System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting biological information of a user; means for securely transmitting the biological information to a cloud server; means for analyzing the biological information by the cloud server; means for generating health advice customized based on a result of the analysis; means for transmitting the health advice to a user terminal; and means for notifying the user of the health advice by the user terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In modern society, it is difficult for many people to objectively grasp their individual health conditions and lifestyle habits. This often leads to inappropriate nutritional intake, lack of exercise, and excessive stress, which have a negative impact on health. Furthermore, medical intervention is often not performed at the appropriate time, and illnesses are often only noticed after they have progressed. There is a need for a system that can improve this situation and enable users to always receive optimal health management. [Means for solving the problem]

[0005] The present invention provides a system including means for collecting a user's biometric information, means for securely transmitting the biometric information to a cloud server, means for the cloud server to analyze the biometric information, means for generating customized health advice based on the analysis results, means for transmitting the health advice to a user terminal, and means for the user terminal to notify the user of the health advice. In particular, the health advice is generated based on sleep duration, dietary content, or stress level. Furthermore, the cloud server includes means for analyzing medical data with the user's permission and suggesting early warning or preventive measures for disease. In this way, users can effectively manage their health status and prevent disease before it occurs. This is expected to improve the user's overall health.

[0006] "Biometric information" refers to data about the user's body, specifically information such as sleep duration, dietary habits, stress level, body temperature, blood pressure, and heart rate.

[0007] A "cloud server" is a remote server accessible via the Internet that has the functionality to store, analyze, and process data.

[0008] "Analysis" refers to the process of analyzing collected biometric information and deriving useful information and trends from it.

[0009] "Health advice" refers to specific lifestyle improvement suggestions and recommended behavioral guidelines provided to users based on the analysis results.

[0010] A "user terminal" is an electronic device used by a user, and includes smartphones, tablets, personal computers, smartwatches, etc.

[0011] "Secure transmission" refers to a technology or method for transmitting data to a cloud server in a manner that prevents biometric information from being tampered with and protects it from unauthorized access.

[0012] "Notification" refers to the act of displaying information in the form of an alert or message on a user device.

[0013] "Medical Data" refers to health-related data, including a user's past diagnoses, treatments, medication history, test results, etc.

[0014] "Early disease warning" refers to analyzing a user's biometric and medical data to warn them of an increased risk of developing a disease.

[0015] "Preventive measures" refer to specific actions or measures taken to prevent disease or health problems before they occur. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention relates to a system that manages a user's health information comprehensively and individually and provides optimal health advice. The system includes a user terminal, a cloud server, and a means for collecting and analyzing the user's health information.

[0038] The devices used by users (e.g., smartphones, smartwatches, and fitness trackers) first collect biometric information from users. For example, smartwatches record users' sleep data and heart rate, while smartphone apps collect data by inputting dietary habits and stress levels.

[0039] The device transmits the collected biometric information to a cloud server at regular intervals. This transmission is performed using a secure protocol (e.g., HTTPS) to ensure user privacy.

[0040] The cloud server analyzes the received biometric information and generates optimal health advice for each individual user. This analysis uses machine learning models and generative AI models to comprehensively evaluate the user's sleep patterns, diet, stress levels, etc. For example, if a user's sleep data is analyzed and a regular sleep pattern is not observed, specific advice on how to get enough sleep is generated.

[0041] The generated health advice is sent from the cloud server to the user's device, which then notifies the user of the advice via push notifications or in-app messages. For example, a notification might appear on a smartphone saying, "You haven't been getting enough sleep recently. We recommend going to bed one hour earlier each night."

[0042] The cloud server also analyzes medical data with the user's permission. This analysis includes the user's past diagnosis and test data, and provides early warnings and suggests preventative measures for illness. For example, if a user's blood pressure shows high levels, the cloud server will notify them of their risk of hypertension and recommend that they seek medical attention at an appropriate medical institution.

[0043] As a concrete example, consider a user wearing a smartwatch. The smartwatch records the time the user falls asleep and wakes up. This data is sent to a cloud server each night. The cloud server analyzes the user's sleep patterns and determines that the average sleep time is six hours or less. Based on this information, it generates advice such as "Try to get at least seven hours of sleep each night" and sends a notification to the user's smartphone.

[0044] In this way, the system of the present invention comprehensively manages the user's lifestyle habits and provides individually customized health advice to help the user maintain their health. In doing so, the user can always understand their health status and take appropriate action. This system is expected to improve the user's overall health.

[0045] The processing flow will be explained below.

[0046] Step 1:

[0047] The device collects the user's biometric information. Specifically, the smartwatch records sleep duration and heart rate, and the user inputs dietary habits and stress levels into a smartphone app.

[0048] Step 2:

[0049] The device formats the collected biometric data and prepares it for transmission to the cloud server, ensuring that the data is in the correct format and encoding it if necessary.

[0050] Step 3:

[0051] The device sends the formatted biometric data to the cloud server using a secure communication protocol (e.g., HTTPS). If an error occurs during transmission, the device attempts to retransmit the data.

[0052] Step 4:

[0053] The server receives the biometric data sent from the device, checks the data integrity, and verifies that there are no abnormal values ​​before storing it in a database.

[0054] Step 5:

[0055] The server analyzes the biometric information stored in the database and utilizes machine learning and generative AI models to comprehensively evaluate the user's lifestyle habits.

[0056] Step 6:

[0057] The server generates customized health advice based on the analysis results, such as suggesting to a user who is getting insufficient sleep on average to go to bed a certain amount earlier each night.

[0058] Step 7:

[0059] The server generates and transmits the generated health advice to the user's terminal in an appropriate format.

[0060] Step 8:

[0061] The device notifies the user of the health advice received from the server, displaying the advice content using push notifications or in-app messages.

[0062] Step 9:

[0063] The user checks the notification from the device and takes action based on the presented advice, such as reviewing their bedtime schedule in accordance with the advice.

[0064] Step 10:

[0065] The device continuously monitors how well the user followed the advice, collecting data again and sending it to the server for further analysis.

[0066] Step 11:

[0067] The server performs a new analysis based on the retransmitted data and generates new advice as necessary, thereby always providing the user with up-to-date support for health management.

[0068] Example 1

[0069] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0070] In recent years, the importance of health management has increased, and many users want to collect individual health information and understand their own health status. However, existing systems do not standardize data collection and analysis methods, and the advice provided to users cannot be said to be uniform and effective. Furthermore, advanced privacy protection and data analysis technologies are required for users to safely manage their own health information and receive accurate health advice. To address these challenges, a system is needed that can effectively collect and analyze users' biometric information and provide individually customized health advice.

[0071] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0072] In this invention, the server includes means for collecting a user's biometric information, means for transmitting the biometric information to a cloud server using a secure protocol, means for the cloud server to analyze the biometric information, means for generating customized health advice based on the analysis results using a generative AI model, means for transmitting the health advice to a user terminal, means for the user terminal to notify the user of the health advice, and means for analyzing medical data and proposing early warning or preventive measures for diseases with the user's permission, thereby enabling users to consistently manage their own health information and receive comprehensive and individually customized advice.

[0073] "User" refers to an individual who utilizes the system to manage their own health information and receive health advice.

[0074] "Biometric information" refers to data relating to the user's health condition, and specifically includes heart rate, sleep data, dietary details, stress level, and the like.

[0075] A "cloud server" is a server system that provides data storage and computing power over the Internet and is used to analyze a user's biometric information.

[0076] "Protocol" refers to the communication rules that ensure safety and accuracy in sending and receiving data. Examples include HTTPS.

[0077] A "generative AI model" is a type of machine learning algorithm used to analyze a user's biometric information and generate customized health advice.

[0078] "Advice" refers to recommended actions and precautions generated based on the user's health condition.

[0079] A "terminal" is a device used by a user, and specifically includes a smartphone, a smartwatch, a fitness tracker, etc.

[0080] "Medical data" refers to information obtained by medical institutions, such as a user's past diagnosis results and test data.

[0081] "Analysis" refers to the process of analyzing information based on collected data and drawing conclusions or predictions.

[0082] "Notification" refers to the transmission of information to a user via a device, and specifically includes push notifications and in-app messages.

[0083] MODE FOR CARRYING OUT THE INVENTION

[0084] This invention relates to a system that comprehensively and individually manages a user's health information and provides optimal health advice. This system uses a user device, a cloud server, a generative AI model, and prompts to collect and analyze the user's health information and provide customized advice.

[0085] 1. Hardware and Software Configuration

[0086] Devices: Devices used by users include smartphones, smartwatches, fitness trackers, etc. For example, there is the smartwatch "Apple Watch" and the smartphone "iPhone." These devices are used to collect users' biometric information.

[0087] Cloud servers: Cloud servers, including Amazon Web Services (AWS), are used to analyze data and generate advice. Cloud servers have high computing power and storage capacity, providing the computing resources to analyze the received data.

[0088] Generative AI model: The generative AI model runs on a cloud server and uses machine learning libraries such as TensorFlow and Scikit-learn to generate optimal health advice for users.

[0089] Prompt example: Prompts are used to generate health advice. For example, "Generate optimal health advice based on the user's latest sleep data. For example, if the average sleep time is 6 hours, provide specific advice on how many hours of sleep the user should get."

[0090] 2. System Operation

[0091] User biometric information collection:

[0092] The devices collect the user's biometric information. Specifically, the smartwatch "Apple Watch" measures the user's heart rate and sleep data, and the smartphone app "MyFitnessPal" collects information by inputting the user's diet and stress level.

[0093] Sending data to cloud server:

[0094] The collected data is sent to a cloud server at a fixed time (for example, at night) using a secure protocol (for example, HTTPS), which ensures safe transfer while preventing data leakage and tampering.

[0095] Data analysis and health advice generation:

[0096] The cloud server analyzes the received data. Python 3.9 and TensorFlow 2.4 are used for analysis, performing pattern recognition and evaluation of the data. The generative AI model integrates and evaluates the user's sleep patterns, diet, stress level, etc., to generate optimal advice for each individual user. For example, it might generate advice such as, "Make sure your average sleep time is under six hours, and aim to get at least seven hours of sleep each night."

[0097] Health Advice Notice:

[0098] The generated advice is sent from the cloud server to the user's device, and the user's device notifies the user via push notification or in-app message. For example, an iPhone might display a notification to the user saying, "We recommend getting at least seven hours of sleep each night."

[0099] Medical data analysis and additional advice:

[0100] With permission from the user, the cloud server analyzes past diagnostic results and test data to provide early warnings and preventative measures. Machine learning libraries such as "Scikit-learn" are used for the analysis. For example, if blood pressure data indicates high levels, the cloud server generates and notifies the user with advice such as, "You are at risk of high blood pressure. Please seek medical attention at an appropriate medical institution."

[0101] As a result, a system is realized that manages a user's health information comprehensively and individually, and contributes to maintaining the user's health through appropriate advice.

[0102] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0103] Specific explanation of program processing

[0104] Step 1:

[0105] Collecting user biometric information

[0106] Device:

[0107] (Input) User activity information (heart rate, sleep data, dietary details, stress level)

[0108] (Operation) The user wears a "smartwatch" that automatically collects sleep data and heart rate data. The user also inputs their diet and stress level into a "smartphone app."

[0109] (Output) Collected biometric data (e.g., daily heart rate data, sleep duration, and dietary records)

[0110] Step 2:

[0111] Sending collected data to a cloud server

[0112] Device:

[0113] (Input) Collected biometric data

[0114] (Operation) At regular intervals, the device sends the collected data to the cloud server using a secure protocol (e.g., HTTPS).

[0115] (Output) Biometric data sent to a cloud server via a secure protocol

[0116] Step 3:

[0117] Data analysis on a cloud server

[0118] server:

[0119] (Input) Biometric data sent to the cloud server

[0120] (Operation) The cloud server uses Python 3.9 and TensorFlow 2.4 to analyze the received data, specifically evaluating the user's average heart rate, sleep patterns, dietary habits, etc.

[0121] (Output) Analysis result data (e.g. average sleep time, average heart rate)

[0122] Step 4:

[0123] Generating health advice

[0124] server:

[0125] (Input) Analysis result data

[0126] (Operation) The server uses the generative AI model to generate optimal health advice for the user based on the analysis results. For example, the "generative AI model" analyzes sleep data and generates specific advice such as "Try to get at least seven hours of sleep every night."

[0127] (Output) The generated health advice

[0128] Step 5:

[0129] Sending and notifying health advice to user devices

[0130] Server and Device:

[0131] (Input) Generated health advice

[0132] (Operation) The cloud server sends the generated advice to the user's device, which notifies the user via push notification or in-app message. The smartphone receives the notification and displays the advice to the user.

[0133] (Output) Advice notice displayed on the user's device (e.g., "Try to get at least 7 hours of sleep each night")

[0134] Step 6:

[0135] Medical data analysis and additional advice

[0136] server:

[0137] (Input) Medical data (diagnosis results, test data, etc.) with permission from the user

[0138] (Operation) The cloud server uses machine learning libraries such as "Scikit-learn" to analyze medical data and provide early warnings of diseases and suggest preventive measures. Specifically, if blood pressure data indicates high values, the cloud server generates advice to the user saying, "You are at risk of high blood pressure. Please seek medical attention at an appropriate medical institution."

[0139] (Output) Additional advice generated based on medical data

[0140] Specific steps and details of each step

[0141] Step 1:

[0142] Device:

[0143] The collected biometric data is obtained using a smartwatch worn by the user or a smartphone app that the user enters into, and this information is temporarily stored in a database on the device.

[0144] Step 2:

[0145] Device:

[0146] At a fixed time, such as midnight or at a specified time, data is sent to the cloud server via a secure protocol (HTTPS). The sent data then travels over the Internet to the cloud server.

[0147] Step 3:

[0148] server:

[0149] A Python 3.9 script runs periodically on the cloud server to analyze the data, using TensorFlow 2.4 to analyze complex data patterns and assess the user's health status.

[0150] Step 4:

[0151] server:

[0152] Based on the analysis results, the AI ​​model generates appropriate health advice according to prompts, such as calculating the average sleep time from sleep data and providing advice on necessary measures.

[0153] Step 5:

[0154] Server and Device:

[0155] Once advice is generated, it is sent from the cloud server to the user's device (such as a smartphone). The device immediately notifies the user of the received advice so that the user can review the information.

[0156] Step 6:

[0157] server:

[0158] With the user's permission, the cloud server also analyzes the medical data using machine learning libraries to predict disease risk and generate additional advice for the user on preventative measures.

[0159] In this way, the system of the present invention collects, safely manages, and analyzes the user's biometric information, and provides individually customized health advice, thereby helping the user maintain their health.

[0160] (Application example 1)

[0161] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0162] Conventional health management systems collect users' biometric information and provide health advice, but there are few cases where that advice leads to concrete actions. In particular, dietary advice is limited to providing information and is difficult to lead to the execution of actual meal plans. This creates a problem in that it is difficult for users to take concrete actions to maintain and improve their health.

[0163] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0164] In this invention, the server includes means for collecting biometric information of a user, means for securely transmitting the biometric information to a cloud server, means for the cloud server to analyze the biometric information, means for generating customized health advice based on the analysis results, means for proposing an optimal meal plan based on the health advice, means for managing food item information for providing the meal plan, means for transmitting the health advice and the meal plan to a user terminal, and means for the user terminal to notify the user of the health advice and the meal plan. This enables the user to receive a specific and feasible meal plan based on their health condition and to take actual action.

[0165] "User's biological information" is data related to the user's health condition, including heart rate, sleep time, dietary content, stress level, and the like.

[0166] A "cloud server" is a remote server accessible via the Internet, and is a virtualized server environment for storing and processing data.

[0167] "Analytical means" refers to a processing system that uses mathematical and statistical methods to analyze collected data and derive specific results and recommendations.

[0168] "Customized health advice" refers to specific guidance and advice for maintaining and improving health that is generated based on each user's individual health data.

[0169] The "means for proposing a meal plan" is a function that specifically selects and proposes a meal menu suitable for the user based on the analysis results.

[0170] The "means for managing food item information" is a system for creating a database of nutritional information, calories, ingredients, etc. of available meal menus and managing them appropriately.

[0171] A "user terminal" is a communication device used by a user, and includes mobile devices such as smartphones and tablets.

[0172] The "notification means" is a function that transmits the generated health advice and meal plan to the user terminal and displays the information to the user.

[0173] "Early warning or prevention of disease" refers to the use of a user's health data to predict the risk of developing a disease in the future and provide specific measures or guidelines for action to reduce that risk.

[0174] The present invention relates to a system that manages a user's health information comprehensively and individually and provides optimal health advice. The system includes a user terminal, a cloud server, and a means for collecting and analyzing the user's health information.

[0175] First, the user uses a communication device (user terminal) such as a smartphone or smartwatch to collect their own biometric information. The smartwatch records the user's heart rate and sleep time, and data on diet and stress level is entered into the smartphone app. This information is periodically sent to a cloud server.

[0176] The cloud server analyzes the received biometric information using a machine learning model or a generative AI model. A common machine learning framework (e.g., TensorFlow) is used as the model. Based on the analyzed data, the cloud server generates optimal health advice for each individual user. For example, this health advice might be, "You haven't been getting enough sleep recently. We recommend that you go to bed one hour earlier each night."

[0177] The cloud server then proposes a specific meal plan based on the analysis results. The meal plan is proposed using a database that manages food item information. The meal plan takes into account nutritional information, calories, ingredients, etc.

[0178] Health advice and meal plans are sent from the cloud server to the user's device and are notified to the user via push notifications and in-app messages, allowing the user to create a specific action plan.

[0179] A concrete example is the following scenario: If the user's sleep data shows an average of six hours or less, the cloud server will suggest a nutritionally balanced meal plan along with advice such as, "Your average sleep time is short. It is important to get enough sleep." For example, a "chicken breast salad (rich in protein, under 500 kcal)" might be recommended.

[0180] An example of a prompt to be input into the generative AI model is, "If the user has had less than six hours of sleep and their recent heart rate and stress level are high, recommend a low-calorie, high-protein meal."

[0181] In this way, the system of the present invention comprehensively manages the user's lifestyle habits and provides individually customized health advice and specific action plans to help the user maintain their health. The user can always understand their health status and take appropriate action. This system is expected to improve the user's overall health.

[0182] This completes the description of the "Description of the Preferred Embodiments."

[0183] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0184] Step 1:

[0185] The user device collects biometric information

[0186] The user wears a smartphone or smartwatch, which records biometric information such as heart rate, sleep time, dietary intake, and stress level. Input data includes dietary intake and stress level that the user manually enters into the app. Output data includes various measurement data, including this biometric information.

[0187] Step 2:

[0188] The user device sends biometric information to the cloud server.

[0189] The collected biometric information is sent from the user device to the cloud server at regular intervals via a secure protocol (e.g., HTTPS). The input data is the biometric information stored on the user device, and the output data is the data transferred to the cloud server.

[0190] Step 3:

[0191] Cloud server analyzes biometric information

[0192] The cloud server analyzes the received biometric information using machine learning models and generative AI models, which includes data preprocessing (data normalization and invalid data removal). The input data is the biometric information stored on the cloud server, and the output data is the analysis results.

[0193] Step 4:

[0194] A cloud server generates customized health advice

[0195] Based on the analysis results, the cloud server generates personalized health advice for the user, often using a generative AI model. The input data is the analysis results, and the output data is specific health advice.

[0196] Step 5:

[0197] Cloud server proposes meal plans

[0198] Based on the generated health advice, the cloud server proposes an optimal meal plan. It references a database that manages food item information and makes suggestions based on nutritional information, calories, ingredients, etc. The input data are health advice and food item information, and the output data is the proposed meal plan.

[0199] Step 6:

[0200] The cloud server sends health advice and meal plans to the user's device.

[0201] The cloud server transmits the generated health advice and meal plan to the user terminal, and the output data is the health advice and meal plan transmitted from the cloud server.

[0202] Step 7:

[0203] The user device notifies the user of health advice and meal plans

[0204] The user device notifies the user of the received health advice and meal plan via push notifications or in-app messages. The input data are the health advice and meal plan sent from the cloud server, and the output data are the notification content displayed to the user.

[0205] Through these steps, users can receive and implement specific advice and meal plans tailored to their health condition.

[0206] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0207] The present invention relates to a system that manages a user's health information comprehensively and individually and provides optimal health advice. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions. The system includes a user terminal, a cloud server, and a means for collecting and analyzing the user's health information and emotion information.

[0208] The devices used by users (e.g., smartphones, smartwatches, and fitness trackers) first collect the user's biometric and emotional information. For example, smartwatches record the user's sleep data and heart rate, while smartphone apps collect dietary information, stress levels, and even voice and facial expression data. Furthermore, emotion engines analyze the user's facial, voice, and text data to recognize the user's emotional state.

[0209] The device formats the collected biometric and emotional information and transmits it to a cloud server using a secure protocol (e.g., HTTPS) to ensure user privacy.

[0210] The cloud server analyzes the received information and generates optimal health advice for each individual user. This analysis uses machine learning models, generative AI models, and an emotion engine to comprehensively evaluate the user's sleep patterns, diet, stress level, emotional state, etc. For example, if a user's sleep data is analyzed and a regular sleep pattern is not observed, specific advice on how to get enough sleep is generated. In addition, if the user is under stress, the emotion engine will add health advice that takes their emotional state into account.

[0211] The generated health advice is sent from the cloud server to the user's device. The user's device then notifies the user of the advice via push notifications or in-app messages. For example, a notification might appear on the smartphone saying, "You've been getting insufficient sleep recently. We recommend going to bed one hour earlier each night." At the same time, additional advice based on the user's emotional state might be displayed, such as, "You seem to be feeling stressed. We recommend you practice relaxation."

[0212] The cloud server also analyzes medical data with the user's permission. This analysis includes the user's past diagnosis and test data, and provides early warnings and suggests preventative measures for illness. For example, if a user's blood pressure shows high levels, the cloud server will notify them of their risk of hypertension and recommend that they seek medical attention at an appropriate medical institution.

[0213] As a concrete example, consider a case where a user wears a smartwatch and uses a smartphone app equipped with an emotion analysis function. The smartwatch records the time the user goes to sleep and wakes up. The smartphone app records the user's meals and simultaneously analyzes the user's emotional state from voice and facial expressions. This data is sent to a cloud server every night. The cloud server analyzes the user's sleep patterns, diet, and emotional state, and verifies that the average sleep time is six hours or less. Based on this information, it generates advice such as "Try to get at least seven hours of sleep each night" and sends a notification to the smartphone. At the same time, if the user's emotional state indicates stress, it also generates advice such as "You are feeling stressed. We recommend that you practice meditation and relaxation."

[0214] In this way, the system of the present invention comprehensively manages the user's lifestyle habits and emotional state and provides individually customized health advice to help the user maintain their health. In doing so, the user can always understand their own health and emotional state and take appropriate action. This system is expected to improve the user's overall health.

[0215] The processing flow will be explained below.

[0216] Step 1:

[0217] The device collects the user's biometric information. Specifically, the smartwatch records sleep duration and heart rate, and the user inputs their diet and stress level via a smartphone app.

[0218] Step 2:

[0219] The device collects the user's emotional information. Specifically, the smartphone app analyzes the user's voice and facial expression data and uses an emotion engine to recognize the user's emotional state.

[0220] Step 3:

[0221] The device formats the collected biometric and emotional information and prepares it for transmission to the cloud server, ensuring that the data is in the correct format and encoding it if necessary.

[0222] Step 4:

[0223] The device transmits the formatted biometric and emotional information to the cloud server using a secure communication protocol (e.g., HTTPS). If an error occurs during transmission, the device attempts to retransmit the information.

[0224] Step 5:

[0225] The server receives the biometric and emotional data sent from the device, checks the data integrity, and verifies whether there are any outliers before storing them in a database.

[0226] Step 6:

[0227] The server analyzes the biometric and emotional information stored in the database, and utilizes machine learning models, generative AI models, and an emotion engine to comprehensively evaluate the user's lifestyle habits and emotional state.

[0228] Step 7:

[0229] The server generates customized health advice based on the analysis results. For example, it may advise a user who is not getting enough sleep on average to go to bed earlier each night. It also suggests stress reduction measures based on the emotion engine's analysis results.

[0230] Step 8:

[0231] The server generates and transmits the generated health advice to the user terminal in an appropriate format.

[0232] Step 9:

[0233] The device notifies the user of the health advice received from the server, displaying specific advice content using push notifications and in-app messages.

[0234] Step 10:

[0235] The user checks the notification from the device and takes action based on the advice provided, such as reviewing their bedtime schedule or practicing meditation or relaxation to reduce stress.

[0236] Step 11:

[0237] The device continuously monitors how well the user followed the advice, collecting data again and sending it to the server for further analysis.

[0238] Step 12:

[0239] The server performs a new analysis based on the retransmitted data and generates new advice as necessary, thereby always providing the user with up-to-date support for health management.

[0240] Example 2

[0241] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0242] Conventional health management systems focus on collecting and analyzing biometric information, but do not provide advice that takes into account the user's emotional information. Furthermore, they face challenges such as security issues and a lack of individually tailored advice. This creates a need for a system that can comprehensively manage a user's health and emotional state and provide individually tailored health advice.

[0243] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0244] In this invention, the server includes means for collecting biometric information and emotional information of a user, means for securely transmitting the biometric information and emotional information to a data processing device, means for the data processing device to analyze the biometric information and emotional information, means for generating customized health advice based on the analysis results, means for transmitting the health advice to a user terminal, and means for the user terminal to notify the user of the health advice. This allows the user to comprehensively understand their own health and emotional state and take appropriate actions to improve their overall health.

[0245] "Biometric information" refers to data relating to the user's physical condition, such as heart rate, sleep data, and number of steps taken.

[0246] "Emotion information" is data relating to the user's emotional state analyzed from facial expressions, voice, text data, and the like.

[0247] The "data processing device" is a device that receives and analyzes biometric information and emotional information sent from a user.

[0248] "Analysis" refers to the process of evaluating the received biometric and emotional information to comprehensively determine the user's health and emotional state.

[0249] A "generative AI model" is an artificial intelligence model that assesses a user's health and emotional state and generates customized health advice.

[0250] "Health advice" refers to individually customized guidance and recommendations for maintaining health based on the analysis of the user's biometric and emotional information.

[0251] A "user terminal" is a device worn or used by a user, such as a smartphone, smartwatch, or fitness tracker.

[0252] A "secure protocol" is a communication protocol for securely sending and receiving data, and examples include HTTPS.

[0253] "Medical data" refers to information relating to the user's medical care, such as past diagnostic results, test data, and drug therapy history.

[0254] The present invention relates to a system that manages a user's health information comprehensively and individually and provides optimal health advice. It also incorporates an emotion engine that recognizes the user's emotions. The system includes a device used by the user (e.g., a smartphone, a smartwatch, a fitness tracker), a data processing device (a cloud server), and an integrated analysis means.

[0255] composition:

[0256] Device configuration:

[0257] The devices used by users include smartwatches, smartphones, etc. These devices collect information such as:

[0258] The smartwatch tracks heart rate, steps, and sleep data.

[0259] The smartphone collects and analyzes dietary habits, stress levels, and facial and voice data using an emotion engine.

[0260] Data collection and transmission:

[0261] The biometric and emotional information collected on the device is sent to the cloud server through data processing and secure protocols (e.g., HTTPS), ensuring user privacy.

[0262] Data processing unit configuration:

[0263] The cloud server is equipped with the following analytical tools:

[0264] Machine learning models

[0265] Generative AI Models

[0266] Emotion Engine

[0267] These elements allow the cloud server to analyze the biometric and emotional information sent by the user and generate individually customized health advice.

[0268] Health advice generation and notification:

[0269] Health advice generated by the cloud server is based on the analysis results. For example, if a user's sleep data lacks regularity, advice such as "Try to get at least seven hours of sleep every night" will be generated. Furthermore, if the analysis results of the emotion engine indicate high stress levels, additional advice such as "We recommend you practice relaxation" will be generated.

[0270] The generated health advice is securely sent to the user's device, which then notifies the user via push notifications or in-app messages. For example, a notification might appear on a smartphone saying, "You haven't been getting enough sleep recently. We recommend going to bed one hour earlier each night."

[0271] Usage example

[0272] Consider a case where a user is wearing a smartwatch and using a smartphone app equipped with emotion analysis functionality. The smartwatch records the user's sleep and wake-up times. The smartphone app records the user's meals and simultaneously analyzes their emotional state from their voice and facial expressions. These data are sent to a cloud server each night. The cloud server analyzes the user's sleep patterns, diet, and emotional state, and verifies that their average sleep time is six hours or less. Based on this information, it generates advice such as "Try to get at least seven hours of sleep each night" and sends a notification to the smartphone. At the same time, if the user's emotional state indicates stress, it also generates advice such as "You are feeling stressed. We recommend that you practice meditation and relaxation."

[0273] Example prompt sentence:

[0274] "Analyze the user's sleep data and emotional state and generate appropriate health advice. For example, if the average sleep time is less than six hours, recommend getting at least seven hours of sleep each night, or recommend relaxation if the user is stressed."

[0275] This system allows users to comprehensively understand their health and emotional state, and by taking appropriate actions based on the health advice provided individually, it is expected that their overall health will improve.

[0276] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0277] Step 1:

[0278] The user uses the terminal to collect biometric information and emotional information.

[0279] Specifically, the smartwatch records heart rate, steps, and sleep data (e.g., time of falling asleep and waking up), while the smartphone prompts the user to enter their diet, weight, and stress level, and uses a camera and microphone to collect facial and voice data.

[0280] Input: User's biometric information (e.g., heart rate, sleep data), emotional information (e.g., facial expressions, voice)

[0281] Output: A dataset of collected biometric and emotional information

[0282] Step 2:

[0283] The device formats the biometric and emotional information collected and sends it to a cloud server using a secure protocol.

[0284] Specifically, the device encrypts data using the HTTPS protocol and periodically sends it to the cloud server.

[0285] Input: Dataset of collected biometric and emotional information

[0286] Output: Formatted data sent to the cloud server

[0287] Step 3:

[0288] The server parses the received data.

[0289] Specifically, the cloud server formats the data into a coherent format and analyzes it using machine learning models, generative AI models, and emotion engines, including assessing the user's sleep patterns, diet, stress levels, and emotional state.

[0290] Input: Formatted data sent to the cloud server

[0291] Output: Analysis results (e.g., user's sleep patterns, emotional state)

[0292] Step 4:

[0293] The server generates customized health advice based on the analysis results.

[0294] Specifically, the generative AI model generates personalized health advice for each user based on the analysis results. For example, for a user who is not getting enough sleep, the advice "Try to get at least seven hours of sleep each night" is generated. If the emotion engine detects a state of stress, the additional advice "We recommend you practice relaxation" is generated.

[0295] Input: Analysis results (e.g., user's sleep patterns, emotional state)

[0296] Output: Generated customized health advice

[0297] Step 5:

[0298] The server transmits the generated health advice to the user terminal.

[0299] Specifically, the generated advice is converted into JSON format or similar, encrypted using the HTTPS protocol, and sent to the terminal.

[0300] Input: Generated customized health advice

[0301] Output: Health advice sent to the user device

[0302] Step 6:

[0303] The user terminal notifies the user of the advice.

[0304] Specifically, the smartphone will send advice to the user via push notifications or in-app messages. For example, a notification will be displayed saying, "You haven't been getting enough sleep recently. We recommend going to bed one hour earlier each night." Additional advice based on your emotional state will also be provided.

[0305] Input: Health advice sent to user device

[0306] Output: Advice given to the user

[0307] The above are the specific processing steps of the program for this system.

[0308] (Application example 2)

[0309] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0310] Nowadays, many people are interested in health management, but it is not easy to obtain appropriate advice based on their individual health status. Furthermore, there is a lack of means in physical stores to provide personalized health advice and product recommendations in real time based on users' health and emotional data. Under these circumstances, it is difficult for users to take optimal actions to maintain their health, and overall health improvement cannot be expected. Therefore, there is a need for a system that collects and analyzes users' biometric and emotional information and provides appropriate health advice and product recommendations in real time.

[0311] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0312] In this invention, the server includes means for collecting biometric information from a user, means for securely transmitting the biometric information to a cloud server, means for the cloud server to analyze the biometric information and emotional information, means for generating customized health advice based on the analysis results, means for transmitting the health advice to a user terminal, means for the user terminal to notify the user of the health advice, and means for analyzing the user's health data and emotional data in real time at a physical store and providing health advice and product suggestions. This enables users to receive individually customized health advice in real time even in a physical store, and to select appropriate products and take actions to maintain their health.

[0313] The "means for collecting user's biometric information" refers to a device or software for acquiring physiological data such as the user's heart rate and sleep data.

[0314] The "means for securely transmitting the biometric information to the cloud server" refers to protocols and technologies for transferring collected biometric information to the cloud server while protecting the privacy of the data.

[0315] The "means by which the cloud server analyzes the biometric information and emotional information" refers to a machine learning model or algorithm for collecting and analyzing the user's biometric information and emotional information on the cloud.

[0316] The "means for generating customized health advice based on the analysis results" is a technology that generates advice related to health management that is optimized for each user based on the analyzed data.

[0317] The "means for transmitting the health advice to the user terminal" is a technology for transmitting health advice generated by a cloud server to a terminal such as a user's smartphone or smartwatch.

[0318] The "means for the user terminal to notify the user of the health advice" refers to a technology for displaying the received health advice to the user on the user terminal as a push notification or an in-app message.

[0319] "Means for analyzing a user's health and emotional data in real time in a physical store and providing health advice and product suggestions" refers to a technology that instantly analyzes a user's health and emotional state in a physical store environment and, based on the results, provides the user with optimal health-related advice and product suggestions.

[0320] This invention is a system for collecting and analyzing a user's biometric and emotional information and providing appropriate health advice and product suggestions in real time at a physical store. The system includes a user terminal, a cloud server, and a means for analyzing this data and generating advice.

[0321] User devices refer to devices such as smartphones, smartwatches, and smart glasses. These devices have the ability to collect biometric and emotional information, such as a user's heart rate, sleep data, dietary information, voice, and facial expressions. For example, a smartwatch records a user's heart rate and sleep data, while a smartphone app records dietary information and stress levels. They also include emotion engines that analyze the user's voice and facial expression data.

[0322] The collected data is formatted and sent to a cloud server using a secure protocol (e.g., HTTPS). The cloud server analyzes the received data using machine learning models (e.g., TensorFlow, PyTorch) or generative AI models (e.g., GPT-4). The emotion engine analyzes the user's emotional state and comprehensively evaluates this data.

[0323] The analysis results are used to generate customized health advice based on the user's individual health data. For example, analyzing a user's sleep data and determining that their average sleep time is six hours or less generates the advice, "Try to get at least seven hours of sleep each night." If the user's emotional state indicates stress, the system adds the advice, "You are feeling stressed. We recommend that you practice meditation and relaxation."

[0324] To improve the user experience in physical stores, the system analyzes users' health and emotional data in real time and makes specific product suggestions. For example, while shopping in a store, a product suggestion could be displayed such as, "Since your recent sleep data has been insufficient, why not try this health pillow?"

[0325] For example, the following prompts can be used:

[0326] "Due to the user's recent sleep data and increasing stress levels, we want to generate optimal health advice for him.

[0327] (input data)

[0328] Average sleep time: 5.5 hours

[0329] Recent stress level: High

[0330] Health Goal: Stress Management

[0331] (generated advice)

[0332] Aim to get at least seven hours of sleep each night and incorporate relaxation activities like deep breathing and meditation."

[0333] This system allows users to constantly monitor their health and emotional state and take appropriate action. By receiving real-time health advice and product suggestions in physical stores, users are expected to maintain their health and improve their quality of life.

[0334] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0335] Step 1:

[0336] Devices (e.g., smartwatches and smartphones) collect biometric and emotional information such as the user's heart rate, sleep data, dietary information, voice, and facial expressions. These data are input from sensors, cameras, and microphones. For example, a smartwatch records the user's heart rate every second, and a smartphone app inputs the user's dietary information using image recognition technology.

[0337] Step 2:

[0338] The device preprocesses the collected biometric and emotional information and formats the data. For example, it calculates heart rate data as a minute-by-minute average and converts voice data into text. This preprocessed data is sent to a cloud server using the HTTPS protocol. The input is raw data, and the output is preprocessed structured data.

[0339] Step 3:

[0340] The cloud server analyzes the received data using machine learning models (e.g., TensorFlow, PyTorch) and generative AI models (e.g., GPT-4). This analysis step comprehensively evaluates the user's sleep patterns, heart rate, dietary content, and emotional state. The input is preprocessed structured data, and the output is evaluation data as the analysis result.

[0341] Step 4:

[0342] The server generates customized health advice based on the analysis results. The generated advice specifically corresponds to the user's health and emotional state. For example, if the sleep data is insufficient, the advice generated might be, "Try to get at least seven hours of sleep each night," or if the stress level is high, the advice generated might be, "We recommend meditating and relaxing." The input is the evaluation data from the analysis results, and the output is specific advice text.

[0343] Step 5:

[0344] The generated health advice is sent from the cloud server to the user's device using a secure protocol such as HTTPS. The input is the advice text, and the output is the data sent to the user's device.

[0345] Step 6:

[0346] The device notifies the user of the received health advice as a push notification or an in-app message. For example, a message such as "You haven't been getting enough sleep recently. We recommend that you go to bed one hour earlier each night" is displayed on the user's smartphone screen. The input is the received advice text, and the output is the notification to the user.

[0347] Step 7:

[0348] In a physical store, the device analyzes the user's health and emotional data in real time and makes product suggestions based on the results. For example, while shopping in a store, a message might appear saying, "Your recent sleep data has been insufficient. Why not try this health pillow?" The input is real-time health and emotional data, and the output is a display of product suggestions.

[0349] Through these steps, the system can efficiently collect and analyze the user's biometric and emotional information, and provide optimal health advice and product recommendations in real time.

[0350] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0351] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0352] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0353] [Second embodiment]

[0354] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0355] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0356] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0357] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0358] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0359] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0360] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0361] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0362] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0363] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0364] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0365] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0366] The present invention relates to a system that manages a user's health information comprehensively and individually and provides optimal health advice. The system includes a user terminal, a cloud server, and a means for collecting and analyzing the user's health information.

[0367] The devices used by users (e.g., smartphones, smartwatches, and fitness trackers) first collect biometric information from users. For example, smartwatches record users' sleep data and heart rate, while smartphone apps collect data by inputting dietary habits and stress levels.

[0368] The device transmits the collected biometric information to a cloud server at regular intervals. This transmission is performed using a secure protocol (e.g., HTTPS) to ensure user privacy.

[0369] The cloud server analyzes the received biometric information and generates optimal health advice for each individual user. This analysis uses machine learning models and generative AI models to comprehensively evaluate the user's sleep patterns, diet, stress levels, etc. For example, if a user's sleep data is analyzed and a regular sleep pattern is not observed, specific advice on how to get enough sleep is generated.

[0370] The generated health advice is sent from the cloud server to the user's device, which then notifies the user of the advice via push notifications or in-app messages. For example, a notification might appear on a smartphone saying, "You haven't been getting enough sleep recently. We recommend going to bed one hour earlier each night."

[0371] The cloud server also analyzes medical data with the user's permission. This analysis includes the user's past diagnosis and test data, and provides early warnings and suggests preventative measures for illness. For example, if a user's blood pressure shows high levels, the cloud server will notify them of their risk of hypertension and recommend that they seek medical attention at an appropriate medical institution.

[0372] As a concrete example, consider a user wearing a smartwatch. The smartwatch records the time the user falls asleep and wakes up. This data is sent to a cloud server each night. The cloud server analyzes the user's sleep patterns and determines that the average sleep time is six hours or less. Based on this information, it generates advice such as "Try to get at least seven hours of sleep each night" and sends a notification to the user's smartphone.

[0373] In this way, the system of the present invention comprehensively manages the user's lifestyle habits and provides individually customized health advice to help the user maintain their health. In doing so, the user can always understand their health status and take appropriate action. This system is expected to improve the user's overall health.

[0374] The processing flow will be explained below.

[0375] Step 1:

[0376] The device collects the user's biometric information. Specifically, the smartwatch records sleep duration and heart rate, and the user inputs dietary habits and stress levels into a smartphone app.

[0377] Step 2:

[0378] The device formats the collected biometric data and prepares it for transmission to the cloud server, ensuring that the data is in the correct format and encoding it if necessary.

[0379] Step 3:

[0380] The device sends the formatted biometric data to the cloud server using a secure communication protocol (e.g., HTTPS). If an error occurs during transmission, the device attempts to retransmit the data.

[0381] Step 4:

[0382] The server receives the biometric data sent from the device, checks the data integrity, and verifies that there are no abnormal values ​​before storing it in a database.

[0383] Step 5:

[0384] The server analyzes the biometric information stored in the database and utilizes machine learning and generative AI models to comprehensively evaluate the user's lifestyle habits.

[0385] Step 6:

[0386] The server generates customized health advice based on the analysis results, such as suggesting to a user who is getting insufficient sleep on average to go to bed a certain amount earlier each night.

[0387] Step 7:

[0388] The server generates and transmits the generated health advice to the user's terminal in an appropriate format.

[0389] Step 8:

[0390] The device notifies the user of the health advice received from the server, displaying the advice content using push notifications or in-app messages.

[0391] Step 9:

[0392] The user checks the notification from the device and takes action based on the presented advice, such as reviewing their bedtime schedule in accordance with the advice.

[0393] Step 10:

[0394] The device continuously monitors how well the user followed the advice, collecting data again and sending it to the server for further analysis.

[0395] Step 11:

[0396] The server performs a new analysis based on the retransmitted data and generates new advice as necessary, thereby always providing the user with up-to-date support for health management.

[0397] Example 1

[0398] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0399] In recent years, the importance of health management has increased, and many users want to collect individual health information and understand their own health status. However, existing systems do not standardize data collection and analysis methods, and the advice provided to users cannot be said to be uniform and effective. Furthermore, advanced privacy protection and data analysis technologies are required for users to safely manage their own health information and receive accurate health advice. To address these challenges, a system is needed that can effectively collect and analyze users' biometric information and provide individually customized health advice.

[0400] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0401] In this invention, the server includes means for collecting a user's biometric information, means for transmitting the biometric information to a cloud server using a secure protocol, means for the cloud server to analyze the biometric information, means for generating customized health advice based on the analysis results using a generative AI model, means for transmitting the health advice to a user terminal, means for the user terminal to notify the user of the health advice, and means for analyzing medical data and proposing early warning or preventive measures for diseases with the user's permission, thereby enabling users to consistently manage their own health information and receive comprehensive and individually customized advice.

[0402] "User" refers to an individual who utilizes the system to manage their own health information and receive health advice.

[0403] "Biometric information" refers to data relating to the user's health condition, and specifically includes heart rate, sleep data, dietary details, stress level, and the like.

[0404] A "cloud server" is a server system that provides data storage and computing power over the Internet and is used to analyze a user's biometric information.

[0405] "Protocol" refers to the communication rules that ensure safety and accuracy in sending and receiving data. Examples include HTTPS.

[0406] A "generative AI model" is a type of machine learning algorithm used to analyze a user's biometric information and generate customized health advice.

[0407] "Advice" refers to recommended actions and precautions generated based on the user's health condition.

[0408] A "terminal" is a device used by a user, and specifically includes a smartphone, a smartwatch, a fitness tracker, etc.

[0409] "Medical data" refers to information obtained by medical institutions, such as a user's past diagnosis results and test data.

[0410] "Analysis" refers to the process of analyzing information based on collected data and drawing conclusions or predictions.

[0411] "Notification" refers to the transmission of information to a user via a device, and specifically includes push notifications and in-app messages.

[0412] MODE FOR CARRYING OUT THE INVENTION

[0413] This invention relates to a system that comprehensively and individually manages a user's health information and provides optimal health advice. This system uses a user device, a cloud server, a generative AI model, and prompts to collect and analyze the user's health information and provide customized advice.

[0414] 1. Hardware and Software Configuration

[0415] Devices: Devices used by users include smartphones, smartwatches, fitness trackers, etc. For example, there is the smartwatch "Apple Watch" and the smartphone "iPhone." These devices are used to collect users' biometric information.

[0416] Cloud servers: Cloud servers, including Amazon Web Services (AWS), are used to analyze data and generate advice. Cloud servers have high computing power and storage capacity, providing the computing resources to analyze the received data.

[0417] Generative AI model: The generative AI model runs on a cloud server and uses machine learning libraries such as TensorFlow and Scikit-learn to generate optimal health advice for users.

[0418] Prompt example: Prompts are used to generate health advice. For example, "Generate optimal health advice based on the user's latest sleep data. For example, if the average sleep time is 6 hours, provide specific advice on how many hours of sleep the user should get."

[0419] 2. System Operation

[0420] User biometric information collection:

[0421] The devices collect the user's biometric information. Specifically, the smartwatch "Apple Watch" measures the user's heart rate and sleep data, and the smartphone app "MyFitnessPal" collects information by inputting the user's diet and stress level.

[0422] Sending data to cloud server:

[0423] The collected data is sent to a cloud server at a fixed time (for example, at night) using a secure protocol (for example, HTTPS), which ensures safe transfer while preventing data leakage and tampering.

[0424] Data analysis and health advice generation:

[0425] The cloud server analyzes the received data. Python 3.9 and TensorFlow 2.4 are used for analysis, performing pattern recognition and evaluation of the data. The generative AI model integrates and evaluates the user's sleep patterns, diet, stress level, etc., to generate optimal advice for each individual user. For example, it might generate advice such as, "Make sure your average sleep time is under six hours, and aim to get at least seven hours of sleep each night."

[0426] Health Advice Notice:

[0427] The generated advice is sent from the cloud server to the user's device, and the user's device notifies the user via push notification or in-app message. For example, an iPhone might display a notification to the user saying, "We recommend getting at least seven hours of sleep each night."

[0428] Medical data analysis and additional advice:

[0429] With permission from the user, the cloud server analyzes past diagnostic results and test data to provide early warnings and preventative measures. Machine learning libraries such as "Scikit-learn" are used for the analysis. For example, if blood pressure data indicates high levels, the cloud server generates and notifies the user with advice such as, "You are at risk of high blood pressure. Please seek medical attention at an appropriate medical institution."

[0430] As a result, a system is realized that manages a user's health information comprehensively and individually, and contributes to maintaining the user's health through appropriate advice.

[0431] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0432] Specific explanation of program processing

[0433] Step 1:

[0434] Collecting user biometric information

[0435] Device:

[0436] (Input) User activity information (heart rate, sleep data, dietary details, stress level)

[0437] (Operation) The user wears a "smartwatch" that automatically collects sleep data and heart rate data. The user also inputs their diet and stress level into a "smartphone app."

[0438] (Output) Collected biometric data (e.g., daily heart rate data, sleep duration, and dietary records)

[0439] Step 2:

[0440] Sending collected data to a cloud server

[0441] Device:

[0442] (Input) Collected biometric data

[0443] (Operation) At regular intervals, the device sends the collected data to the cloud server using a secure protocol (e.g., HTTPS).

[0444] (Output) Biometric data sent to a cloud server via a secure protocol

[0445] Step 3:

[0446] Data analysis on a cloud server

[0447] server:

[0448] (Input) Biometric data sent to the cloud server

[0449] (Operation) The cloud server uses Python 3.9 and TensorFlow 2.4 to analyze the received data, specifically evaluating the user's average heart rate, sleep patterns, dietary habits, etc.

[0450] (Output) Analysis result data (e.g. average sleep time, average heart rate)

[0451] Step 4:

[0452] Generating health advice

[0453] server:

[0454] (Input) Analysis result data

[0455] (Operation) The server uses the generative AI model to generate optimal health advice for the user based on the analysis results. For example, the "generative AI model" analyzes sleep data and generates specific advice such as "Try to get at least seven hours of sleep every night."

[0456] (Output) The generated health advice

[0457] Step 5:

[0458] Sending and notifying health advice to user devices

[0459] Server and Device:

[0460] (Input) Generated health advice

[0461] (Operation) The cloud server sends the generated advice to the user's device, which notifies the user via push notification or in-app message. The smartphone receives the notification and displays the advice to the user.

[0462] (Output) Advice notice displayed on the user's device (e.g., "Try to get at least 7 hours of sleep each night")

[0463] Step 6:

[0464] Medical data analysis and additional advice

[0465] server:

[0466] (Input) Medical data (diagnosis results, test data, etc.) with permission from the user

[0467] (Operation) The cloud server uses machine learning libraries such as "Scikit-learn" to analyze medical data and provide early warnings of diseases and suggest preventive measures. Specifically, if blood pressure data indicates high values, the cloud server generates advice to the user saying, "You are at risk of high blood pressure. Please seek medical attention at an appropriate medical institution."

[0468] (Output) Additional advice generated based on medical data

[0469] Specific steps and details of each step

[0470] Step 1:

[0471] Device:

[0472] The collected biometric data is obtained using a smartwatch worn by the user or a smartphone app that the user enters into, and this information is temporarily stored in a database on the device.

[0473] Step 2:

[0474] Device:

[0475] At a fixed time, such as midnight or at a specified time, data is sent to the cloud server via a secure protocol (HTTPS). The sent data then travels over the Internet to the cloud server.

[0476] Step 3:

[0477] server:

[0478] A Python 3.9 script runs periodically on the cloud server to analyze the data, using TensorFlow 2.4 to analyze complex data patterns and assess the user's health status.

[0479] Step 4:

[0480] server:

[0481] Based on the analysis results, the AI ​​model generates appropriate health advice according to prompts, such as calculating the average sleep time from sleep data and providing advice on necessary measures.

[0482] Step 5:

[0483] Server and Device:

[0484] Once advice is generated, it is sent from the cloud server to the user's device (such as a smartphone). The device immediately notifies the user of the received advice so that the user can review the information.

[0485] Step 6:

[0486] server:

[0487] With the user's permission, the cloud server also analyzes the medical data using machine learning libraries to predict disease risk and generate additional advice for the user on preventative measures.

[0488] In this way, the system of the present invention collects, safely manages, and analyzes the user's biometric information, and provides individually customized health advice, thereby helping the user maintain their health.

[0489] (Application example 1)

[0490] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0491] Conventional health management systems collect users' biometric information and provide health advice, but there are few cases where that advice leads to concrete actions. In particular, dietary advice is limited to providing information and is difficult to lead to the execution of actual meal plans. This creates a problem in that it is difficult for users to take concrete actions to maintain and improve their health.

[0492] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0493] In this invention, the server includes means for collecting biometric information of a user, means for securely transmitting the biometric information to a cloud server, means for the cloud server to analyze the biometric information, means for generating customized health advice based on the analysis results, means for proposing an optimal meal plan based on the health advice, means for managing food item information for providing the meal plan, means for transmitting the health advice and the meal plan to a user terminal, and means for the user terminal to notify the user of the health advice and the meal plan. This enables the user to receive a specific and feasible meal plan based on their health condition and to take actual action.

[0494] "User's biological information" is data related to the user's health condition, including heart rate, sleep time, dietary content, stress level, and the like.

[0495] A "cloud server" is a remote server accessible via the Internet, and is a virtualized server environment for storing and processing data.

[0496] "Analytical means" refers to a processing system that uses mathematical and statistical methods to analyze collected data and derive specific results and recommendations.

[0497] "Customized health advice" refers to specific guidance and advice for maintaining and improving health that is generated based on each user's individual health data.

[0498] The "means for proposing a meal plan" is a function that specifically selects and proposes a meal menu suitable for the user based on the analysis results.

[0499] The "means for managing food item information" is a system for creating a database of nutritional information, calories, ingredients, etc. of available meal menus and managing them appropriately.

[0500] A "user terminal" is a communication device used by a user, and includes mobile devices such as smartphones and tablets.

[0501] The "notification means" is a function that transmits the generated health advice and meal plan to the user terminal and displays the information to the user.

[0502] "Early warning or prevention of disease" refers to the use of a user's health data to predict the risk of developing a disease in the future and provide specific measures or guidelines for action to reduce that risk.

[0503] The present invention relates to a system that manages a user's health information comprehensively and individually and provides optimal health advice. The system includes a user terminal, a cloud server, and a means for collecting and analyzing the user's health information.

[0504] First, the user uses a communication device (user terminal) such as a smartphone or smartwatch to collect their own biometric information. The smartwatch records the user's heart rate and sleep time, and data on diet and stress level is entered into the smartphone app. This information is periodically sent to a cloud server.

[0505] The cloud server analyzes the received biometric information using a machine learning model or a generative AI model. A common machine learning framework (e.g., TensorFlow) is used as the model. Based on the analyzed data, the cloud server generates optimal health advice for each individual user. For example, this health advice might be, "You haven't been getting enough sleep recently. We recommend that you go to bed one hour earlier each night."

[0506] The cloud server then proposes a specific meal plan based on the analysis results. The meal plan is proposed using a database that manages food item information. The meal plan takes into account nutritional information, calories, ingredients, etc.

[0507] Health advice and meal plans are sent from the cloud server to the user's device and are notified to the user via push notifications and in-app messages, allowing the user to create a specific action plan.

[0508] A concrete example is the following scenario: If the user's sleep data shows an average of six hours or less, the cloud server will suggest a nutritionally balanced meal plan along with advice such as, "Your average sleep time is short. It is important to get enough sleep." For example, a "chicken breast salad (rich in protein, under 500 kcal)" might be recommended.

[0509] An example of a prompt to be input into the generative AI model is, "If the user has had less than six hours of sleep and their recent heart rate and stress level are high, recommend a low-calorie, high-protein meal."

[0510] In this way, the system of the present invention comprehensively manages the user's lifestyle habits and provides individually customized health advice and specific action plans to help the user maintain their health. The user can always understand their health status and take appropriate action. This system is expected to improve the user's overall health.

[0511] This completes the description of the "Description of the Preferred Embodiments."

[0512] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0513] Step 1:

[0514] The user device collects biometric information

[0515] The user wears a smartphone or smartwatch, which records biometric information such as heart rate, sleep time, dietary intake, and stress level. Input data includes dietary intake and stress level that the user manually enters into the app. Output data includes various measurement data, including this biometric information.

[0516] Step 2:

[0517] The user device sends biometric information to the cloud server.

[0518] The collected biometric information is sent from the user device to the cloud server at regular intervals via a secure protocol (e.g., HTTPS). The input data is the biometric information stored on the user device, and the output data is the data transferred to the cloud server.

[0519] Step 3:

[0520] Cloud server analyzes biometric information

[0521] The cloud server analyzes the received biometric information using machine learning models and generative AI models, which includes data preprocessing (data normalization and invalid data removal). The input data is the biometric information stored on the cloud server, and the output data is the analysis results.

[0522] Step 4:

[0523] A cloud server generates customized health advice

[0524] Based on the analysis results, the cloud server generates personalized health advice for the user, often using a generative AI model. The input data is the analysis results, and the output data is specific health advice.

[0525] Step 5:

[0526] Cloud server proposes meal plans

[0527] Based on the generated health advice, the cloud server proposes an optimal meal plan. It references a database that manages food item information and makes suggestions based on nutritional information, calories, ingredients, etc. The input data are health advice and food item information, and the output data is the proposed meal plan.

[0528] Step 6:

[0529] The cloud server sends health advice and meal plans to the user's device.

[0530] The cloud server transmits the generated health advice and meal plan to the user terminal, and the output data is the health advice and meal plan transmitted from the cloud server.

[0531] Step 7:

[0532] The user device notifies the user of health advice and meal plans

[0533] The user device notifies the user of the received health advice and meal plan via push notifications or in-app messages. The input data are the health advice and meal plan sent from the cloud server, and the output data are the notification content displayed to the user.

[0534] Through these steps, users can receive and implement specific advice and meal plans tailored to their health condition.

[0535] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0536] The present invention relates to a system that manages a user's health information comprehensively and individually and provides optimal health advice. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions. The system includes a user terminal, a cloud server, and a means for collecting and analyzing the user's health information and emotion information.

[0537] The devices used by users (e.g., smartphones, smartwatches, and fitness trackers) first collect the user's biometric and emotional information. For example, smartwatches record the user's sleep data and heart rate, while smartphone apps collect dietary information, stress levels, and even voice and facial expression data. Furthermore, emotion engines analyze the user's facial, voice, and text data to recognize the user's emotional state.

[0538] The device formats the collected biometric and emotional information and transmits it to a cloud server using a secure protocol (e.g., HTTPS) to ensure user privacy.

[0539] The cloud server analyzes the received information and generates optimal health advice for each individual user. This analysis uses machine learning models, generative AI models, and an emotion engine to comprehensively evaluate the user's sleep patterns, diet, stress level, emotional state, etc. For example, if a user's sleep data is analyzed and a regular sleep pattern is not observed, specific advice on how to get enough sleep is generated. In addition, if the user is under stress, the emotion engine will add health advice that takes their emotional state into account.

[0540] The generated health advice is sent from the cloud server to the user's device. The user's device then notifies the user of the advice via push notifications or in-app messages. For example, a notification might appear on the smartphone saying, "You've been getting insufficient sleep recently. We recommend going to bed one hour earlier each night." At the same time, additional advice based on the user's emotional state might be displayed, such as, "You seem to be feeling stressed. We recommend you practice relaxation."

[0541] The cloud server also analyzes medical data with the user's permission. This analysis includes the user's past diagnosis and test data, and provides early warnings and suggests preventative measures for illness. For example, if a user's blood pressure shows high levels, the cloud server will notify them of their risk of hypertension and recommend that they seek medical attention at an appropriate medical institution.

[0542] As a concrete example, consider a case where a user wears a smartwatch and uses a smartphone app equipped with an emotion analysis function. The smartwatch records the time the user goes to sleep and wakes up. The smartphone app records the user's meals and simultaneously analyzes the user's emotional state from voice and facial expressions. This data is sent to a cloud server every night. The cloud server analyzes the user's sleep patterns, diet, and emotional state, and verifies that the average sleep time is six hours or less. Based on this information, it generates advice such as "Try to get at least seven hours of sleep each night" and sends a notification to the smartphone. At the same time, if the user's emotional state indicates stress, it also generates advice such as "You are feeling stressed. We recommend that you practice meditation and relaxation."

[0543] In this way, the system of the present invention comprehensively manages the user's lifestyle habits and emotional state and provides individually customized health advice to help the user maintain their health. In doing so, the user can always understand their own health and emotional state and take appropriate action. This system is expected to improve the user's overall health.

[0544] The processing flow will be explained below.

[0545] Step 1:

[0546] The device collects the user's biometric information. Specifically, the smartwatch records sleep duration and heart rate, and the user inputs their diet and stress level via a smartphone app.

[0547] Step 2:

[0548] The device collects the user's emotional information. Specifically, the smartphone app analyzes the user's voice and facial expression data and uses an emotion engine to recognize the user's emotional state.

[0549] Step 3:

[0550] The device formats the collected biometric and emotional information and prepares it for transmission to the cloud server, ensuring that the data is in the correct format and encoding it if necessary.

[0551] Step 4:

[0552] The device transmits the formatted biometric and emotional information to the cloud server using a secure communication protocol (e.g., HTTPS). If an error occurs during transmission, the device attempts to retransmit the information.

[0553] Step 5:

[0554] The server receives the biometric and emotional data sent from the device, checks the data integrity, and verifies whether there are any outliers before storing them in a database.

[0555] Step 6:

[0556] The server analyzes the biometric and emotional information stored in the database, and utilizes machine learning models, generative AI models, and an emotion engine to comprehensively evaluate the user's lifestyle habits and emotional state.

[0557] Step 7:

[0558] The server generates customized health advice based on the analysis results. For example, it may advise a user who is not getting enough sleep on average to go to bed earlier each night. It also suggests stress reduction measures based on the emotion engine's analysis results.

[0559] Step 8:

[0560] The server generates and transmits the generated health advice to the user terminal in an appropriate format.

[0561] Step 9:

[0562] The device notifies the user of the health advice received from the server, displaying specific advice content using push notifications and in-app messages.

[0563] Step 10:

[0564] The user checks the notification from the device and takes action based on the advice provided, such as reviewing their bedtime schedule or practicing meditation or relaxation to reduce stress.

[0565] Step 11:

[0566] The device continuously monitors how well the user followed the advice, collecting data again and sending it to the server for further analysis.

[0567] Step 12:

[0568] The server performs a new analysis based on the retransmitted data and generates new advice as necessary, thereby always providing the user with up-to-date support for health management.

[0569] Example 2

[0570] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0571] Conventional health management systems focus on collecting and analyzing biometric information, but do not provide advice that takes into account the user's emotional information. Furthermore, they face challenges such as security issues and a lack of individually tailored advice. This creates a need for a system that can comprehensively manage a user's health and emotional state and provide individually tailored health advice.

[0572] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0573] In this invention, the server includes means for collecting biometric information and emotional information of a user, means for securely transmitting the biometric information and emotional information to a data processing device, means for the data processing device to analyze the biometric information and emotional information, means for generating customized health advice based on the analysis results, means for transmitting the health advice to a user terminal, and means for the user terminal to notify the user of the health advice. This allows the user to comprehensively understand their own health and emotional state and take appropriate actions to improve their overall health.

[0574] "Biometric information" refers to data relating to the user's physical condition, such as heart rate, sleep data, and number of steps taken.

[0575] "Emotion information" is data relating to the user's emotional state analyzed from facial expressions, voice, text data, and the like.

[0576] The "data processing device" is a device that receives and analyzes biometric information and emotional information sent from a user.

[0577] "Analysis" refers to the process of evaluating the received biometric and emotional information to comprehensively determine the user's health and emotional state.

[0578] A "generative AI model" is an artificial intelligence model that assesses a user's health and emotional state and generates customized health advice.

[0579] "Health advice" refers to individually customized guidance and recommendations for maintaining health based on the analysis of the user's biometric and emotional information.

[0580] A "user terminal" is a device worn or used by a user, such as a smartphone, smartwatch, or fitness tracker.

[0581] A "secure protocol" is a communication protocol for securely sending and receiving data, and examples include HTTPS.

[0582] "Medical data" refers to information relating to the user's medical care, such as past diagnostic results, test data, and drug therapy history.

[0583] The present invention relates to a system that manages a user's health information comprehensively and individually and provides optimal health advice. It also incorporates an emotion engine that recognizes the user's emotions. The system includes a device used by the user (e.g., a smartphone, a smartwatch, a fitness tracker), a data processing device (a cloud server), and an integrated analysis means.

[0584] composition:

[0585] Device configuration:

[0586] The devices used by users include smartwatches, smartphones, etc. These devices collect information such as:

[0587] The smartwatch tracks heart rate, steps, and sleep data.

[0588] The smartphone collects and analyzes dietary habits, stress levels, and facial and voice data using an emotion engine.

[0589] Data collection and transmission:

[0590] The biometric and emotional information collected on the device is sent to the cloud server through data processing and secure protocols (e.g., HTTPS), ensuring user privacy.

[0591] Data processing unit configuration:

[0592] The cloud server is equipped with the following analytical tools:

[0593] Machine learning models

[0594] Generative AI Models

[0595] Emotion Engine

[0596] These elements allow the cloud server to analyze the biometric and emotional information sent by the user and generate individually customized health advice.

[0597] Health advice generation and notification:

[0598] Health advice generated by the cloud server is based on the analysis results. For example, if a user's sleep data lacks regularity, advice such as "Try to get at least seven hours of sleep every night" will be generated. Furthermore, if the analysis results of the emotion engine indicate high stress levels, additional advice such as "We recommend you practice relaxation" will be generated.

[0599] The generated health advice is securely sent to the user's device, which then notifies the user via push notifications or in-app messages. For example, a notification might appear on a smartphone saying, "You haven't been getting enough sleep recently. We recommend going to bed one hour earlier each night."

[0600] Usage example

[0601] Consider a case where a user is wearing a smartwatch and using a smartphone app equipped with emotion analysis functionality. The smartwatch records the user's sleep and wake-up times. The smartphone app records the user's meals and simultaneously analyzes their emotional state from their voice and facial expressions. These data are sent to a cloud server each night. The cloud server analyzes the user's sleep patterns, diet, and emotional state, and verifies that their average sleep time is six hours or less. Based on this information, it generates advice such as "Try to get at least seven hours of sleep each night" and sends a notification to the smartphone. At the same time, if the user's emotional state indicates stress, it also generates advice such as "You are feeling stressed. We recommend that you practice meditation and relaxation."

[0602] Example prompt sentence:

[0603] "Analyze the user's sleep data and emotional state and generate appropriate health advice. For example, if the average sleep time is less than six hours, recommend getting at least seven hours of sleep each night, or recommend relaxation if the user is stressed."

[0604] This system allows users to comprehensively understand their health and emotional state, and by taking appropriate actions based on the health advice provided individually, it is expected that their overall health will improve.

[0605] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0606] Step 1:

[0607] The user uses the terminal to collect biometric information and emotional information.

[0608] Specifically, the smartwatch records heart rate, steps, and sleep data (e.g., time of falling asleep and waking up), while the smartphone prompts the user to enter their diet, weight, and stress level, and uses a camera and microphone to collect facial and voice data.

[0609] Input: User's biometric information (e.g., heart rate, sleep data), emotional information (e.g., facial expressions, voice)

[0610] Output: A dataset of collected biometric and emotional information

[0611] Step 2:

[0612] The device formats the biometric and emotional information collected and sends it to a cloud server using a secure protocol.

[0613] Specifically, the device encrypts data using the HTTPS protocol and periodically sends it to the cloud server.

[0614] Input: Dataset of collected biometric and emotional information

[0615] Output: Formatted data sent to the cloud server

[0616] Step 3:

[0617] The server parses the received data.

[0618] Specifically, the cloud server formats the data into a coherent format and analyzes it using machine learning models, generative AI models, and emotion engines, including assessing the user's sleep patterns, diet, stress levels, and emotional state.

[0619] Input: Formatted data sent to the cloud server

[0620] Output: Analysis results (e.g., user's sleep patterns, emotional state)

[0621] Step 4:

[0622] The server generates customized health advice based on the analysis results.

[0623] Specifically, the generative AI model generates personalized health advice for each user based on the analysis results. For example, for a user who is not getting enough sleep, the advice "Try to get at least seven hours of sleep each night" is generated. If the emotion engine detects a state of stress, the additional advice "We recommend you practice relaxation" is generated.

[0624] Input: Analysis results (e.g., user's sleep patterns, emotional state)

[0625] Output: Generated customized health advice

[0626] Step 5:

[0627] The server transmits the generated health advice to the user terminal.

[0628] Specifically, the generated advice is converted into JSON format or similar, encrypted using the HTTPS protocol, and sent to the terminal.

[0629] Input: Generated customized health advice

[0630] Output: Health advice sent to the user device

[0631] Step 6:

[0632] The user terminal notifies the user of the advice.

[0633] Specifically, the smartphone will send advice to the user via push notifications or in-app messages. For example, a notification will be displayed saying, "You haven't been getting enough sleep recently. We recommend going to bed one hour earlier each night." Additional advice based on your emotional state will also be provided.

[0634] Input: Health advice sent to user device

[0635] Output: Advice given to the user

[0636] The above are the specific processing steps of the program for this system.

[0637] (Application example 2)

[0638] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0639] Nowadays, many people are interested in health management, but it is not easy to obtain appropriate advice based on their individual health status. Furthermore, there is a lack of means in physical stores to provide personalized health advice and product recommendations in real time based on users' health and emotional data. Under these circumstances, it is difficult for users to take optimal actions to maintain their health, and overall health improvement cannot be expected. Therefore, there is a need for a system that collects and analyzes users' biometric and emotional information and provides appropriate health advice and product recommendations in real time.

[0640] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0641] In this invention, the server includes means for collecting biometric information from a user, means for securely transmitting the biometric information to a cloud server, means for the cloud server to analyze the biometric information and emotional information, means for generating customized health advice based on the analysis results, means for transmitting the health advice to a user terminal, means for the user terminal to notify the user of the health advice, and means for analyzing the user's health data and emotional data in real time at a physical store and providing health advice and product suggestions. This enables users to receive individually customized health advice in real time even in a physical store, and to select appropriate products and take actions to maintain their health.

[0642] The "means for collecting user's biometric information" refers to a device or software for acquiring physiological data such as the user's heart rate and sleep data.

[0643] The "means for securely transmitting the biometric information to the cloud server" refers to protocols and technologies for transferring collected biometric information to the cloud server while protecting the privacy of the data.

[0644] The "means by which the cloud server analyzes the biometric information and emotional information" refers to a machine learning model or algorithm for collecting and analyzing the user's biometric information and emotional information on the cloud.

[0645] The "means for generating customized health advice based on the analysis results" is a technology that generates advice related to health management that is optimized for each user based on the analyzed data.

[0646] The "means for transmitting the health advice to the user terminal" is a technology for transmitting health advice generated by a cloud server to a terminal such as a user's smartphone or smartwatch.

[0647] The "means for the user terminal to notify the user of the health advice" refers to a technology for displaying the received health advice to the user on the user terminal as a push notification or an in-app message.

[0648] "Means for analyzing a user's health and emotional data in real time in a physical store and providing health advice and product suggestions" refers to a technology that instantly analyzes a user's health and emotional state in a physical store environment and, based on the results, provides the user with optimal health-related advice and product suggestions.

[0649] This invention is a system for collecting and analyzing a user's biometric and emotional information and providing appropriate health advice and product suggestions in real time at a physical store. The system includes a user terminal, a cloud server, and a means for analyzing this data and generating advice.

[0650] User devices refer to devices such as smartphones, smartwatches, and smart glasses. These devices have the ability to collect biometric and emotional information, such as a user's heart rate, sleep data, dietary information, voice, and facial expressions. For example, a smartwatch records a user's heart rate and sleep data, while a smartphone app records dietary information and stress levels. They also include emotion engines that analyze the user's voice and facial expression data.

[0651] The collected data is formatted and sent to a cloud server using a secure protocol (e.g., HTTPS). The cloud server analyzes the received data using machine learning models (e.g., TensorFlow, PyTorch) or generative AI models (e.g., GPT-4). The emotion engine analyzes the user's emotional state and comprehensively evaluates this data.

[0652] The analysis results are used to generate customized health advice based on the user's individual health data. For example, analyzing a user's sleep data and determining that their average sleep time is six hours or less generates the advice, "Try to get at least seven hours of sleep each night." If the user's emotional state indicates stress, the system adds the advice, "You are feeling stressed. We recommend that you practice meditation and relaxation."

[0653] To improve the user experience in physical stores, the system analyzes users' health and emotional data in real time and makes specific product suggestions. For example, while shopping in a store, a product suggestion could be displayed such as, "Since your recent sleep data has been insufficient, why not try this health pillow?"

[0654] For example, the following prompts can be used:

[0655] "Due to the user's recent sleep data and increasing stress levels, we want to generate optimal health advice for him.

[0656] (input data)

[0657] Average sleep time: 5.5 hours

[0658] Recent stress level: High

[0659] Health Goal: Stress Management

[0660] (generated advice)

[0661] Aim to get at least seven hours of sleep each night and incorporate relaxation activities like deep breathing and meditation."

[0662] This system allows users to constantly monitor their health and emotional state and take appropriate action. By receiving real-time health advice and product suggestions in physical stores, users are expected to maintain their health and improve their quality of life.

[0663] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0664] Step 1:

[0665] Devices (e.g., smartwatches and smartphones) collect biometric and emotional information such as the user's heart rate, sleep data, dietary information, voice, and facial expressions. These data are input from sensors, cameras, and microphones. For example, a smartwatch records the user's heart rate every second, and a smartphone app inputs the user's dietary information using image recognition technology.

[0666] Step 2:

[0667] The device preprocesses the collected biometric and emotional information and formats the data. For example, it calculates heart rate data as a minute-by-minute average and converts voice data into text. This preprocessed data is sent to a cloud server using the HTTPS protocol. The input is raw data, and the output is preprocessed structured data.

[0668] Step 3:

[0669] The cloud server analyzes the received data using machine learning models (e.g., TensorFlow, PyTorch) and generative AI models (e.g., GPT-4). This analysis step comprehensively evaluates the user's sleep patterns, heart rate, dietary content, and emotional state. The input is preprocessed structured data, and the output is evaluation data as the analysis result.

[0670] Step 4:

[0671] The server generates customized health advice based on the analysis results. The generated advice specifically corresponds to the user's health and emotional state. For example, if the sleep data is insufficient, the advice generated might be, "Try to get at least seven hours of sleep each night," or if the stress level is high, the advice generated might be, "We recommend meditating and relaxing." The input is the evaluation data from the analysis results, and the output is specific advice text.

[0672] Step 5:

[0673] The generated health advice is sent from the cloud server to the user's device using a secure protocol such as HTTPS. The input is the advice text, and the output is the data sent to the user's device.

[0674] Step 6:

[0675] The device notifies the user of the received health advice as a push notification or an in-app message. For example, a message such as "You haven't been getting enough sleep recently. We recommend that you go to bed one hour earlier each night" is displayed on the user's smartphone screen. The input is the received advice text, and the output is the notification to the user.

[0676] Step 7:

[0677] In a physical store, the device analyzes the user's health and emotional data in real time and makes product suggestions based on the results. For example, while shopping in a store, a message might appear saying, "Your recent sleep data has been insufficient. Why not try this health pillow?" The input is real-time health and emotional data, and the output is a display of product suggestions.

[0678] Through these steps, the system can efficiently collect and analyze the user's biometric and emotional information, and provide optimal health advice and product recommendations in real time.

[0679] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0680] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0681] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0682] [Third embodiment]

[0683] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0684] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0685] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0686] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0687] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0688] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0689] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0690] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0691] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0692] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0693] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0694] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0695] The present invention relates to a system that manages a user's health information comprehensively and individually and provides optimal health advice. The system includes a user terminal, a cloud server, and a means for collecting and analyzing the user's health information.

[0696] The devices used by users (e.g., smartphones, smartwatches, and fitness trackers) first collect biometric information from users. For example, smartwatches record users' sleep data and heart rate, while smartphone apps collect data by inputting dietary habits and stress levels.

[0697] The device transmits the collected biometric information to a cloud server at regular intervals. This transmission is performed using a secure protocol (e.g., HTTPS) to ensure user privacy.

[0698] The cloud server analyzes the received biometric information and generates optimal health advice for each individual user. This analysis uses machine learning models and generative AI models to comprehensively evaluate the user's sleep patterns, diet, stress levels, etc. For example, if a user's sleep data is analyzed and a regular sleep pattern is not observed, specific advice on how to get enough sleep is generated.

[0699] The generated health advice is sent from the cloud server to the user's device, which then notifies the user of the advice via push notifications or in-app messages. For example, a notification might appear on a smartphone saying, "You haven't been getting enough sleep recently. We recommend going to bed one hour earlier each night."

[0700] The cloud server also analyzes medical data with the user's permission. This analysis includes the user's past diagnosis and test data, and provides early warnings and suggests preventative measures for illness. For example, if a user's blood pressure shows high levels, the cloud server will notify them of their risk of hypertension and recommend that they seek medical attention at an appropriate medical institution.

[0701] As a concrete example, consider a user wearing a smartwatch. The smartwatch records the time the user falls asleep and wakes up. This data is sent to a cloud server each night. The cloud server analyzes the user's sleep patterns and determines that the average sleep time is six hours or less. Based on this information, it generates advice such as "Try to get at least seven hours of sleep each night" and sends a notification to the user's smartphone.

[0702] In this way, the system of the present invention comprehensively manages the user's lifestyle habits and provides individually customized health advice to help the user maintain their health. In doing so, the user can always understand their health status and take appropriate action. This system is expected to improve the user's overall health.

[0703] The processing flow will be explained below.

[0704] Step 1:

[0705] The device collects the user's biometric information. Specifically, the smartwatch records sleep duration and heart rate, and the user inputs dietary habits and stress levels into a smartphone app.

[0706] Step 2:

[0707] The device formats the collected biometric data and prepares it for transmission to the cloud server, ensuring that the data is in the correct format and encoding it if necessary.

[0708] Step 3:

[0709] The device sends the formatted biometric data to the cloud server using a secure communication protocol (e.g., HTTPS). If an error occurs during transmission, the device attempts to retransmit the data.

[0710] Step 4:

[0711] The server receives the biometric data sent from the device, checks the data integrity, and verifies that there are no abnormal values ​​before storing it in a database.

[0712] Step 5:

[0713] The server analyzes the biometric information stored in the database and utilizes machine learning and generative AI models to comprehensively evaluate the user's lifestyle habits.

[0714] Step 6:

[0715] The server generates customized health advice based on the analysis results, such as suggesting to a user who is getting insufficient sleep on average to go to bed a certain amount earlier each night.

[0716] Step 7:

[0717] The server generates and transmits the generated health advice to the user's terminal in an appropriate format.

[0718] Step 8:

[0719] The device notifies the user of the health advice received from the server, displaying the advice content using push notifications or in-app messages.

[0720] Step 9:

[0721] The user checks the notification from the device and takes action based on the presented advice, such as reviewing their bedtime schedule in accordance with the advice.

[0722] Step 10:

[0723] The device continuously monitors how well the user followed the advice, collecting data again and sending it to the server for further analysis.

[0724] Step 11:

[0725] The server performs a new analysis based on the retransmitted data and generates new advice as necessary, thereby always providing the user with up-to-date support for health management.

[0726] Example 1

[0727] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0728] In recent years, the importance of health management has increased, and many users want to collect individual health information and understand their own health status. However, existing systems do not standardize data collection and analysis methods, and the advice provided to users cannot be said to be uniform and effective. Furthermore, advanced privacy protection and data analysis technologies are required for users to safely manage their own health information and receive accurate health advice. To address these challenges, a system is needed that can effectively collect and analyze users' biometric information and provide individually customized health advice.

[0729] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0730] In this invention, the server includes means for collecting a user's biometric information, means for transmitting the biometric information to a cloud server using a secure protocol, means for the cloud server to analyze the biometric information, means for generating customized health advice based on the analysis results using a generative AI model, means for transmitting the health advice to a user terminal, means for the user terminal to notify the user of the health advice, and means for analyzing medical data and proposing early warning or preventive measures for diseases with the user's permission, thereby enabling users to consistently manage their own health information and receive comprehensive and individually customized advice.

[0731] "User" refers to an individual who utilizes the system to manage their own health information and receive health advice.

[0732] "Biometric information" refers to data relating to the user's health condition, and specifically includes heart rate, sleep data, dietary details, stress level, and the like.

[0733] A "cloud server" is a server system that provides data storage and computing power over the Internet and is used to analyze a user's biometric information.

[0734] "Protocol" refers to the communication rules that ensure safety and accuracy in sending and receiving data. Examples include HTTPS.

[0735] A "generative AI model" is a type of machine learning algorithm used to analyze a user's biometric information and generate customized health advice.

[0736] "Advice" refers to recommended actions and precautions generated based on the user's health condition.

[0737] A "terminal" is a device used by a user, and specifically includes a smartphone, a smartwatch, a fitness tracker, etc.

[0738] "Medical data" refers to information obtained by medical institutions, such as a user's past diagnosis results and test data.

[0739] "Analysis" refers to the process of analyzing information based on collected data and drawing conclusions or predictions.

[0740] "Notification" refers to the transmission of information to a user via a device, and specifically includes push notifications and in-app messages.

[0741] MODE FOR CARRYING OUT THE INVENTION

[0742] This invention relates to a system that comprehensively and individually manages a user's health information and provides optimal health advice. This system uses a user device, a cloud server, a generative AI model, and prompts to collect and analyze the user's health information and provide customized advice.

[0743] 1. Hardware and Software Configuration

[0744] Devices: Devices used by users include smartphones, smartwatches, fitness trackers, etc. For example, there is the smartwatch "Apple Watch" and the smartphone "iPhone." These devices are used to collect users' biometric information.

[0745] Cloud servers: Cloud servers, including Amazon Web Services (AWS), are used to analyze data and generate advice. Cloud servers have high computing power and storage capacity, providing the computing resources to analyze the received data.

[0746] Generative AI model: The generative AI model runs on a cloud server and uses machine learning libraries such as TensorFlow and Scikit-learn to generate optimal health advice for users.

[0747] Prompt example: Prompts are used to generate health advice. For example, "Generate optimal health advice based on the user's latest sleep data. For example, if the average sleep time is 6 hours, provide specific advice on how many hours of sleep the user should get."

[0748] 2. System Operation

[0749] User biometric information collection:

[0750] The devices collect the user's biometric information. Specifically, the smartwatch "Apple Watch" measures the user's heart rate and sleep data, and the smartphone app "MyFitnessPal" collects information by inputting the user's diet and stress level.

[0751] Sending data to cloud server:

[0752] The collected data is sent to a cloud server at a fixed time (for example, at night) using a secure protocol (for example, HTTPS), which ensures safe transfer while preventing data leakage and tampering.

[0753] Data analysis and health advice generation:

[0754] The cloud server analyzes the received data. Python 3.9 and TensorFlow 2.4 are used for analysis, performing pattern recognition and evaluation of the data. The generative AI model integrates and evaluates the user's sleep patterns, diet, stress level, etc., to generate optimal advice for each individual user. For example, it might generate advice such as, "Make sure your average sleep time is under six hours, and aim to get at least seven hours of sleep each night."

[0755] Health Advice Notice:

[0756] The generated advice is sent from the cloud server to the user's device, and the user's device notifies the user via push notification or in-app message. For example, an iPhone might display a notification to the user saying, "We recommend getting at least seven hours of sleep each night."

[0757] Medical data analysis and additional advice:

[0758] With permission from the user, the cloud server analyzes past diagnostic results and test data to provide early warnings and preventative measures. Machine learning libraries such as "Scikit-learn" are used for the analysis. For example, if blood pressure data indicates high levels, the cloud server generates and notifies the user with advice such as, "You are at risk of high blood pressure. Please seek medical attention at an appropriate medical institution."

[0759] As a result, a system is realized that manages a user's health information comprehensively and individually, and contributes to maintaining the user's health through appropriate advice.

[0760] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0761] Specific explanation of program processing

[0762] Step 1:

[0763] Collecting user biometric information

[0764] Device:

[0765] (Input) User activity information (heart rate, sleep data, dietary details, stress level)

[0766] (Operation) The user wears a "smartwatch" that automatically collects sleep data and heart rate data. The user also inputs their diet and stress level into a "smartphone app."

[0767] (Output) Collected biometric data (e.g., daily heart rate data, sleep duration, and dietary records)

[0768] Step 2:

[0769] Sending collected data to a cloud server

[0770] Device:

[0771] (Input) Collected biometric data

[0772] (Operation) At regular intervals, the device sends the collected data to the cloud server using a secure protocol (e.g., HTTPS).

[0773] (Output) Biometric data sent to a cloud server via a secure protocol

[0774] Step 3:

[0775] Data analysis on a cloud server

[0776] server:

[0777] (Input) Biometric data sent to the cloud server

[0778] (Operation) The cloud server uses Python 3.9 and TensorFlow 2.4 to analyze the received data, specifically evaluating the user's average heart rate, sleep patterns, dietary habits, etc.

[0779] (Output) Analysis result data (e.g. average sleep time, average heart rate)

[0780] Step 4:

[0781] Generating health advice

[0782] server:

[0783] (Input) Analysis result data

[0784] (Operation) The server uses the generative AI model to generate optimal health advice for the user based on the analysis results. For example, the "generative AI model" analyzes sleep data and generates specific advice such as "Try to get at least seven hours of sleep every night."

[0785] (Output) The generated health advice

[0786] Step 5:

[0787] Sending and notifying health advice to user devices

[0788] Server and Device:

[0789] (Input) Generated health advice

[0790] (Operation) The cloud server sends the generated advice to the user's device, which notifies the user via push notification or in-app message. The smartphone receives the notification and displays the advice to the user.

[0791] (Output) Advice notice displayed on the user's device (e.g., "Try to get at least 7 hours of sleep each night")

[0792] Step 6:

[0793] Medical data analysis and additional advice

[0794] server:

[0795] (Input) Medical data (diagnosis results, test data, etc.) with permission from the user

[0796] (Operation) The cloud server uses machine learning libraries such as "Scikit-learn" to analyze medical data and provide early warnings of diseases and suggest preventive measures. Specifically, if blood pressure data indicates high values, the cloud server generates advice to the user saying, "You are at risk of high blood pressure. Please seek medical attention at an appropriate medical institution."

[0797] (Output) Additional advice generated based on medical data

[0798] Specific steps and details of each step

[0799] Step 1:

[0800] Device:

[0801] The collected biometric data is obtained using a smartwatch worn by the user or a smartphone app that the user enters into, and this information is temporarily stored in a database on the device.

[0802] Step 2:

[0803] Device:

[0804] At a fixed time, such as midnight or at a specified time, data is sent to the cloud server via a secure protocol (HTTPS). The sent data then travels over the Internet to the cloud server.

[0805] Step 3:

[0806] server:

[0807] A Python 3.9 script runs periodically on the cloud server to analyze the data, using TensorFlow 2.4 to analyze complex data patterns and assess the user's health status.

[0808] Step 4:

[0809] server:

[0810] Based on the analysis results, the AI ​​model generates appropriate health advice according to prompts, such as calculating the average sleep time from sleep data and providing advice on necessary measures.

[0811] Step 5:

[0812] Server and Device:

[0813] Once advice is generated, it is sent from the cloud server to the user's device (such as a smartphone). The device immediately notifies the user of the received advice so that the user can review the information.

[0814] Step 6:

[0815] server:

[0816] With the user's permission, the cloud server also analyzes the medical data using machine learning libraries to predict disease risk and generate additional advice for the user on preventative measures.

[0817] In this way, the system of the present invention collects, safely manages, and analyzes the user's biometric information, and provides individually customized health advice, thereby helping the user maintain their health.

[0818] (Application example 1)

[0819] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0820] Conventional health management systems collect users' biometric information and provide health advice, but there are few cases where that advice leads to concrete actions. In particular, dietary advice is limited to providing information and is difficult to lead to the execution of actual meal plans. This creates a problem in that it is difficult for users to take concrete actions to maintain and improve their health.

[0821] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0822] In this invention, the server includes means for collecting biometric information of a user, means for securely transmitting the biometric information to a cloud server, means for the cloud server to analyze the biometric information, means for generating customized health advice based on the analysis results, means for proposing an optimal meal plan based on the health advice, means for managing food item information for providing the meal plan, means for transmitting the health advice and the meal plan to a user terminal, and means for the user terminal to notify the user of the health advice and the meal plan. This enables the user to receive a specific and feasible meal plan based on their health condition and to take actual action.

[0823] "User's biological information" is data related to the user's health condition, including heart rate, sleep time, dietary content, stress level, and the like.

[0824] A "cloud server" is a remote server accessible via the Internet, and is a virtualized server environment for storing and processing data.

[0825] "Analytical means" refers to a processing system that uses mathematical and statistical methods to analyze collected data and derive specific results and recommendations.

[0826] "Customized health advice" refers to specific guidance and advice for maintaining and improving health that is generated based on each user's individual health data.

[0827] The "means for proposing a meal plan" is a function that specifically selects and proposes a meal menu suitable for the user based on the analysis results.

[0828] The "means for managing food item information" is a system for creating a database of nutritional information, calories, ingredients, etc. of available meal menus and managing them appropriately.

[0829] A "user terminal" is a communication device used by a user, and includes mobile devices such as smartphones and tablets.

[0830] The "notification means" is a function that transmits the generated health advice and meal plan to the user terminal and displays the information to the user.

[0831] "Early warning or prevention of disease" refers to the use of a user's health data to predict the risk of developing a disease in the future and provide specific measures or guidelines for action to reduce that risk.

[0832] The present invention relates to a system that manages a user's health information comprehensively and individually and provides optimal health advice. The system includes a user terminal, a cloud server, and a means for collecting and analyzing the user's health information.

[0833] First, the user uses a communication device (user terminal) such as a smartphone or smartwatch to collect their own biometric information. The smartwatch records the user's heart rate and sleep time, and data on diet and stress level is entered into the smartphone app. This information is periodically sent to a cloud server.

[0834] The cloud server analyzes the received biometric information using a machine learning model or a generative AI model. A common machine learning framework (e.g., TensorFlow) is used as the model. Based on the analyzed data, the cloud server generates optimal health advice for each individual user. For example, this health advice might be, "You haven't been getting enough sleep recently. We recommend that you go to bed one hour earlier each night."

[0835] The cloud server then proposes a specific meal plan based on the analysis results. The meal plan is proposed using a database that manages food item information. The meal plan takes into account nutritional information, calories, ingredients, etc.

[0836] Health advice and meal plans are sent from the cloud server to the user's device and are notified to the user via push notifications and in-app messages, allowing the user to create a specific action plan.

[0837] A concrete example is the following scenario: If the user's sleep data shows an average of six hours or less, the cloud server will suggest a nutritionally balanced meal plan along with advice such as, "Your average sleep time is short. It is important to get enough sleep." For example, a "chicken breast salad (rich in protein, under 500 kcal)" might be recommended.

[0838] An example of a prompt to be input into the generative AI model is, "If the user has had less than six hours of sleep and their recent heart rate and stress level are high, recommend a low-calorie, high-protein meal."

[0839] In this way, the system of the present invention comprehensively manages the user's lifestyle habits and provides individually customized health advice and specific action plans to help the user maintain their health. The user can always understand their health status and take appropriate action. This system is expected to improve the user's overall health.

[0840] This completes the description of the "Description of the Preferred Embodiments."

[0841] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0842] Step 1:

[0843] The user device collects biometric information

[0844] The user wears a smartphone or smartwatch, which records biometric information such as heart rate, sleep time, dietary intake, and stress level. Input data includes dietary intake and stress level that the user manually enters into the app. Output data includes various measurement data, including this biometric information.

[0845] Step 2:

[0846] The user device sends biometric information to the cloud server.

[0847] The collected biometric information is sent from the user device to the cloud server at regular intervals via a secure protocol (e.g., HTTPS). The input data is the biometric information stored on the user device, and the output data is the data transferred to the cloud server.

[0848] Step 3:

[0849] Cloud server analyzes biometric information

[0850] The cloud server analyzes the received biometric information using machine learning models and generative AI models, which includes data preprocessing (data normalization and invalid data removal). The input data is the biometric information stored on the cloud server, and the output data is the analysis results.

[0851] Step 4:

[0852] A cloud server generates customized health advice

[0853] Based on the analysis results, the cloud server generates personalized health advice for the user, often using a generative AI model. The input data is the analysis results, and the output data is specific health advice.

[0854] Step 5:

[0855] Cloud server proposes meal plans

[0856] Based on the generated health advice, the cloud server proposes an optimal meal plan. It references a database that manages food item information and makes suggestions based on nutritional information, calories, ingredients, etc. The input data are health advice and food item information, and the output data is the proposed meal plan.

[0857] Step 6:

[0858] The cloud server sends health advice and meal plans to the user's device.

[0859] The cloud server transmits the generated health advice and meal plan to the user terminal, and the output data is the health advice and meal plan transmitted from the cloud server.

[0860] Step 7:

[0861] The user device notifies the user of health advice and meal plans

[0862] The user device notifies the user of the received health advice and meal plan via push notifications or in-app messages. The input data are the health advice and meal plan sent from the cloud server, and the output data are the notification content displayed to the user.

[0863] Through these steps, users can receive and implement specific advice and meal plans tailored to their health condition.

[0864] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0865] The present invention relates to a system that manages a user's health information comprehensively and individually and provides optimal health advice. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions. The system includes a user terminal, a cloud server, and a means for collecting and analyzing the user's health information and emotion information.

[0866] The devices used by users (e.g., smartphones, smartwatches, and fitness trackers) first collect the user's biometric and emotional information. For example, smartwatches record the user's sleep data and heart rate, while smartphone apps collect dietary information, stress levels, and even voice and facial expression data. Furthermore, emotion engines analyze the user's facial, voice, and text data to recognize the user's emotional state.

[0867] The device formats the collected biometric and emotional information and transmits it to a cloud server using a secure protocol (e.g., HTTPS) to ensure user privacy.

[0868] The cloud server analyzes the received information and generates optimal health advice for each individual user. This analysis uses machine learning models, generative AI models, and an emotion engine to comprehensively evaluate the user's sleep patterns, diet, stress level, emotional state, etc. For example, if a user's sleep data is analyzed and a regular sleep pattern is not observed, specific advice on how to get enough sleep is generated. In addition, if the user is under stress, the emotion engine will add health advice that takes their emotional state into account.

[0869] The generated health advice is sent from the cloud server to the user's device. The user's device then notifies the user of the advice via push notifications or in-app messages. For example, a notification might appear on the smartphone saying, "You've been getting insufficient sleep recently. We recommend going to bed one hour earlier each night." At the same time, additional advice based on the user's emotional state might be displayed, such as, "You seem to be feeling stressed. We recommend you practice relaxation."

[0870] The cloud server also analyzes medical data with the user's permission. This analysis includes the user's past diagnosis and test data, and provides early warnings and suggests preventative measures for illness. For example, if a user's blood pressure shows high levels, the cloud server will notify them of their risk of hypertension and recommend that they seek medical attention at an appropriate medical institution.

[0871] As a concrete example, consider a case where a user wears a smartwatch and uses a smartphone app equipped with an emotion analysis function. The smartwatch records the time the user goes to sleep and wakes up. The smartphone app records the user's meals and simultaneously analyzes the user's emotional state from voice and facial expressions. This data is sent to a cloud server every night. The cloud server analyzes the user's sleep patterns, diet, and emotional state, and verifies that the average sleep time is six hours or less. Based on this information, it generates advice such as "Try to get at least seven hours of sleep each night" and sends a notification to the smartphone. At the same time, if the user's emotional state indicates stress, it also generates advice such as "You are feeling stressed. We recommend that you practice meditation and relaxation."

[0872] In this way, the system of the present invention comprehensively manages the user's lifestyle habits and emotional state and provides individually customized health advice to help the user maintain their health. In doing so, the user can always understand their own health and emotional state and take appropriate action. This system is expected to improve the user's overall health.

[0873] The processing flow will be explained below.

[0874] Step 1:

[0875] The device collects the user's biometric information. Specifically, the smartwatch records sleep duration and heart rate, and the user inputs their diet and stress level via a smartphone app.

[0876] Step 2:

[0877] The device collects the user's emotional information. Specifically, the smartphone app analyzes the user's voice and facial expression data and uses an emotion engine to recognize the user's emotional state.

[0878] Step 3:

[0879] The device formats the collected biometric and emotional information and prepares it for transmission to the cloud server, ensuring that the data is in the correct format and encoding it if necessary.

[0880] Step 4:

[0881] The device transmits the formatted biometric and emotional information to the cloud server using a secure communication protocol (e.g., HTTPS). If an error occurs during transmission, the device attempts to retransmit the information.

[0882] Step 5:

[0883] The server receives the biometric and emotional data sent from the device, checks the data integrity, and verifies whether there are any outliers before storing them in a database.

[0884] Step 6:

[0885] The server analyzes the biometric and emotional information stored in the database, and utilizes machine learning models, generative AI models, and an emotion engine to comprehensively evaluate the user's lifestyle habits and emotional state.

[0886] Step 7:

[0887] The server generates customized health advice based on the analysis results. For example, it may advise a user who is not getting enough sleep on average to go to bed earlier each night. It also suggests stress reduction measures based on the emotion engine's analysis results.

[0888] Step 8:

[0889] The server generates and transmits the generated health advice to the user terminal in an appropriate format.

[0890] Step 9:

[0891] The device notifies the user of the health advice received from the server, displaying specific advice content using push notifications and in-app messages.

[0892] Step 10:

[0893] The user checks the notification from the device and takes action based on the advice provided, such as reviewing their bedtime schedule or practicing meditation or relaxation to reduce stress.

[0894] Step 11:

[0895] The device continuously monitors how well the user followed the advice, collecting data again and sending it to the server for further analysis.

[0896] Step 12:

[0897] The server performs a new analysis based on the retransmitted data and generates new advice as necessary, thereby always providing the user with up-to-date support for health management.

[0898] Example 2

[0899] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0900] Conventional health management systems focus on collecting and analyzing biometric information, but do not provide advice that takes into account the user's emotional information. Furthermore, they face challenges such as security issues and a lack of individually tailored advice. This creates a need for a system that can comprehensively manage a user's health and emotional state and provide individually tailored health advice.

[0901] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0902] In this invention, the server includes means for collecting biometric information and emotional information of a user, means for securely transmitting the biometric information and emotional information to a data processing device, means for the data processing device to analyze the biometric information and emotional information, means for generating customized health advice based on the analysis results, means for transmitting the health advice to a user terminal, and means for the user terminal to notify the user of the health advice. This allows the user to comprehensively understand their own health and emotional state and take appropriate actions to improve their overall health.

[0903] "Biometric information" refers to data relating to the user's physical condition, such as heart rate, sleep data, and number of steps taken.

[0904] "Emotion information" is data relating to the user's emotional state analyzed from facial expressions, voice, text data, and the like.

[0905] The "data processing device" is a device that receives and analyzes biometric information and emotional information sent from a user.

[0906] "Analysis" refers to the process of evaluating the received biometric and emotional information to comprehensively determine the user's health and emotional state.

[0907] A "generative AI model" is an artificial intelligence model that assesses a user's health and emotional state and generates customized health advice.

[0908] "Health advice" refers to individually customized guidance and recommendations for maintaining health based on the analysis of the user's biometric and emotional information.

[0909] A "user terminal" is a device worn or used by a user, such as a smartphone, smartwatch, or fitness tracker.

[0910] A "secure protocol" is a communication protocol for securely sending and receiving data, and examples include HTTPS.

[0911] "Medical data" refers to information relating to the user's medical care, such as past diagnostic results, test data, and drug therapy history.

[0912] The present invention relates to a system that manages a user's health information comprehensively and individually and provides optimal health advice. It also incorporates an emotion engine that recognizes the user's emotions. The system includes a device used by the user (e.g., a smartphone, a smartwatch, a fitness tracker), a data processing device (a cloud server), and an integrated analysis means.

[0913] composition:

[0914] Device configuration:

[0915] The devices used by users include smartwatches, smartphones, etc. These devices collect information such as:

[0916] The smartwatch tracks heart rate, steps, and sleep data.

[0917] The smartphone collects and analyzes dietary habits, stress levels, and facial and voice data using an emotion engine.

[0918] Data collection and transmission:

[0919] The biometric and emotional information collected on the device is sent to the cloud server through data processing and secure protocols (e.g., HTTPS), ensuring user privacy.

[0920] Data processing unit configuration:

[0921] The cloud server is equipped with the following analytical tools:

[0922] Machine learning models

[0923] Generative AI Models

[0924] Emotion Engine

[0925] These elements allow the cloud server to analyze the biometric and emotional information sent by the user and generate individually customized health advice.

[0926] Health advice generation and notification:

[0927] Health advice generated by the cloud server is based on the analysis results. For example, if a user's sleep data lacks regularity, advice such as "Try to get at least seven hours of sleep every night" will be generated. Furthermore, if the analysis results of the emotion engine indicate high stress levels, additional advice such as "We recommend you practice relaxation" will be generated.

[0928] The generated health advice is securely sent to the user's device, which then notifies the user via push notifications or in-app messages. For example, a notification might appear on a smartphone saying, "You haven't been getting enough sleep recently. We recommend going to bed one hour earlier each night."

[0929] Usage example

[0930] Consider a case where a user is wearing a smartwatch and using a smartphone app equipped with emotion analysis functionality. The smartwatch records the user's sleep and wake-up times. The smartphone app records the user's meals and simultaneously analyzes their emotional state from their voice and facial expressions. These data are sent to a cloud server each night. The cloud server analyzes the user's sleep patterns, diet, and emotional state, and verifies that their average sleep time is six hours or less. Based on this information, it generates advice such as "Try to get at least seven hours of sleep each night" and sends a notification to the smartphone. At the same time, if the user's emotional state indicates stress, it also generates advice such as "You are feeling stressed. We recommend that you practice meditation and relaxation."

[0931] Example prompt sentence:

[0932] "Analyze the user's sleep data and emotional state and generate appropriate health advice. For example, if the average sleep time is less than six hours, recommend getting at least seven hours of sleep each night, or recommend relaxation if the user is stressed."

[0933] This system allows users to comprehensively understand their health and emotional state, and by taking appropriate actions based on the health advice provided individually, it is expected that their overall health will improve.

[0934] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0935] Step 1:

[0936] The user uses the terminal to collect biometric information and emotional information.

[0937] Specifically, the smartwatch records heart rate, steps, and sleep data (e.g., time of falling asleep and waking up), while the smartphone prompts the user to enter their diet, weight, and stress level, and uses a camera and microphone to collect facial and voice data.

[0938] Input: User's biometric information (e.g., heart rate, sleep data), emotional information (e.g., facial expressions, voice)

[0939] Output: A dataset of collected biometric and emotional information

[0940] Step 2:

[0941] The device formats the biometric and emotional information collected and sends it to a cloud server using a secure protocol.

[0942] Specifically, the device encrypts data using the HTTPS protocol and periodically sends it to the cloud server.

[0943] Input: Dataset of collected biometric and emotional information

[0944] Output: Formatted data sent to the cloud server

[0945] Step 3:

[0946] The server parses the received data.

[0947] Specifically, the cloud server formats the data into a coherent format and analyzes it using machine learning models, generative AI models, and emotion engines, including assessing the user's sleep patterns, diet, stress levels, and emotional state.

[0948] Input: Formatted data sent to the cloud server

[0949] Output: Analysis results (e.g., user's sleep patterns, emotional state)

[0950] Step 4:

[0951] The server generates customized health advice based on the analysis results.

[0952] Specifically, the generative AI model generates personalized health advice for each user based on the analysis results. For example, for a user who is not getting enough sleep, the advice "Try to get at least seven hours of sleep each night" is generated. If the emotion engine detects a state of stress, the additional advice "We recommend you practice relaxation" is generated.

[0953] Input: Analysis results (e.g., user's sleep patterns, emotional state)

[0954] Output: Generated customized health advice

[0955] Step 5:

[0956] The server transmits the generated health advice to the user terminal.

[0957] Specifically, the generated advice is converted into JSON format or similar, encrypted using the HTTPS protocol, and sent to the terminal.

[0958] Input: Generated customized health advice

[0959] Output: Health advice sent to the user device

[0960] Step 6:

[0961] The user terminal notifies the user of the advice.

[0962] Specifically, the smartphone will send advice to the user via push notifications or in-app messages. For example, a notification will be displayed saying, "You haven't been getting enough sleep recently. We recommend going to bed one hour earlier each night." Additional advice based on your emotional state will also be provided.

[0963] Input: Health advice sent to user device

[0964] Output: Advice given to the user

[0965] The above are the specific processing steps of the program for this system.

[0966] (Application example 2)

[0967] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0968] Nowadays, many people are interested in health management, but it is not easy to obtain appropriate advice based on their individual health status. Furthermore, there is a lack of means in physical stores to provide personalized health advice and product recommendations in real time based on users' health and emotional data. Under these circumstances, it is difficult for users to take optimal actions to maintain their health, and overall health improvement cannot be expected. Therefore, there is a need for a system that collects and analyzes users' biometric and emotional information and provides appropriate health advice and product recommendations in real time.

[0969] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0970] In this invention, the server includes means for collecting biometric information from a user, means for securely transmitting the biometric information to a cloud server, means for the cloud server to analyze the biometric information and emotional information, means for generating customized health advice based on the analysis results, means for transmitting the health advice to a user terminal, means for the user terminal to notify the user of the health advice, and means for analyzing the user's health data and emotional data in real time at a physical store and providing health advice and product suggestions. This enables users to receive individually customized health advice in real time even in a physical store, and to select appropriate products and take actions to maintain their health.

[0971] The "means for collecting user's biometric information" refers to a device or software for acquiring physiological data such as the user's heart rate and sleep data.

[0972] The "means for securely transmitting the biometric information to the cloud server" refers to protocols and technologies for transferring collected biometric information to the cloud server while protecting the privacy of the data.

[0973] The "means by which the cloud server analyzes the biometric information and emotional information" refers to a machine learning model or algorithm for collecting and analyzing the user's biometric information and emotional information on the cloud.

[0974] The "means for generating customized health advice based on the analysis results" is a technology that generates advice related to health management that is optimized for each user based on the analyzed data.

[0975] The "means for transmitting the health advice to the user terminal" is a technology for transmitting health advice generated by a cloud server to a terminal such as a user's smartphone or smartwatch.

[0976] The "means for the user terminal to notify the user of the health advice" refers to a technology for displaying the received health advice to the user on the user terminal as a push notification or an in-app message.

[0977] "Means for analyzing a user's health and emotional data in real time in a physical store and providing health advice and product suggestions" refers to a technology that instantly analyzes a user's health and emotional state in a physical store environment and, based on the results, provides the user with optimal health-related advice and product suggestions.

[0978] This invention is a system for collecting and analyzing a user's biometric and emotional information and providing appropriate health advice and product suggestions in real time at a physical store. The system includes a user terminal, a cloud server, and a means for analyzing this data and generating advice.

[0979] User devices refer to devices such as smartphones, smartwatches, and smart glasses. These devices have the ability to collect biometric and emotional information, such as a user's heart rate, sleep data, dietary information, voice, and facial expressions. For example, a smartwatch records a user's heart rate and sleep data, while a smartphone app records dietary information and stress levels. They also include emotion engines that analyze the user's voice and facial expression data.

[0980] The collected data is formatted and sent to a cloud server using a secure protocol (e.g., HTTPS). The cloud server analyzes the received data using machine learning models (e.g., TensorFlow, PyTorch) or generative AI models (e.g., GPT-4). The emotion engine analyzes the user's emotional state and comprehensively evaluates this data.

[0981] The analysis results are used to generate customized health advice based on the user's individual health data. For example, analyzing a user's sleep data and determining that their average sleep time is six hours or less generates the advice, "Try to get at least seven hours of sleep each night." If the user's emotional state indicates stress, the system adds the advice, "You are feeling stressed. We recommend that you practice meditation and relaxation."

[0982] To improve the user experience in physical stores, the system analyzes users' health and emotional data in real time and makes specific product suggestions. For example, while shopping in a store, a product suggestion could be displayed such as, "Since your recent sleep data has been insufficient, why not try this health pillow?"

[0983] For example, the following prompts can be used:

[0984] "Due to the user's recent sleep data and increasing stress levels, we want to generate optimal health advice for him.

[0985] (input data)

[0986] Average sleep time: 5.5 hours

[0987] Recent stress level: High

[0988] Health Goal: Stress Management

[0989] (generated advice)

[0990] Aim to get at least seven hours of sleep each night and incorporate relaxation activities like deep breathing and meditation."

[0991] This system allows users to constantly monitor their health and emotional state and take appropriate action. By receiving real-time health advice and product suggestions in physical stores, users are expected to maintain their health and improve their quality of life.

[0992] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0993] Step 1:

[0994] Devices (e.g., smartwatches and smartphones) collect biometric and emotional information such as the user's heart rate, sleep data, dietary information, voice, and facial expressions. These data are input from sensors, cameras, and microphones. For example, a smartwatch records the user's heart rate every second, and a smartphone app inputs the user's dietary information using image recognition technology.

[0995] Step 2:

[0996] The device preprocesses the collected biometric and emotional information and formats the data. For example, it calculates heart rate data as a minute-by-minute average and converts voice data into text. This preprocessed data is sent to a cloud server using the HTTPS protocol. The input is raw data, and the output is preprocessed structured data.

[0997] Step 3:

[0998] The cloud server analyzes the received data using machine learning models (e.g., TensorFlow, PyTorch) and generative AI models (e.g., GPT-4). This analysis step comprehensively evaluates the user's sleep patterns, heart rate, dietary content, and emotional state. The input is preprocessed structured data, and the output is evaluation data as the analysis result.

[0999] Step 4:

[1000] The server generates customized health advice based on the analysis results. The generated advice specifically corresponds to the user's health and emotional state. For example, if the sleep data is insufficient, the advice generated might be, "Try to get at least seven hours of sleep each night," or if the stress level is high, the advice generated might be, "We recommend meditating and relaxing." The input is the evaluation data from the analysis results, and the output is specific advice text.

[1001] Step 5:

[1002] The generated health advice is sent from the cloud server to the user's device using a secure protocol such as HTTPS. The input is the advice text, and the output is the data sent to the user's device.

[1003] Step 6:

[1004] The device notifies the user of the received health advice as a push notification or an in-app message. For example, a message such as "You haven't been getting enough sleep recently. We recommend that you go to bed one hour earlier each night" is displayed on the user's smartphone screen. The input is the received advice text, and the output is the notification to the user.

[1005] Step 7:

[1006] In a physical store, the device analyzes the user's health and emotional data in real time and makes product suggestions based on the results. For example, while shopping in a store, a message might appear saying, "Your recent sleep data has been insufficient. Why not try this health pillow?" The input is real-time health and emotional data, and the output is a display of product suggestions.

[1007] Through these steps, the system can efficiently collect and analyze the user's biometric and emotional information, and provide optimal health advice and product recommendations in real time.

[1008] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1009] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1010] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1011] [Fourth embodiment]

[1012] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1013] 7, a 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.

[1014] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1015] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1016] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1017] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1018] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1019] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1020] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1021] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1022] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1023] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1024] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1025] The present invention relates to a system that manages a user's health information comprehensively and individually and provides optimal health advice. The system includes a user terminal, a cloud server, and a means for collecting and analyzing the user's health information.

[1026] The devices used by users (e.g., smartphones, smartwatches, and fitness trackers) first collect biometric information from users. For example, smartwatches record users' sleep data and heart rate, while smartphone apps collect data by inputting dietary habits and stress levels.

[1027] The device transmits the collected biometric information to a cloud server at regular intervals. This transmission is performed using a secure protocol (e.g., HTTPS) to ensure user privacy.

[1028] The cloud server analyzes the received biometric information and generates optimal health advice for each individual user. This analysis uses machine learning models and generative AI models to comprehensively evaluate the user's sleep patterns, diet, stress levels, etc. For example, if a user's sleep data is analyzed and a regular sleep pattern is not observed, specific advice on how to get enough sleep is generated.

[1029] The generated health advice is sent from the cloud server to the user's device, which then notifies the user of the advice via push notifications or in-app messages. For example, a notification might appear on a smartphone saying, "You haven't been getting enough sleep recently. We recommend going to bed one hour earlier each night."

[1030] The cloud server also analyzes medical data with the user's permission. This analysis includes the user's past diagnosis and test data, and provides early warnings and suggests preventative measures for illness. For example, if a user's blood pressure shows high levels, the cloud server will notify them of their risk of hypertension and recommend that they seek medical attention at an appropriate medical institution.

[1031] As a concrete example, consider a user wearing a smartwatch. The smartwatch records the time the user falls asleep and wakes up. This data is sent to a cloud server each night. The cloud server analyzes the user's sleep patterns and determines that the average sleep time is six hours or less. Based on this information, it generates advice such as "Try to get at least seven hours of sleep each night" and sends a notification to the user's smartphone.

[1032] In this way, the system of the present invention comprehensively manages the user's lifestyle habits and provides individually customized health advice to help the user maintain their health. In doing so, the user can always understand their health status and take appropriate action. This system is expected to improve the user's overall health.

[1033] The processing flow will be explained below.

[1034] Step 1:

[1035] The device collects the user's biometric information. Specifically, the smartwatch records sleep duration and heart rate, and the user inputs dietary habits and stress levels into a smartphone app.

[1036] Step 2:

[1037] The device formats the collected biometric data and prepares it for transmission to the cloud server, ensuring that the data is in the correct format and encoding it if necessary.

[1038] Step 3:

[1039] The device sends the formatted biometric data to the cloud server using a secure communication protocol (e.g., HTTPS). If an error occurs during transmission, the device attempts to retransmit the data.

[1040] Step 4:

[1041] The server receives the biometric data sent from the device, checks the data integrity, and verifies that there are no abnormal values ​​before storing it in a database.

[1042] Step 5:

[1043] The server analyzes the biometric information stored in the database and utilizes machine learning and generative AI models to comprehensively evaluate the user's lifestyle habits.

[1044] Step 6:

[1045] The server generates customized health advice based on the analysis results, such as suggesting to a user who is getting insufficient sleep on average to go to bed a certain amount earlier each night.

[1046] Step 7:

[1047] The server generates and transmits the generated health advice to the user's terminal in an appropriate format.

[1048] Step 8:

[1049] The device notifies the user of the health advice received from the server, displaying the advice content using push notifications or in-app messages.

[1050] Step 9:

[1051] The user checks the notification from the device and takes action based on the presented advice, such as reviewing their bedtime schedule in accordance with the advice.

[1052] Step 10:

[1053] The device continuously monitors how well the user followed the advice, collecting data again and sending it to the server for further analysis.

[1054] Step 11:

[1055] The server performs a new analysis based on the retransmitted data and generates new advice as necessary, thereby always providing the user with up-to-date support for health management.

[1056] Example 1

[1057] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1058] In recent years, the importance of health management has increased, and many users want to collect individual health information and understand their own health status. However, existing systems do not standardize data collection and analysis methods, and the advice provided to users cannot be said to be uniform and effective. Furthermore, advanced privacy protection and data analysis technologies are required for users to safely manage their own health information and receive accurate health advice. To address these challenges, a system is needed that can effectively collect and analyze users' biometric information and provide individually customized health advice.

[1059] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1060] In this invention, the server includes means for collecting a user's biometric information, means for transmitting the biometric information to a cloud server using a secure protocol, means for the cloud server to analyze the biometric information, means for generating customized health advice based on the analysis results using a generative AI model, means for transmitting the health advice to a user terminal, means for the user terminal to notify the user of the health advice, and means for analyzing medical data and proposing early warning or preventive measures for diseases with the user's permission, thereby enabling users to consistently manage their own health information and receive comprehensive and individually customized advice.

[1061] "User" refers to an individual who utilizes the system to manage their own health information and receive health advice.

[1062] "Biometric information" refers to data relating to the user's health condition, and specifically includes heart rate, sleep data, dietary details, stress level, and the like.

[1063] A "cloud server" is a server system that provides data storage and computing power over the Internet and is used to analyze a user's biometric information.

[1064] "Protocol" refers to the communication rules that ensure safety and accuracy in sending and receiving data. Examples include HTTPS.

[1065] A "generative AI model" is a type of machine learning algorithm used to analyze a user's biometric information and generate customized health advice.

[1066] "Advice" refers to recommended actions and precautions generated based on the user's health condition.

[1067] A "terminal" is a device used by a user, and specifically includes a smartphone, a smartwatch, a fitness tracker, etc.

[1068] "Medical data" refers to information obtained by medical institutions, such as a user's past diagnosis results and test data.

[1069] "Analysis" refers to the process of analyzing information based on collected data and drawing conclusions or predictions.

[1070] "Notification" refers to the transmission of information to a user via a device, and specifically includes push notifications and in-app messages.

[1071] MODE FOR CARRYING OUT THE INVENTION

[1072] This invention relates to a system that comprehensively and individually manages a user's health information and provides optimal health advice. This system uses a user device, a cloud server, a generative AI model, and prompts to collect and analyze the user's health information and provide customized advice.

[1073] 1. Hardware and Software Configuration

[1074] Devices: Devices used by users include smartphones, smartwatches, fitness trackers, etc. For example, there is the smartwatch "Apple Watch" and the smartphone "iPhone." These devices are used to collect users' biometric information.

[1075] Cloud servers: Cloud servers, including Amazon Web Services (AWS), are used to analyze data and generate advice. Cloud servers have high computing power and storage capacity, providing the computing resources to analyze the received data.

[1076] Generative AI model: The generative AI model runs on a cloud server and uses machine learning libraries such as TensorFlow and Scikit-learn to generate optimal health advice for users.

[1077] Prompt example: Prompts are used to generate health advice. For example, "Generate optimal health advice based on the user's latest sleep data. For example, if the average sleep time is 6 hours, provide specific advice on how many hours of sleep the user should get."

[1078] 2. System Operation

[1079] User biometric information collection:

[1080] The devices collect the user's biometric information. Specifically, the smartwatch "Apple Watch" measures the user's heart rate and sleep data, and the smartphone app "MyFitnessPal" collects information by inputting the user's diet and stress level.

[1081] Sending data to cloud server:

[1082] The collected data is sent to a cloud server at a fixed time (for example, at night) using a secure protocol (for example, HTTPS), which ensures safe transfer while preventing data leakage and tampering.

[1083] Data analysis and health advice generation:

[1084] The cloud server analyzes the received data. Python 3.9 and TensorFlow 2.4 are used for analysis, performing pattern recognition and evaluation of the data. The generative AI model integrates and evaluates the user's sleep patterns, diet, stress level, etc., to generate optimal advice for each individual user. For example, it might generate advice such as, "Make sure your average sleep time is under six hours, and aim to get at least seven hours of sleep each night."

[1085] Health Advice Notice:

[1086] The generated advice is sent from the cloud server to the user's device, and the user's device notifies the user via push notification or in-app message. For example, an iPhone might display a notification to the user saying, "We recommend getting at least seven hours of sleep each night."

[1087] Medical data analysis and additional advice:

[1088] With permission from the user, the cloud server analyzes past diagnostic results and test data to provide early warnings and preventative measures. Machine learning libraries such as "Scikit-learn" are used for the analysis. For example, if blood pressure data indicates high levels, the cloud server generates and notifies the user with advice such as, "You are at risk of high blood pressure. Please seek medical attention at an appropriate medical institution."

[1089] As a result, a system is realized that manages a user's health information comprehensively and individually, and contributes to maintaining the user's health through appropriate advice.

[1090] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1091] Specific explanation of program processing

[1092] Step 1:

[1093] Collecting user biometric information

[1094] Device:

[1095] (Input) User activity information (heart rate, sleep data, dietary details, stress level)

[1096] (Operation) The user wears a "smartwatch" that automatically collects sleep data and heart rate data. The user also inputs their diet and stress level into a "smartphone app."

[1097] (Output) Collected biometric data (e.g., daily heart rate data, sleep duration, and dietary records)

[1098] Step 2:

[1099] Sending collected data to a cloud server

[1100] Device:

[1101] (Input) Collected biometric data

[1102] (Operation) At regular intervals, the device sends the collected data to the cloud server using a secure protocol (e.g., HTTPS).

[1103] (Output) Biometric data sent to a cloud server via a secure protocol

[1104] Step 3:

[1105] Data analysis on a cloud server

[1106] server:

[1107] (Input) Biometric data sent to the cloud server

[1108] (Operation) The cloud server uses Python 3.9 and TensorFlow 2.4 to analyze the received data, specifically evaluating the user's average heart rate, sleep patterns, dietary habits, etc.

[1109] (Output) Analysis result data (e.g. average sleep time, average heart rate)

[1110] Step 4:

[1111] Generating health advice

[1112] server:

[1113] (Input) Analysis result data

[1114] (Operation) The server uses the generative AI model to generate optimal health advice for the user based on the analysis results. For example, the "generative AI model" analyzes sleep data and generates specific advice such as "Try to get at least seven hours of sleep every night."

[1115] (Output) The generated health advice

[1116] Step 5:

[1117] Sending and notifying health advice to user devices

[1118] Server and Device:

[1119] (Input) Generated health advice

[1120] (Operation) The cloud server sends the generated advice to the user's device, which notifies the user via push notification or in-app message. The smartphone receives the notification and displays the advice to the user.

[1121] (Output) Advice notice displayed on the user's device (e.g., "Try to get at least 7 hours of sleep each night")

[1122] Step 6:

[1123] Medical data analysis and additional advice

[1124] server:

[1125] (Input) Medical data (diagnosis results, test data, etc.) with permission from the user

[1126] (Operation) The cloud server uses machine learning libraries such as "Scikit-learn" to analyze medical data and provide early warnings of diseases and suggest preventive measures. Specifically, if blood pressure data indicates high values, the cloud server generates advice to the user saying, "You are at risk of high blood pressure. Please seek medical attention at an appropriate medical institution."

[1127] (Output) Additional advice generated based on medical data

[1128] Specific steps and details of each step

[1129] Step 1:

[1130] Device:

[1131] The collected biometric data is obtained using a smartwatch worn by the user or a smartphone app that the user enters into, and this information is temporarily stored in a database on the device.

[1132] Step 2:

[1133] Device:

[1134] At a fixed time, such as midnight or at a specified time, data is sent to the cloud server via a secure protocol (HTTPS). The sent data then travels over the Internet to the cloud server.

[1135] Step 3:

[1136] server:

[1137] A Python 3.9 script runs periodically on the cloud server to analyze the data, using TensorFlow 2.4 to analyze complex data patterns and assess the user's health status.

[1138] Step 4:

[1139] server:

[1140] Based on the analysis results, the AI ​​model generates appropriate health advice according to prompts, such as calculating the average sleep time from sleep data and providing advice on necessary measures.

[1141] Step 5:

[1142] Server and Device:

[1143] Once advice is generated, it is sent from the cloud server to the user's device (such as a smartphone). The device immediately notifies the user of the received advice so that the user can review the information.

[1144] Step 6:

[1145] server:

[1146] With the user's permission, the cloud server also analyzes the medical data using machine learning libraries to predict disease risk and generate additional advice for the user on preventative measures.

[1147] In this way, the system of the present invention collects, safely manages, and analyzes the user's biometric information, and provides individually customized health advice, thereby helping the user maintain their health.

[1148] (Application example 1)

[1149] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1150] Conventional health management systems collect users' biometric information and provide health advice, but there are few cases where that advice leads to concrete actions. In particular, dietary advice is limited to providing information and is difficult to lead to the execution of actual meal plans. This creates a problem in that it is difficult for users to take concrete actions to maintain and improve their health.

[1151] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1152] In this invention, the server includes means for collecting biometric information of a user, means for securely transmitting the biometric information to a cloud server, means for the cloud server to analyze the biometric information, means for generating customized health advice based on the analysis results, means for proposing an optimal meal plan based on the health advice, means for managing food item information for providing the meal plan, means for transmitting the health advice and the meal plan to a user terminal, and means for the user terminal to notify the user of the health advice and the meal plan. This enables the user to receive a specific and feasible meal plan based on their health condition and to take actual action.

[1153] "User's biological information" is data related to the user's health condition, including heart rate, sleep time, dietary content, stress level, and the like.

[1154] A "cloud server" is a remote server accessible via the Internet, and is a virtualized server environment for storing and processing data.

[1155] "Analytical means" refers to a processing system that uses mathematical and statistical methods to analyze collected data and derive specific results and recommendations.

[1156] "Customized health advice" refers to specific guidance and advice for maintaining and improving health that is generated based on each user's individual health data.

[1157] The "means for proposing a meal plan" is a function that specifically selects and proposes a meal menu suitable for the user based on the analysis results.

[1158] The "means for managing food item information" is a system for creating a database of nutritional information, calories, ingredients, etc. of available meal menus and managing them appropriately.

[1159] A "user terminal" is a communication device used by a user, and includes mobile devices such as smartphones and tablets.

[1160] The "notification means" is a function that transmits the generated health advice and meal plan to the user terminal and displays the information to the user.

[1161] "Early warning or prevention of disease" refers to the use of a user's health data to predict the risk of developing a disease in the future and provide specific measures or guidelines for action to reduce that risk.

[1162] The present invention relates to a system that manages a user's health information comprehensively and individually and provides optimal health advice. The system includes a user terminal, a cloud server, and a means for collecting and analyzing the user's health information.

[1163] First, the user uses a communication device (user terminal) such as a smartphone or smartwatch to collect their own biometric information. The smartwatch records the user's heart rate and sleep time, and data on diet and stress level is entered into the smartphone app. This information is periodically sent to a cloud server.

[1164] The cloud server analyzes the received biometric information using a machine learning model or a generative AI model. A common machine learning framework (e.g., TensorFlow) is used as the model. Based on the analyzed data, the cloud server generates optimal health advice for each individual user. For example, this health advice might be, "You haven't been getting enough sleep recently. We recommend that you go to bed one hour earlier each night."

[1165] The cloud server then proposes a specific meal plan based on the analysis results. The meal plan is proposed using a database that manages food item information. The meal plan takes into account nutritional information, calories, ingredients, etc.

[1166] Health advice and meal plans are sent from the cloud server to the user's device and are notified to the user via push notifications and in-app messages, allowing the user to create a specific action plan.

[1167] A concrete example is the following scenario: If the user's sleep data shows an average of six hours or less, the cloud server will suggest a nutritionally balanced meal plan along with advice such as, "Your average sleep time is short. It is important to get enough sleep." For example, a "chicken breast salad (rich in protein, under 500 kcal)" might be recommended.

[1168] An example of a prompt to be input into the generative AI model is, "If the user has had less than six hours of sleep and their recent heart rate and stress level are high, recommend a low-calorie, high-protein meal."

[1169] In this way, the system of the present invention comprehensively manages the user's lifestyle habits and provides individually customized health advice and specific action plans to help the user maintain their health. The user can always understand their health status and take appropriate action. This system is expected to improve the user's overall health.

[1170] This completes the description of the "Description of the Preferred Embodiments."

[1171] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1172] Step 1:

[1173] The user device collects biometric information

[1174] The user wears a smartphone or smartwatch, which records biometric information such as heart rate, sleep time, dietary intake, and stress level. Input data includes dietary intake and stress level that the user manually enters into the app. Output data includes various measurement data, including this biometric information.

[1175] Step 2:

[1176] The user device sends biometric information to the cloud server.

[1177] The collected biometric information is sent from the user device to the cloud server at regular intervals via a secure protocol (e.g., HTTPS). The input data is the biometric information stored on the user device, and the output data is the data transferred to the cloud server.

[1178] Step 3:

[1179] Cloud server analyzes biometric information

[1180] The cloud server analyzes the received biometric information using machine learning models and generative AI models, which includes data preprocessing (data normalization and invalid data removal). The input data is the biometric information stored on the cloud server, and the output data is the analysis results.

[1181] Step 4:

[1182] A cloud server generates customized health advice

[1183] Based on the analysis results, the cloud server generates personalized health advice for the user, often using a generative AI model. The input data is the analysis results, and the output data is specific health advice.

[1184] Step 5:

[1185] Cloud server proposes meal plans

[1186] Based on the generated health advice, the cloud server proposes an optimal meal plan. It references a database that manages food item information and makes suggestions based on nutritional information, calories, ingredients, etc. The input data are health advice and food item information, and the output data is the proposed meal plan.

[1187] Step 6:

[1188] The cloud server sends health advice and meal plans to the user's device.

[1189] The cloud server transmits the generated health advice and meal plan to the user terminal, and the output data is the health advice and meal plan transmitted from the cloud server.

[1190] Step 7:

[1191] The user device notifies the user of health advice and meal plans

[1192] The user device notifies the user of the received health advice and meal plan via push notifications or in-app messages. The input data are the health advice and meal plan sent from the cloud server, and the output data are the notification content displayed to the user.

[1193] Through these steps, users can receive and implement specific advice and meal plans tailored to their health condition.

[1194] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1195] The present invention relates to a system that manages a user's health information comprehensively and individually and provides optimal health advice. Furthermore, this system incorporates an emotion engine that recognizes the user's emotions. The system includes a user terminal, a cloud server, and a means for collecting and analyzing the user's health information and emotion information.

[1196] The devices used by users (e.g., smartphones, smartwatches, and fitness trackers) first collect the user's biometric and emotional information. For example, smartwatches record the user's sleep data and heart rate, while smartphone apps collect dietary information, stress levels, and even voice and facial expression data. Furthermore, emotion engines analyze the user's facial, voice, and text data to recognize the user's emotional state.

[1197] The device formats the collected biometric and emotional information and transmits it to a cloud server using a secure protocol (e.g., HTTPS) to ensure user privacy.

[1198] The cloud server analyzes the received information and generates optimal health advice for each individual user. This analysis uses machine learning models, generative AI models, and an emotion engine to comprehensively evaluate the user's sleep patterns, diet, stress level, emotional state, etc. For example, if a user's sleep data is analyzed and a regular sleep pattern is not observed, specific advice on how to get enough sleep is generated. In addition, if the user is under stress, the emotion engine will add health advice that takes their emotional state into account.

[1199] The generated health advice is sent from the cloud server to the user's device. The user's device then notifies the user of the advice via push notifications or in-app messages. For example, a notification might appear on the smartphone saying, "You've been getting insufficient sleep recently. We recommend going to bed one hour earlier each night." At the same time, additional advice based on the user's emotional state might be displayed, such as, "You seem to be feeling stressed. We recommend you practice relaxation."

[1200] The cloud server also analyzes medical data with the user's permission. This analysis includes the user's past diagnosis and test data, and provides early warnings and suggests preventative measures for illness. For example, if a user's blood pressure shows high levels, the cloud server will notify them of their risk of hypertension and recommend that they seek medical attention at an appropriate medical institution.

[1201] As a concrete example, consider a case where a user wears a smartwatch and uses a smartphone app equipped with an emotion analysis function. The smartwatch records the time the user goes to sleep and wakes up. The smartphone app records the user's meals and simultaneously analyzes the user's emotional state from voice and facial expressions. This data is sent to a cloud server every night. The cloud server analyzes the user's sleep patterns, diet, and emotional state, and verifies that the average sleep time is six hours or less. Based on this information, it generates advice such as "Try to get at least seven hours of sleep each night" and sends a notification to the smartphone. At the same time, if the user's emotional state indicates stress, it also generates advice such as "You are feeling stressed. We recommend that you practice meditation and relaxation."

[1202] In this way, the system of the present invention comprehensively manages the user's lifestyle habits and emotional state and provides individually customized health advice to help the user maintain their health. In doing so, the user can always understand their own health and emotional state and take appropriate action. This system is expected to improve the user's overall health.

[1203] The processing flow will be explained below.

[1204] Step 1:

[1205] The device collects the user's biometric information. Specifically, the smartwatch records sleep duration and heart rate, and the user inputs their diet and stress level via a smartphone app.

[1206] Step 2:

[1207] The device collects the user's emotional information. Specifically, the smartphone app analyzes the user's voice and facial expression data and uses an emotion engine to recognize the user's emotional state.

[1208] Step 3:

[1209] The device formats the collected biometric and emotional information and prepares it for transmission to the cloud server, ensuring that the data is in the correct format and encoding it if necessary.

[1210] Step 4:

[1211] The device transmits the formatted biometric and emotional information to the cloud server using a secure communication protocol (e.g., HTTPS). If an error occurs during transmission, the device attempts to retransmit the information.

[1212] Step 5:

[1213] The server receives the biometric and emotional data sent from the device, checks the data integrity, and verifies whether there are any outliers before storing them in a database.

[1214] Step 6:

[1215] The server analyzes the biometric and emotional information stored in the database, and utilizes machine learning models, generative AI models, and an emotion engine to comprehensively evaluate the user's lifestyle habits and emotional state.

[1216] Step 7:

[1217] The server generates customized health advice based on the analysis results. For example, it may advise a user who is not getting enough sleep on average to go to bed earlier each night. It also suggests stress reduction measures based on the emotion engine's analysis results.

[1218] Step 8:

[1219] The server generates and transmits the generated health advice to the user terminal in an appropriate format.

[1220] Step 9:

[1221] The device notifies the user of the health advice received from the server, displaying specific advice content using push notifications and in-app messages.

[1222] Step 10:

[1223] The user checks the notification from the device and takes action based on the advice provided, such as reviewing their bedtime schedule or practicing meditation or relaxation to reduce stress.

[1224] Step 11:

[1225] The device continuously monitors how well the user followed the advice, collecting data again and sending it to the server for further analysis.

[1226] Step 12:

[1227] The server performs a new analysis based on the retransmitted data and generates new advice as necessary, thereby always providing the user with up-to-date support for health management.

[1228] Example 2

[1229] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1230] Conventional health management systems focus on collecting and analyzing biometric information, but do not provide advice that takes into account the user's emotional information. Furthermore, they face challenges such as security issues and a lack of individually tailored advice. This creates a need for a system that can comprehensively manage a user's health and emotional state and provide individually tailored health advice.

[1231] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1232] In this invention, the server includes means for collecting biometric information and emotional information of a user, means for securely transmitting the biometric information and emotional information to a data processing device, means for the data processing device to analyze the biometric information and emotional information, means for generating customized health advice based on the analysis results, means for transmitting the health advice to a user terminal, and means for the user terminal to notify the user of the health advice. This allows the user to comprehensively understand their own health and emotional state and take appropriate actions to improve their overall health.

[1233] "Biometric information" refers to data relating to the user's physical condition, such as heart rate, sleep data, and number of steps taken.

[1234] "Emotion information" is data relating to the user's emotional state analyzed from facial expressions, voice, text data, and the like.

[1235] The "data processing device" is a device that receives and analyzes biometric information and emotional information sent from a user.

[1236] "Analysis" refers to the process of evaluating the received biometric and emotional information to comprehensively determine the user's health and emotional state.

[1237] A "generative AI model" is an artificial intelligence model that assesses a user's health and emotional state and generates customized health advice.

[1238] "Health advice" refers to individually customized guidance and recommendations for maintaining health based on the analysis of the user's biometric and emotional information.

[1239] A "user terminal" is a device worn or used by a user, such as a smartphone, smartwatch, or fitness tracker.

[1240] A "secure protocol" is a communication protocol for securely sending and receiving data, and examples include HTTPS.

[1241] "Medical data" refers to information relating to the user's medical care, such as past diagnostic results, test data, and drug therapy history.

[1242] The present invention relates to a system that manages a user's health information comprehensively and individually and provides optimal health advice. It also incorporates an emotion engine that recognizes the user's emotions. The system includes a device used by the user (e.g., a smartphone, a smartwatch, a fitness tracker), a data processing device (a cloud server), and an integrated analysis means.

[1243] composition:

[1244] Device configuration:

[1245] The devices used by users include smartwatches, smartphones, etc. These devices collect information such as:

[1246] The smartwatch tracks heart rate, steps, and sleep data.

[1247] The smartphone collects and analyzes dietary habits, stress levels, and facial and voice data using an emotion engine.

[1248] Data collection and transmission:

[1249] The biometric and emotional information collected on the device is sent to the cloud server through data processing and secure protocols (e.g., HTTPS), ensuring user privacy.

[1250] Data processing unit configuration:

[1251] The cloud server is equipped with the following analytical tools:

[1252] Machine learning models

[1253] Generative AI Models

[1254] Emotion Engine

[1255] These elements allow the cloud server to analyze the biometric and emotional information sent by the user and generate individually customized health advice.

[1256] Health advice generation and notification:

[1257] Health advice generated by the cloud server is based on the analysis results. For example, if a user's sleep data lacks regularity, advice such as "Try to get at least seven hours of sleep every night" will be generated. Furthermore, if the analysis results of the emotion engine indicate high stress levels, additional advice such as "We recommend you practice relaxation" will be generated.

[1258] The generated health advice is securely sent to the user's device, which then notifies the user via push notifications or in-app messages. For example, a notification might appear on a smartphone saying, "You haven't been getting enough sleep recently. We recommend going to bed one hour earlier each night."

[1259] Usage example

[1260] Consider a case where a user is wearing a smartwatch and using a smartphone app equipped with emotion analysis functionality. The smartwatch records the user's sleep and wake-up times. The smartphone app records the user's meals and simultaneously analyzes their emotional state from their voice and facial expressions. These data are sent to a cloud server each night. The cloud server analyzes the user's sleep patterns, diet, and emotional state, and verifies that their average sleep time is six hours or less. Based on this information, it generates advice such as "Try to get at least seven hours of sleep each night" and sends a notification to the smartphone. At the same time, if the user's emotional state indicates stress, it also generates advice such as "You are feeling stressed. We recommend that you practice meditation and relaxation."

[1261] Example prompt sentence:

[1262] "Analyze the user's sleep data and emotional state and generate appropriate health advice. For example, if the average sleep time is less than six hours, recommend getting at least seven hours of sleep each night, or recommend relaxation if the user is stressed."

[1263] This system allows users to comprehensively understand their health and emotional state, and by taking appropriate actions based on the health advice provided individually, it is expected that their overall health will improve.

[1264] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1265] Step 1:

[1266] The user uses the terminal to collect biometric information and emotional information.

[1267] Specifically, the smartwatch records heart rate, steps, and sleep data (e.g., time of falling asleep and waking up), while the smartphone prompts the user to enter their diet, weight, and stress level, and uses a camera and microphone to collect facial and voice data.

[1268] Input: User's biometric information (e.g., heart rate, sleep data), emotional information (e.g., facial expressions, voice)

[1269] Output: A dataset of collected biometric and emotional information

[1270] Step 2:

[1271] The device formats the biometric and emotional information collected and sends it to a cloud server using a secure protocol.

[1272] Specifically, the device encrypts data using the HTTPS protocol and periodically sends it to the cloud server.

[1273] Input: Dataset of collected biometric and emotional information

[1274] Output: Formatted data sent to the cloud server

[1275] Step 3:

[1276] The server parses the received data.

[1277] Specifically, the cloud server formats the data into a coherent format and analyzes it using machine learning models, generative AI models, and emotion engines, including assessing the user's sleep patterns, diet, stress levels, and emotional state.

[1278] Input: Formatted data sent to the cloud server

[1279] Output: Analysis results (e.g., user's sleep patterns, emotional state)

[1280] Step 4:

[1281] The server generates customized health advice based on the analysis results.

[1282] Specifically, the generative AI model generates personalized health advice for each user based on the analysis results. For example, for a user who is not getting enough sleep, the advice "Try to get at least seven hours of sleep each night" is generated. If the emotion engine detects a state of stress, the additional advice "We recommend you practice relaxation" is generated.

[1283] Input: Analysis results (e.g., user's sleep patterns, emotional state)

[1284] Output: Generated customized health advice

[1285] Step 5:

[1286] The server transmits the generated health advice to the user terminal.

[1287] Specifically, the generated advice is converted into JSON format or similar, encrypted using the HTTPS protocol, and sent to the terminal.

[1288] Input: Generated customized health advice

[1289] Output: Health advice sent to the user device

[1290] Step 6:

[1291] The user terminal notifies the user of the advice.

[1292] Specifically, the smartphone will send advice to the user via push notifications or in-app messages. For example, a notification will be displayed saying, "You haven't been getting enough sleep recently. We recommend going to bed one hour earlier each night." Additional advice based on your emotional state will also be provided.

[1293] Input: Health advice sent to user device

[1294] Output: Advice given to the user

[1295] The above are the specific processing steps of the program for this system.

[1296] (Application example 2)

[1297] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1298] Nowadays, many people are interested in health management, but it is not easy to obtain appropriate advice based on their individual health status. Furthermore, there is a lack of means in physical stores to provide personalized health advice and product recommendations in real time based on users' health and emotional data. Under these circumstances, it is difficult for users to take optimal actions to maintain their health, and overall health improvement cannot be expected. Therefore, there is a need for a system that collects and analyzes users' biometric and emotional information and provides appropriate health advice and product recommendations in real time.

[1299] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1300] In this invention, the server includes means for collecting biometric information from a user, means for securely transmitting the biometric information to a cloud server, means for the cloud server to analyze the biometric information and emotional information, means for generating customized health advice based on the analysis results, means for transmitting the health advice to a user terminal, means for the user terminal to notify the user of the health advice, and means for analyzing the user's health data and emotional data in real time at a physical store and providing health advice and product suggestions. This enables users to receive individually customized health advice in real time even in a physical store, and to select appropriate products and take actions to maintain their health.

[1301] The "means for collecting user's biometric information" refers to a device or software for acquiring physiological data such as the user's heart rate and sleep data.

[1302] The "means for securely transmitting the biometric information to the cloud server" refers to protocols and technologies for transferring collected biometric information to the cloud server while protecting the privacy of the data.

[1303] The "means by which the cloud server analyzes the biometric information and emotional information" refers to a machine learning model or algorithm for collecting and analyzing the user's biometric information and emotional information on the cloud.

[1304] The "means for generating customized health advice based on the analysis results" is a technology that generates advice related to health management that is optimized for each user based on the analyzed data.

[1305] The "means for transmitting the health advice to the user terminal" is a technology for transmitting health advice generated by a cloud server to a terminal such as a user's smartphone or smartwatch.

[1306] The "means for the user terminal to notify the user of the health advice" refers to a technology for displaying the received health advice to the user on the user terminal as a push notification or an in-app message.

[1307] "Means for analyzing a user's health and emotional data in real time in a physical store and providing health advice and product suggestions" refers to a technology that instantly analyzes a user's health and emotional state in a physical store environment and, based on the results, provides the user with optimal health-related advice and product suggestions.

[1308] This invention is a system for collecting and analyzing a user's biometric and emotional information and providing appropriate health advice and product suggestions in real time at a physical store. The system includes a user terminal, a cloud server, and a means for analyzing this data and generating advice.

[1309] User devices refer to devices such as smartphones, smartwatches, and smart glasses. These devices have the ability to collect biometric and emotional information, such as a user's heart rate, sleep data, dietary information, voice, and facial expressions. For example, a smartwatch records a user's heart rate and sleep data, while a smartphone app records dietary information and stress levels. They also include emotion engines that analyze the user's voice and facial expression data.

[1310] The collected data is formatted and sent to a cloud server using a secure protocol (e.g., HTTPS). The cloud server analyzes the received data using machine learning models (e.g., TensorFlow, PyTorch) or generative AI models (e.g., GPT-4). The emotion engine analyzes the user's emotional state and comprehensively evaluates this data.

[1311] The analysis results are used to generate customized health advice based on the user's individual health data. For example, analyzing a user's sleep data and determining that their average sleep time is six hours or less generates the advice, "Try to get at least seven hours of sleep each night." If the user's emotional state indicates stress, the system adds the advice, "You are feeling stressed. We recommend that you practice meditation and relaxation."

[1312] To improve the user experience in physical stores, the system analyzes users' health and emotional data in real time and makes specific product suggestions. For example, while shopping in a store, a product suggestion could be displayed such as, "Since your recent sleep data has been insufficient, why not try this health pillow?"

[1313] For example, the following prompts can be used:

[1314] "Due to the user's recent sleep data and increasing stress levels, we want to generate optimal health advice for him.

[1315] (input data)

[1316] Average sleep time: 5.5 hours

[1317] Recent stress level: High

[1318] Health Goal: Stress Management

[1319] (generated advice)

[1320] Aim to get at least seven hours of sleep each night and incorporate relaxation activities like deep breathing and meditation."

[1321] This system allows users to constantly monitor their health and emotional state and take appropriate action. By receiving real-time health advice and product suggestions in physical stores, users are expected to maintain their health and improve their quality of life.

[1322] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1323] Step 1:

[1324] Devices (e.g., smartwatches and smartphones) collect biometric and emotional information such as the user's heart rate, sleep data, dietary information, voice, and facial expressions. These data are input from sensors, cameras, and microphones. For example, a smartwatch records the user's heart rate every second, and a smartphone app inputs the user's dietary information using image recognition technology.

[1325] Step 2:

[1326] The device preprocesses the collected biometric and emotional information and formats the data. For example, it calculates heart rate data as a minute-by-minute average and converts voice data into text. This preprocessed data is sent to a cloud server using the HTTPS protocol. The input is raw data, and the output is preprocessed structured data.

[1327] Step 3:

[1328] The cloud server analyzes the received data using machine learning models (e.g., TensorFlow, PyTorch) and generative AI models (e.g., GPT-4). This analysis step comprehensively evaluates the user's sleep patterns, heart rate, dietary content, and emotional state. The input is preprocessed structured data, and the output is evaluation data as the analysis result.

[1329] Step 4:

[1330] The server generates customized health advice based on the analysis results. The generated advice specifically corresponds to the user's health and emotional state. For example, if the sleep data is insufficient, the advice generated might be, "Try to get at least seven hours of sleep each night," or if the stress level is high, the advice generated might be, "We recommend meditating and relaxing." The input is the evaluation data from the analysis results, and the output is specific advice text.

[1331] Step 5:

[1332] The generated health advice is sent from the cloud server to the user's device using a secure protocol such as HTTPS. The input is the advice text, and the output is the data sent to the user's device.

[1333] Step 6:

[1334] The device notifies the user of the received health advice as a push notification or an in-app message. For example, a message such as "You haven't been getting enough sleep recently. We recommend that you go to bed one hour earlier each night" is displayed on the user's smartphone screen. The input is the received advice text, and the output is the notification to the user.

[1335] Step 7:

[1336] In a physical store, the device analyzes the user's health and emotional data in real time and makes product suggestions based on the results. For example, while shopping in a store, a message might appear saying, "Your recent sleep data has been insufficient. Why not try this health pillow?" The input is real-time health and emotional data, and the output is a display of product suggestions.

[1337] Through these steps, the system can efficiently collect and analyze the user's biometric and emotional information, and provide optimal health advice and product recommendations in real time.

[1338] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1339] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1340] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1341] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1342] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1343] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1344] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1345] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1346] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1347] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1348] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1349] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1350] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1351] 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.

[1352] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1353] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1354] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1355] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1356] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1357] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1358] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1359] The following is further disclosed regarding the above embodiment.

[1360] (Claim 1)

[1361] means for collecting biometric information of a user;

[1362] means for securely transmitting the biometric information to a cloud server;

[1363] A means for analyzing the biological information by the cloud server;

[1364] means for generating customized health advice based on the analysis results;

[1365] means for transmitting the health advice to a user terminal;

[1366] The system includes a means for notifying the user of the health advice, wherein the user terminal includes a means for notifying the user of the health advice.

[1367] (Claim 2)

[1368] 10. The system of claim 1, wherein the health advice is generated based on sleep duration, dietary content, or stress level.

[1369] (Claim 3)

[1370] 2. The system according to claim 1, wherein the cloud server includes means for analyzing medical data with permission from the user and suggesting early warning or preventive measures for diseases.

[1371] "Example 1"

[1372] (Claim 1)

[1373] means for collecting biometric information of a user;

[1374] means for transmitting the biometric information to a cloud server using a secure protocol;

[1375] A means for analyzing the biological information by the cloud server;

[1376] means for generating customized health advice based on the analysis results using a generative AI model;

[1377] means for transmitting the health advice to a user terminal;

[1378] means for notifying the user of the health advice by the user terminal;

[1379] With user permission, a means for analyzing medical data and providing early warning or preventative measures for disease;

[1380] A system including:

[1381] (Claim 2)

[1382] 10. The system of claim 1, wherein the health advice is generated based on the user's sleep time, diet, or stress level.

[1383] (Claim 3)

[1384] 2. The system of claim 1, wherein the generative AI model includes means for generating prompt sentences based on individual data of a user.

[1385] "Application Example 1"

[1386] (Claim 1)

[1387] means for collecting biometric information of a user;

[1388] means for securely transmitting the biometric information to a cloud server;

[1389] A means for analyzing the biological information by the cloud server;

[1390] means for generating customized health advice based on the analysis results;

[1391] A means for proposing an optimal meal plan based on the health advice;

[1392] means for managing food item information for providing said meal plan;

[1393] means for transmitting said health advice and meal plan to a user terminal;

[1394] The system wherein the user terminal includes means for notifying the user of the health advice and meal plan.

[1395] (Claim 2)

[1396] 10. The system of claim 1, wherein the health advice is generated based on sleep duration, dietary content, or stress level.

[1397] (Claim 3)

[1398] 2. The system according to claim 1, wherein the cloud server includes means for analyzing medical data with permission from the user and suggesting early warning or preventive measures for diseases.

[1399] "Example 2: Combining Emotion Engines"

[1400] (Claim 1)

[1401] means for collecting biometric and emotional information of a user;

[1402] means for securely transmitting said biometric information and emotional information to a data processing device;

[1403] means for analyzing the biometric information and emotional information in the data processing device;

[1404] means for generating customized health advice based on the analysis results;

[1405] means for transmitting the health advice to a user terminal;

[1406] The system includes a means for notifying the user of the health advice, wherein the user terminal includes a means for notifying the user of the health advice.

[1407] (Claim 2)

[1408] 10. The system of claim 1, wherein the health advice is generated based on sleep duration, diet, stress level, or emotional state.

[1409] (Claim 3)

[1410] 10. The system of claim 1, wherein the data processing device includes means for analyzing medical data with permission from the user and suggesting early warning or preventive measures for disease.

[1411] "Application example 2 when combining emotion engines"

[1412] (Claim 1)

[1413] means for collecting biometric information of a user;

[1414] means for securely transmitting the biometric information to a cloud server;

[1415] means for analyzing the biometric information and emotional information by the cloud server;

[1416] means for generating customized health advice based on the analysis results;

[1417] means for transmitting the health advice to a user terminal;

[1418] means for notifying the user of the health advice by the user terminal;

[1419] A system that includes a means to analyze users' health and emotional data in real time in physical stores and provide health advice and product suggestions.

[1420] (Claim 2)

[1421] 10. The system of claim 1, wherein the health advice is generated based on sleep duration, dietary content, or stress level.

[1422] (Claim 3)

[1423] 2. The system according to claim 1, wherein the cloud server includes means for analyzing medical data with permission from the user and suggesting early warning or preventive measures for diseases. [Explanation of symbols]

[1424] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for collecting biometric information of a user; means for securely transmitting the biometric information to a cloud server; A means for analyzing the biological information by the cloud server; means for generating customized health advice based on the analysis results; means for transmitting the health advice to a user terminal; The system includes a means for notifying the user of the health advice, wherein the user terminal includes a means for notifying the user of the health advice.

2. The system of claim 1 , wherein the health advice is generated based on sleep duration, dietary content, or stress level.

3. The system according to claim 1 , wherein the cloud server includes means for analyzing medical data with permission from the user and proposing early warning or preventive measures for diseases.

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