines multiple devices and technologies to effectively manage personal health information.

[0462] First, the user wears a wearable device to collect daily health information in real time. The receiving device uses the Bluetooth communication protocol to automatically transmit this data to a server. The collected data includes heart rate and daily activity levels.

[0463] After receiving the data, the server uses analysis software such as Python or TensorFlow to analyze the health information using artificial intelligence models. This makes it possible to identify patterns and anomalies related to the user's health status.

[0464] Next, the server generates personalized health management information based on the analysis results. During this process, API access to external medical information databases is performed, incorporating the latest medical information to improve the accuracy of user advice. This information is generated by a generator and provided to the user's notification device.

[0465] A specific example of its use is that if the robot detects that a user is experiencing excessive stress, it might provide voice advice such as, "Your heart rate has recently increased. We recommend you take three minutes of deep breathing." In this way, users can monitor their health in their daily lives and take necessary actions.

[0466] The generating AI model is used to suggest lifestyle improvements based on health information and is instructed by an example prompt: "Generate lifestyle improvement advice based on the user's health data. Provide relaxation methods to cope with recent increases in heart rate." This prompt allows the AI ​​model to generate specific advice on stress reduction methods and daily habits.

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

[0468] Step 1:

[0469] The user wears a wearable device to collect health information. The wearable device measures heart rate and activity level, and transmits this data to the terminal in real time via Bluetooth communication. The input is raw data about the user's physical condition, and the output is formatted data transmitted to the terminal via Bluetooth.

[0470] Step 2:

[0471] The terminal transfers the received raw data to the server. The server receives the health information sent from the terminal and stores it in a database. The input in this step is formatted health information data, and the output is data in an available format such as JSON stored in the database.

[0472] Step 3:

[0473] The server analyzes the stored data using Python and TensorFlow. The server initiates the analysis, using a generated AI model to detect anomalies and perform trend analysis. The input is user health information stored in a database, and the output is diagnosed data and identified health risk information.

[0474] Step 4:

[0475] The server generates personalized health management information using a generation device based on the analysis results. It obtains the latest medical information from an external API and creates advice that takes this information into account. The input is the analyzed health information and the latest information obtained from the medical database, and the output is personalized, specific health management advice for the user.

[0476] Step 5:

[0477] The generated health management advice is sent to the terminal via a notification device, and the terminal uses a robot to present the information to the user. In this process, the analyzed information is presented as audio and visual messages. The input is the generated advice information, and the output is a display or audio output as a notification intended for the user to receive and understand.

[0478] Step 6:

[0479] The user receives advice from the robot and implements it in their daily life. Specifically, the robot guides the user through recommended actions, and the user follows these instructions to manage their health. The input is the advice from the robot, and the output is the user's improved actions.

[0480] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0481] The healthcare system described in this patent collects and analyzes individual health and emotional data to provide users with appropriate health management advice. Specifically, it centers on the interaction between the server, terminal, and user.

[0482] The server receives user health and emotional data. Through wearable devices and smartphones worn by the user, it acquires physiological data such as heart rate, body temperature, and activity levels, as well as emotional data derived from voice data and behavioral patterns. The server stores this data in a database.

[0483] In the analysis, the server uses an AI model to analyze health data and evaluate the health status of individual users. Emotional data is also analyzed using speech recognition technology and machine learning models to understand the user's emotions. As a result, a comprehensive health profile is formed that combines the user's physical and emotional state.

[0484] The server generates personalized health management advice based on the analysis results. This generation method designs advice considering not only the user's physical condition but also their emotional state. For example, it generates advice that includes suggestions for relaxation methods for users who are feeling stressed. It also links with a database of medical institutions to reflect the latest medical information.

[0485] The device notifies the user of health management advice received from the server. Through a smartphone or tablet app, the user is presented with specific action plans to incorporate into their daily life and health suggestions tailored to their emotional state.

[0486] As a concrete example, consider a case where user B regularly wears a wearable device and records their emotional state in a smartphone app. The server acquires heart rate data and emotional data from voice from the device and analyzes them with an AI model. If the system determines that user B's stress level has recently increased and their activity level has decreased, it provides a notification via the device suggesting yoga or meditation to help them relax. In this way, specific advice based on the user's health and emotional state is realized.

[0487] The following describes the processing flow.

[0488] Step 1:

[0489] Users record health data such as heart rate, activity levels, and voice input, as well as emotional data, using wearable devices and smartphones. This data is transferred from the device to the smartphone via Bluetooth or Wi-Fi.

[0490] Step 2:

[0491] The device sends received health and emotional data to the server. This transmission uses a secure communication method and is updated regularly in real time.

[0492] Step 3:

[0493] The server stores the received data in a database and begins analyzing the health status using an AI model. It analyzes changes in heart rate and activity levels from the health data, and analyzes emotional states based on voice patterns and behavioral data from the emotional data.

[0494] Step 4:

[0495] Based on the analysis results, the server comprehensively evaluates the user's health and emotional state. For example, if an increase in stress levels or a decrease in activity levels is detected, this will be taken into consideration.

[0496] Step 5:

[0497] The server generates personalized health management advice based on the user's physical and emotional state. This advice includes programs for relaxation and instructions to encourage positive activity. It also accesses a database of healthcare institutions to incorporate the latest medical information into the advice.

[0498] Step 6:

[0499] The device notifies the user of advice received from the server. The app provides the user with detailed action plans and suggestions for improving lifestyle habits, encouraging them to take action based on these suggestions.

[0500] Step 7:

[0501] Users receive advice from their devices and adjust their lifestyles as needed. They provide feedback on their actions through the app and send additional data to the server. This data is used for future analysis.

[0502] (Example 2)

[0503] 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."

[0504] In modern society, comprehensively managing an individual's health and mental state is becoming increasingly important. However, conventional systems treat physiological and emotional information separately, resulting in insufficient comprehensive health management. In particular, providing personalized health advice that takes emotional states into account has been difficult.

[0505] 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.

[0506] In this invention, the server includes means for acquiring personal physiological and emotional information, means for storing the acquired physiological and emotional information, and means for analyzing the physiological and emotional state using the stored information. This makes it possible to provide health advice optimized for the user.

[0507] "Physiological information" refers to information that indicates an individual's physical condition, such as heart rate, body temperature, and activity level.

[0508] "Emotional information" refers to information that indicates an individual's emotional state, extracted from audio data and behavioral patterns.

[0509] "Means of acquisition" refers to devices and processes for collecting physiological and emotional information from individuals.

[0510] "Storage means" refers to a mechanism for storing acquired physiological and emotional information in a database or storage device.

[0511] "Analysis means" refers to methods and techniques used to evaluate an individual's physiological and emotional state using acquired and stored information.

[0512] "Generation method" refers to the process of creating individually tailored health advice based on the analysis results.

[0513] "Notification means" refers to methods or devices for transmitting generated health advice to the user's information terminal.

[0514] A "medical institution's information repository" is a database or information system where the latest medical information is stored and accessible.

[0515] A "machine learning model" refers to an algorithm or framework used for data analysis; it is a mathematical model that learns patterns from data and makes predictions.

[0516] The system for implementing this invention is realized through interaction between a server, a user's terminal, and the user themselves. The user wears a wearable device or smartphone to collect physiological information. These devices acquire physiological information such as heart rate, body temperature, and activity level in real time, and further extract emotional information from voice data using speech recognition technology.

[0517] The device continuously transmits acquired physiological and emotional information to the server. The server organizes and stores this information in a database. The server also uses machine learning models on the stored information to evaluate the user's health and emotional state. Specifically, by running the AI ​​model, the health state of each user is quantified and their emotional state is analyzed.

[0518] To generate health advice optimized for the user, the server uses a generating AI model based on data provided by the AI ​​model. This generating AI model creates advice aimed at improving lifestyle habits and providing mental support, based on the user's analysis results. An example of this prompt is, "Please suggest a specific action plan that addresses the user's changing health condition."

[0519] As a concrete example, suppose a user wears a wearable device daily. This device records heart rate variability and sends it to a server along with audio recordings from a smartphone. The server analyzes the data and, if it determines that the user has been experiencing stress recently, suggests yoga exercises to promote relaxation. This notification appears on the user's device, allowing them to learn specific steps to improve their health.

[0520] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0521] Step 1:

[0522] Users collect physiological and emotional information using wearable devices and smartphones. Specifically, the wearable device measures heart rate, body temperature, and activity level, while the smartphone acquires voice data to collect emotional information. Input data includes heart rate, body temperature, activity level, and voice data, and the output is a set of these data.

[0523] Step 2:

[0524] The device sends collected physiological and emotional information to the server. Specifically, the device packets the measurement data and sends a request to the server's API endpoint. The input data is a dataset sent from the wearable device and smartphone, and the output is the data sent to the server.

[0525] Step 3:

[0526] The server stores and organizes the received data in a database. Specifically, physiological and emotional information is written to user-specific database tables and organized along a timeline. The input is data sent from the terminal, and the output is the information stored in the database.

[0527] Step 4:

[0528] The server uses stored data to perform analysis with an AI model. Specifically, it applies machine learning algorithms to evaluate and quantify the user's health and emotional state. The input is information obtained from the database, and the output is the health and emotional state quantified from the analysis results.

[0529] Step 5:

[0530] Using a generative AI model, the server generates health advice optimized for the user. During this process, it takes the analysis results as input and executes the prompt message, "Please propose a specific action plan that addresses changes in the user's health condition." The output is the generated health advice.

[0531] Step 6:

[0532] The device receives advice generated from the server and notifies the user. For example, a smartphone app displays a pop-up notification for the user to review. The input is health advice from the server, and the output is a specific action plan displayed on the user's screen.

[0533] (Application Example 2)

[0534] 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."

[0535] In modern urban areas, maintaining individual health management and emotional balance is crucial, but there is a lack of systems to effectively support this. In particular, there is a need for methods to simultaneously collect individual health and emotional data and provide appropriate health management and advice tailored to their emotional state based on this data. Furthermore, effectively utilizing this data to coordinate with urban medical resources and relaxation facilities to provide individualized support remains challenging.

[0536] 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.

[0537] In this invention, the server includes receiving means for collecting individual health and emotional data, analyzing means for analyzing the collected health and emotional data and evaluating the health and emotional state of individual users, and generating means for generating personalized health management and emotional advice based on the analysis results. This makes it possible to effectively support the balance of health and emotional well-being for individual citizens in urban areas.

[0538] "Personal health data" refers to numerical values ​​and indicators that show the physical condition of individual users, such as heart rate, activity level, and body temperature.

[0539] "Emotional data" refers to information that indicates a user's mental state and emotional tendencies, obtained by analyzing their voice and behavioral patterns.

[0540] "Receiving means" refers to a system or device used to collect an individual's health data and emotional data.

[0541] "Analysis means" refers to a method or apparatus used to evaluate a user's health and emotional state using collected health data and emotional data.

[0542] A "generation method" is a system or device that constructs personalized health management advice and recommendations tailored to emotional states based on analyzed data.

[0543] "Notification means" refers to a method or device for promptly and appropriately conveying generated advice to the user's information processing terminal.

[0544] "Information processing equipment" refers to electronic devices such as mobile terminals and computers that users use on a daily basis.

[0545] A "system" is a collection of devices or methods in which individual components work together to achieve a specific purpose.

[0546] The system for implementing this invention mainly consists of a server, a terminal, and a wearable device or information processing device used by the user. The program is implemented through a series of processes that automatically perform everything from data collection to analysis and notification.

[0547] The server receives personal health and emotional data from wearable devices and smartphones worn by users. Specifically, health data includes heart rate, body temperature, and activity levels, while emotional data includes voice data and emotional states based on behavioral patterns. The data is stored on a cloud server and used for analysis. Hardware used includes Apple Watches and typical smartphones, and cloud computing technology is applied for data processing.

[0548] As an analysis method, the server uses Python to run machine learning models and evaluate health and emotional data. This process utilizes AI frameworks such as TensorFlow and PyTorch. Based on the analysis results, the system comprehensively evaluates the user's health and emotional state and generates appropriate health management advice. In particular, the health advice is personalized based on the individual's health condition and emotions.

[0549] The device itself is responsible for quickly notifying users of the generated advice on their smartphones. This notification includes specific action plans tailored to the user's current situation and suggestions for activities utilizing local healthcare resources.

[0550] As a concrete example, suppose a user inputs data for one month, and the analysis results indicate an increase in stress levels. In this case, the system would provide a notification recommending a yoga session at a relaxation facility in the city. Another example of a prompt for the generating AI model is, "Generate appropriate health management advice based on the user's health and emotional data from the past week."

[0551] This system will enable individual users to effectively manage their own health and emotional state, supporting a healthy lifestyle within urban environments.

[0552] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0553] Step 1:

[0554] A wearable device and smartphone worn by the user periodically measure personal health data (e.g., heart rate, body temperature, activity level) and emotional data (e.g., emotional state from voice), and transmit this data to a server. This data is collected, and the server prepares to store this input data in a database. The collected data is stored as information necessary for subsequent analysis.

[0555] Step 2:

[0556] The server retrieves health and emotional data stored in a database. The input data also includes historical measurement data. The server uses this data as input to perform data analysis using a machine learning model (e.g., a model using TensorFlow). Specifically, it evaluates the user's current health status and emotional tendencies from the data. This analysis outputs indicators related to the user's health and emotional state.

[0557] Step 3:

[0558] Based on the analysis results, the server generates health advice. This generation process takes the analyzed indicators as input and outputs health recommendations and advice tailored to the user's emotional state. This process utilizes a pre-configured rule-based system and a generative AI model. The generated advice includes specific action plans and suggestions for utilizing medical resources to provide to the user.

[0559] Step 4:

[0560] The server sends the generated advice to the terminal, which then notifies the user's smartphone. Here, the generated advice is taken as input and output in an appropriate format for display on the user's screen. This notification is presented in real time for the user to review.

[0561] Step 5:

[0562] Users review the notifications they receive and take action based on the presented health and emotional care plans. As users take action, a feedback loop is formed throughout the system, influencing subsequent data acquisition.

[0563] Through the processes described above, the system continues to support the user's health and emotional management.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] [Fourth Embodiment]

[0568] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0569] 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.

[0570] 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).

[0571] 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.

[0572] 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.

[0573] 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).

[0574] 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.

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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".

[0581] The healthcare system described in this patent effectively collects and analyzes individual health data to provide users with appropriate health management advice. This system consists of a server, terminals, and users.

[0582] The server receives health data from the user. For example, heart rate and activity levels recorded by the user wearing a wearable device are sent to the server via smartphone. This data is stored in a database on the server and then analyzed by a designated AI model.

[0583] In the analysis, the server assesses the user's health status. An AI model assists in this process, detecting anomalies and assessing risks. This identifies health risks specific to each user. For example, if the heart rate exceeds the normal range, it suggests an increase in stress levels.

[0584] The server uses the analysis results to generate personalized health management advice. This requires up-to-date medical information, so it accesses databases of medical institutions to obtain that information. For example, it can determine the need for vaccination based on the influenza season.

[0585] The generated advice is notified to the device. The device provides information to the user in the form of a smartphone or tablet. The notification includes suggestions for improving lifestyle habits and recommendations for regular health checkups. This serves as an opportunity for the user to take action on their daily health management.

[0586] As a concrete example, consider a case where user A regularly records their heart rate and activity level through a device. If the server analyzes this data and finds a recent decrease in activity and an increase in heart rate, it determines that stress management is necessary. Therefore, the server suggests stretching and relaxation methods and notifies user A through the device. In this way, specific advice tailored to individual health needs is provided.

[0587] The following describes the processing flow.

[0588] Step 1:

[0589] The server receives the user's health data. The system works by transmitting data from the user's wearable device via a smartphone and storing it in the server's database. This data includes heart rate, body temperature, and activity level.

[0590] Step 2:

[0591] The server processes the accumulated data through an AI model to analyze the user's health status. Through regular data analysis, it understands the user's daily health patterns and detects unexpected changes or anomalies. This analysis identifies specific health risks.

[0592] Step 3:

[0593] Based on the analysis results, the server generates personalized health management advice. To do this, the server accesses databases of medical institutions to obtain the latest medical information. This ensures that the advice reflects current medical guidelines and disease information.

[0594] Step 4:

[0595] The device notifies the user of health management advice received from the server. This notification is displayed as a message or alert in the smartphone app. Specific action plans and health management suggestions are then provided.

[0596] Step 5:

[0597] Based on notifications from their devices, users incorporate the provided health management advice into their daily lives. For example, they might review their lifestyle habits or implement recommended exercise programs. Furthermore, as needed, users can send feedback to the server via their devices, contributing to the system's analysis accuracy by providing additional data.

[0598] (Example 1)

[0599] 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".

[0600] In recent years, the importance of individuals managing their health in a more precise and personalized way has increased. However, conventional healthcare systems struggle to effectively monitor the dynamic health status of individual users and provide timely and appropriate health management advice. Solving this challenge is crucial to contributing to health maintenance and disease prevention.

[0601] 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.

[0602] In this invention, the server includes receiving means for collecting personal health information, analysis means for analyzing the collected health information and evaluating the user's health status, notification means for notifying the user's device of the generated advice, and feedback receiving means for collecting the user's responses from the device and using them for future analysis. This makes it possible to analyze the dynamic health status of the user in real time and to quickly provide personalized health management advice.

[0603] "Personal health information" refers to biometric data and activity data related to the user's physical condition, including heart rate and activity level.

[0604] "Receiving means" refers to the function that allows a server to acquire health information transmitted from wearable devices or terminals.

[0605] "Analysis means" refers to the process of evaluating the user's health status based on received health information, and specifically includes the function of analyzing data using an AI model.

[0606] "Generating means" refers to the function that generates personalized health management advice for each user based on the analyzed results.

[0607] "Notification means" refers to the function that sends generated health management advice to the user's device to inform the user.

[0608] "Means for receiving feedback" refers to a function that collects responses from users and utilizes them for future analysis.

[0609] A "generative AI model" refers to an artificial intelligence model used to analyze health information, and includes functions for detecting anomalies and assessing risks.

[0610] A "medical institution's information repository" refers to a database where the latest medical information is stored, and it is used to retrieve necessary medical information.

[0611] This invention is a system for supporting personal health management, consisting of a server, a terminal, and a user. The server is equipped with a receiving means to receive health information from a wearable device worn by the user. The received information includes heart rate and activity level. The terminal takes the form of a smartphone or tablet and is responsible for collecting data from the wearable device and transmitting it to the server.

[0612] The server has analytical capabilities to analyze the collected health information. This analysis uses a generative AI model to assess the user's health status. The AI ​​model detects anomalies and assesses risks, providing insights into the user's health. For example, if the heart rate exceeds the normal range, it may suggest that stress levels may be increasing.

[0613] Based on the analysis results, the server is equipped with a generation mechanism to generate personalized health management advice for the user. This advice generation takes into account the latest medical information. Therefore, the server accesses the information storage of medical institutions to obtain the necessary information. For example, it can provide vaccination recommendations depending on the influenza season.

[0614] The generated health management advice is sent from the server to the terminal. The terminal has a means of notifying the user and conveys the advice to the user. This advice includes suggestions for improving lifestyle habits and recommendations for regular health checkups. The user receives the notification and takes daily health management actions based on it.

[0615] As a concrete example, consider a case where user A records their heart rate and activity level through a wearable device. This data is sent to a server and analyzed by an AI model. If the analysis reveals a recent decrease in activity level and an increase in heart rate, the server provides stress management advice. This advice may include suggestions for stretching and relaxation techniques, and this information is sent to user A's device.

[0616] A concrete example of a prompt message for the generating AI model is, "Based on the analysis of user A's recent heart rate and activity data, generate appropriate health management advice." This will provide advice tailored to the user's health condition.

[0617] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0618] Step 1:

[0619] The user wears a wearable device to collect health information such as heart rate and activity level. This information is recorded in real time by the device. The input is biometric data related to the user's physical activity. The output is health data stored in the wearable device.

[0620] Step 2:

[0621] The terminal receives data from wearable devices via Bluetooth or Wi-Fi. The input is health data transmitted from the wearable device. The terminal formats this data and prepares it for transmission to the server. The output is the prepared set of health data, which is stored for further processing.

[0622] Step 3:

[0623] The server receives health data transmitted from the terminal. This data is stored in a database on the server. The input is the data transmitted from the terminal. Storing it in the database allows for historical data accumulation, which is useful for later analysis. The output is the health data as it is stored in the database.

[0624] Step 4:

[0625] The server analyzes data using a generative AI model. The input is health data stored in a database. The AI ​​model analyzes the data to detect anomalies and assess risks. For example, it analyzes heart rate trends and detects abnormal increases. This provides insights for evaluating the user's health status. The output is the analysis result, which is information indicating the user's health status.

[0626] Step 5:

[0627] The server generates personalized health management advice based on the analysis results. The inputs are the AI ​​analysis results and the latest medical information. The server accesses the information repository of healthcare institutions to retrieve relevant medical information and supplement the advice. For example, it might make recommendations about stretching or relaxation. The output is the health management advice provided to the user.

[0628] Step 6:

[0629] The server sends the generated advice to the terminal. The input is the generated health management advice. The terminal receives this information and displays it on the screen or provides alerts to notify the user. The output is the advice information the user receives through the terminal.

[0630] Step 7:

[0631] Users adjust their lifestyles based on the advice provided. Input is health management advice received via the device. Users can provide feedback via the device. Output is user feedback, which will be used for future analysis.

[0632] (Application Example 1)

[0633] 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".

[0634] In modern society, there is a need to effectively manage individual health information and provide appropriate health advice in real time. However, existing systems struggle to provide personalized health management information in real time. Furthermore, the introduction of advice that reflects the latest medical information and in a form that can be easily used at home is lagging behind. Therefore, the challenge is to realize a system that efficiently and in real time monitors an individual's health status and provides optimal health advice.

[0635] 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.

[0636] In this invention, the server includes a receiving device for collecting personal health information, an analysis device for analyzing the collected health information and evaluating the user's health status, a generating device for generating personalized health management information based on the analysis results, a notification device for notifying the user's device of the generated information, and a robot for monitoring the user's condition in real time and providing appropriate advice. This makes it possible to grasp an individual's health status in real time and easily obtain specific and effective health management information based on the latest medical information within the home.

[0637] A "receiving device for collecting personal health information" refers to a device that acquires health-related data from wearable devices and sensors.

[0638] An "analysis device" is a system used to evaluate and analyze a user's health status based on acquired health information.

[0639] A "generator" is a device that creates individually customized health management information based on the analysis results.

[0640] A "notification device" refers to a terminal or interface used to inform users of generated health management information.

[0641] A "robot" is an automated device that monitors the user's health status in real time within the home and provides appropriate health advice.

[0642] An "artificial intelligence model" is an algorithm or technology that analyzes acquired health information and identifies patterns to evaluate fluctuations in health status.

[0643] "Personalized health management information" refers to information that provides recommendations for maintaining and improving health, tailored to each user's health condition and lifestyle.

[0644] The system for implementing this invention combines multiple devices and technologies to effectively manage personal health information.

[0645] First, the user wears a wearable device to collect daily health information in real time. The receiving device uses the Bluetooth communication protocol to automatically transmit this data to a server. The collected data includes heart rate and daily activity levels.

[0646] After receiving the data, the server uses analysis software such as Python or TensorFlow to analyze the health information using artificial intelligence models. This makes it possible to identify patterns and anomalies related to the user's health status.

[0647] Next, the server generates personalized health management information based on the analysis results. During this process, API access to external medical information databases is performed, incorporating the latest medical information to improve the accuracy of user advice. This information is generated by a generator and provided to the user's notification device.

[0648] A specific example of its use is that if the robot detects that a user is experiencing excessive stress, it might provide voice advice such as, "Your heart rate has recently increased. We recommend you take three minutes of deep breathing." In this way, users can monitor their health in their daily lives and take necessary actions.

[0649] The generating AI model is used to suggest lifestyle improvements based on health information and is instructed by an example prompt: "Generate lifestyle improvement advice based on the user's health data. Provide relaxation methods to cope with recent increases in heart rate." This prompt allows the AI ​​model to generate specific advice on stress reduction methods and daily habits.

[0650] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0651] Step 1:

[0652] The user wears a wearable device to collect health information. The wearable device measures heart rate and activity level, and transmits this data to the terminal in real time via Bluetooth communication. The input is raw data about the user's physical condition, and the output is formatted data transmitted to the terminal via Bluetooth.

[0653] Step 2:

[0654] The terminal transfers the received raw data to the server. The server receives the health information sent from the terminal and stores it in a database. The input in this step is formatted health information data, and the output is data in an available format such as JSON stored in the database.

[0655] Step 3:

[0656] The server analyzes the stored data using Python and TensorFlow. The server initiates the analysis, using a generated AI model to detect anomalies and perform trend analysis. The input is user health information stored in a database, and the output is diagnosed data and identified health risk information.

[0657] Step 4:

[0658] The server generates personalized health management information using a generation device based on the analysis results. It obtains the latest medical information from an external API and creates advice that takes this information into account. The input is the analyzed health information and the latest information obtained from the medical database, and the output is personalized, specific health management advice for the user.

[0659] Step 5:

[0660] The generated health management advice is sent to the terminal via a notification device, and the terminal uses a robot to present the information to the user. In this process, the analyzed information is presented as audio and visual messages. The input is the generated advice information, and the output is a display or audio output as a notification intended for the user to receive and understand.

[0661] Step 6:

[0662] The user receives advice from the robot and implements it in their daily life. Specifically, the robot guides the user through recommended actions, and the user follows these instructions to manage their health. The input is the advice from the robot, and the output is the user's improved actions.

[0663] 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.

[0664] The healthcare system described in this patent collects and analyzes individual health and emotional data to provide users with appropriate health management advice. Specifically, it centers on the interaction between the server, terminal, and user.

[0665] The server receives user health and emotional data. Through wearable devices and smartphones worn by the user, it acquires physiological data such as heart rate, body temperature, and activity levels, as well as emotional data derived from voice data and behavioral patterns. The server stores this data in a database.

[0666] In the analysis, the server uses an AI model to analyze health data and evaluate the health status of individual users. Emotional data is also analyzed using speech recognition technology and machine learning models to understand the user's emotions. As a result, a comprehensive health profile is formed that combines the user's physical and emotional state.

[0667] The server generates personalized health management advice based on the analysis results. This generation method designs advice considering not only the user's physical condition but also their emotional state. For example, it generates advice that includes suggestions for relaxation methods for users who are feeling stressed. It also links with a database of medical institutions to reflect the latest medical information.

[0668] The device notifies the user of health management advice received from the server. Through a smartphone or tablet app, the user is presented with specific action plans to incorporate into their daily life and health suggestions tailored to their emotional state.

[0669] As a concrete example, consider a case where user B regularly wears a wearable device and records their emotional state in a smartphone app. The server acquires heart rate data and emotional data from voice from the device and analyzes them with an AI model. If the system determines that user B's stress level has recently increased and their activity level has decreased, it provides a notification via the device suggesting yoga or meditation to help them relax. In this way, specific advice based on the user's health and emotional state is realized.

[0670] The following describes the processing flow.

[0671] Step 1:

[0672] Users record health data such as heart rate, activity levels, and voice input, as well as emotional data, using wearable devices and smartphones. This data is transferred from the device to the smartphone via Bluetooth or Wi-Fi.

[0673] Step 2:

[0674] The device sends received health and emotional data to the server. This transmission uses a secure communication method and is updated regularly in real time.

[0675] Step 3:

[0676] The server stores the received data in a database and begins analyzing the health status using an AI model. It analyzes changes in heart rate and activity levels from the health data, and analyzes emotional states based on voice patterns and behavioral data from the emotional data.

[0677] Step 4:

[0678] Based on the analysis results, the server comprehensively evaluates the user's health and emotional state. For example, if an increase in stress levels or a decrease in activity levels is detected, this will be taken into consideration.

[0679] Step 5:

[0680] The server generates personalized health management advice based on the user's physical and emotional state. This advice includes programs for relaxation and instructions to encourage positive activity. It also accesses a database of healthcare institutions to incorporate the latest medical information into the advice.

[0681] Step 6:

[0682] The device notifies the user of advice received from the server. The app provides the user with detailed action plans and suggestions for improving lifestyle habits, encouraging them to take action based on these suggestions.

[0683] Step 7:

[0684] Users receive advice from their devices and adjust their lifestyles as needed. They provide feedback on their actions through the app and send additional data to the server. This data is used for future analysis.

[0685] (Example 2)

[0686] 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".

[0687] In modern society, comprehensively managing an individual's health and mental state is becoming increasingly important. However, conventional systems treat physiological and emotional information separately, resulting in insufficient comprehensive health management. In particular, providing personalized health advice that takes emotional states into account has been difficult.

[0688] 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.

[0689] In this invention, the server includes means for acquiring personal physiological and emotional information, means for storing the acquired physiological and emotional information, and means for analyzing the physiological and emotional state using the stored information. This makes it possible to provide health advice optimized for the user.

[0690] "Physiological information" refers to information that indicates an individual's physical condition, such as heart rate, body temperature, and activity level.

[0691] "Emotional information" refers to information that indicates an individual's emotional state, extracted from audio data and behavioral patterns.

[0692] "Means of acquisition" refers to devices and processes for collecting physiological and emotional information from individuals.

[0693] "Storage means" refers to a mechanism for storing acquired physiological and emotional information in a database or storage device.

[0694] "Analysis means" refers to methods and techniques used to evaluate an individual's physiological and emotional state using acquired and stored information.

[0695] "Generation method" refers to the process of creating individually tailored health advice based on the analysis results.

[0696] "Notification means" refers to methods or devices for transmitting generated health advice to the user's information terminal.

[0697] A "medical institution's information repository" is a database or information system where the latest medical information is stored and accessible.

[0698] A "machine learning model" refers to an algorithm or framework used for data analysis; it is a mathematical model that learns patterns from data and makes predictions.

[0699] The system for implementing this invention is realized through interaction between a server, a user's terminal, and the user themselves. The user wears a wearable device or smartphone to collect physiological information. These devices acquire physiological information such as heart rate, body temperature, and activity level in real time, and further extract emotional information from voice data using speech recognition technology.

[0700] The device continuously transmits acquired physiological and emotional information to the server. The server organizes and stores this information in a database. The server also uses machine learning models on the stored information to evaluate the user's health and emotional state. Specifically, by running the AI ​​model, the health state of each user is quantified and their emotional state is analyzed.

[0701] To generate health advice optimized for the user, the server uses a generating AI model based on data provided by the AI ​​model. This generating AI model creates advice aimed at improving lifestyle habits and providing mental support, based on the user's analysis results. An example of this prompt is, "Please suggest a specific action plan that addresses the user's changing health condition."

[0702] As a concrete example, suppose a user wears a wearable device daily. This device records heart rate variability and sends it to a server along with audio recordings from a smartphone. The server analyzes the data and, if it determines that the user has been experiencing stress recently, suggests yoga exercises to promote relaxation. This notification appears on the user's device, allowing them to learn specific steps to improve their health.

[0703] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0704] Step 1:

[0705] Users collect physiological and emotional information using wearable devices and smartphones. Specifically, the wearable device measures heart rate, body temperature, and activity level, while the smartphone acquires voice data to collect emotional information. Input data includes heart rate, body temperature, activity level, and voice data, and the output is a set of these data.

[0706] Step 2:

[0707] The device sends collected physiological and emotional information to the server. Specifically, the device packets the measurement data and sends a request to the server's API endpoint. The input data is a dataset sent from the wearable device and smartphone, and the output is the data sent to the server.

[0708] Step 3:

[0709] The server stores and organizes the received data in a database. Specifically, physiological and emotional information is written to user-specific database tables and organized along a timeline. The input is data sent from the terminal, and the output is the information stored in the database.

[0710] Step 4:

[0711] The server uses stored data to perform analysis with an AI model. Specifically, it applies machine learning algorithms to evaluate and quantify the user's health and emotional state. The input is information obtained from the database, and the output is the health and emotional state quantified from the analysis results.

[0712] Step 5:

[0713] Using a generative AI model, the server generates health advice optimized for the user. During this process, it takes the analysis results as input and executes the prompt message, "Please propose a specific action plan that addresses changes in the user's health condition." The output is the generated health advice.

[0714] Step 6:

[0715] The device receives advice generated from the server and notifies the user. For example, a smartphone app displays a pop-up notification for the user to review. The input is health advice from the server, and the output is a specific action plan displayed on the user's screen.

[0716] (Application Example 2)

[0717] 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".

[0718] In modern urban areas, maintaining individual health management and emotional balance is crucial, but there is a lack of systems to effectively support this. In particular, there is a need for methods to simultaneously collect individual health and emotional data and provide appropriate health management and advice tailored to their emotional state based on this data. Furthermore, effectively utilizing this data to coordinate with urban medical resources and relaxation facilities to provide individualized support remains challenging.

[0719] 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.

[0720] In this invention, the server includes receiving means for collecting individual health and emotional data, analyzing means for analyzing the collected health and emotional data and evaluating the health and emotional state of individual users, and generating means for generating personalized health management and emotional advice based on the analysis results. This makes it possible to effectively support the balance of health and emotional well-being for individual citizens in urban areas.

[0721] "Personal health data" refers to numerical values ​​and indicators that show the physical condition of individual users, such as heart rate, activity level, and body temperature.

[0722] "Emotional data" refers to information that indicates a user's mental state and emotional tendencies, obtained by analyzing their voice and behavioral patterns.

[0723] "Receiving means" refers to a system or device used to collect an individual's health data and emotional data.

[0724] "Analysis means" refers to a method or apparatus used to evaluate a user's health and emotional state using collected health data and emotional data.

[0725] A "generation method" is a system or device that constructs personalized health management advice and recommendations tailored to emotional states based on analyzed data.

[0726] "Notification means" refers to a method or device for promptly and appropriately conveying generated advice to the user's information processing terminal.

[0727] "Information processing equipment" refers to electronic devices such as mobile terminals and computers that users use on a daily basis.

[0728] A "system" is a collection of devices or methods in which individual components work together to achieve a specific purpose.

[0729] The system for implementing this invention mainly consists of a server, a terminal, and a wearable device or information processing device used by the user. The program is implemented through a series of processes that automatically perform everything from data collection to analysis and notification.

[0730] The server receives personal health and emotional data from wearable devices and smartphones worn by users. Specifically, health data includes heart rate, body temperature, and activity levels, while emotional data includes voice data and emotional states based on behavioral patterns. The data is stored on a cloud server and used for analysis. Hardware used includes Apple Watches and typical smartphones, and cloud computing technology is applied for data processing.

[0731] As an analysis method, the server uses Python to run machine learning models and evaluate health and emotional data. This process utilizes AI frameworks such as TensorFlow and PyTorch. Based on the analysis results, the system comprehensively evaluates the user's health and emotional state and generates appropriate health management advice. In particular, the health advice is personalized based on the individual's health condition and emotions.

[0732] The device itself is responsible for quickly notifying users of the generated advice on their smartphones. This notification includes specific action plans tailored to the user's current situation and suggestions for activities utilizing local healthcare resources.

[0733] As a concrete example, suppose a user inputs data for one month, and the analysis results indicate an increase in stress levels. In this case, the system would provide a notification recommending a yoga session at a relaxation facility in the city. Another example of a prompt for the generating AI model is, "Generate appropriate health management advice based on the user's health and emotional data from the past week."

[0734] This system will enable individual users to effectively manage their own health and emotional state, supporting a healthy lifestyle within urban environments.

[0735] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0736] Step 1:

[0737] A wearable device and smartphone worn by the user periodically measure personal health data (e.g., heart rate, body temperature, activity level) and emotional data (e.g., emotional state from voice), and transmit this data to a server. This data is collected, and the server prepares to store this input data in a database. The collected data is stored as information necessary for subsequent analysis.

[0738] Step 2:

[0739] The server retrieves health and emotional data stored in a database. The input data also includes historical measurement data. The server uses this data as input to perform data analysis using a machine learning model (e.g., a model using TensorFlow). Specifically, it evaluates the user's current health status and emotional tendencies from the data. This analysis outputs indicators related to the user's health and emotional state.

[0740] Step 3:

[0741] Based on the analysis results, the server generates health advice. This generation process takes the analyzed indicators as input and outputs health recommendations and advice tailored to the user's emotional state. This process utilizes a pre-configured rule-based system and a generative AI model. The generated advice includes specific action plans and suggestions for utilizing medical resources to provide to the user.

[0742] Step 4:

[0743] The server sends the generated advice to the terminal, which then notifies the user's smartphone. Here, the generated advice is taken as input and output in an appropriate format for display on the user's screen. This notification is presented in real time for the user to review.

[0744] Step 5:

[0745] Users review the notifications they receive and take action based on the presented health and emotional care plans. As users take action, a feedback loop is formed throughout the system, influencing subsequent data acquisition.

[0746] Through the processes described above, the system continues to support the user's health and emotional management.

[0747] 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.

[0748] 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.

[0749] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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."

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] The following is further disclosed regarding the embodiments described above.

[0769] (Claim 1)

[0770] A means of receiving personal health data,

[0771] An analytical means for analyzing collected health data and evaluating the user's health status,

[0772] A generation means for generating personalized health management advice based on analysis results,

[0773] A notification method that notifies the user's device of the generated advice,

[0774] A healthcare system that includes this.

[0775] (Claim 2)

[0776] The healthcare system according to claim 1, further comprising means for accessing a database of medical institutions and obtaining the latest medical information.

[0777] (Claim 3)

[0778] The healthcare system according to claim 1, characterized in that the analysis means analyzes individual health data using an AI model.

[0779] "Example 1"

[0780] (Claim 1)

[0781] A means of receiving personal health information,

[0782] An analytical means for analyzing collected health information and evaluating the user's health status,

[0783] A generation means for generating personalized health management advice based on analysis results,

[0784] A notification means for notifying the user's device of the generated advice,

[0785] A means for receiving feedback to collect user responses from the device and use them for future analysis,

[0786] A system that includes this.

[0787] (Claim 2)

[0788] The system according to claim 1, further comprising means for accessing the information storage of a medical institution and obtaining the latest medical information.

[0789] (Claim 3)

[0790] The system according to claim 1, characterized in that the analysis means analyzes individual health information using a generating AI model.

[0791] "Application Example 1"

[0792] (Claim 1)

[0793] A receiving device for collecting personal health information,

[0794] An analysis device that analyzes collected health information and evaluates the user's health status,

[0795] A generator that generates personalized health management information based on the analysis results,

[0796] A notification device that notifies the user's device of the generated information,

[0797] Including a robot that monitors the user's condition in real time and provides appropriate advice,

[0798] A system that includes this.

[0799] (Claim 2)

[0800] The system according to claim 1, further comprising means for accessing an information base of a medical institution and obtaining the latest medical information.

[0801] (Claim 3)

[0802] The system according to claim 1, characterized in that the analysis device analyzes individual health information using an artificial intelligence model and monitors the health status in real time.

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

[0804] (Claim 1)

[0805] Means for acquiring personal physiological and emotional information,

[0806] A storage means for storing acquired physiological and emotional information,

[0807] An analytical means for evaluating physiological and emotional states using stored information,

[0808] A generation means that generates health advice optimized for the user based on the results evaluated by the analysis means,

[0809] A notification means that notifies the user's information terminal of the generated health advice,

[0810] A system that includes this.

[0811] (Claim 2)

[0812] The system according to claim 1, further comprising means for accessing the information storage of a medical institution and obtaining the latest medical information.

[0813] (Claim 3)

[0814] The system according to claim 1, characterized in that the analysis means analyzes individual information data using a machine learning model.

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

[0816] (Claim 1)

[0817] Receiving means for collecting personal health data and emotional data,

[0818] An analytical means for analyzing collected health and emotional data to evaluate the health and emotional state of individual users,

[0819] A generation means that generates personalized health management and emotional state-based advice based on the analysis results,

[0820] A notification means that, in conjunction with information on urban medical resources and relaxation facilities, notifies the user's information processing device of the generated advice,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, further comprising means for accessing information sources from medical institutions and obtaining the latest medical knowledge and relaxation information.

[0824] (Claim 3)

[0825] The system according to claim 1, characterized in that the analysis means analyzes individual health data and emotional data using a machine learning model. [Explanation of Symbols]

[0826] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving personal health data, An analytical means for analyzing collected health data and evaluating the user's health status, A generation means for generating personalized health management advice based on analysis results, A notification method that notifies the user's device of the generated advice, A healthcare system that includes this.

2. The healthcare system according to claim 1, further comprising means for accessing a database of medical institutions and obtaining the latest medical information.

3. The healthcare system according to claim 1, characterized in that the analysis means analyzes individual health data using an AI model.

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