System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting health data from a user; generative model means for analyzing the health data to evaluate a health condition of the user; means for generating personalized health advice based on an evaluation result of the generative model means; and means for transmitting the health advice to a terminal of the user.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, individual health management is extremely time-consuming, as it requires adapting to individual health conditions and lifestyles. For people who cannot afford expensive personal trainers or nutritionists, it is difficult to obtain appropriate health advice. Given this background, there is a demand for a system that allows users to easily manage their own health data and receive personalized advice based on that data in real time. [Means for solving the problem]

[0005] The present invention is a system that analyzes health data collected from a user and provides optimal advice for each individual's health condition. Specifically, the system includes a means for collecting health data from the user, a generative modeling means for analyzing the collected data, a means for generating personalized advice based on the analysis results, and a means for transmitting the generated advice to a user terminal, thereby enabling easier and more effective individual health management. Furthermore, the system provides comprehensive support for the user's health management by periodically collecting health data and evaluating progress, and by assessing potential health risks and suggesting countermeasures.

[0006] "User" refers to an individual who uses the system.

[0007] "Health data" refers to information about a user's physical and lifestyle habits, such as sleep time, eating habits, and exercise habits.

[0008] "Means of collection" refers to the system's functions and mechanisms for inputting or obtaining health data from users.

[0009] "Generative model means" refers to artificial intelligence or machine learning algorithms that analyze collected health data and assess a user's health status.

[0010] "Personalized health advice" refers to guidelines or recommendations for optimal health maintenance or improvement for an individual user based on the evaluation results of a generative model means.

[0011] "Transmission means" refers to the system's functions and mechanisms for transmitting the generated health advice to the user's terminal.

[0012] "Terminal" refers to the device (e.g., smartphone, tablet, PC, etc.) used by a user to access the system.

[0013] "Means for assessing progress" refers to the system's functions and mechanisms for analyzing the user's regularly collected health data and assessing progress toward the initially set health goals.

[0014] "Potential health risks" refers to health problems or risks that may occur in the future based on the user's health data.

[0015] "Countermeasures" refer to specific actions or measures proposed to reduce or avoid potential health risks. [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] This invention relates to a system that analyzes health data collected from a user and provides personalized health advice. The system includes three main components: a user, a terminal, and a server.

[0038] 1. User information collection module

[0039] First, the user launches the application and creates a new account or logs in. The device prompts the user and provides a form where they can enter their health data (e.g., sleep time, dietary habits, exercise habits, etc.). The user enters the data according to the form and submits it. The device encrypts the submitted data and sends it to the server.

[0040] 2. Data storage module

[0041] The server decrypts the received data, stores it in the database, and sends a save confirmation response to the terminal. The terminal then confirms that the data has been successfully saved and notifies the user.

[0042] 3. Data Analysis Module

[0043] The server inputs the stored data into a generative model, which then analyzes the data and evaluates the user's health status. Based on the analysis results, the model generates personalized health advice.

[0044] Example 1:

[0045] If a user reports exercising three times a week but at a low intensity, the server uses the generative AI model to generate advice such as "Recommend to increase exercise intensity."

[0046] Example 2:

[0047] If a user averages 5 hours of sleep, the server uses the generative model to provide guidelines such as "you should aim for 7-8 hours of sleep per day."

[0048] 4. Advice sending module

[0049] The server prepares the generated advice and sends it to the user's device. The device displays the received advice to the user, who then confirms the advice and takes action to improve their lifestyle habits.

[0050] 5. Regular progress check module

[0051] The server periodically (e.g., once a week) sends a request for new data to the user. The device displays this as a notification to the user, prompting them to enter data. The user enters the new data and sends it to the server via the device. The server analyzes the new data and evaluates progress. Depending on the progress, the server can provide further advice and corrections.

[0052] Example 3:

[0053] If the user has been exercising for a month but has not seen any weight loss, the server will recommend that the user "review their diet" or "increase the intensity of their exercise."

[0054] 6. Potential Risk Monitoring Module

[0055] The server continuously monitors user data and evaluates the possibility of health risks occurring. If a risk is detected, the server immediately generates a warning. The server then sends a warning message and instructions on how to respond to the user's device, and the device notifies the user of the warning so that they can confirm the appropriate measures.

[0056] Example 4:

[0057] If the user has recently been eating a lot of high-calorie food and is gaining weight, the server will warn the user that "if this continues, your cardiovascular risk will increase" and suggest measures to the user, such as "eating more low-calorie meals."

[0058] In this way, users can easily manage their own health and receive necessary advice in a timely manner. This system also enables users who do not have abundant resources to obtain effective health advice.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The user launches the application and either creates a new account or logs in. The user follows the in-app prompts to enter and submit health data (e.g., sleep duration, dietary habits, exercise habits, etc.).

[0062] Step 2:

[0063] The terminal encrypts the transmitted data and sends it to the server, and also notifies the user of the success of the transmission.

[0064] Step 3:

[0065] The server decrypts the received data and stores it in the database. The server then sends a save confirmation response to the terminal to confirm that the save was successful.

[0066] Step 4:

[0067] The server inputs the stored data into a generative model, which analyzes the data, evaluates the user's health status, and generates personalized health advice based on the analysis results.

[0068] Step 5:

[0069] The server prepares the generated advice and sends it to the user's terminal, which displays the received advice to the user.

[0070] Step 6:

[0071] Users review the advice, implement it to improve their lifestyle habits, and provide feedback within the app if needed.

[0072] Step 7:

[0073] The server periodically (e.g., once a week) sends a request for new data to the user, which the device displays as a notification to the user, prompting them to enter the data.

[0074] Step 8:

[0075] The user enters new data and sends it through the terminal to the server, which analyzes the new data and assesses progress. Depending on progress, the server can provide further advice and corrections.

[0076] Step 9:

[0077] The server continuously monitors user data and assesses potential health risks. If a risk is detected, the server generates an immediate alert.

[0078] Step 10:

[0079] The server sends a warning message and instructions on how to deal with the problem to the user's device, which then notifies the user of the warning and allows them to confirm the corrective action.

[0080] Step 11:

[0081] The user checks the warning and takes necessary measures, then provides feedback to the server via the app and requests another analysis.

[0082] Example 1

[0083] 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."

[0084] In modern society, it is important for individuals to properly understand and manage their own health status, but in many cases, specialized knowledge and resources are required, and there is a lack of easy ways to collect information and receive effective health advice. Furthermore, there are challenges in protecting the security and privacy of collected health data, as well as regular progress checks and early detection of potential risks.

[0085] 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.

[0086] In this invention, the server includes: means for collecting health data from a user; means for encrypting the health data using a generating prompt and transmitting it to the server; server means for decrypting the encrypted health data and storing it in a database; generative AI model means for analyzing the health data to evaluate the user's health status; means for generating personalized health advice based on the evaluation results of the generative AI model means; means for transmitting the generated health advice to the user's terminal; and terminal means for displaying the transmitted health advice to the user. This enables effective and safe health management for users without specialized knowledge or resources. It also enables regular progress checks and early detection of potential risks.

[0087] "User" refers to an individual who uses the system to input their own health data and receive analysis results and advice.

[0088] "Device" refers to the device used by the user (e.g., smartphone, tablet, computer, etc.) to input health data and display advice.

[0089] A "server" is a computer system that receives, analyzes, and stores input health data and transmits generated advice to the user.

[0090] "Health Data" refers to information related to a user's health (e.g., sleep duration, diet, exercise habits, etc.) entered by the user.

[0091] "Generated prompts" are guides or hints used by users when entering health data.

[0092] "Encryption" refers to the process of converting health data sent from a device into a secure format to protect it from unauthorized access.

[0093] "Database" means a data repository where the health data received by the server is systematically stored and used for later analysis.

[0094] A "generative AI model" is an artificial intelligence model used to analyze a user's health data and assess their health status.

[0095] "Health advice" refers to recommendations and guidelines for improving a user's health based on the results analyzed by the generative AI model.

[0096] "Progress" refers to changes and improvements in the user's health condition over time, obtained through regular collection and analysis of health data.

[0097] "Potential risk" refers to the likelihood of future health problems or disorders estimated based on the user's health data.

[0098] This invention relates to a system that analyzes health data collected from a user and provides personalized health advice. The system includes three main components: a user, a terminal, and a server.

[0099] User information collection module

[0100] First, a user launches a dedicated application on a device such as a smartphone or computer, and then creates a new account or logs into an existing account. The device then displays a form for the user to enter health data (e.g., sleep time, diet, exercise habits, etc.). After the user enters the health data and presses the submit button, the device encrypts the data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0101] Data Storage Module

[0102] The server receives the encrypted data sent from the terminal and first decrypts it. The decrypted data is stored in a database and a response indicating the completion of storage is generated. The server sends this response to the terminal, and the terminal displays a notification to the user confirming the storage.

[0103] Data Analysis Module

[0104] The server inputs the user's health data stored in the database into an AI model. The generative AI model (e.g., a multi-layer neural network using TensorFlow or PyTorch) analyzes the data and evaluates the user's health status. Based on the results of this analysis, it generates personalized health advice for each user.

[0105] Example 1:

[0106] If a user reports exercising three times a week but at a low intensity, the server uses a generative AI model to generate advice such as "recommend increasing exercise intensity."

[0107] Example 2:

[0108] If a user averages 5 hours of sleep, the server uses the generative model to provide a guideline such as "you should aim for 7-8 hours of sleep per day."

[0109] Advice sending module

[0110] The server sends the generated advice to the user. This advice is formatted in JSON format or similar and sent to the device via a secure communication method (e.g., HTTPS). The device displays the received advice to the user, allowing the user to take action to improve their health.

[0111] Regular progress check module

[0112] The server periodically (e.g., weekly) requests the user to provide new health data. The device displays this request as a notification, prompting the user to enter new data. After the user enters and submits the new health data, the server analyzes it again and evaluates the user's progress. Based on this progress evaluation, the server provides more detailed advice and corrections.

[0113] Example 3:

[0114] If the user has been exercising for a month but has not seen any weight loss, the server will recommend that the user "review their diet" or "increase the intensity of their exercise."

[0115] Potential Risk Monitoring Module

[0116] The server continuously monitors the user's health data and evaluates potential health risks. If a risk is detected, the server immediately generates a warning message and countermeasures. These warnings and countermeasures are sent to the user's device, which then notifies the user via an in-app alert or email.

[0117] Example 4:

[0118] If the user has recently been eating a lot of high-calorie food and is gaining weight, the server will warn the user that "if this continues, your cardiovascular risk will increase" and suggest measures to the user, such as "eating more low-calorie meals."

[0119] This system allows users to easily manage their own health and receive necessary advice in a timely manner, and by using this system, effective and safe health management can be achieved without expensive resources.

[0120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0121] Step 1: Launch the application and log in

[0122] A user launches a dedicated application on a device such as a smartphone or computer. The user creates a new account or logs in to an existing account. The device sends the entered authentication information to the server, which verifies it. If authentication is successful, the server generates an authentication token and sends it to the device.

[0123] Input: User credentials (username, password)

[0124] Output: Authentication token

[0125] Step 2: Enter your health data

[0126] Within the application, the user accesses a form to enter health data (e.g., sleep duration, dietary habits, exercise habits, etc.). The device displays prompts and receives the entered data. Once the user has completed the entry, they press the submit button. The device then encrypts this data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0127] Input: User health data

[0128] Output: Encrypted health data

[0129] Step 3: Receiving and storing data

[0130] The server receives the encrypted data sent from the terminal and first decrypts it. The decrypted data is saved in the database. If the data is saved successfully, the server generates a save completion response and sends it to the terminal. The terminal then displays a save confirmation notification to the user.

[0131] Input: Encrypted health data

[0132] Output: Decrypted data stored in the database, storage completion response

[0133] Step 4: Data analysis

[0134] The server inputs the user's health data stored in the database into a generative AI model. The generative model (e.g., using TensorFlow or PyTorch) analyzes the user's health status. Based on the analysis results, personalized health advice is generated.

[0135] Input: Health data stored in a database

[0136] Output: Health advice

[0137] Step 5: Send advice

[0138] The server sends the generated health advice to the user's device. The advice is formatted in an appropriate format, such as JSON, and transmitted using a secure communication method. The device then displays the received advice to the user.

[0139] Enter: Health Advice

[0140] Output: Advice displayed on the user's terminal

[0141] Step 6: Periodic data requests

[0142] The server periodically (e.g., once a week) sends a request to the user to provide new health data. The device displays this request as a notification, prompting the user to enter new data. When the user enters and submits new health data, the device sends the data to the server.

[0143] Input: Periodic request timer

[0144] Output: Data request displayed to the user, new health data sent to the server

[0145] Step 7: Progress assessment

[0146] The server analyzes the newly received health data and compares it with previous data to assess the user's progress, and provides further detailed advice and corrections based on the assessment results.

[0147] Input: New health data, Previous health data

[0148] Output: Progress evaluation results, improvement advice

[0149] Step 8: Potential Risk Monitoring

[0150] The server continuously monitors the user's health data and evaluates potential health risks. If a risk is detected, it immediately generates a warning message and countermeasures. The warning and countermeasures are sent to the user's device, which then notifies the user.

[0151] Input: User's cumulative health data

[0152] Output: Risk warning message, notification of countermeasures

[0153] In this way, users can receive appropriate advice in real time based on their own health data and manage their health. In addition, regular progress checks and early detection of potential risks enable effective and safe health management.

[0154] (Application example 1)

[0155] 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."

[0156] Conventional health management systems analyze health data provided by users and provide advice on an individual basis, and lack the means to provide real-time health-related information, particularly in collaboration with physical stores. Furthermore, data encryption and regular progress management are insufficient, making it difficult to provide early warnings of health risks and provide specific countermeasures.

[0157] 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.

[0158] In this invention, the server includes means for collecting health data from a user, generative model means for analyzing the health data to evaluate the user's health status, means for generating personalized health advice based on the evaluation results of the generative model means, means for transmitting the health advice to the user's device, means for encrypting the health data and transmitting it to the server, means for displaying the generated advice as a notification on the user's device, and means for periodically requesting new data. This allows the user to understand their own health status in real time and receive personalized advice and product suggestions. Furthermore, data encryption ensures security, enabling regular progress management and early warning of potential health risks.

[0159] "User's health data" refers to information about the user's health, such as their sleep time, eating habits, and exercise habits.

[0160] "Generative model means" refers to means that use AI models or machine learning algorithms to analyze a user's health data and assess their health status.

[0161] "Encryption" refers to the process of converting data using a specific algorithm to make it unreadable to third parties in order to maintain the confidentiality of the information.

[0162] "Personalized health advice" refers to advice for individually optimal health improvement that is generated based on the user's individual health data.

[0163] "Terminal" refers to an electronic device such as a smartphone or tablet used by a user.

[0164] "Physical store" refers to a physically existing store such as a fitness club or drugstore.

[0165] "Real-time" refers to the immediacy of time in which results are obtained as soon as data is generated and processed.

[0166] "Notification" refers to a message function that notifies the user of information through an application.

[0167] "New data request" refers to a request from the server to the user to input new health data at regular intervals.

[0168] "Health risk assessment" refers to predicting possible future health risks based on the user's current health data.

[0169] "Warning and countermeasures" refers to notifying the user of a warning when a health risk is detected and providing specific remedial measures.

[0170] This invention is a system in which a user provides their own health data and receives personalized health advice in real time based on that data. The system includes three main components: a user, a terminal, and a server. Each component is described in detail below.

[0171] User information collection module

[0172] A user launches the application using a device such as a smartphone or tablet. After creating a new account or logging in, the user follows the prompts to enter health data (e.g., sleep time, dietary habits, exercise habits, etc.). The entered data is encrypted and sent to the server.

[0173] Data Storage Module

[0174] The server receives the encrypted data sent by the user, decrypts it, and stores it in the database. If the data is successfully saved, the server sends a save completion response to the device, and the device displays a notification to the user.

[0175] Data Analysis Module

[0176] The saved data is input into a generative AI model on the server. This AI model analyzes the data and evaluates the user's health status. Based on the analysis results, personalized health advice is generated. For example, if a user only exercises twice a week, the server will generate advice such as "Adding one more exercise session would be effective." Also, if the user's sleep time is short at five hours, the server will provide a guideline such as "Aim for 7-8 hours of sleep per day."

[0177] Advice sending module

[0178] The generated advice is sent from the server to the user's device, and the device displays the advice as a notification to the user. The user can then confirm the advice and take action to improve their lifestyle habits.

[0179] Regular progress check module

[0180] The server periodically sends the user a request for new health data. The device displays the request as a notification, and the user enters the new data and sends it to the server. The server analyzes the new data, evaluates progress, and provides further advice or corrections as needed.

[0181] Potential Risk Monitoring Module

[0182] The server continuously monitors the user's health data and evaluates the possibility of a health risk occurring. If a risk is detected, the server immediately generates a warning and sends a warning message and instructions on how to respond to the user's device. The device notifies the user of the warning, allowing the user to confirm countermeasures. For example, if the user has recently been eating a lot of high-calorie food and has gained weight, the server warns that "if this continues, your cardiovascular risk will increase" and suggests countermeasures such as "eating more low-calorie foods."

[0183] Hardware and software used

[0184] Hardware: Smartphones, tablets

[0185] Software: Encryption library (Fernet), data transmission library (requests), generative AI model

[0186] Examples of concrete examples and prompts

[0187] For example, if a user's average sleep time is five hours, the app will provide a guideline such as, "You should aim for seven to eight hours of sleep a day." If the user only exercises twice a week, the app will display real-time advice on their smartphone, such as, "Adding one more exercise session would be beneficial."

[0188] Example prompt sentence:

[0189] Generate personalized health advice based on the following user data:

[0190] Sleep time: 5 hours

[0191] Eating habits: High-calorie diet

[0192] Exercise habits: Once a week

[0193] This allows users to understand their own health condition in real time and receive optimized health advice.

[0194] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0195] Step 1:

[0196] A user uses a smartphone or tablet to launch the application and create a new account or log in. Then, they input their health data (e.g., sleep duration, eating habits, exercise habits, etc.). This data is encrypted on the device. The input data is used by an interface that allows the user to provide specific health information to the device. Specific actions include entering data into an input form and pressing the submit button.

[0197] Input: Health data such as sleep time, eating habits, exercise habits, etc.

[0198] Output: Encrypted health data

[0199] Step 2:

[0200] The device sends encrypted health data to the server using a secure protocol such as HTTPS. The device sends data to the server and manages the transmission status at the same time. Specific operations include the process of sending encrypted data to the server via the network.

[0201] Input: Encrypted health data

[0202] Output: Health data sent to the server

[0203] Step 3:

[0204] The server decrypts the received health data and performs pre-processing to store it in the database, including steps to verify the consistency and integrity of the data. Specific operations include writing the decrypted data to the database.

[0205] Input: Encrypted health data

[0206] Output: Health data stored in a database

[0207] Step 4:

[0208] The server inputs the stored health data into a generative AI model and performs data analysis. The generative AI model analyzes the data and evaluates the user's health condition. Based on the evaluation results, personalized health advice is generated. Specific operations include the process of inputting data into the AI ​​model and obtaining the evaluation results.

[0209] Input: Health data stored in a database

[0210] Output: Analyzed health status assessment and personalized health advice

[0211] Step 5:

[0212] The server sends the generated health advice to the user's device, which then displays the advice to the user as a notification. This process incorporates visual elements based on UI / UX design. Specific operations include displaying the advice notification and prompting confirmation.

[0213] Enter: personalized health advice

[0214] Output: Health advice displayed on the user's device

[0215] Step 6:

[0216] The server periodically sends a request for new data to the user, and the device displays the request as a notification. The user can then enter new health data and send it back to the server. Specific operations include requesting data via notifications and receiving new data from the user.

[0217] Input: New data request

[0218] Output: New health data

[0219] Step 7:

[0220] The server analyzes new health data and periodically evaluates progress, providing further advice and corrections as needed. Specific actions include inputting new data into the generative AI model and evaluating progress.

[0221] Input: New health data

[0222] Output: Progress assessment and additional health advice

[0223] In this way, processing steps across the system work together to enable users to receive personalized health advice on an ongoing basis.

[0224] By clarifying the specific operations and processes of input and output, it becomes possible to concretely understand the actual function of the system.

[0225] 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.

[0226] This invention relates to a system that analyzes health and emotion data collected from a user and provides personalized health and emotion care advice. The system includes four main components: a user, a terminal, a server, and an emotion engine.

[0227] 1. User information collection module

[0228] First, a user launches the application and either creates a new account or logs in. Then, following the prompts within the app, the user inputs and submits health data (e.g., sleep duration, dietary habits, exercise habits, etc.) and emotional data. Emotional data is collected through self-reporting by the user or through automatic identification using an emotion engine.

[0229] 2. Data storage module

[0230] The device encrypts the data and sends it to the server. The server decrypts the received data and stores it in the database. It then sends a save confirmation response to the device, notifying it that the save was successful.

[0231] 3. Data Analysis Module

[0232] The server inputs the stored health and emotional data into the generative model, which analyzes the data and evaluates the user's health and emotional state. Based on the analysis results, the model generates personalized health and emotional care advice.

[0233] Example 1:

[0234] If a user reports exercising three times a week but at a low intensity, the server can use the generative AI model to generate advice such as "Recommend increasing exercise intensity." On the other hand, if emotion data indicates that the user is stressed, the server can also recommend relaxation exercises.

[0235] Example 2:

[0236] If the user averages 5 hours of sleep, the server uses the generative model to provide guidelines such as "You should aim for 7-8 hours of sleep a day." If the emotion data indicates that the user is feeling anxious, the server suggests "practicing relaxation meditation before bed."

[0237] 4. Advice sending module

[0238] The server prepares and sends the generated advice to the user's terminal, which displays the received health and emotional care advice to the user.

[0239] 5. Regular progress check module

[0240] The server periodically (e.g., once a week) sends a request for new data to the user. The device displays this as a notification to the user, prompting them to enter data. The user enters new health and emotional data and sends it to the server via the device. The server analyzes the new data and evaluates progress. Depending on the progress, it provides more detailed advice and corrections.

[0241] Example 3:

[0242] If a user has been exercising for a month but has not seen any weight loss and the emotional data indicates an increase in stress, the server will suggest that the user "reconsider their diet," "increase the intensity of their exercise," and "reconsider their stress management methods."

[0243] 6. Potential Risk Monitoring Module

[0244] The server continuously monitors user data and evaluates the possibility of health and emotional risks occurring. If a risk is detected, the server immediately generates a warning. The server then sends a warning message and instructions on how to respond to the user's device, which then notifies the user of the warning and allows them to confirm countermeasures.

[0245] Example 4:

[0246] If a user has recently been eating a lot of high-calorie food and gaining weight, and emotional data indicates that they are feeling depressed, the server will warn them that "if they continue this way, their cardiovascular risk and mental stress will increase," and suggest measures to the user, such as "eating more low-calorie meals" and "adding mental health exercise."

[0247] In this way, users can easily manage their own health data and emotion data in an integrated manner and receive necessary advice in a timely manner. In addition, by combining it with the emotion engine, users can manage their overall health more comprehensively and effectively.

[0248] The processing flow will be explained below.

[0249] Step 1:

[0250] A user launches the application and creates a new account or logs in. They follow the in-app prompts to enter and submit health data (e.g., sleep duration, dietary habits, exercise habits, etc.) and emotional data (e.g., self-reported or entered into an emotion recognition engine).

[0251] Step 2:

[0252] The device encrypts the transmitted health and emotion data and transmits it to the server, and also notifies the user of the success of the transmission.

[0253] Step 3:

[0254] The server decodes the received health and emotion data and stores it in a database. The server then sends a save confirmation response to the device, notifying the user that the save was successful.

[0255] Step 4:

[0256] The server inputs the stored health and emotional data into a generative model, which analyzes the data and evaluates the user's health and emotional state. Based on the analysis results, the model generates personalized health and emotional care advice.

[0257] Step 5:

[0258] The server sends the generated advice to the user's terminal, which displays the received health and emotional care advice to the user.

[0259] Step 6:

[0260] Users can review the advice and implement it to improve their lifestyle. They can also provide feedback within the app, which will be used for the next analysis.

[0261] Step 7:

[0262] The server periodically (e.g., once a week) sends a request for new data to the user, which the device displays as a notification to the user, prompting them to enter the data.

[0263] Step 8:

[0264] Users input new health and emotional data and send it to the server via their device. The server analyzes the new data and assesses progress. Depending on progress, the server can provide further detailed advice and corrections.

[0265] Step 9:

[0266] The server continuously monitors user data and assesses the likelihood of health and emotional risks occurring. If a risk is detected, the server generates an immediate alert.

[0267] Step 10:

[0268] The server sends a warning message and instructions on how to deal with the problem to the user's device, which then notifies the user of the warning and allows them to confirm the corrective action.

[0269] Step 11:

[0270] The user checks the warning and takes necessary measures, then provides feedback to the server via the app and requests another analysis.

[0271] Example 2

[0272] 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."

[0273] Conventional health management systems primarily provide advice based solely on a user's health data, making it difficult to provide personalized advice that also takes into account the user's emotional state. Furthermore, regular progress checks and monitoring of potential health and emotional risks are insufficient, making it difficult for users to receive appropriate care instructions when needed. Furthermore, the lack of a means to comprehensively evaluate health and emotional data has prevented more comprehensive health management from being realized.

[0274] 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.

[0275] In this invention, the server includes means for collecting health data and emotional data from a user, means for encrypting the health data and emotional data and transmitting the data to the server, means for decrypting the health data and emotional data stored on the server and storing them in a secure database, means for inputting the health data and emotional data into a generative AI model for analysis and evaluating the user's health and emotional state, means for generating personalized health and emotional care advice based on the evaluation results of the generative AI model, and means for transmitting the generated advice to the user's device and displaying it. This allows for integrated management of the user's health data and emotional data, providing more comprehensive and personalized advice, and enabling regular progress checks and monitoring of potential risks.

[0276] "Health data" refers to data used to measure and evaluate a user's health status, such as the user's physical activity level, sleep time, eating habits, and exercise habits.

[0277] "Emotion data" refers to information self-reported by a user about their own emotional state, or data about an emotional state automatically identified by an emotion identification engine.

[0278] A "generative AI model" is an artificial intelligence model that takes health data and emotional data as input, analyzes this data, and evaluates the user's health and emotional state.

[0279] "Encryption" is a technical measure that makes transmitted data unreadable to third parties.

[0280] The "Server" is a device that receives, decodes, stores, and analyzes user-submitted health and emotional data using a generative AI model.

[0281] "Personalized health and emotional care advice" refers to user-specific health and emotional care instructions that are generated based on an analysis of the user's individual health and emotional data.

[0282] A "database" is a collection of health and emotional data stored securely within a server.

[0283] "Progress assessment" is the process of assessing changes in a user's health and emotional state based on new health and emotional data collected periodically from the user.

[0284] "Potential risk" is an assessment result that indicates the possibility of a physical or psychological risk occurring in the future, based on the user's health data and emotional data.

[0285] A "warning" is a message issued to the user to warn them when a potential risk is detected.

[0286] A "countermeasure" is specific instructions or advice provided to a user to address a detected risk.

[0287] This invention relates to a system that analyzes health and emotional data collected from a user and provides personalized health and emotional care advice, which specifically includes four main components: a user, a terminal, a server, and an emotion engine.

[0288] User information collection module

[0289] First, a user launches the application and creates a new account or logs in. Then, following the prompts within the app, the user inputs and submits health data (e.g., sleep duration, dietary habits, exercise habits, etc.) and emotional data. Emotional data is collected through self-reporting by the user or through automatic identification using an emotion engine.

[0290] A specific example would be a user opening a smartphone app and entering the amount of sleep they got last night or their emotional state today. For example, the user might enter "7 hours of sleep" and "stress."

[0291] Data Storage Module

[0292] The device encrypts the data using the TLS encryption protocol and sends it to the server. The server then decrypts the received data and stores it in a secure database. If the data is successfully saved, the server notifies the device, and the device displays a confirmation message to the user.

[0293] As a specific example, the device may display the message "Saving..." and after the data has been successfully saved, display a notification to the user saying "Data saved successfully."

[0294] Data Analysis Module

[0295] The server inputs the stored health and emotional data into a generative AI model (such as OpenAI's GPT-4). The generative AI model analyzes this data and evaluates the user's health and emotional state. The server generates personalized health and emotional care advice based on the analysis results.

[0296] As a specific example, if you input data such as "The user exercises three times a week, but at a low intensity," the system will generate advice such as "Increase the intensity of your exercise."

[0297] Advice sending module

[0298] The server prepares the generated advice and sends it to the terminal, which receives it and displays it to the user in an appropriate form.

[0299] For example, advice such as "You should aim to sleep 7-8 hours a day" is generated and sent to the device, which then displays the message "Recommended sleep time: 7-8 hours a day" to the user.

[0300] Regular progress check module

[0301] The server periodically sends a request for new data to the device. The device displays the request as a notification to the user, prompting them to enter data. The user enters new health and emotional data and sends it to the server via the device. The server analyzes the new data and evaluates progress. Based on the analysis results, the server provides detailed advice and corrections.

[0302] As a specific example, the server sends a request to the terminal saying "Please enter new data," and the terminal displays a notification to the user saying "Please enter new health data."

[0303] Potential Risk Monitoring Module

[0304] The server continuously monitors user data and evaluates the likelihood of health and emotional risks. If a risk is detected, the server immediately generates an alert and sends a warning message and instructions on how to respond to the device. The device then notifies the user of the alert and prompts them to take action.

[0305] For example, the server receives data such as "You have recently increased your intake of high-calorie meals and have been experiencing depression," generates a warning that "Your cardiovascular risk and mental stress may be increasing," and sends it to the device. The device then notifies the user to "increase your intake of low-calorie meals and add mental health exercises."

[0306] Prompt Sentence Examples

[0307] "The user exercises at a low intensity three times a week, but is feeling stressed. Please generate appropriate health advice."

[0308] "The user's average sleep time is 5 hours and they are feeling anxious. Please generate appropriate health advice."

[0309] In this way, this system can comprehensively manage the user's health and emotional data, providing more comprehensive and personalized advice. It also allows users to regularly check their progress and monitor potential risks, allowing them to manage their health with peace of mind.

[0310] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0311] Program processing flow

[0312] Step 1: Collect user information

[0313] 1. The user launches the application and either creates a new account or logs in.

[0314] Input: Account information (username, password, etc.)

[0315] Output: Authentication status (login success / failure)

[0316] Specific operation: The user opens the smartphone app and enters their username and password on the login screen.

[0317] 2. The user follows the prompts in the app to enter and submit their health and emotional data.

[0318] Input: Health data (e.g., sleep time, dietary habits, exercise habits), emotional data (e.g., stress level)

[0319] Output: Sending status (success / failure)

[0320] Specific operation: The user enters various health and emotional data into the form and presses the "Submit" button.

[0321] Step 2: Data encryption and transmission

[0322] 1. The device encrypts the transmitted data using the TLS encryption protocol.

[0323] Input: Health and emotion data entered by the user

[0324] Output: Encrypted data

[0325] Specific operation: The terminal encrypts the input data using the encryption library.

[0326] 2. The device sends the encrypted data to the server.

[0327] Input: Encrypted data

[0328] Output: Sending status (success / failure)

[0329] Specific operation: The device sends the encrypted data to the server using an HTTP POST request.

[0330] Step 3: Receiving and storing data

[0331] 1. The server decrypts the received data.

[0332] Input: Encrypted data

[0333] Output: raw decrypted data

[0334] What happens: The server uses a decryption library to decrypt the data.

[0335] 2. The server stores the decrypted data in a secure database.

[0336] Input: raw decrypted data

[0337] Output: Save status (save successful / failed)

[0338] Specific behavior: The server saves the data via the database connection and returns the save status.

[0339] Step 4: Data analysis

[0340] 1. The server inputs the stored health and emotion data into the generative AI model.

[0341] Input: Health and emotion data stored in a database

[0342] Output: Data analysis results

[0343] Specific operation: The server inputs data into the API of the generative AI model and performs analysis.

[0344] 2. The generative AI model analyzes the data and the server assesses the user's health and emotional state.

[0345] Input: Health and emotion data

[0346] Output: Health and emotional state assessment results

[0347] Specific operation: The generative AI model analyzes the data and returns the evaluation results to the server.

[0348] Step 5: Advice generation and sending

[0349] 1. The server generates personalized health and emotional care advice based on the analysis results.

[0350] Input: Health and emotional state assessment results

[0351] Output: Personalized advice

[0352] Specific operation: The server generates advice based on the analysis results of the generative AI model.

[0353] 2. The server sends the generated advice to the device.

[0354] Enter: personalized advice

[0355] Output: Sending status (success / failure)

[0356] Specific operation: The server sends the advice to the device using an HTTP POST request.

[0357] 3. The device receives the advice and displays it to the user.

[0358] Input: Advice received

[0359] Output: Advice displayed

[0360] Specific operation: The terminal application notifies and displays the advice to the user.

[0361] Step 6: Regular progress checks

[0362] 1. The server periodically sends a request for new data to the device.

[0363] Input: Request Send Schedule

[0364] Output: New data request

[0365] Specific operation: The server sends requests to the terminal based on a schedule.

[0366] 2. The device displays a notification to the user, prompting them to enter data.

[0367] Input: New data request

[0368] Output: Data entry prompt notification

[0369] Specific behavior: The terminal notifies the user, "Please enter new data."

[0370] 3. The user inputs new health and emotion data and sends it to the server via the terminal.

[0371] Input: New health and emotion data

[0372] Output: New data submission status

[0373] What happens: The user enters new data and presses the submit button.

[0374] 4. The server analyzes the new data and assesses progress.

[0375] Input: New health and emotion data

[0376] Output: Progress evaluation results

[0377] What it does: The server feeds new data into the generative AI model and evaluates progress.

[0378] 5. The server will provide detailed advice and corrections based on your progress.

[0379] Input: Progress evaluation results

[0380] Output: Detailed advice and fixes

[0381] Specific operation: The server generates new advice based on the evaluation results and sends it to the device.

[0382] Step 7: Potential Risk Monitoring

[0383] 1. The server continuously monitors user data and assesses health and emotional risks.

[0384] Input: Continuously monitored health and emotional data

[0385] Output: Risk assessment results

[0386] What it does: The server periodically checks the data in the database and assesses the risk.

[0387] 2. If a risk is detected, the server generates an immediate alert.

[0388] Input: Risk assessment results

[0389] Output: Warning message

[0390] Specific operation: The server generates a warning based on the risk assessment results.

[0391] 3. The server sends a warning message and instructions on how to respond to the device.

[0392] Input: warning message

[0393] Output: Sending status (success / failure)

[0394] Specific operation: The server sends a warning message to the device using an HTTP POST request.

[0395] 4. The device notifies the user of the warning.

[0396] Input: Received warning message

[0397] Output: The displayed warning

[0398] Specific behavior: The device displays a warning message to the user, informing them that "continuing this behavior will increase cardiovascular risk and mental stress," and suggesting countermeasures such as "increasing low-calorie meals."

[0399] Through these steps, the system can comprehensively manage the user's health and emotional data, provide personalized advice, and periodically check progress and monitor potential risks, allowing users to manage their health with peace of mind.

[0400] (Application example 2)

[0401] 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."

[0402] Conventional health management systems only provide advice based on a user's physical health data, and therefore are unable to provide comprehensive care that takes into account the user's mental health, such as emotional state and stress level. Furthermore, they lack support for users to continuously manage their health, with insufficient regular progress checks and monitoring of potential risks. This increases the burden on users to manage their health themselves, and prevents them from receiving effective healthcare.

[0403] 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.

[0404] In this invention, the server includes a means for collecting health data and emotional data from a user, a generative model means for analyzing the health data and emotional data to evaluate the user's health and emotional state, and a means for generating personalized health and emotional care advice based on the evaluation results of the generative model means. This enables comprehensive management of the user's physical health and emotional state and provides appropriate advice. Furthermore, by regularly collecting data, evaluating progress, and monitoring potential risks, the user can receive support for maintaining a long-term balance between health and emotions.

[0405] A "means for collecting health and emotional data from a user" is a technology or device that receives and collects information about the health and emotional state input or provided by a user.

[0406] "Generative model means for analyzing health data and emotional data to assess a user's health and emotional state" refers to a technology that uses an algorithm or AI model to analyze a user's physical and mental health state based on the collected data and evaluates that state quantitatively or qualitatively.

[0407] The "means for generating personalized health and emotional care advice based on the evaluation results of the generative model means" is a technology for creating optimal health management and emotional care advice for individual users from the analysis results.

[0408] "Means for sending health and emotional care advice to a user's device" refers to technology that sends the generated advice to the user's device (smartphone, tablet, PC, etc.) and makes it viewable.

[0409] "Means for periodically collecting the user's health and emotional data and evaluating progress" refers to technology that collects new data from the user at regular intervals and analyzes and evaluates the progress and degree of improvement.

[0410] "Means for assessing potential health and emotional risks based on a user's health and emotional data and generating warnings and countermeasures" refers to technology that analyzes health and emotional risks that may occur in the future from collected data and provides warnings and countermeasures for those risks.

[0411] MODE FOR CARRYING OUT THE INVENTION

[0412] System Overview

[0413] This invention relates to a system that collects a user's health and emotional data and provides personalized health and emotional care advice based on the analysis results. The system is implemented by combining a user's device, a server, and a generative AI model.

[0414] Generating a Program

[0415] The system works in the following steps: The user's device collects health and emotional data, encrypts it, and sends it to the server. The server receives the data and inputs it into the generative AI model for analysis. Based on the analysis results, the server generates personalized advice and sends it to the user's device. The user's device displays the received advice to the user.

[0416] Natural language explanation

[0417] User's device

[0418] The user's device (smartphone, tablet, PC, etc.) collects health data (e.g., sleep time, exercise frequency, dietary content, etc.) and emotional data (e.g., stress level, mood) from the user through a dedicated application. The collected data is encrypted using methods such as AES encryption to maintain security.

[0419] Server and Generative AI Model

[0420] The server receives encrypted data from the user's device. The data is decrypted and stored in a database. The stored data is then input into a generative AI model implemented in Python. The model analyzes the data using frameworks such as TensorFlow and PyTorch. The analysis results provide an assessment of the user's current health and emotional state and generate personalized health and emotional care advice.

[0421] Providing advice

[0422] The server sends the generated advice to the user's device, which receives it and displays it on the application, allowing the user to take specific actions to put into practice in their daily lives.

[0423] Specific examples

[0424] For example, if a user inputs "average sleep time 5 hours," "low exercise intensity," and "stress level 7," the server will generate advice such as "get more sleep" and "increase exercise intensity" and notify the user.

[0425] Prompt Sentence Examples

[0426] User: "I average five hours of sleep and exercise three days a week, but the intensity is low. I've also been feeling a lot of stress lately."

[0427] System: "I recommend trying to increase your average sleep time to 7-8 hours and increasing your exercise intensity to a moderate level. Also, try relaxation techniques to manage stress."

[0428] This invention allows users to manage comprehensive health and emotional data in an integrated manner, receive personalized advice, and provide support for maintaining long-term health and emotional balance through regular data collection, progress evaluation, and monitoring of potential risks.

[0429] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0430] Step 1:

[0431] The user's device collects health and emotional data from the user through a dedicated application, including sleep duration, exercise frequency, dietary intake, stress level, and mood. The user enters this data into an input form, or the application automatically collects the data from sensors and other devices.

[0432] Input: Health data, emotion data

[0433] Output: Collected data

[0434] Step 2:

[0435] The device encrypts the collected data using a method such as AES encryption, which ensures data security, and then sends the encrypted data to the server.

[0436] Input: Collected data

[0437] Output: Encrypted data

[0438] Step 3:

[0439] The server receives the encrypted data sent from the device and decrypts it. The decrypted data is stored in a database. The server prepares the stored data for analysis.

[0440] Input: Encrypted data

[0441] Output: Decrypted data

[0442] Step 4:

[0443] The server then feeds the decoded data into a generative AI model, which uses frameworks such as TensorFlow and PyTorch to analyze the data and assess the user's health and emotional state.

[0444] Input: Decrypted data

[0445] Output: Analysis results

[0446] Step 5:

[0447] The server generates personalized health and emotional care advice based on the analysis results, including specific instructions for action and suggestions for improvement for the user.

[0448] Input: Analysis results

[0449] Output: Personalized advice

[0450] Step 6:

[0451] The server transmits the generated advice to the user's terminal.

[0452] Input: Personalized advice

[0453] Output: Advice sent

[0454] Step 7:

[0455] The user's device displays the received advice, and the application presents the advice to the user in a visually easy-to-understand manner. The user can use this advice to adjust their daily activities.

[0456] Input: Submitted advice

[0457] Output: Advice displayed

[0458] Step 8:

[0459] At regular intervals, the server requests new data from the user. This request appears as a notification on the user's device. The user enters new health and emotional data, which is then sent back to the server. This process continues, periodically assessing the user's progress.

[0460] Input: New health and emotion data

[0461] Output: Continuous progress assessment

[0462] Step 9:

[0463] The server continuously monitors user data and evaluates the likelihood of potential health or emotional risks. If a risk is detected, the server immediately generates a warning message and instructions for response and sends them to the user's device. The user's device displays the warning and the user confirms the measures.

[0464] Input: Continuously collected data

[0465] Output: Warnings and actions to take

[0466] 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.

[0467] 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.

[0468] 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.

[0469] [Second embodiment]

[0470] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0471] 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.

[0472] 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).

[0473] 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.

[0474] 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.

[0475] 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).

[0476] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0477] 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.

[0478] 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.

[0479] 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.

[0480] 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.

[0481] 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."

[0482] This invention relates to a system that analyzes health data collected from a user and provides personalized health advice. The system includes three main components: a user, a terminal, and a server.

[0483] 1. User information collection module

[0484] First, the user launches the application and creates a new account or logs in. The device prompts the user and provides a form where they can enter their health data (e.g., sleep time, dietary habits, exercise habits, etc.). The user enters the data according to the form and submits it. The device encrypts the submitted data and sends it to the server.

[0485] 2. Data storage module

[0486] The server decrypts the received data, stores it in the database, and sends a save confirmation response to the terminal. The terminal then confirms that the data has been successfully saved and notifies the user.

[0487] 3. Data Analysis Module

[0488] The server inputs the stored data into a generative model, which then analyzes the data and evaluates the user's health status. Based on the analysis results, the model generates personalized health advice.

[0489] Example 1:

[0490] If a user reports exercising three times a week but at a low intensity, the server uses the generative AI model to generate advice such as "Recommend to increase exercise intensity."

[0491] Example 2:

[0492] If a user averages 5 hours of sleep, the server uses the generative model to provide guidelines such as "you should aim for 7-8 hours of sleep per day."

[0493] 4. Advice sending module

[0494] The server prepares the generated advice and sends it to the user's device. The device displays the received advice to the user, who then confirms the advice and takes action to improve their lifestyle habits.

[0495] 5. Regular progress check module

[0496] The server periodically (e.g., once a week) sends a request for new data to the user. The device displays this as a notification to the user, prompting them to enter data. The user enters the new data and sends it to the server via the device. The server analyzes the new data and evaluates progress. Depending on the progress, the server can provide further advice and corrections.

[0497] Example 3:

[0498] If the user has been exercising for a month but has not seen any weight loss, the server will recommend that the user "review their diet" or "increase the intensity of their exercise."

[0499] 6. Potential Risk Monitoring Module

[0500] The server continuously monitors user data and evaluates the possibility of health risks occurring. If a risk is detected, the server immediately generates a warning. The server then sends a warning message and instructions on how to respond to the user's device, and the device notifies the user of the warning so that they can confirm the appropriate measures.

[0501] Example 4:

[0502] If the user has recently been eating a lot of high-calorie food and is gaining weight, the server will warn the user that "if this continues, your cardiovascular risk will increase" and suggest measures to the user, such as "eating more low-calorie meals."

[0503] In this way, users can easily manage their own health and receive necessary advice in a timely manner. This system also enables users who do not have abundant resources to obtain effective health advice.

[0504] The processing flow will be explained below.

[0505] Step 1:

[0506] The user launches the application and either creates a new account or logs in. The user follows the in-app prompts to enter and submit health data (e.g., sleep duration, dietary habits, exercise habits, etc.).

[0507] Step 2:

[0508] The terminal encrypts the transmitted data and sends it to the server, and also notifies the user of the success of the transmission.

[0509] Step 3:

[0510] The server decrypts the received data and stores it in the database. The server then sends a save confirmation response to the terminal to confirm that the save was successful.

[0511] Step 4:

[0512] The server inputs the stored data into a generative model, which analyzes the data, evaluates the user's health status, and generates personalized health advice based on the analysis results.

[0513] Step 5:

[0514] The server prepares the generated advice and sends it to the user's terminal, which displays the received advice to the user.

[0515] Step 6:

[0516] Users review the advice, implement it to improve their lifestyle habits, and provide feedback within the app if needed.

[0517] Step 7:

[0518] The server periodically (e.g., once a week) sends a request for new data to the user, which the device displays as a notification to the user, prompting them to enter the data.

[0519] Step 8:

[0520] The user enters new data and sends it through the terminal to the server, which analyzes the new data and assesses progress. Depending on progress, the server can provide further advice and corrections.

[0521] Step 9:

[0522] The server continuously monitors user data and assesses potential health risks. If a risk is detected, the server generates an immediate alert.

[0523] Step 10:

[0524] The server sends a warning message and instructions on how to deal with the problem to the user's device, which then notifies the user of the warning and allows them to confirm the corrective action.

[0525] Step 11:

[0526] The user checks the warning and takes necessary measures, then provides feedback to the server via the app and requests another analysis.

[0527] Example 1

[0528] 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."

[0529] In modern society, it is important for individuals to properly understand and manage their own health status, but in many cases, specialized knowledge and resources are required, and there is a lack of easy ways to collect information and receive effective health advice. Furthermore, there are challenges in protecting the security and privacy of collected health data, as well as regular progress checks and early detection of potential risks.

[0530] 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.

[0531] In this invention, the server includes: means for collecting health data from a user; means for encrypting the health data using a generating prompt and transmitting it to the server; server means for decrypting the encrypted health data and storing it in a database; generative AI model means for analyzing the health data to evaluate the user's health status; means for generating personalized health advice based on the evaluation results of the generative AI model means; means for transmitting the generated health advice to the user's terminal; and terminal means for displaying the transmitted health advice to the user. This enables effective and safe health management for users without specialized knowledge or resources. It also enables regular progress checks and early detection of potential risks.

[0532] "User" refers to an individual who uses the system to input their own health data and receive analysis results and advice.

[0533] "Device" refers to the device used by the user (e.g., smartphone, tablet, computer, etc.) to input health data and display advice.

[0534] A "server" is a computer system that receives, analyzes, and stores input health data and transmits generated advice to the user.

[0535] "Health Data" refers to information related to a user's health (e.g., sleep duration, diet, exercise habits, etc.) entered by the user.

[0536] "Generated prompts" are guides or hints used by users when entering health data.

[0537] "Encryption" refers to the process of converting health data sent from a device into a secure format to protect it from unauthorized access.

[0538] "Database" means a data repository where the health data received by the server is systematically stored and used for later analysis.

[0539] A "generative AI model" is an artificial intelligence model used to analyze a user's health data and assess their health status.

[0540] "Health advice" refers to recommendations and guidelines for improving a user's health based on the results analyzed by the generative AI model.

[0541] "Progress" refers to changes and improvements in the user's health condition over time, obtained through regular collection and analysis of health data.

[0542] "Potential risk" refers to the likelihood of future health problems or disorders estimated based on the user's health data.

[0543] This invention relates to a system that analyzes health data collected from a user and provides personalized health advice. The system includes three main components: a user, a terminal, and a server.

[0544] User information collection module

[0545] First, a user launches a dedicated application on a device such as a smartphone or computer, and then creates a new account or logs into an existing account. The device then displays a form for the user to enter health data (e.g., sleep time, diet, exercise habits, etc.). After the user enters the health data and presses the submit button, the device encrypts the data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0546] Data Storage Module

[0547] The server receives the encrypted data sent from the terminal and first decrypts it. The decrypted data is stored in a database and a response indicating the completion of storage is generated. The server sends this response to the terminal, and the terminal displays a notification to the user confirming the storage.

[0548] Data Analysis Module

[0549] The server inputs the user's health data stored in the database into an AI model. The generative AI model (e.g., a multi-layer neural network using TensorFlow or PyTorch) analyzes the data and evaluates the user's health status. Based on the results of this analysis, it generates personalized health advice for each user.

[0550] Example 1:

[0551] If a user reports exercising three times a week but at a low intensity, the server uses a generative AI model to generate advice such as "recommend increasing exercise intensity."

[0552] Example 2:

[0553] If a user averages 5 hours of sleep, the server uses the generative model to provide a guideline such as "you should aim for 7-8 hours of sleep per day."

[0554] Advice sending module

[0555] The server sends the generated advice to the user. This advice is formatted in JSON format or similar and sent to the device via a secure communication method (e.g., HTTPS). The device displays the received advice to the user, allowing the user to take action to improve their health.

[0556] Regular progress check module

[0557] The server periodically (e.g., weekly) requests the user to provide new health data. The device displays this request as a notification, prompting the user to enter new data. After the user enters and submits the new health data, the server analyzes it again and evaluates the user's progress. Based on this progress evaluation, the server provides more detailed advice and corrections.

[0558] Example 3:

[0559] If the user has been exercising for a month but has not seen any weight loss, the server will recommend that the user "review their diet" or "increase the intensity of their exercise."

[0560] Potential Risk Monitoring Module

[0561] The server continuously monitors the user's health data and evaluates potential health risks. If a risk is detected, the server immediately generates a warning message and countermeasures. These warnings and countermeasures are sent to the user's device, which then notifies the user via an in-app alert or email.

[0562] Example 4:

[0563] If the user has recently been eating a lot of high-calorie food and is gaining weight, the server will warn the user that "if this continues, your cardiovascular risk will increase" and suggest measures to the user, such as "eating more low-calorie meals."

[0564] This system allows users to easily manage their own health and receive necessary advice in a timely manner, and by using this system, effective and safe health management can be achieved without expensive resources.

[0565] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0566] Step 1: Launch the application and log in

[0567] A user launches a dedicated application on a device such as a smartphone or computer. The user creates a new account or logs in to an existing account. The device sends the entered authentication information to the server, which verifies it. If authentication is successful, the server generates an authentication token and sends it to the device.

[0568] Input: User credentials (username, password)

[0569] Output: Authentication token

[0570] Step 2: Enter your health data

[0571] Within the application, the user accesses a form to enter health data (e.g., sleep duration, dietary habits, exercise habits, etc.). The device displays prompts and receives the entered data. Once the user has completed the entry, they press the submit button. The device then encrypts this data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0572] Input: User health data

[0573] Output: Encrypted health data

[0574] Step 3: Receiving and storing data

[0575] The server receives the encrypted data sent from the terminal and first decrypts it. The decrypted data is saved in the database. If the data is saved successfully, the server generates a save completion response and sends it to the terminal. The terminal then displays a save confirmation notification to the user.

[0576] Input: Encrypted health data

[0577] Output: Decrypted data stored in the database, storage completion response

[0578] Step 4: Data analysis

[0579] The server inputs the user's health data stored in the database into a generative AI model. The generative model (e.g., using TensorFlow or PyTorch) analyzes the user's health status. Based on the analysis results, personalized health advice is generated.

[0580] Input: Health data stored in a database

[0581] Output: Health advice

[0582] Step 5: Send advice

[0583] The server sends the generated health advice to the user's device. The advice is formatted in an appropriate format, such as JSON, and transmitted using a secure communication method. The device then displays the received advice to the user.

[0584] Enter: Health Advice

[0585] Output: Advice displayed on the user's terminal

[0586] Step 6: Periodic data requests

[0587] The server periodically (e.g., once a week) sends a request to the user to provide new health data. The device displays this request as a notification, prompting the user to enter new data. When the user enters and submits new health data, the device sends the data to the server.

[0588] Input: Periodic request timer

[0589] Output: Data request displayed to the user, new health data sent to the server

[0590] Step 7: Progress assessment

[0591] The server analyzes the newly received health data and compares it with previous data to assess the user's progress, and provides further detailed advice and corrections based on the assessment results.

[0592] Input: New health data, Previous health data

[0593] Output: Progress evaluation results, improvement advice

[0594] Step 8: Potential Risk Monitoring

[0595] The server continuously monitors the user's health data and evaluates potential health risks. If a risk is detected, it immediately generates a warning message and countermeasures. The warning and countermeasures are sent to the user's device, which then notifies the user.

[0596] Input: User's cumulative health data

[0597] Output: Risk warning message, notification of countermeasures

[0598] In this way, users can receive appropriate advice in real time based on their own health data and manage their health. In addition, regular progress checks and early detection of potential risks enable effective and safe health management.

[0599] (Application example 1)

[0600] 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."

[0601] Conventional health management systems analyze health data provided by users and provide advice on an individual basis, and lack the means to provide real-time health-related information, particularly in collaboration with physical stores. Furthermore, data encryption and regular progress management are insufficient, making it difficult to provide early warnings of health risks and provide specific countermeasures.

[0602] 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.

[0603] In this invention, the server includes means for collecting health data from a user, generative model means for analyzing the health data to evaluate the user's health status, means for generating personalized health advice based on the evaluation results of the generative model means, means for transmitting the health advice to the user's device, means for encrypting the health data and transmitting it to the server, means for displaying the generated advice as a notification on the user's device, and means for periodically requesting new data. This allows the user to understand their own health status in real time and receive personalized advice and product suggestions. Furthermore, data encryption ensures security, enabling regular progress management and early warning of potential health risks.

[0604] "User's health data" refers to information about the user's health, such as their sleep time, eating habits, and exercise habits.

[0605] "Generative model means" refers to means that use AI models or machine learning algorithms to analyze a user's health data and assess their health status.

[0606] "Encryption" refers to the process of converting data using a specific algorithm to make it unreadable to third parties in order to maintain the confidentiality of the information.

[0607] "Personalized health advice" refers to advice for individually optimal health improvement that is generated based on the user's individual health data.

[0608] "Terminal" refers to an electronic device such as a smartphone or tablet used by a user.

[0609] "Physical store" refers to a physically existing store such as a fitness club or drugstore.

[0610] "Real-time" refers to the immediacy of time in which results are obtained as soon as data is generated and processed.

[0611] "Notification" refers to a message function that notifies the user of information through an application.

[0612] "New data request" refers to a request from the server to the user to input new health data at regular intervals.

[0613] "Health risk assessment" refers to predicting possible future health risks based on the user's current health data.

[0614] "Warning and countermeasures" refers to notifying the user of a warning when a health risk is detected and providing specific remedial measures.

[0615] This invention is a system in which a user provides their own health data and receives personalized health advice in real time based on that data. The system includes three main components: a user, a terminal, and a server. Each component is described in detail below.

[0616] User information collection module

[0617] A user launches the application using a device such as a smartphone or tablet. After creating a new account or logging in, the user follows the prompts to enter health data (e.g., sleep time, dietary habits, exercise habits, etc.). The entered data is encrypted and sent to the server.

[0618] Data Storage Module

[0619] The server receives the encrypted data sent by the user, decrypts it, and stores it in the database. If the data is successfully saved, the server sends a save completion response to the device, and the device displays a notification to the user.

[0620] Data Analysis Module

[0621] The saved data is input into a generative AI model on the server. This AI model analyzes the data and evaluates the user's health status. Based on the analysis results, personalized health advice is generated. For example, if a user only exercises twice a week, the server will generate advice such as "Adding one more exercise session would be effective." Also, if the user's sleep time is short at five hours, the server will provide a guideline such as "Aim for 7-8 hours of sleep per day."

[0622] Advice sending module

[0623] The generated advice is sent from the server to the user's device, and the device displays the advice as a notification to the user. The user can then confirm the advice and take action to improve their lifestyle habits.

[0624] Regular progress check module

[0625] The server periodically sends the user a request for new health data. The device displays the request as a notification, and the user enters the new data and sends it to the server. The server analyzes the new data, evaluates progress, and provides further advice or corrections as needed.

[0626] Potential Risk Monitoring Module

[0627] The server continuously monitors the user's health data and evaluates the possibility of a health risk occurring. If a risk is detected, the server immediately generates a warning and sends a warning message and instructions on how to respond to the user's device. The device notifies the user of the warning, allowing the user to confirm countermeasures. For example, if the user has recently been eating a lot of high-calorie food and has gained weight, the server warns that "if this continues, your cardiovascular risk will increase" and suggests countermeasures such as "eating more low-calorie foods."

[0628] Hardware and software used

[0629] Hardware: Smartphones, tablets

[0630] Software: Encryption library (Fernet), data transmission library (requests), generative AI model

[0631] Examples of concrete examples and prompts

[0632] For example, if a user's average sleep time is five hours, the app will provide a guideline such as, "You should aim for seven to eight hours of sleep a day." If the user only exercises twice a week, the app will display real-time advice on their smartphone, such as, "Adding one more exercise session would be beneficial."

[0633] Example prompt sentence:

[0634] Generate personalized health advice based on the following user data:

[0635] Sleep time: 5 hours

[0636] Eating habits: High-calorie diet

[0637] Exercise habits: Once a week

[0638] This allows users to understand their own health condition in real time and receive optimized health advice.

[0639] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0640] Step 1:

[0641] A user uses a smartphone or tablet to launch the application and create a new account or log in. Then, they input their health data (e.g., sleep duration, eating habits, exercise habits, etc.). This data is encrypted on the device. The input data is used by an interface that allows the user to provide specific health information to the device. Specific actions include entering data into an input form and pressing the submit button.

[0642] Input: Health data such as sleep time, eating habits, exercise habits, etc.

[0643] Output: Encrypted health data

[0644] Step 2:

[0645] The device sends encrypted health data to the server using a secure protocol such as HTTPS. The device sends data to the server and manages the transmission status at the same time. Specific operations include the process of sending encrypted data to the server via the network.

[0646] Input: Encrypted health data

[0647] Output: Health data sent to the server

[0648] Step 3:

[0649] The server decrypts the received health data and performs pre-processing to store it in the database, including steps to verify the consistency and integrity of the data. Specific operations include writing the decrypted data to the database.

[0650] Input: Encrypted health data

[0651] Output: Health data stored in a database

[0652] Step 4:

[0653] The server inputs the stored health data into a generative AI model and performs data analysis. The generative AI model analyzes the data and evaluates the user's health condition. Based on the evaluation results, personalized health advice is generated. Specific operations include the process of inputting data into the AI ​​model and obtaining the evaluation results.

[0654] Input: Health data stored in a database

[0655] Output: Analyzed health status assessment and personalized health advice

[0656] Step 5:

[0657] The server sends the generated health advice to the user's device, which then displays the advice to the user as a notification. This process incorporates visual elements based on UI / UX design. Specific operations include displaying the advice notification and prompting confirmation.

[0658] Enter: personalized health advice

[0659] Output: Health advice displayed on the user's device

[0660] Step 6:

[0661] The server periodically sends a request for new data to the user, and the device displays the request as a notification. The user can then enter new health data and send it back to the server. Specific operations include requesting data via notifications and receiving new data from the user.

[0662] Input: New data request

[0663] Output: New health data

[0664] Step 7:

[0665] The server analyzes new health data and periodically evaluates progress, providing further advice and corrections as needed. Specific actions include inputting new data into the generative AI model and evaluating progress.

[0666] Input: New health data

[0667] Output: Progress assessment and additional health advice

[0668] In this way, processing steps across the system work together to enable users to receive personalized health advice on an ongoing basis.

[0669] By clarifying the specific operations and processes of input and output, it becomes possible to concretely understand the actual function of the system.

[0670] 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.

[0671] This invention relates to a system that analyzes health and emotion data collected from a user and provides personalized health and emotion care advice. The system includes four main components: a user, a terminal, a server, and an emotion engine.

[0672] 1. User information collection module

[0673] First, a user launches the application and either creates a new account or logs in. Then, following the prompts within the app, the user inputs and submits health data (e.g., sleep duration, dietary habits, exercise habits, etc.) and emotional data. Emotional data is collected through self-reporting by the user or through automatic identification using an emotion engine.

[0674] 2. Data storage module

[0675] The device encrypts the data and sends it to the server. The server decrypts the received data and stores it in the database. It then sends a save confirmation response to the device, notifying it that the save was successful.

[0676] 3. Data Analysis Module

[0677] The server inputs the stored health and emotional data into the generative model, which analyzes the data and evaluates the user's health and emotional state. Based on the analysis results, the model generates personalized health and emotional care advice.

[0678] Example 1:

[0679] If a user reports exercising three times a week but at a low intensity, the server can use the generative AI model to generate advice such as "Recommend increasing exercise intensity." On the other hand, if emotion data indicates that the user is stressed, the server can also recommend relaxation exercises.

[0680] Example 2:

[0681] If the user averages 5 hours of sleep, the server uses the generative model to provide guidelines such as "You should aim for 7-8 hours of sleep a day." If the emotion data indicates that the user is feeling anxious, the server suggests "practicing relaxation meditation before bed."

[0682] 4. Advice sending module

[0683] The server prepares and sends the generated advice to the user's terminal, which displays the received health and emotional care advice to the user.

[0684] 5. Regular progress check module

[0685] The server periodically (e.g., once a week) sends a request for new data to the user. The device displays this as a notification to the user, prompting them to enter data. The user enters new health and emotional data and sends it to the server via the device. The server analyzes the new data and evaluates progress. Depending on the progress, it provides more detailed advice and corrections.

[0686] Example 3:

[0687] If a user has been exercising for a month but has not seen any weight loss and the emotional data indicates an increase in stress, the server will suggest that the user "reconsider their diet," "increase the intensity of their exercise," and "reconsider their stress management methods."

[0688] 6. Potential Risk Monitoring Module

[0689] The server continuously monitors user data and evaluates the possibility of health and emotional risks occurring. If a risk is detected, the server immediately generates a warning. The server then sends a warning message and instructions on how to respond to the user's device, which then notifies the user of the warning and allows them to confirm countermeasures.

[0690] Example 4:

[0691] If a user has recently been eating a lot of high-calorie food and gaining weight, and emotional data indicates that they are feeling depressed, the server will warn them that "if they continue this way, their cardiovascular risk and mental stress will increase," and suggest measures to the user, such as "eating more low-calorie meals" and "adding mental health exercise."

[0692] In this way, users can easily manage their own health data and emotion data in an integrated manner and receive necessary advice in a timely manner. In addition, by combining it with the emotion engine, users can manage their overall health more comprehensively and effectively.

[0693] The processing flow will be explained below.

[0694] Step 1:

[0695] A user launches the application and creates a new account or logs in. They follow the in-app prompts to enter and submit health data (e.g., sleep duration, dietary habits, exercise habits, etc.) and emotional data (e.g., self-reported or entered into an emotion recognition engine).

[0696] Step 2:

[0697] The device encrypts the transmitted health and emotion data and transmits it to the server, and also notifies the user of the success of the transmission.

[0698] Step 3:

[0699] The server decodes the received health and emotion data and stores it in a database. The server then sends a save confirmation response to the device, notifying the user that the save was successful.

[0700] Step 4:

[0701] The server inputs the stored health and emotional data into a generative model, which analyzes the data and evaluates the user's health and emotional state. Based on the analysis results, the model generates personalized health and emotional care advice.

[0702] Step 5:

[0703] The server sends the generated advice to the user's terminal, which displays the received health and emotional care advice to the user.

[0704] Step 6:

[0705] Users can review the advice and implement it to improve their lifestyle. They can also provide feedback within the app, which will be used for the next analysis.

[0706] Step 7:

[0707] The server periodically (e.g., once a week) sends a request for new data to the user, which the device displays as a notification to the user, prompting them to enter the data.

[0708] Step 8:

[0709] Users input new health and emotional data and send it to the server via their device. The server analyzes the new data and assesses progress. Depending on progress, the server can provide further detailed advice and corrections.

[0710] Step 9:

[0711] The server continuously monitors user data and assesses the likelihood of health and emotional risks occurring. If a risk is detected, the server generates an immediate alert.

[0712] Step 10:

[0713] The server sends a warning message and instructions on how to deal with the problem to the user's device, which then notifies the user of the warning and allows them to confirm the corrective action.

[0714] Step 11:

[0715] The user checks the warning and takes necessary measures, then provides feedback to the server via the app and requests another analysis.

[0716] Example 2

[0717] 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."

[0718] Conventional health management systems primarily provide advice based solely on a user's health data, making it difficult to provide personalized advice that also takes into account the user's emotional state. Furthermore, regular progress checks and monitoring of potential health and emotional risks are insufficient, making it difficult for users to receive appropriate care instructions when needed. Furthermore, the lack of a means to comprehensively evaluate health and emotional data has prevented more comprehensive health management from being realized.

[0719] 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.

[0720] In this invention, the server includes means for collecting health data and emotional data from a user, means for encrypting the health data and emotional data and transmitting the data to the server, means for decrypting the health data and emotional data stored on the server and storing them in a secure database, means for inputting the health data and emotional data into a generative AI model for analysis and evaluating the user's health and emotional state, means for generating personalized health and emotional care advice based on the evaluation results of the generative AI model, and means for transmitting the generated advice to the user's device and displaying it. This allows for integrated management of the user's health data and emotional data, providing more comprehensive and personalized advice, and enabling regular progress checks and monitoring of potential risks.

[0721] "Health data" refers to data used to measure and evaluate a user's health status, such as the user's physical activity level, sleep time, eating habits, and exercise habits.

[0722] "Emotion data" refers to information self-reported by a user about their own emotional state, or data about an emotional state automatically identified by an emotion identification engine.

[0723] A "generative AI model" is an artificial intelligence model that takes health data and emotional data as input, analyzes this data, and evaluates the user's health and emotional state.

[0724] "Encryption" is a technical measure that makes transmitted data unreadable to third parties.

[0725] The "Server" is a device that receives, decodes, stores, and analyzes user-submitted health and emotional data using a generative AI model.

[0726] "Personalized health and emotional care advice" refers to user-specific health and emotional care instructions that are generated based on an analysis of the user's individual health and emotional data.

[0727] A "database" is a collection of health and emotional data stored securely within a server.

[0728] "Progress assessment" is the process of assessing changes in a user's health and emotional state based on new health and emotional data collected periodically from the user.

[0729] "Potential risk" is an assessment result that indicates the possibility of a physical or psychological risk occurring in the future, based on the user's health data and emotional data.

[0730] A "warning" is a message issued to the user to warn them when a potential risk is detected.

[0731] A "countermeasure" is specific instructions or advice provided to a user to address a detected risk.

[0732] This invention relates to a system that analyzes health and emotional data collected from a user and provides personalized health and emotional care advice, which specifically includes four main components: a user, a terminal, a server, and an emotion engine.

[0733] User information collection module

[0734] First, a user launches the application and creates a new account or logs in. Then, following the prompts within the app, the user inputs and submits health data (e.g., sleep duration, dietary habits, exercise habits, etc.) and emotional data. Emotional data is collected through self-reporting by the user or through automatic identification using an emotion engine.

[0735] A specific example would be a user opening a smartphone app and entering the amount of sleep they got last night or their emotional state today. For example, the user might enter "7 hours of sleep" and "stress."

[0736] Data Storage Module

[0737] The device encrypts the data using the TLS encryption protocol and sends it to the server. The server then decrypts the received data and stores it in a secure database. If the data is successfully saved, the server notifies the device, and the device displays a confirmation message to the user.

[0738] As a specific example, the device may display the message "Saving..." and after the data has been successfully saved, display a notification to the user saying "Data saved successfully."

[0739] Data Analysis Module

[0740] The server inputs the stored health and emotional data into a generative AI model (such as OpenAI's GPT-4). The generative AI model analyzes this data and evaluates the user's health and emotional state. The server generates personalized health and emotional care advice based on the analysis results.

[0741] As a specific example, if you input data such as "The user exercises three times a week, but at a low intensity," the system will generate advice such as "Increase the intensity of your exercise."

[0742] Advice sending module

[0743] The server prepares the generated advice and sends it to the terminal, which receives it and displays it to the user in an appropriate form.

[0744] For example, advice such as "You should aim to sleep 7-8 hours a day" is generated and sent to the device, which then displays the message "Recommended sleep time: 7-8 hours a day" to the user.

[0745] Regular progress check module

[0746] The server periodically sends a request for new data to the device. The device displays the request as a notification to the user, prompting them to enter data. The user enters new health and emotional data and sends it to the server via the device. The server analyzes the new data and evaluates progress. Based on the analysis results, the server provides detailed advice and corrections.

[0747] As a specific example, the server sends a request to the terminal saying "Please enter new data," and the terminal displays a notification to the user saying "Please enter new health data."

[0748] Potential Risk Monitoring Module

[0749] The server continuously monitors user data and evaluates the likelihood of health and emotional risks. If a risk is detected, the server immediately generates an alert and sends a warning message and instructions on how to respond to the device. The device then notifies the user of the alert and prompts them to take action.

[0750] For example, the server receives data such as "You have recently increased your intake of high-calorie meals and have been experiencing depression," generates a warning that "Your cardiovascular risk and mental stress may be increasing," and sends it to the device. The device then notifies the user to "increase your intake of low-calorie meals and add mental health exercises."

[0751] Prompt Sentence Examples

[0752] "The user exercises at a low intensity three times a week, but is feeling stressed. Please generate appropriate health advice."

[0753] "The user's average sleep time is 5 hours and they are feeling anxious. Please generate appropriate health advice."

[0754] In this way, this system can comprehensively manage the user's health and emotional data, providing more comprehensive and personalized advice. It also allows users to regularly check their progress and monitor potential risks, allowing them to manage their health with peace of mind.

[0755] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0756] Program processing flow

[0757] Step 1: Collect user information

[0758] 1. The user launches the application and either creates a new account or logs in.

[0759] Input: Account information (username, password, etc.)

[0760] Output: Authentication status (login success / failure)

[0761] Specific operation: The user opens the smartphone app and enters their username and password on the login screen.

[0762] 2. The user follows the prompts in the app to enter and submit their health and emotional data.

[0763] Input: Health data (e.g., sleep time, dietary habits, exercise habits), emotional data (e.g., stress level)

[0764] Output: Sending status (success / failure)

[0765] Specific operation: The user enters various health and emotional data into the form and presses the "Submit" button.

[0766] Step 2: Data encryption and transmission

[0767] 1. The device encrypts the transmitted data using the TLS encryption protocol.

[0768] Input: Health and emotion data entered by the user

[0769] Output: Encrypted data

[0770] Specific operation: The terminal encrypts the input data using the encryption library.

[0771] 2. The device sends the encrypted data to the server.

[0772] Input: Encrypted data

[0773] Output: Sending status (success / failure)

[0774] Specific operation: The device sends the encrypted data to the server using an HTTP POST request.

[0775] Step 3: Receiving and storing data

[0776] 1. The server decrypts the received data.

[0777] Input: Encrypted data

[0778] Output: raw decrypted data

[0779] What happens: The server uses a decryption library to decrypt the data.

[0780] 2. The server stores the decrypted data in a secure database.

[0781] Input: raw decrypted data

[0782] Output: Save status (save successful / failed)

[0783] Specific behavior: The server saves the data via the database connection and returns the save status.

[0784] Step 4: Data analysis

[0785] 1. The server inputs the stored health and emotion data into the generative AI model.

[0786] Input: Health and emotion data stored in a database

[0787] Output: Data analysis results

[0788] Specific operation: The server inputs data into the API of the generative AI model and performs analysis.

[0789] 2. The generative AI model analyzes the data and the server assesses the user's health and emotional state.

[0790] Input: Health and emotion data

[0791] Output: Health and emotional state assessment results

[0792] Specific operation: The generative AI model analyzes the data and returns the evaluation results to the server.

[0793] Step 5: Advice generation and sending

[0794] 1. The server generates personalized health and emotional care advice based on the analysis results.

[0795] Input: Health and emotional state assessment results

[0796] Output: Personalized advice

[0797] Specific operation: The server generates advice based on the analysis results of the generative AI model.

[0798] 2. The server sends the generated advice to the device.

[0799] Enter: personalized advice

[0800] Output: Sending status (success / failure)

[0801] Specific operation: The server sends the advice to the device using an HTTP POST request.

[0802] 3. The device receives the advice and displays it to the user.

[0803] Input: Advice received

[0804] Output: Advice displayed

[0805] Specific operation: The terminal application notifies and displays the advice to the user.

[0806] Step 6: Regular progress checks

[0807] 1. The server periodically sends a request for new data to the device.

[0808] Input: Request Send Schedule

[0809] Output: New data request

[0810] Specific operation: The server sends requests to the terminal based on a schedule.

[0811] 2. The device displays a notification to the user, prompting them to enter data.

[0812] Input: New data request

[0813] Output: Data entry prompt notification

[0814] Specific behavior: The terminal notifies the user, "Please enter new data."

[0815] 3. The user inputs new health and emotion data and sends it to the server via the terminal.

[0816] Input: New health and emotion data

[0817] Output: New data submission status

[0818] What happens: The user enters new data and presses the submit button.

[0819] 4. The server analyzes the new data and assesses progress.

[0820] Input: New health and emotion data

[0821] Output: Progress evaluation results

[0822] What it does: The server feeds new data into the generative AI model and evaluates progress.

[0823] 5. The server will provide detailed advice and corrections based on your progress.

[0824] Input: Progress evaluation results

[0825] Output: Detailed advice and fixes

[0826] Specific operation: The server generates new advice based on the evaluation results and sends it to the device.

[0827] Step 7: Potential Risk Monitoring

[0828] 1. The server continuously monitors user data and assesses health and emotional risks.

[0829] Input: Continuously monitored health and emotional data

[0830] Output: Risk assessment results

[0831] What it does: The server periodically checks the data in the database and assesses the risk.

[0832] 2. If a risk is detected, the server generates an immediate alert.

[0833] Input: Risk assessment results

[0834] Output: Warning message

[0835] Specific operation: The server generates a warning based on the risk assessment results.

[0836] 3. The server sends a warning message and instructions on how to respond to the device.

[0837] Input: warning message

[0838] Output: Sending status (success / failure)

[0839] Specific operation: The server sends a warning message to the device using an HTTP POST request.

[0840] 4. The device notifies the user of the warning.

[0841] Input: Received warning message

[0842] Output: The displayed warning

[0843] Specific behavior: The device displays a warning message to the user, informing them that "continuing this behavior will increase cardiovascular risk and mental stress," and suggesting countermeasures such as "increasing low-calorie meals."

[0844] Through these steps, the system can comprehensively manage the user's health and emotional data, provide personalized advice, and periodically check progress and monitor potential risks, allowing users to manage their health with peace of mind.

[0845] (Application example 2)

[0846] 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."

[0847] Conventional health management systems only provide advice based on a user's physical health data, and therefore are unable to provide comprehensive care that takes into account the user's mental health, such as emotional state and stress level. Furthermore, they lack support for users to continuously manage their health, with insufficient regular progress checks and monitoring of potential risks. This increases the burden on users to manage their health themselves, and prevents them from receiving effective healthcare.

[0848] 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.

[0849] In this invention, the server includes a means for collecting health data and emotional data from a user, a generative model means for analyzing the health data and emotional data to evaluate the user's health and emotional state, and a means for generating personalized health and emotional care advice based on the evaluation results of the generative model means. This enables comprehensive management of the user's physical health and emotional state and provides appropriate advice. Furthermore, by regularly collecting data, evaluating progress, and monitoring potential risks, the user can receive support for maintaining a long-term balance between health and emotions.

[0850] A "means for collecting health and emotional data from a user" is a technology or device that receives and collects information about the health and emotional state input or provided by a user.

[0851] "Generative model means for analyzing health data and emotional data to assess a user's health and emotional state" refers to a technology that uses an algorithm or AI model to analyze a user's physical and mental health state based on the collected data and evaluates that state quantitatively or qualitatively.

[0852] The "means for generating personalized health and emotional care advice based on the evaluation results of the generative model means" is a technology for creating optimal health management and emotional care advice for individual users from the analysis results.

[0853] "Means for sending health and emotional care advice to a user's device" refers to technology that sends the generated advice to the user's device (smartphone, tablet, PC, etc.) and makes it viewable.

[0854] "Means for periodically collecting the user's health and emotional data and evaluating progress" refers to technology that collects new data from the user at regular intervals and analyzes and evaluates the progress and degree of improvement.

[0855] "Means for assessing potential health and emotional risks based on a user's health and emotional data and generating warnings and countermeasures" refers to technology that analyzes health and emotional risks that may occur in the future from collected data and provides warnings and countermeasures for those risks.

[0856] MODE FOR CARRYING OUT THE INVENTION

[0857] System Overview

[0858] This invention relates to a system that collects a user's health and emotional data and provides personalized health and emotional care advice based on the analysis results. The system is implemented by combining a user's device, a server, and a generative AI model.

[0859] Generating a Program

[0860] The system works in the following steps: The user's device collects health and emotional data, encrypts it, and sends it to the server. The server receives the data and inputs it into the generative AI model for analysis. Based on the analysis results, the server generates personalized advice and sends it to the user's device. The user's device displays the received advice to the user.

[0861] Natural language explanation

[0862] User's device

[0863] The user's device (smartphone, tablet, PC, etc.) collects health data (e.g., sleep time, exercise frequency, dietary content, etc.) and emotional data (e.g., stress level, mood) from the user through a dedicated application. The collected data is encrypted using methods such as AES encryption to maintain security.

[0864] Server and Generative AI Model

[0865] The server receives encrypted data from the user's device. The data is decrypted and stored in a database. The stored data is then input into a generative AI model implemented in Python. The model analyzes the data using frameworks such as TensorFlow and PyTorch. The analysis results provide an assessment of the user's current health and emotional state and generate personalized health and emotional care advice.

[0866] Providing advice

[0867] The server sends the generated advice to the user's device, which receives it and displays it on the application, allowing the user to take specific actions to put into practice in their daily lives.

[0868] Specific examples

[0869] For example, if a user inputs "average sleep time 5 hours," "low exercise intensity," and "stress level 7," the server will generate advice such as "get more sleep" and "increase exercise intensity" and notify the user.

[0870] Prompt Sentence Examples

[0871] User: "I average five hours of sleep and exercise three days a week, but the intensity is low. I've also been feeling a lot of stress lately."

[0872] System: "I recommend trying to increase your average sleep time to 7-8 hours and increasing your exercise intensity to a moderate level. Also, try relaxation techniques to manage stress."

[0873] This invention allows users to manage comprehensive health and emotional data in an integrated manner, receive personalized advice, and provide support for maintaining long-term health and emotional balance through regular data collection, progress evaluation, and monitoring of potential risks.

[0874] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0875] Step 1:

[0876] The user's device collects health and emotional data from the user through a dedicated application, including sleep duration, exercise frequency, dietary intake, stress level, and mood. The user enters this data into an input form, or the application automatically collects the data from sensors and other devices.

[0877] Input: Health data, emotion data

[0878] Output: Collected data

[0879] Step 2:

[0880] The device encrypts the collected data using a method such as AES encryption, which ensures data security, and then sends the encrypted data to the server.

[0881] Input: Collected data

[0882] Output: Encrypted data

[0883] Step 3:

[0884] The server receives the encrypted data sent from the device and decrypts it. The decrypted data is stored in a database. The server prepares the stored data for analysis.

[0885] Input: Encrypted data

[0886] Output: Decrypted data

[0887] Step 4:

[0888] The server then feeds the decoded data into a generative AI model, which uses frameworks such as TensorFlow and PyTorch to analyze the data and assess the user's health and emotional state.

[0889] Input: Decrypted data

[0890] Output: Analysis results

[0891] Step 5:

[0892] The server generates personalized health and emotional care advice based on the analysis results, including specific instructions for action and suggestions for improvement for the user.

[0893] Input: Analysis results

[0894] Output: Personalized advice

[0895] Step 6:

[0896] The server transmits the generated advice to the user's terminal.

[0897] Input: Personalized advice

[0898] Output: Advice sent

[0899] Step 7:

[0900] The user's device displays the received advice, and the application presents the advice to the user in a visually easy-to-understand manner. The user can use this advice to adjust their daily activities.

[0901] Input: Submitted advice

[0902] Output: Advice displayed

[0903] Step 8:

[0904] At regular intervals, the server requests new data from the user. This request appears as a notification on the user's device. The user enters new health and emotional data, which is then sent back to the server. This process continues, periodically assessing the user's progress.

[0905] Input: New health and emotion data

[0906] Output: Continuous progress assessment

[0907] Step 9:

[0908] The server continuously monitors user data and evaluates the likelihood of potential health or emotional risks. If a risk is detected, the server immediately generates a warning message and instructions for response and sends them to the user's device. The user's device displays the warning and the user confirms the measures.

[0909] Input: Continuously collected data

[0910] Output: Warnings and actions to take

[0911] 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.

[0912] 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.

[0913] 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.

[0914] [Third embodiment]

[0915] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0916] 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.

[0917] 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).

[0918] 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.

[0919] 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.

[0920] 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).

[0921] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[0922] 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.

[0923] 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.

[0924] 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.

[0925] 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.

[0926] 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."

[0927] This invention relates to a system that analyzes health data collected from a user and provides personalized health advice. The system includes three main components: a user, a terminal, and a server.

[0928] 1. User information collection module

[0929] First, the user launches the application and creates a new account or logs in. The device prompts the user and provides a form where they can enter their health data (e.g., sleep time, dietary habits, exercise habits, etc.). The user enters the data according to the form and submits it. The device encrypts the submitted data and sends it to the server.

[0930] 2. Data storage module

[0931] The server decrypts the received data, stores it in the database, and sends a save confirmation response to the terminal. The terminal then confirms that the data has been successfully saved and notifies the user.

[0932] 3. Data Analysis Module

[0933] The server inputs the stored data into a generative model, which then analyzes the data and evaluates the user's health status. Based on the analysis results, the model generates personalized health advice.

[0934] Example 1:

[0935] If a user reports exercising three times a week but at a low intensity, the server uses the generative AI model to generate advice such as "Recommend to increase exercise intensity."

[0936] Example 2:

[0937] If a user averages 5 hours of sleep, the server uses the generative model to provide guidelines such as "you should aim for 7-8 hours of sleep per day."

[0938] 4. Advice sending module

[0939] The server prepares the generated advice and sends it to the user's device. The device displays the received advice to the user, who then confirms the advice and takes action to improve their lifestyle habits.

[0940] 5. Regular progress check module

[0941] The server periodically (e.g., once a week) sends a request for new data to the user. The device displays this as a notification to the user, prompting them to enter data. The user enters the new data and sends it to the server via the device. The server analyzes the new data and evaluates progress. Depending on the progress, the server can provide further advice and corrections.

[0942] Example 3:

[0943] If the user has been exercising for a month but has not seen any weight loss, the server will recommend that the user "review their diet" or "increase the intensity of their exercise."

[0944] 6. Potential Risk Monitoring Module

[0945] The server continuously monitors user data and evaluates the possibility of health risks occurring. If a risk is detected, the server immediately generates a warning. The server then sends a warning message and instructions on how to respond to the user's device, and the device notifies the user of the warning so that they can confirm the appropriate measures.

[0946] Example 4:

[0947] If the user has recently been eating a lot of high-calorie food and is gaining weight, the server will warn the user that "if this continues, your cardiovascular risk will increase" and suggest measures to the user, such as "eating more low-calorie meals."

[0948] In this way, users can easily manage their own health and receive necessary advice in a timely manner. This system also enables users who do not have abundant resources to obtain effective health advice.

[0949] The processing flow will be explained below.

[0950] Step 1:

[0951] The user launches the application and either creates a new account or logs in. The user follows the in-app prompts to enter and submit health data (e.g., sleep duration, dietary habits, exercise habits, etc.).

[0952] Step 2:

[0953] The terminal encrypts the transmitted data and sends it to the server, and also notifies the user of the success of the transmission.

[0954] Step 3:

[0955] The server decrypts the received data and stores it in the database. The server then sends a save confirmation response to the terminal to confirm that the save was successful.

[0956] Step 4:

[0957] The server inputs the stored data into a generative model, which analyzes the data, evaluates the user's health status, and generates personalized health advice based on the analysis results.

[0958] Step 5:

[0959] The server prepares the generated advice and sends it to the user's terminal, which displays the received advice to the user.

[0960] Step 6:

[0961] Users review the advice, implement it to improve their lifestyle habits, and provide feedback within the app if needed.

[0962] Step 7:

[0963] The server periodically (e.g., once a week) sends a request for new data to the user, which the device displays as a notification to the user, prompting them to enter the data.

[0964] Step 8:

[0965] The user enters new data and sends it through the terminal to the server, which analyzes the new data and assesses progress. Depending on progress, the server can provide further advice and corrections.

[0966] Step 9:

[0967] The server continuously monitors user data and assesses potential health risks. If a risk is detected, the server generates an immediate alert.

[0968] Step 10:

[0969] The server sends a warning message and instructions on how to deal with the problem to the user's device, which then notifies the user of the warning and allows them to confirm the corrective action.

[0970] Step 11:

[0971] The user checks the warning and takes necessary measures, then provides feedback to the server via the app and requests another analysis.

[0972] Example 1

[0973] 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."

[0974] In modern society, it is important for individuals to properly understand and manage their own health status, but in many cases, specialized knowledge and resources are required, and there is a lack of easy ways to collect information and receive effective health advice. Furthermore, there are challenges in protecting the security and privacy of collected health data, as well as regular progress checks and early detection of potential risks.

[0975] 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.

[0976] In this invention, the server includes: means for collecting health data from a user; means for encrypting the health data using a generating prompt and transmitting it to the server; server means for decrypting the encrypted health data and storing it in a database; generative AI model means for analyzing the health data to evaluate the user's health status; means for generating personalized health advice based on the evaluation results of the generative AI model means; means for transmitting the generated health advice to the user's terminal; and terminal means for displaying the transmitted health advice to the user. This enables effective and safe health management for users without specialized knowledge or resources. It also enables regular progress checks and early detection of potential risks.

[0977] "User" refers to an individual who uses the system to input their own health data and receive analysis results and advice.

[0978] "Device" refers to the device used by the user (e.g., smartphone, tablet, computer, etc.) to input health data and display advice.

[0979] A "server" is a computer system that receives, analyzes, and stores input health data and transmits generated advice to the user.

[0980] "Health Data" refers to information related to a user's health (e.g., sleep duration, diet, exercise habits, etc.) entered by the user.

[0981] "Generated prompts" are guides or hints used by users when entering health data.

[0982] "Encryption" refers to the process of converting health data sent from a device into a secure format to protect it from unauthorized access.

[0983] "Database" means a data repository where the health data received by the server is systematically stored and used for later analysis.

[0984] A "generative AI model" is an artificial intelligence model used to analyze a user's health data and assess their health status.

[0985] "Health advice" refers to recommendations and guidelines for improving a user's health based on the results analyzed by the generative AI model.

[0986] "Progress" refers to changes and improvements in the user's health condition over time, obtained through regular collection and analysis of health data.

[0987] "Potential risk" refers to the likelihood of future health problems or disorders estimated based on the user's health data.

[0988] This invention relates to a system that analyzes health data collected from a user and provides personalized health advice. The system includes three main components: a user, a terminal, and a server.

[0989] User information collection module

[0990] First, a user launches a dedicated application on a device such as a smartphone or computer, and then creates a new account or logs into an existing account. The device then displays a form for the user to enter health data (e.g., sleep time, diet, exercise habits, etc.). After the user enters the health data and presses the submit button, the device encrypts the data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[0991] Data Storage Module

[0992] The server receives the encrypted data sent from the terminal and first decrypts it. The decrypted data is stored in a database and a response indicating the completion of storage is generated. The server sends this response to the terminal, and the terminal displays a notification to the user confirming the storage.

[0993] Data Analysis Module

[0994] The server inputs the user's health data stored in the database into an AI model. The generative AI model (e.g., a multi-layer neural network using TensorFlow or PyTorch) analyzes the data and evaluates the user's health status. Based on the results of this analysis, it generates personalized health advice for each user.

[0995] Example 1:

[0996] If a user reports exercising three times a week but at a low intensity, the server uses a generative AI model to generate advice such as "recommend increasing exercise intensity."

[0997] Example 2:

[0998] If a user averages 5 hours of sleep, the server uses the generative model to provide a guideline such as "you should aim for 7-8 hours of sleep per day."

[0999] Advice sending module

[1000] The server sends the generated advice to the user. This advice is formatted in JSON format or similar and sent to the device via a secure communication method (e.g., HTTPS). The device displays the received advice to the user, allowing the user to take action to improve their health.

[1001] Regular progress check module

[1002] The server periodically (e.g., weekly) requests the user to provide new health data. The device displays this request as a notification, prompting the user to enter new data. After the user enters and submits the new health data, the server analyzes it again and evaluates the user's progress. Based on this progress evaluation, the server provides more detailed advice and corrections.

[1003] Example 3:

[1004] If the user has been exercising for a month but has not seen any weight loss, the server will recommend that the user "review their diet" or "increase the intensity of their exercise."

[1005] Potential Risk Monitoring Module

[1006] The server continuously monitors the user's health data and evaluates potential health risks. If a risk is detected, the server immediately generates a warning message and countermeasures. These warnings and countermeasures are sent to the user's device, which then notifies the user via an in-app alert or email.

[1007] Example 4:

[1008] If the user has recently been eating a lot of high-calorie food and is gaining weight, the server will warn the user that "if this continues, your cardiovascular risk will increase" and suggest measures to the user, such as "eating more low-calorie meals."

[1009] This system allows users to easily manage their own health and receive necessary advice in a timely manner, and by using this system, effective and safe health management can be achieved without expensive resources.

[1010] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1011] Step 1: Launch the application and log in

[1012] A user launches a dedicated application on a device such as a smartphone or computer. The user creates a new account or logs in to an existing account. The device sends the entered authentication information to the server, which verifies it. If authentication is successful, the server generates an authentication token and sends it to the device.

[1013] Input: User credentials (username, password)

[1014] Output: Authentication token

[1015] Step 2: Enter your health data

[1016] Within the application, the user accesses a form to enter health data (e.g., sleep duration, dietary habits, exercise habits, etc.). The device displays prompts and receives the entered data. Once the user has completed the entry, they press the submit button. The device then encrypts this data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1017] Input: User health data

[1018] Output: Encrypted health data

[1019] Step 3: Receiving and storing data

[1020] The server receives the encrypted data sent from the terminal and first decrypts it. The decrypted data is saved in the database. If the data is saved successfully, the server generates a save completion response and sends it to the terminal. The terminal then displays a save confirmation notification to the user.

[1021] Input: Encrypted health data

[1022] Output: Decrypted data stored in the database, storage completion response

[1023] Step 4: Data analysis

[1024] The server inputs the user's health data stored in the database into a generative AI model. The generative model (e.g., using TensorFlow or PyTorch) analyzes the user's health status. Based on the analysis results, personalized health advice is generated.

[1025] Input: Health data stored in a database

[1026] Output: Health advice

[1027] Step 5: Send advice

[1028] The server sends the generated health advice to the user's device. The advice is formatted in an appropriate format, such as JSON, and transmitted using a secure communication method. The device then displays the received advice to the user.

[1029] Enter: Health Advice

[1030] Output: Advice displayed on the user's terminal

[1031] Step 6: Periodic data requests

[1032] The server periodically (e.g., once a week) sends a request to the user to provide new health data. The device displays this request as a notification, prompting the user to enter new data. When the user enters and submits new health data, the device sends the data to the server.

[1033] Input: Periodic request timer

[1034] Output: Data request displayed to the user, new health data sent to the server

[1035] Step 7: Progress assessment

[1036] The server analyzes the newly received health data and compares it with previous data to assess the user's progress, and provides further detailed advice and corrections based on the assessment results.

[1037] Input: New health data, Previous health data

[1038] Output: Progress evaluation results, improvement advice

[1039] Step 8: Potential Risk Monitoring

[1040] The server continuously monitors the user's health data and evaluates potential health risks. If a risk is detected, it immediately generates a warning message and countermeasures. The warning and countermeasures are sent to the user's device, which then notifies the user.

[1041] Input: User's cumulative health data

[1042] Output: Risk warning message, notification of countermeasures

[1043] In this way, users can receive appropriate advice in real time based on their own health data and manage their health. In addition, regular progress checks and early detection of potential risks enable effective and safe health management.

[1044] (Application example 1)

[1045] 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."

[1046] Conventional health management systems analyze health data provided by users and provide advice on an individual basis, and lack the means to provide real-time health-related information, particularly in collaboration with physical stores. Furthermore, data encryption and regular progress management are insufficient, making it difficult to provide early warnings of health risks and provide specific countermeasures.

[1047] 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.

[1048] In this invention, the server includes means for collecting health data from a user, generative model means for analyzing the health data to evaluate the user's health status, means for generating personalized health advice based on the evaluation results of the generative model means, means for transmitting the health advice to the user's device, means for encrypting the health data and transmitting it to the server, means for displaying the generated advice as a notification on the user's device, and means for periodically requesting new data. This allows the user to understand their own health status in real time and receive personalized advice and product suggestions. Furthermore, data encryption ensures security, enabling regular progress management and early warning of potential health risks.

[1049] "User's health data" refers to information about the user's health, such as their sleep time, eating habits, and exercise habits.

[1050] "Generative model means" refers to means that use AI models or machine learning algorithms to analyze a user's health data and assess their health status.

[1051] "Encryption" refers to the process of converting data using a specific algorithm to make it unreadable to third parties in order to maintain the confidentiality of the information.

[1052] "Personalized health advice" refers to advice for individually optimal health improvement that is generated based on the user's individual health data.

[1053] "Terminal" refers to an electronic device such as a smartphone or tablet used by a user.

[1054] "Physical store" refers to a physically existing store such as a fitness club or drugstore.

[1055] "Real-time" refers to the immediacy of time in which results are obtained as soon as data is generated and processed.

[1056] "Notification" refers to a message function that notifies the user of information through an application.

[1057] "New data request" refers to a request from the server to the user to input new health data at regular intervals.

[1058] "Health risk assessment" refers to predicting possible future health risks based on the user's current health data.

[1059] "Warning and countermeasures" refers to notifying the user of a warning when a health risk is detected and providing specific remedial measures.

[1060] This invention is a system in which a user provides their own health data and receives personalized health advice in real time based on that data. The system includes three main components: a user, a terminal, and a server. Each component is described in detail below.

[1061] User information collection module

[1062] A user launches the application using a device such as a smartphone or tablet. After creating a new account or logging in, the user follows the prompts to enter health data (e.g., sleep time, dietary habits, exercise habits, etc.). The entered data is encrypted and sent to the server.

[1063] Data Storage Module

[1064] The server receives the encrypted data sent by the user, decrypts it, and stores it in the database. If the data is successfully saved, the server sends a save completion response to the device, and the device displays a notification to the user.

[1065] Data Analysis Module

[1066] The saved data is input into a generative AI model on the server. This AI model analyzes the data and evaluates the user's health status. Based on the analysis results, personalized health advice is generated. For example, if a user only exercises twice a week, the server will generate advice such as "Adding one more exercise session would be effective." Also, if the user's sleep time is short at five hours, the server will provide a guideline such as "Aim for 7-8 hours of sleep per day."

[1067] Advice sending module

[1068] The generated advice is sent from the server to the user's device, and the device displays the advice as a notification to the user. The user can then confirm the advice and take action to improve their lifestyle habits.

[1069] Regular progress check module

[1070] The server periodically sends the user a request for new health data. The device displays the request as a notification, and the user enters the new data and sends it to the server. The server analyzes the new data, evaluates progress, and provides further advice or corrections as needed.

[1071] Potential Risk Monitoring Module

[1072] The server continuously monitors the user's health data and evaluates the possibility of a health risk occurring. If a risk is detected, the server immediately generates a warning and sends a warning message and instructions on how to respond to the user's device. The device notifies the user of the warning, allowing the user to confirm countermeasures. For example, if the user has recently been eating a lot of high-calorie food and has gained weight, the server warns that "if this continues, your cardiovascular risk will increase" and suggests countermeasures such as "eating more low-calorie foods."

[1073] Hardware and software used

[1074] Hardware: Smartphones, tablets

[1075] Software: Encryption library (Fernet), data transmission library (requests), generative AI model

[1076] Examples of concrete examples and prompts

[1077] For example, if a user's average sleep time is five hours, the app will provide a guideline such as, "You should aim for seven to eight hours of sleep a day." If the user only exercises twice a week, the app will display real-time advice on their smartphone, such as, "Adding one more exercise session would be beneficial."

[1078] Example prompt sentence:

[1079] Generate personalized health advice based on the following user data:

[1080] Sleep time: 5 hours

[1081] Eating habits: High-calorie diet

[1082] Exercise habits: Once a week

[1083] This allows users to understand their own health condition in real time and receive optimized health advice.

[1084] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1085] Step 1:

[1086] A user uses a smartphone or tablet to launch the application and create a new account or log in. Then, they input their health data (e.g., sleep duration, eating habits, exercise habits, etc.). This data is encrypted on the device. The input data is used by an interface that allows the user to provide specific health information to the device. Specific actions include entering data into an input form and pressing the submit button.

[1087] Input: Health data such as sleep time, eating habits, exercise habits, etc.

[1088] Output: Encrypted health data

[1089] Step 2:

[1090] The device sends encrypted health data to the server using a secure protocol such as HTTPS. The device sends data to the server and manages the transmission status at the same time. Specific operations include the process of sending encrypted data to the server via the network.

[1091] Input: Encrypted health data

[1092] Output: Health data sent to the server

[1093] Step 3:

[1094] The server decrypts the received health data and performs pre-processing to store it in the database, including steps to verify the consistency and integrity of the data. Specific operations include writing the decrypted data to the database.

[1095] Input: Encrypted health data

[1096] Output: Health data stored in a database

[1097] Step 4:

[1098] The server inputs the stored health data into a generative AI model and performs data analysis. The generative AI model analyzes the data and evaluates the user's health condition. Based on the evaluation results, personalized health advice is generated. Specific operations include the process of inputting data into the AI ​​model and obtaining the evaluation results.

[1099] Input: Health data stored in a database

[1100] Output: Analyzed health status assessment and personalized health advice

[1101] Step 5:

[1102] The server sends the generated health advice to the user's device, which then displays the advice to the user as a notification. This process incorporates visual elements based on UI / UX design. Specific operations include displaying the advice notification and prompting confirmation.

[1103] Enter: personalized health advice

[1104] Output: Health advice displayed on the user's device

[1105] Step 6:

[1106] The server periodically sends a request for new data to the user, and the device displays the request as a notification. The user can then enter new health data and send it back to the server. Specific operations include requesting data via notifications and receiving new data from the user.

[1107] Input: New data request

[1108] Output: New health data

[1109] Step 7:

[1110] The server analyzes new health data and periodically evaluates progress, providing further advice and corrections as needed. Specific actions include inputting new data into the generative AI model and evaluating progress.

[1111] Input: New health data

[1112] Output: Progress assessment and additional health advice

[1113] In this way, processing steps across the system work together to enable users to receive personalized health advice on an ongoing basis.

[1114] By clarifying the specific operations and processes of input and output, it becomes possible to concretely understand the actual function of the system.

[1115] 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.

[1116] This invention relates to a system that analyzes health and emotion data collected from a user and provides personalized health and emotion care advice. The system includes four main components: a user, a terminal, a server, and an emotion engine.

[1117] 1. User information collection module

[1118] First, a user launches the application and either creates a new account or logs in. Then, following the prompts within the app, the user inputs and submits health data (e.g., sleep duration, dietary habits, exercise habits, etc.) and emotional data. Emotional data is collected through self-reporting by the user or through automatic identification using an emotion engine.

[1119] 2. Data storage module

[1120] The device encrypts the data and sends it to the server. The server decrypts the received data and stores it in the database. It then sends a save confirmation response to the device, notifying it that the save was successful.

[1121] 3. Data Analysis Module

[1122] The server inputs the stored health and emotional data into the generative model, which analyzes the data and evaluates the user's health and emotional state. Based on the analysis results, the model generates personalized health and emotional care advice.

[1123] Example 1:

[1124] If a user reports exercising three times a week but at a low intensity, the server can use the generative AI model to generate advice such as "Recommend increasing exercise intensity." On the other hand, if emotion data indicates that the user is stressed, the server can also recommend relaxation exercises.

[1125] Example 2:

[1126] If the user averages 5 hours of sleep, the server uses the generative model to provide guidelines such as "You should aim for 7-8 hours of sleep a day." If the emotion data indicates that the user is feeling anxious, the server suggests "practicing relaxation meditation before bed."

[1127] 4. Advice sending module

[1128] The server prepares and sends the generated advice to the user's terminal, which displays the received health and emotional care advice to the user.

[1129] 5. Regular progress check module

[1130] The server periodically (e.g., once a week) sends a request for new data to the user. The device displays this as a notification to the user, prompting them to enter data. The user enters new health and emotional data and sends it to the server via the device. The server analyzes the new data and evaluates progress. Depending on the progress, it provides more detailed advice and corrections.

[1131] Example 3:

[1132] If a user has been exercising for a month but has not seen any weight loss and the emotional data indicates an increase in stress, the server will suggest that the user "reconsider their diet," "increase the intensity of their exercise," and "reconsider their stress management methods."

[1133] 6. Potential Risk Monitoring Module

[1134] The server continuously monitors user data and evaluates the possibility of health and emotional risks occurring. If a risk is detected, the server immediately generates a warning. The server then sends a warning message and instructions on how to respond to the user's device, which then notifies the user of the warning and allows them to confirm countermeasures.

[1135] Example 4:

[1136] If a user has recently been eating a lot of high-calorie food and gaining weight, and emotional data indicates that they are feeling depressed, the server will warn them that "if they continue this way, their cardiovascular risk and mental stress will increase," and suggest measures to the user, such as "eating more low-calorie meals" and "adding mental health exercise."

[1137] In this way, users can easily manage their own health data and emotion data in an integrated manner and receive necessary advice in a timely manner. In addition, by combining it with the emotion engine, users can manage their overall health more comprehensively and effectively.

[1138] The processing flow will be explained below.

[1139] Step 1:

[1140] A user launches the application and creates a new account or logs in. They follow the in-app prompts to enter and submit health data (e.g., sleep duration, dietary habits, exercise habits, etc.) and emotional data (e.g., self-reported or entered into an emotion recognition engine).

[1141] Step 2:

[1142] The device encrypts the transmitted health and emotion data and transmits it to the server, and also notifies the user of the success of the transmission.

[1143] Step 3:

[1144] The server decodes the received health and emotion data and stores it in a database. The server then sends a save confirmation response to the device, notifying the user that the save was successful.

[1145] Step 4:

[1146] The server inputs the stored health and emotional data into a generative model, which analyzes the data and evaluates the user's health and emotional state. Based on the analysis results, the model generates personalized health and emotional care advice.

[1147] Step 5:

[1148] The server sends the generated advice to the user's terminal, which displays the received health and emotional care advice to the user.

[1149] Step 6:

[1150] Users can review the advice and implement it to improve their lifestyle. They can also provide feedback within the app, which will be used for the next analysis.

[1151] Step 7:

[1152] The server periodically (e.g., once a week) sends a request for new data to the user, which the device displays as a notification to the user, prompting them to enter the data.

[1153] Step 8:

[1154] Users input new health and emotional data and send it to the server via their device. The server analyzes the new data and assesses progress. Depending on progress, the server can provide further detailed advice and corrections.

[1155] Step 9:

[1156] The server continuously monitors user data and assesses the likelihood of health and emotional risks occurring. If a risk is detected, the server generates an immediate alert.

[1157] Step 10:

[1158] The server sends a warning message and instructions on how to deal with the problem to the user's device, which then notifies the user of the warning and allows them to confirm the corrective action.

[1159] Step 11:

[1160] The user checks the warning and takes necessary measures, then provides feedback to the server via the app and requests another analysis.

[1161] Example 2

[1162] 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."

[1163] Conventional health management systems primarily provide advice based solely on a user's health data, making it difficult to provide personalized advice that also takes into account the user's emotional state. Furthermore, regular progress checks and monitoring of potential health and emotional risks are insufficient, making it difficult for users to receive appropriate care instructions when needed. Furthermore, the lack of a means to comprehensively evaluate health and emotional data has prevented more comprehensive health management from being realized.

[1164] 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.

[1165] In this invention, the server includes means for collecting health data and emotional data from a user, means for encrypting the health data and emotional data and transmitting the data to the server, means for decrypting the health data and emotional data stored on the server and storing them in a secure database, means for inputting the health data and emotional data into a generative AI model for analysis and evaluating the user's health and emotional state, means for generating personalized health and emotional care advice based on the evaluation results of the generative AI model, and means for transmitting the generated advice to the user's device and displaying it. This allows for integrated management of the user's health data and emotional data, providing more comprehensive and personalized advice, and enabling regular progress checks and monitoring of potential risks.

[1166] "Health data" refers to data used to measure and evaluate a user's health status, such as the user's physical activity level, sleep time, eating habits, and exercise habits.

[1167] "Emotion data" refers to information self-reported by a user about their own emotional state, or data about an emotional state automatically identified by an emotion identification engine.

[1168] A "generative AI model" is an artificial intelligence model that takes health data and emotional data as input, analyzes this data, and evaluates the user's health and emotional state.

[1169] "Encryption" is a technical measure that makes transmitted data unreadable to third parties.

[1170] The "Server" is a device that receives, decodes, stores, and analyzes user-submitted health and emotional data using a generative AI model.

[1171] "Personalized health and emotional care advice" refers to user-specific health and emotional care instructions that are generated based on an analysis of the user's individual health and emotional data.

[1172] A "database" is a collection of health and emotional data stored securely within a server.

[1173] "Progress assessment" is the process of assessing changes in a user's health and emotional state based on new health and emotional data collected periodically from the user.

[1174] "Potential risk" is an assessment result that indicates the possibility of a physical or psychological risk occurring in the future, based on the user's health data and emotional data.

[1175] A "warning" is a message issued to the user to warn them when a potential risk is detected.

[1176] A "countermeasure" is specific instructions or advice provided to a user to address a detected risk.

[1177] This invention relates to a system that analyzes health and emotional data collected from a user and provides personalized health and emotional care advice, which specifically includes four main components: a user, a terminal, a server, and an emotion engine.

[1178] User information collection module

[1179] First, a user launches the application and creates a new account or logs in. Then, following the prompts within the app, the user inputs and submits health data (e.g., sleep duration, dietary habits, exercise habits, etc.) and emotional data. Emotional data is collected through self-reporting by the user or through automatic identification using an emotion engine.

[1180] A specific example would be a user opening a smartphone app and entering the amount of sleep they got last night or their emotional state today. For example, the user might enter "7 hours of sleep" and "stress."

[1181] Data Storage Module

[1182] The device encrypts the data using the TLS encryption protocol and sends it to the server. The server then decrypts the received data and stores it in a secure database. If the data is successfully saved, the server notifies the device, and the device displays a confirmation message to the user.

[1183] As a specific example, the device may display the message "Saving..." and after the data has been successfully saved, display a notification to the user saying "Data saved successfully."

[1184] Data Analysis Module

[1185] The server inputs the stored health and emotional data into a generative AI model (such as OpenAI's GPT-4). The generative AI model analyzes this data and evaluates the user's health and emotional state. The server generates personalized health and emotional care advice based on the analysis results.

[1186] As a specific example, if you input data such as "The user exercises three times a week, but at a low intensity," the system will generate advice such as "Increase the intensity of your exercise."

[1187] Advice sending module

[1188] The server prepares the generated advice and sends it to the terminal, which receives it and displays it to the user in an appropriate form.

[1189] For example, advice such as "You should aim to sleep 7-8 hours a day" is generated and sent to the device, which then displays the message "Recommended sleep time: 7-8 hours a day" to the user.

[1190] Regular progress check module

[1191] The server periodically sends a request for new data to the device. The device displays the request as a notification to the user, prompting them to enter data. The user enters new health and emotional data and sends it to the server via the device. The server analyzes the new data and evaluates progress. Based on the analysis results, the server provides detailed advice and corrections.

[1192] As a specific example, the server sends a request to the terminal saying "Please enter new data," and the terminal displays a notification to the user saying "Please enter new health data."

[1193] Potential Risk Monitoring Module

[1194] The server continuously monitors user data and evaluates the likelihood of health and emotional risks. If a risk is detected, the server immediately generates an alert and sends a warning message and instructions on how to respond to the device. The device then notifies the user of the alert and prompts them to take action.

[1195] For example, the server receives data such as "You have recently increased your intake of high-calorie meals and have been experiencing depression," generates a warning that "Your cardiovascular risk and mental stress may be increasing," and sends it to the device. The device then notifies the user to "increase your intake of low-calorie meals and add mental health exercises."

[1196] Prompt Sentence Examples

[1197] "The user exercises at a low intensity three times a week, but is feeling stressed. Please generate appropriate health advice."

[1198] "The user's average sleep time is 5 hours and they are feeling anxious. Please generate appropriate health advice."

[1199] In this way, this system can comprehensively manage the user's health and emotional data, providing more comprehensive and personalized advice. It also allows users to regularly check their progress and monitor potential risks, allowing them to manage their health with peace of mind.

[1200] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1201] Program processing flow

[1202] Step 1: Collect user information

[1203] 1. The user launches the application and either creates a new account or logs in.

[1204] Input: Account information (username, password, etc.)

[1205] Output: Authentication status (login success / failure)

[1206] Specific operation: The user opens the smartphone app and enters their username and password on the login screen.

[1207] 2. The user follows the prompts in the app to enter and submit their health and emotional data.

[1208] Input: Health data (e.g., sleep time, dietary habits, exercise habits), emotional data (e.g., stress level)

[1209] Output: Sending status (success / failure)

[1210] Specific operation: The user enters various health and emotional data into the form and presses the "Submit" button.

[1211] Step 2: Data encryption and transmission

[1212] 1. The device encrypts the transmitted data using the TLS encryption protocol.

[1213] Input: Health and emotion data entered by the user

[1214] Output: Encrypted data

[1215] Specific operation: The terminal encrypts the input data using the encryption library.

[1216] 2. The device sends the encrypted data to the server.

[1217] Input: Encrypted data

[1218] Output: Sending status (success / failure)

[1219] Specific operation: The device sends the encrypted data to the server using an HTTP POST request.

[1220] Step 3: Receiving and storing data

[1221] 1. The server decrypts the received data.

[1222] Input: Encrypted data

[1223] Output: raw decrypted data

[1224] What happens: The server uses a decryption library to decrypt the data.

[1225] 2. The server stores the decrypted data in a secure database.

[1226] Input: raw decrypted data

[1227] Output: Save status (save successful / failed)

[1228] Specific behavior: The server saves the data via the database connection and returns the save status.

[1229] Step 4: Data analysis

[1230] 1. The server inputs the stored health and emotion data into the generative AI model.

[1231] Input: Health and emotion data stored in a database

[1232] Output: Data analysis results

[1233] Specific operation: The server inputs data into the API of the generative AI model and performs analysis.

[1234] 2. The generative AI model analyzes the data and the server assesses the user's health and emotional state.

[1235] Input: Health and emotion data

[1236] Output: Health and emotional state assessment results

[1237] Specific operation: The generative AI model analyzes the data and returns the evaluation results to the server.

[1238] Step 5: Advice generation and sending

[1239] 1. The server generates personalized health and emotional care advice based on the analysis results.

[1240] Input: Health and emotional state assessment results

[1241] Output: Personalized advice

[1242] Specific operation: The server generates advice based on the analysis results of the generative AI model.

[1243] 2. The server sends the generated advice to the device.

[1244] Enter: personalized advice

[1245] Output: Sending status (success / failure)

[1246] Specific operation: The server sends the advice to the device using an HTTP POST request.

[1247] 3. The device receives the advice and displays it to the user.

[1248] Input: Advice received

[1249] Output: Advice displayed

[1250] Specific operation: The terminal application notifies and displays the advice to the user.

[1251] Step 6: Regular progress checks

[1252] 1. The server periodically sends a request for new data to the device.

[1253] Input: Request Send Schedule

[1254] Output: New data request

[1255] Specific operation: The server sends requests to the terminal based on a schedule.

[1256] 2. The device displays a notification to the user, prompting them to enter data.

[1257] Input: New data request

[1258] Output: Data entry prompt notification

[1259] Specific behavior: The terminal notifies the user, "Please enter new data."

[1260] 3. The user inputs new health and emotion data and sends it to the server via the terminal.

[1261] Input: New health and emotion data

[1262] Output: New data submission status

[1263] What happens: The user enters new data and presses the submit button.

[1264] 4. The server analyzes the new data and assesses progress.

[1265] Input: New health and emotion data

[1266] Output: Progress evaluation results

[1267] What it does: The server feeds new data into the generative AI model and evaluates progress.

[1268] 5. The server will provide detailed advice and corrections based on your progress.

[1269] Input: Progress evaluation results

[1270] Output: Detailed advice and fixes

[1271] Specific operation: The server generates new advice based on the evaluation results and sends it to the device.

[1272] Step 7: Potential Risk Monitoring

[1273] 1. The server continuously monitors user data and assesses health and emotional risks.

[1274] Input: Continuously monitored health and emotional data

[1275] Output: Risk assessment results

[1276] What it does: The server periodically checks the data in the database and assesses the risk.

[1277] 2. If a risk is detected, the server generates an immediate alert.

[1278] Input: Risk assessment results

[1279] Output: Warning message

[1280] Specific operation: The server generates a warning based on the risk assessment results.

[1281] 3. The server sends a warning message and instructions on how to respond to the device.

[1282] Input: warning message

[1283] Output: Sending status (success / failure)

[1284] Specific operation: The server sends a warning message to the device using an HTTP POST request.

[1285] 4. The device notifies the user of the warning.

[1286] Input: Received warning message

[1287] Output: The displayed warning

[1288] Specific behavior: The device displays a warning message to the user, informing them that "continuing this behavior will increase cardiovascular risk and mental stress," and suggesting countermeasures such as "increasing low-calorie meals."

[1289] Through these steps, the system can comprehensively manage the user's health and emotional data, provide personalized advice, and periodically check progress and monitor potential risks, allowing users to manage their health with peace of mind.

[1290] (Application example 2)

[1291] 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."

[1292] Conventional health management systems only provide advice based on a user's physical health data, and therefore are unable to provide comprehensive care that takes into account the user's mental health, such as emotional state and stress level. Furthermore, they lack support for users to continuously manage their health, with insufficient regular progress checks and monitoring of potential risks. This increases the burden on users to manage their health themselves, and prevents them from receiving effective healthcare.

[1293] 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.

[1294] In this invention, the server includes a means for collecting health data and emotional data from a user, a generative model means for analyzing the health data and emotional data to evaluate the user's health and emotional state, and a means for generating personalized health and emotional care advice based on the evaluation results of the generative model means. This enables comprehensive management of the user's physical health and emotional state and provides appropriate advice. Furthermore, by regularly collecting data, evaluating progress, and monitoring potential risks, the user can receive support for maintaining a long-term balance between health and emotions.

[1295] A "means for collecting health and emotional data from a user" is a technology or device that receives and collects information about the health and emotional state input or provided by a user.

[1296] "Generative model means for analyzing health data and emotional data to assess a user's health and emotional state" refers to a technology that uses an algorithm or AI model to analyze a user's physical and mental health state based on the collected data and evaluates that state quantitatively or qualitatively.

[1297] The "means for generating personalized health and emotional care advice based on the evaluation results of the generative model means" is a technology for creating optimal health management and emotional care advice for individual users from the analysis results.

[1298] "Means for sending health and emotional care advice to a user's device" refers to technology that sends the generated advice to the user's device (smartphone, tablet, PC, etc.) and makes it viewable.

[1299] "Means for periodically collecting the user's health and emotional data and evaluating progress" refers to technology that collects new data from the user at regular intervals and analyzes and evaluates the progress and degree of improvement.

[1300] "Means for assessing potential health and emotional risks based on a user's health and emotional data and generating warnings and countermeasures" refers to technology that analyzes health and emotional risks that may occur in the future from collected data and provides warnings and countermeasures for those risks.

[1301] MODE FOR CARRYING OUT THE INVENTION

[1302] System Overview

[1303] This invention relates to a system that collects a user's health and emotional data and provides personalized health and emotional care advice based on the analysis results. The system is implemented by combining a user's device, a server, and a generative AI model.

[1304] Generating a Program

[1305] The system works in the following steps: The user's device collects health and emotional data, encrypts it, and sends it to the server. The server receives the data and inputs it into the generative AI model for analysis. Based on the analysis results, the server generates personalized advice and sends it to the user's device. The user's device displays the received advice to the user.

[1306] Natural language explanation

[1307] User's device

[1308] The user's device (smartphone, tablet, PC, etc.) collects health data (e.g., sleep time, exercise frequency, dietary content, etc.) and emotional data (e.g., stress level, mood) from the user through a dedicated application. The collected data is encrypted using methods such as AES encryption to maintain security.

[1309] Server and Generative AI Model

[1310] The server receives encrypted data from the user's device. The data is decrypted and stored in a database. The stored data is then input into a generative AI model implemented in Python. The model analyzes the data using frameworks such as TensorFlow and PyTorch. The analysis results provide an assessment of the user's current health and emotional state and generate personalized health and emotional care advice.

[1311] Providing advice

[1312] The server sends the generated advice to the user's device, which receives it and displays it on the application, allowing the user to take specific actions to put into practice in their daily lives.

[1313] Specific examples

[1314] For example, if a user inputs "average sleep time 5 hours," "low exercise intensity," and "stress level 7," the server will generate advice such as "get more sleep" and "increase exercise intensity" and notify the user.

[1315] Prompt Sentence Examples

[1316] User: "I average five hours of sleep and exercise three days a week, but the intensity is low. I've also been feeling a lot of stress lately."

[1317] System: "I recommend trying to increase your average sleep time to 7-8 hours and increasing your exercise intensity to a moderate level. Also, try relaxation techniques to manage stress."

[1318] This invention allows users to manage comprehensive health and emotional data in an integrated manner, receive personalized advice, and provide support for maintaining long-term health and emotional balance through regular data collection, progress evaluation, and monitoring of potential risks.

[1319] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1320] Step 1:

[1321] The user's device collects health and emotional data from the user through a dedicated application, including sleep duration, exercise frequency, dietary intake, stress level, and mood. The user enters this data into an input form, or the application automatically collects the data from sensors and other devices.

[1322] Input: Health data, emotion data

[1323] Output: Collected data

[1324] Step 2:

[1325] The device encrypts the collected data using a method such as AES encryption, which ensures data security, and then sends the encrypted data to the server.

[1326] Input: Collected data

[1327] Output: Encrypted data

[1328] Step 3:

[1329] The server receives the encrypted data sent from the device and decrypts it. The decrypted data is stored in a database. The server prepares the stored data for analysis.

[1330] Input: Encrypted data

[1331] Output: Decrypted data

[1332] Step 4:

[1333] The server then feeds the decoded data into a generative AI model, which uses frameworks such as TensorFlow and PyTorch to analyze the data and assess the user's health and emotional state.

[1334] Input: Decrypted data

[1335] Output: Analysis results

[1336] Step 5:

[1337] The server generates personalized health and emotional care advice based on the analysis results, including specific instructions for action and suggestions for improvement for the user.

[1338] Input: Analysis results

[1339] Output: Personalized advice

[1340] Step 6:

[1341] The server transmits the generated advice to the user's terminal.

[1342] Input: Personalized advice

[1343] Output: Advice sent

[1344] Step 7:

[1345] The user's device displays the received advice, and the application presents the advice to the user in a visually easy-to-understand manner. The user can use this advice to adjust their daily activities.

[1346] Input: Submitted advice

[1347] Output: Advice displayed

[1348] Step 8:

[1349] At regular intervals, the server requests new data from the user. This request appears as a notification on the user's device. The user enters new health and emotional data, which is then sent back to the server. This process continues, periodically assessing the user's progress.

[1350] Input: New health and emotion data

[1351] Output: Continuous progress assessment

[1352] Step 9:

[1353] The server continuously monitors user data and evaluates the likelihood of potential health or emotional risks. If a risk is detected, the server immediately generates a warning message and instructions for response and sends them to the user's device. The user's device displays the warning and the user confirms the measures.

[1354] Input: Continuously collected data

[1355] Output: Warnings and actions to take

[1356] 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.

[1357] 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.

[1358] 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.

[1359] [Fourth embodiment]

[1360] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1361] 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.

[1362] 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).

[1363] 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.

[1364] 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.

[1365] 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).

[1366] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for 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.

[1367] 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.

[1368] 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.

[1369] 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.

[1370] 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.

[1371] 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.

[1372] 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."

[1373] This invention relates to a system that analyzes health data collected from a user and provides personalized health advice. The system includes three main components: a user, a terminal, and a server.

[1374] 1. User information collection module

[1375] First, the user launches the application and creates a new account or logs in. The device prompts the user and provides a form where they can enter their health data (e.g., sleep time, dietary habits, exercise habits, etc.). The user enters the data according to the form and submits it. The device encrypts the submitted data and sends it to the server.

[1376] 2. Data storage module

[1377] The server decrypts the received data, stores it in the database, and sends a save confirmation response to the terminal. The terminal then confirms that the data has been successfully saved and notifies the user.

[1378] 3. Data Analysis Module

[1379] The server inputs the stored data into a generative model, which then analyzes the data and evaluates the user's health status. Based on the analysis results, the model generates personalized health advice.

[1380] Example 1:

[1381] If a user reports exercising three times a week but at a low intensity, the server uses the generative AI model to generate advice such as "Recommend to increase exercise intensity."

[1382] Example 2:

[1383] If a user averages 5 hours of sleep, the server uses the generative model to provide guidelines such as "you should aim for 7-8 hours of sleep per day."

[1384] 4. Advice sending module

[1385] The server prepares the generated advice and sends it to the user's device. The device displays the received advice to the user, who then confirms the advice and takes action to improve their lifestyle habits.

[1386] 5. Regular progress check module

[1387] The server periodically (e.g., once a week) sends a request for new data to the user. The device displays this as a notification to the user, prompting them to enter data. The user enters the new data and sends it to the server via the device. The server analyzes the new data and evaluates progress. Depending on the progress, the server can provide further advice and corrections.

[1388] Example 3:

[1389] If the user has been exercising for a month but has not seen any weight loss, the server will recommend that the user "review their diet" or "increase the intensity of their exercise."

[1390] 6. Potential Risk Monitoring Module

[1391] The server continuously monitors user data and evaluates the possibility of health risks occurring. If a risk is detected, the server immediately generates a warning. The server then sends a warning message and instructions on how to respond to the user's device, and the device notifies the user of the warning so that they can confirm the appropriate measures.

[1392] Example 4:

[1393] If the user has recently been eating a lot of high-calorie food and is gaining weight, the server will warn the user that "if this continues, your cardiovascular risk will increase" and suggest measures to the user, such as "eating more low-calorie meals."

[1394] In this way, users can easily manage their own health and receive necessary advice in a timely manner. This system also enables users who do not have abundant resources to obtain effective health advice.

[1395] The processing flow will be explained below.

[1396] Step 1:

[1397] The user launches the application and either creates a new account or logs in. The user follows the in-app prompts to enter and submit health data (e.g., sleep duration, dietary habits, exercise habits, etc.).

[1398] Step 2:

[1399] The terminal encrypts the transmitted data and sends it to the server, and also notifies the user of the success of the transmission.

[1400] Step 3:

[1401] The server decrypts the received data and stores it in the database. The server then sends a save confirmation response to the terminal to confirm that the save was successful.

[1402] Step 4:

[1403] The server inputs the stored data into a generative model, which analyzes the data, evaluates the user's health status, and generates personalized health advice based on the analysis results.

[1404] Step 5:

[1405] The server prepares the generated advice and sends it to the user's terminal, which displays the received advice to the user.

[1406] Step 6:

[1407] Users review the advice, implement it to improve their lifestyle habits, and provide feedback within the app if needed.

[1408] Step 7:

[1409] The server periodically (e.g., once a week) sends a request for new data to the user, which the device displays as a notification to the user, prompting them to enter the data.

[1410] Step 8:

[1411] The user enters new data and sends it through the terminal to the server, which analyzes the new data and assesses progress. Depending on progress, the server can provide further advice and corrections.

[1412] Step 9:

[1413] The server continuously monitors user data and assesses potential health risks. If a risk is detected, the server generates an immediate alert.

[1414] Step 10:

[1415] The server sends a warning message and instructions on how to deal with the problem to the user's device, which then notifies the user of the warning and allows them to confirm the corrective action.

[1416] Step 11:

[1417] The user checks the warning and takes necessary measures, then provides feedback to the server via the app and requests another analysis.

[1418] Example 1

[1419] 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."

[1420] In modern society, it is important for individuals to properly understand and manage their own health status, but in many cases, specialized knowledge and resources are required, and there is a lack of easy ways to collect information and receive effective health advice. Furthermore, there are challenges in protecting the security and privacy of collected health data, as well as regular progress checks and early detection of potential risks.

[1421] 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.

[1422] In this invention, the server includes: means for collecting health data from a user; means for encrypting the health data using a generating prompt and transmitting it to the server; server means for decrypting the encrypted health data and storing it in a database; generative AI model means for analyzing the health data to evaluate the user's health status; means for generating personalized health advice based on the evaluation results of the generative AI model means; means for transmitting the generated health advice to the user's terminal; and terminal means for displaying the transmitted health advice to the user. This enables effective and safe health management for users without specialized knowledge or resources. It also enables regular progress checks and early detection of potential risks.

[1423] "User" refers to an individual who uses the system to input their own health data and receive analysis results and advice.

[1424] "Device" refers to the device used by the user (e.g., smartphone, tablet, computer, etc.) to input health data and display advice.

[1425] A "server" is a computer system that receives, analyzes, and stores input health data and transmits generated advice to the user.

[1426] "Health Data" refers to information related to a user's health (e.g., sleep duration, diet, exercise habits, etc.) entered by the user.

[1427] "Generated prompts" are guides or hints used by users when entering health data.

[1428] "Encryption" refers to the process of converting health data sent from a device into a secure format to protect it from unauthorized access.

[1429] "Database" means a data repository where the health data received by the server is systematically stored and used for later analysis.

[1430] A "generative AI model" is an artificial intelligence model used to analyze a user's health data and assess their health status.

[1431] "Health advice" refers to recommendations and guidelines for improving a user's health based on the results analyzed by the generative AI model.

[1432] "Progress" refers to changes and improvements in the user's health condition over time, obtained through regular collection and analysis of health data.

[1433] "Potential risk" refers to the likelihood of future health problems or disorders estimated based on the user's health data.

[1434] This invention relates to a system that analyzes health data collected from a user and provides personalized health advice. The system includes three main components: a user, a terminal, and a server.

[1435] User information collection module

[1436] First, a user launches a dedicated application on a device such as a smartphone or computer, and then creates a new account or logs into an existing account. The device then displays a form for the user to enter health data (e.g., sleep time, diet, exercise habits, etc.). After the user enters the health data and presses the submit button, the device encrypts the data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1437] Data Storage Module

[1438] The server receives the encrypted data sent from the terminal and first decrypts it. The decrypted data is stored in a database and a response indicating the completion of storage is generated. The server sends this response to the terminal, and the terminal displays a notification to the user confirming the storage.

[1439] Data Analysis Module

[1440] The server inputs the user's health data stored in the database into an AI model. The generative AI model (e.g., a multi-layer neural network using TensorFlow or PyTorch) analyzes the data and evaluates the user's health status. Based on the results of this analysis, it generates personalized health advice for each user.

[1441] Example 1:

[1442] If a user reports exercising three times a week but at a low intensity, the server uses a generative AI model to generate advice such as "recommend increasing exercise intensity."

[1443] Example 2:

[1444] If a user averages 5 hours of sleep, the server uses the generative model to provide a guideline such as "you should aim for 7-8 hours of sleep per day."

[1445] Advice sending module

[1446] The server sends the generated advice to the user. This advice is formatted in JSON format or similar and sent to the device via a secure communication method (e.g., HTTPS). The device displays the received advice to the user, allowing the user to take action to improve their health.

[1447] Regular progress check module

[1448] The server periodically (e.g., weekly) requests the user to provide new health data. The device displays this request as a notification, prompting the user to enter new data. After the user enters and submits the new health data, the server analyzes it again and evaluates the user's progress. Based on this progress evaluation, the server provides more detailed advice and corrections.

[1449] Example 3:

[1450] If the user has been exercising for a month but has not seen any weight loss, the server will recommend that the user "review their diet" or "increase the intensity of their exercise."

[1451] Potential Risk Monitoring Module

[1452] The server continuously monitors the user's health data and evaluates potential health risks. If a risk is detected, the server immediately generates a warning message and countermeasures. These warnings and countermeasures are sent to the user's device, which then notifies the user via an in-app alert or email.

[1453] Example 4:

[1454] If the user has recently been eating a lot of high-calorie food and is gaining weight, the server will warn the user that "if this continues, your cardiovascular risk will increase" and suggest measures to the user, such as "eating more low-calorie meals."

[1455] This system allows users to easily manage their own health and receive necessary advice in a timely manner, and by using this system, effective and safe health management can be achieved without expensive resources.

[1456] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1457] Step 1: Launch the application and log in

[1458] A user launches a dedicated application on a device such as a smartphone or computer. The user creates a new account or logs in to an existing account. The device sends the entered authentication information to the server, which verifies it. If authentication is successful, the server generates an authentication token and sends it to the device.

[1459] Input: User credentials (username, password)

[1460] Output: Authentication token

[1461] Step 2: Enter your health data

[1462] Within the application, the user accesses a form to enter health data (e.g., sleep duration, dietary habits, exercise habits, etc.). The device displays prompts and receives the entered data. Once the user has completed the entry, they press the submit button. The device then encrypts this data and sends it to the server using a secure communication protocol (e.g., HTTPS).

[1463] Input: User health data

[1464] Output: Encrypted health data

[1465] Step 3: Receiving and storing data

[1466] The server receives the encrypted data sent from the terminal and first decrypts it. The decrypted data is saved in the database. If the data is saved successfully, the server generates a save completion response and sends it to the terminal. The terminal then displays a save confirmation notification to the user.

[1467] Input: Encrypted health data

[1468] Output: Decrypted data stored in the database, storage completion response

[1469] Step 4: Data analysis

[1470] The server inputs the user's health data stored in the database into a generative AI model. The generative model (e.g., using TensorFlow or PyTorch) analyzes the user's health status. Based on the analysis results, personalized health advice is generated.

[1471] Input: Health data stored in a database

[1472] Output: Health advice

[1473] Step 5: Send advice

[1474] The server sends the generated health advice to the user's device. The advice is formatted in an appropriate format, such as JSON, and transmitted using a secure communication method. The device then displays the received advice to the user.

[1475] Enter: Health Advice

[1476] Output: Advice displayed on the user's terminal

[1477] Step 6: Periodic data requests

[1478] The server periodically (e.g., once a week) sends a request to the user to provide new health data. The device displays this request as a notification, prompting the user to enter new data. When the user enters and submits new health data, the device sends the data to the server.

[1479] Input: Periodic request timer

[1480] Output: Data request displayed to the user, new health data sent to the server

[1481] Step 7: Progress assessment

[1482] The server analyzes the newly received health data and compares it with previous data to assess the user's progress, and provides further detailed advice and corrections based on the assessment results.

[1483] Input: New health data, Previous health data

[1484] Output: Progress evaluation results, improvement advice

[1485] Step 8: Potential Risk Monitoring

[1486] The server continuously monitors the user's health data and evaluates potential health risks. If a risk is detected, it immediately generates a warning message and countermeasures. The warning and countermeasures are sent to the user's device, which then notifies the user.

[1487] Input: User's cumulative health data

[1488] Output: Risk warning message, notification of countermeasures

[1489] In this way, users can receive appropriate advice in real time based on their own health data and manage their health. In addition, regular progress checks and early detection of potential risks enable effective and safe health management.

[1490] (Application example 1)

[1491] 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."

[1492] Conventional health management systems analyze health data provided by users and provide advice on an individual basis, and lack the means to provide real-time health-related information, particularly in collaboration with physical stores. Furthermore, data encryption and regular progress management are insufficient, making it difficult to provide early warnings of health risks and provide specific countermeasures.

[1493] 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.

[1494] In this invention, the server includes means for collecting health data from a user, generative model means for analyzing the health data to evaluate the user's health status, means for generating personalized health advice based on the evaluation results of the generative model means, means for transmitting the health advice to the user's device, means for encrypting the health data and transmitting it to the server, means for displaying the generated advice as a notification on the user's device, and means for periodically requesting new data. This allows the user to understand their own health status in real time and receive personalized advice and product suggestions. Furthermore, data encryption ensures security, enabling regular progress management and early warning of potential health risks.

[1495] "User's health data" refers to information about the user's health, such as their sleep time, eating habits, and exercise habits.

[1496] "Generative model means" refers to means that use AI models or machine learning algorithms to analyze a user's health data and assess their health status.

[1497] "Encryption" refers to the process of converting data using a specific algorithm to make it unreadable to third parties in order to maintain the confidentiality of the information.

[1498] "Personalized health advice" refers to advice for individually optimal health improvement that is generated based on the user's individual health data.

[1499] "Terminal" refers to an electronic device such as a smartphone or tablet used by a user.

[1500] "Physical store" refers to a physically existing store such as a fitness club or drugstore.

[1501] "Real-time" refers to the immediacy of time in which results are obtained as soon as data is generated and processed.

[1502] "Notification" refers to a message function that notifies the user of information through an application.

[1503] "New data request" refers to a request from the server to the user to input new health data at regular intervals.

[1504] "Health risk assessment" refers to predicting possible future health risks based on the user's current health data.

[1505] "Warning and countermeasures" refers to notifying the user of a warning when a health risk is detected and providing specific remedial measures.

[1506] This invention is a system in which a user provides their own health data and receives personalized health advice in real time based on that data. The system includes three main components: a user, a terminal, and a server. Each component is described in detail below.

[1507] User information collection module

[1508] A user launches the application using a device such as a smartphone or tablet. After creating a new account or logging in, the user follows the prompts to enter health data (e.g., sleep time, dietary habits, exercise habits, etc.). The entered data is encrypted and sent to the server.

[1509] Data Storage Module

[1510] The server receives the encrypted data sent by the user, decrypts it, and stores it in the database. If the data is successfully saved, the server sends a save completion response to the device, and the device displays a notification to the user.

[1511] Data Analysis Module

[1512] The saved data is input into a generative AI model on the server. This AI model analyzes the data and evaluates the user's health status. Based on the analysis results, personalized health advice is generated. For example, if a user only exercises twice a week, the server will generate advice such as "Adding one more exercise session would be effective." Also, if the user's sleep time is short at five hours, the server will provide a guideline such as "Aim for 7-8 hours of sleep per day."

[1513] Advice sending module

[1514] The generated advice is sent from the server to the user's device, and the device displays the advice as a notification to the user. The user can then confirm the advice and take action to improve their lifestyle habits.

[1515] Regular progress check module

[1516] The server periodically sends the user a request for new health data. The device displays the request as a notification, and the user enters the new data and sends it to the server. The server analyzes the new data, evaluates progress, and provides further advice or corrections as needed.

[1517] Potential Risk Monitoring Module

[1518] The server continuously monitors the user's health data and evaluates the possibility of a health risk occurring. If a risk is detected, the server immediately generates a warning and sends a warning message and instructions on how to respond to the user's device. The device notifies the user of the warning, allowing the user to confirm countermeasures. For example, if the user has recently been eating a lot of high-calorie food and has gained weight, the server warns that "if this continues, your cardiovascular risk will increase" and suggests countermeasures such as "eating more low-calorie foods."

[1519] Hardware and software used

[1520] Hardware: Smartphones, tablets

[1521] Software: Encryption library (Fernet), data transmission library (requests), generative AI model

[1522] Examples of concrete examples and prompts

[1523] For example, if a user's average sleep time is five hours, the app will provide a guideline such as, "You should aim for seven to eight hours of sleep a day." If the user only exercises twice a week, the app will display real-time advice on their smartphone, such as, "Adding one more exercise session would be beneficial."

[1524] Example prompt sentence:

[1525] Generate personalized health advice based on the following user data:

[1526] Sleep time: 5 hours

[1527] Eating habits: High-calorie diet

[1528] Exercise habits: Once a week

[1529] This allows users to understand their own health condition in real time and receive optimized health advice.

[1530] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1531] Step 1:

[1532] A user uses a smartphone or tablet to launch the application and create a new account or log in. Then, they input their health data (e.g., sleep duration, eating habits, exercise habits, etc.). This data is encrypted on the device. The input data is used by an interface that allows the user to provide specific health information to the device. Specific actions include entering data into an input form and pressing the submit button.

[1533] Input: Health data such as sleep time, eating habits, exercise habits, etc.

[1534] Output: Encrypted health data

[1535] Step 2:

[1536] The device sends encrypted health data to the server using a secure protocol such as HTTPS. The device sends data to the server and manages the transmission status at the same time. Specific operations include the process of sending encrypted data to the server via the network.

[1537] Input: Encrypted health data

[1538] Output: Health data sent to the server

[1539] Step 3:

[1540] The server decrypts the received health data and performs pre-processing to store it in the database, including steps to verify the consistency and integrity of the data. Specific operations include writing the decrypted data to the database.

[1541] Input: Encrypted health data

[1542] Output: Health data stored in a database

[1543] Step 4:

[1544] The server inputs the stored health data into a generative AI model and performs data analysis. The generative AI model analyzes the data and evaluates the user's health condition. Based on the evaluation results, personalized health advice is generated. Specific operations include the process of inputting data into the AI ​​model and obtaining the evaluation results.

[1545] Input: Health data stored in a database

[1546] Output: Analyzed health status assessment and personalized health advice

[1547] Step 5:

[1548] The server sends the generated health advice to the user's device, which then displays the advice to the user as a notification. This process incorporates visual elements based on UI / UX design. Specific operations include displaying the advice notification and prompting confirmation.

[1549] Enter: personalized health advice

[1550] Output: Health advice displayed on the user's device

[1551] Step 6:

[1552] The server periodically sends a request for new data to the user, and the device displays the request as a notification. The user can then enter new health data and send it back to the server. Specific operations include requesting data via notifications and receiving new data from the user.

[1553] Input: New data request

[1554] Output: New health data

[1555] Step 7:

[1556] The server analyzes new health data and periodically evaluates progress, providing further advice and corrections as needed. Specific actions include inputting new data into the generative AI model and evaluating progress.

[1557] Input: New health data

[1558] Output: Progress assessment and additional health advice

[1559] In this way, processing steps across the system work together to enable users to receive personalized health advice on an ongoing basis.

[1560] By clarifying the specific operations and processes of input and output, it becomes possible to concretely understand the actual function of the system.

[1561] 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.

[1562] This invention relates to a system that analyzes health and emotion data collected from a user and provides personalized health and emotion care advice. The system includes four main components: a user, a terminal, a server, and an emotion engine.

[1563] 1. User information collection module

[1564] First, a user launches the application and either creates a new account or logs in. Then, following the prompts within the app, the user inputs and submits health data (e.g., sleep duration, dietary habits, exercise habits, etc.) and emotional data. Emotional data is collected through self-reporting by the user or through automatic identification using an emotion engine.

[1565] 2. Data storage module

[1566] The device encrypts the data and sends it to the server. The server decrypts the received data and stores it in the database. It then sends a save confirmation response to the device, notifying it that the save was successful.

[1567] 3. Data Analysis Module

[1568] The server inputs the stored health and emotional data into the generative model, which analyzes the data and evaluates the user's health and emotional state. Based on the analysis results, the model generates personalized health and emotional care advice.

[1569] Example 1:

[1570] If a user reports exercising three times a week but at a low intensity, the server can use the generative AI model to generate advice such as "Recommend increasing exercise intensity." On the other hand, if emotion data indicates that the user is stressed, the server can also recommend relaxation exercises.

[1571] Example 2:

[1572] If the user averages 5 hours of sleep, the server uses the generative model to provide guidelines such as "You should aim for 7-8 hours of sleep a day." If the emotion data indicates that the user is feeling anxious, the server suggests "practicing relaxation meditation before bed."

[1573] 4. Advice sending module

[1574] The server prepares and sends the generated advice to the user's terminal, which displays the received health and emotional care advice to the user.

[1575] 5. Regular progress check module

[1576] The server periodically (e.g., once a week) sends a request for new data to the user. The device displays this as a notification to the user, prompting them to enter data. The user enters new health and emotional data and sends it to the server via the device. The server analyzes the new data and evaluates progress. Depending on the progress, it provides more detailed advice and corrections.

[1577] Example 3:

[1578] If a user has been exercising for a month but has not seen any weight loss and the emotional data indicates an increase in stress, the server will suggest that the user "reconsider their diet," "increase the intensity of their exercise," and "reconsider their stress management methods."

[1579] 6. Potential Risk Monitoring Module

[1580] The server continuously monitors user data and evaluates the possibility of health and emotional risks occurring. If a risk is detected, the server immediately generates a warning. The server then sends a warning message and instructions on how to respond to the user's device, which then notifies the user of the warning and allows them to confirm countermeasures.

[1581] Example 4:

[1582] If a user has recently been eating a lot of high-calorie food and gaining weight, and emotional data indicates that they are feeling depressed, the server will warn them that "if they continue this way, their cardiovascular risk and mental stress will increase," and suggest measures to the user, such as "eating more low-calorie meals" and "adding mental health exercise."

[1583] In this way, users can easily manage their own health data and emotion data in an integrated manner and receive necessary advice in a timely manner. In addition, by combining it with the emotion engine, users can manage their overall health more comprehensively and effectively.

[1584] The processing flow will be explained below.

[1585] Step 1:

[1586] A user launches the application and creates a new account or logs in. They follow the in-app prompts to enter and submit health data (e.g., sleep duration, dietary habits, exercise habits, etc.) and emotional data (e.g., self-reported or entered into an emotion recognition engine).

[1587] Step 2:

[1588] The device encrypts the transmitted health and emotion data and transmits it to the server, and also notifies the user of the success of the transmission.

[1589] Step 3:

[1590] The server decodes the received health and emotion data and stores it in a database. The server then sends a save confirmation response to the device, notifying the user that the save was successful.

[1591] Step 4:

[1592] The server inputs the stored health and emotional data into a generative model, which analyzes the data and evaluates the user's health and emotional state. Based on the analysis results, the model generates personalized health and emotional care advice.

[1593] Step 5:

[1594] The server sends the generated advice to the user's terminal, which displays the received health and emotional care advice to the user.

[1595] Step 6:

[1596] Users can review the advice and implement it to improve their lifestyle. They can also provide feedback within the app, which will be used for the next analysis.

[1597] Step 7:

[1598] The server periodically (e.g., once a week) sends a request for new data to the user, which the device displays as a notification to the user, prompting them to enter the data.

[1599] Step 8:

[1600] Users input new health and emotional data and send it to the server via their device. The server analyzes the new data and assesses progress. Depending on progress, the server can provide further detailed advice and corrections.

[1601] Step 9:

[1602] The server continuously monitors user data and assesses the likelihood of health and emotional risks occurring. If a risk is detected, the server generates an immediate alert.

[1603] Step 10:

[1604] The server sends a warning message and instructions on how to deal with the problem to the user's device, which then notifies the user of the warning and allows them to confirm the corrective action.

[1605] Step 11:

[1606] The user checks the warning and takes necessary measures, then provides feedback to the server via the app and requests another analysis.

[1607] Example 2

[1608] 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."

[1609] Conventional health management systems primarily provide advice based solely on a user's health data, making it difficult to provide personalized advice that also takes into account the user's emotional state. Furthermore, regular progress checks and monitoring of potential health and emotional risks are insufficient, making it difficult for users to receive appropriate care instructions when needed. Furthermore, the lack of a means to comprehensively evaluate health and emotional data has prevented more comprehensive health management from being realized.

[1610] 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.

[1611] In this invention, the server includes means for collecting health data and emotional data from a user, means for encrypting the health data and emotional data and transmitting the data to the server, means for decrypting the health data and emotional data stored on the server and storing them in a secure database, means for inputting the health data and emotional data into a generative AI model for analysis and evaluating the user's health and emotional state, means for generating personalized health and emotional care advice based on the evaluation results of the generative AI model, and means for transmitting the generated advice to the user's device and displaying it. This allows for integrated management of the user's health data and emotional data, providing more comprehensive and personalized advice, and enabling regular progress checks and monitoring of potential risks.

[1612] "Health data" refers to data used to measure and evaluate a user's health status, such as the user's physical activity level, sleep time, eating habits, and exercise habits.

[1613] "Emotion data" refers to information self-reported by a user about their own emotional state, or data about an emotional state automatically identified by an emotion identification engine.

[1614] A "generative AI model" is an artificial intelligence model that takes health data and emotional data as input, analyzes this data, and evaluates the user's health and emotional state.

[1615] "Encryption" is a technical measure that makes transmitted data unreadable to third parties.

[1616] The "Server" is a device that receives, decodes, stores, and analyzes user-submitted health and emotional data using a generative AI model.

[1617] "Personalized health and emotional care advice" refers to user-specific health and emotional care instructions that are generated based on an analysis of the user's individual health and emotional data.

[1618] A "database" is a collection of health and emotional data stored securely within a server.

[1619] "Progress assessment" is the process of assessing changes in a user's health and emotional state based on new health and emotional data collected periodically from the user.

[1620] "Potential risk" is an assessment result that indicates the possibility of a physical or psychological risk occurring in the future, based on the user's health data and emotional data.

[1621] A "warning" is a message issued to the user to warn them when a potential risk is detected.

[1622] A "countermeasure" is specific instructions or advice provided to a user to address a detected risk.

[1623] This invention relates to a system that analyzes health and emotional data collected from a user and provides personalized health and emotional care advice, which specifically includes four main components: a user, a terminal, a server, and an emotion engine.

[1624] User information collection module

[1625] First, a user launches the application and creates a new account or logs in. Then, following the prompts within the app, the user inputs and submits health data (e.g., sleep duration, dietary habits, exercise habits, etc.) and emotional data. Emotional data is collected through self-reporting by the user or through automatic identification using an emotion engine.

[1626] A specific example would be a user opening a smartphone app and entering the amount of sleep they got last night or their emotional state today. For example, the user might enter "7 hours of sleep" and "stress."

[1627] Data Storage Module

[1628] The device encrypts the data using the TLS encryption protocol and sends it to the server. The server then decrypts the received data and stores it in a secure database. If the data is successfully saved, the server notifies the device, and the device displays a confirmation message to the user.

[1629] As a specific example, the device may display the message "Saving..." and after the data has been successfully saved, display a notification to the user saying "Data saved successfully."

[1630] Data Analysis Module

[1631] The server inputs the stored health and emotional data into a generative AI model (such as OpenAI's GPT-4). The generative AI model analyzes this data and evaluates the user's health and emotional state. The server generates personalized health and emotional care advice based on the analysis results.

[1632] As a specific example, if you input data such as "The user exercises three times a week, but at a low intensity," the system will generate advice such as "Increase the intensity of your exercise."

[1633] Advice sending module

[1634] The server prepares the generated advice and sends it to the terminal, which receives it and displays it to the user in an appropriate form.

[1635] For example, advice such as "You should aim to sleep 7-8 hours a day" is generated and sent to the device, which then displays the message "Recommended sleep time: 7-8 hours a day" to the user.

[1636] Regular progress check module

[1637] The server periodically sends a request for new data to the device. The device displays the request as a notification to the user, prompting them to enter data. The user enters new health and emotional data and sends it to the server via the device. The server analyzes the new data and evaluates progress. Based on the analysis results, the server provides detailed advice and corrections.

[1638] As a specific example, the server sends a request to the terminal saying "Please enter new data," and the terminal displays a notification to the user saying "Please enter new health data."

[1639] Potential Risk Monitoring Module

[1640] The server continuously monitors user data and evaluates the likelihood of health and emotional risks. If a risk is detected, the server immediately generates an alert and sends a warning message and instructions on how to respond to the device. The device then notifies the user of the alert and prompts them to take action.

[1641] For example, the server receives data such as "You have recently increased your intake of high-calorie meals and have been experiencing depression," generates a warning that "Your cardiovascular risk and mental stress may be increasing," and sends it to the device. The device then notifies the user to "increase your intake of low-calorie meals and add mental health exercises."

[1642] Prompt Sentence Examples

[1643] "The user exercises at a low intensity three times a week, but is feeling stressed. Please generate appropriate health advice."

[1644] "The user's average sleep time is 5 hours and they are feeling anxious. Please generate appropriate health advice."

[1645] In this way, this system can comprehensively manage the user's health and emotional data, providing more comprehensive and personalized advice. It also allows users to regularly check their progress and monitor potential risks, allowing them to manage their health with peace of mind.

[1646] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1647] Program processing flow

[1648] Step 1: Collect user information

[1649] 1. The user launches the application and either creates a new account or logs in.

[1650] Input: Account information (username, password, etc.)

[1651] Output: Authentication status (login success / failure)

[1652] Specific operation: The user opens the smartphone app and enters their username and password on the login screen.

[1653] 2. The user follows the prompts in the app to enter and submit their health and emotional data.

[1654] Input: Health data (e.g., sleep time, dietary habits, exercise habits), emotional data (e.g., stress level)

[1655] Output: Sending status (success / failure)

[1656] Specific operation: The user enters various health and emotional data into the form and presses the "Submit" button.

[1657] Step 2: Data encryption and transmission

[1658] 1. The device encrypts the transmitted data using the TLS encryption protocol.

[1659] Input: Health and emotion data entered by the user

[1660] Output: Encrypted data

[1661] Specific operation: The terminal encrypts the input data using the encryption library.

[1662] 2. The device sends the encrypted data to the server.

[1663] Input: Encrypted data

[1664] Output: Sending status (success / failure)

[1665] Specific operation: The device sends the encrypted data to the server using an HTTP POST request.

[1666] Step 3: Receiving and storing data

[1667] 1. The server decrypts the received data.

[1668] Input: Encrypted data

[1669] Output: raw decrypted data

[1670] What happens: The server uses a decryption library to decrypt the data.

[1671] 2. The server stores the decrypted data in a secure database.

[1672] Input: raw decrypted data

[1673] Output: Save status (save successful / failed)

[1674] Specific behavior: The server saves the data via the database connection and returns the save status.

[1675] Step 4: Data analysis

[1676] 1. The server inputs the stored health and emotion data into the generative AI model.

[1677] Input: Health and emotion data stored in a database

[1678] Output: Data analysis results

[1679] Specific operation: The server inputs data into the API of the generative AI model and performs analysis.

[1680] 2. The generative AI model analyzes the data and the server assesses the user's health and emotional state.

[1681] Input: Health and emotion data

[1682] Output: Health and emotional state assessment results

[1683] Specific operation: The generative AI model analyzes the data and returns the evaluation results to the server.

[1684] Step 5: Advice generation and sending

[1685] 1. The server generates personalized health and emotional care advice based on the analysis results.

[1686] Input: Health and emotional state assessment results

[1687] Output: Personalized advice

[1688] Specific operation: The server generates advice based on the analysis results of the generative AI model.

[1689] 2. The server sends the generated advice to the device.

[1690] Enter: personalized advice

[1691] Output: Sending status (success / failure)

[1692] Specific operation: The server sends the advice to the device using an HTTP POST request.

[1693] 3. The device receives the advice and displays it to the user.

[1694] Input: Advice received

[1695] Output: Advice displayed

[1696] Specific operation: The terminal application notifies and displays the advice to the user.

[1697] Step 6: Regular progress checks

[1698] 1. The server periodically sends a request for new data to the device.

[1699] Input: Request Send Schedule

[1700] Output: New data request

[1701] Specific operation: The server sends requests to the terminal based on a schedule.

[1702] 2. The device displays a notification to the user, prompting them to enter data.

[1703] Input: New data request

[1704] Output: Data entry prompt notification

[1705] Specific behavior: The terminal notifies the user, "Please enter new data."

[1706] 3. The user inputs new health and emotion data and sends it to the server via the terminal.

[1707] Input: New health and emotion data

[1708] Output: New data submission status

[1709] What happens: The user enters new data and presses the submit button.

[1710] 4. The server analyzes the new data and assesses progress.

[1711] Input: New health and emotion data

[1712] Output: Progress evaluation results

[1713] What it does: The server feeds new data into the generative AI model and evaluates progress.

[1714] 5. The server will provide detailed advice and corrections based on your progress.

[1715] Input: Progress evaluation results

[1716] Output: Detailed advice and fixes

[1717] Specific operation: The server generates new advice based on the evaluation results and sends it to the device.

[1718] Step 7: Potential Risk Monitoring

[1719] 1. The server continuously monitors user data and assesses health and emotional risks.

[1720] Input: Continuously monitored health and emotional data

[1721] Output: Risk assessment results

[1722] What it does: The server periodically checks the data in the database and assesses the risk.

[1723] 2. If a risk is detected, the server generates an immediate alert.

[1724] Input: Risk assessment results

[1725] Output: Warning message

[1726] Specific operation: The server generates a warning based on the risk assessment results.

[1727] 3. The server sends a warning message and instructions on how to respond to the device.

[1728] Input: warning message

[1729] Output: Sending status (success / failure)

[1730] Specific operation: The server sends a warning message to the device using an HTTP POST request.

[1731] 4. The device notifies the user of the warning.

[1732] Input: Received warning message

[1733] Output: The displayed warning

[1734] Specific behavior: The device displays a warning message to the user, informing them that "continuing this behavior will increase cardiovascular risk and mental stress," and suggesting countermeasures such as "increasing low-calorie meals."

[1735] Through these steps, the system can comprehensively manage the user's health and emotional data, provide personalized advice, and periodically check progress and monitor potential risks, allowing users to manage their health with peace of mind.

[1736] (Application example 2)

[1737] 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."

[1738] Conventional health management systems only provide advice based on a user's physical health data, and therefore are unable to provide comprehensive care that takes into account the user's mental health, such as emotional state and stress level. Furthermore, they lack support for users to continuously manage their health, with insufficient regular progress checks and monitoring of potential risks. This increases the burden on users to manage their health themselves, and prevents them from receiving effective healthcare.

[1739] 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.

[1740] In this invention, the server includes a means for collecting health data and emotional data from a user, a generative model means for analyzing the health data and emotional data to evaluate the user's health and emotional state, and a means for generating personalized health and emotional care advice based on the evaluation results of the generative model means. This enables comprehensive management of the user's physical health and emotional state and provides appropriate advice. Furthermore, by regularly collecting data, evaluating progress, and monitoring potential risks, the user can receive support for maintaining a long-term balance between health and emotions.

[1741] A "means for collecting health and emotional data from a user" is a technology or device that receives and collects information about the health and emotional state input or provided by a user.

[1742] "Generative model means for analyzing health data and emotional data to assess a user's health and emotional state" refers to a technology that uses an algorithm or AI model to analyze a user's physical and mental health state based on the collected data and evaluates that state quantitatively or qualitatively.

[1743] The "means for generating personalized health and emotional care advice based on the evaluation results of the generative model means" is a technology for creating optimal health management and emotional care advice for individual users from the analysis results.

[1744] "Means for sending health and emotional care advice to a user's device" refers to technology that sends the generated advice to the user's device (smartphone, tablet, PC, etc.) and makes it viewable.

[1745] "Means for periodically collecting the user's health and emotional data and evaluating progress" refers to technology that collects new data from the user at regular intervals and analyzes and evaluates the progress and degree of improvement.

[1746] "Means for assessing potential health and emotional risks based on a user's health and emotional data and generating warnings and countermeasures" refers to technology that analyzes health and emotional risks that may occur in the future from collected data and provides warnings and countermeasures for those risks.

[1747] MODE FOR CARRYING OUT THE INVENTION

[1748] System Overview

[1749] This invention relates to a system that collects a user's health and emotional data and provides personalized health and emotional care advice based on the analysis results. The system is implemented by combining a user's device, a server, and a generative AI model.

[1750] Generating a Program

[1751] The system works in the following steps: The user's device collects health and emotional data, encrypts it, and sends it to the server. The server receives the data and inputs it into the generative AI model for analysis. Based on the analysis results, the server generates personalized advice and sends it to the user's device. The user's device displays the received advice to the user.

[1752] Natural language explanation

[1753] User's device

[1754] The user's device (smartphone, tablet, PC, etc.) collects health data (e.g., sleep time, exercise frequency, dietary content, etc.) and emotional data (e.g., stress level, mood) from the user through a dedicated application. The collected data is encrypted using methods such as AES encryption to maintain security.

[1755] Server and Generative AI Model

[1756] The server receives encrypted data from the user's device. The data is decrypted and stored in a database. The stored data is then input into a generative AI model implemented in Python. The model analyzes the data using frameworks such as TensorFlow and PyTorch. The analysis results provide an assessment of the user's current health and emotional state and generate personalized health and emotional care advice.

[1757] Providing advice

[1758] The server sends the generated advice to the user's device, which receives it and displays it on the application, allowing the user to take specific actions to put into practice in their daily lives.

[1759] Specific examples

[1760] For example, if a user inputs "average sleep time 5 hours," "low exercise intensity," and "stress level 7," the server will generate advice such as "get more sleep" and "increase exercise intensity" and notify the user.

[1761] Prompt Sentence Examples

[1762] User: "I average five hours of sleep and exercise three days a week, but the intensity is low. I've also been feeling a lot of stress lately."

[1763] System: "I recommend trying to increase your average sleep time to 7-8 hours and increasing your exercise intensity to a moderate level. Also, try relaxation techniques to manage stress."

[1764] This invention allows users to manage comprehensive health and emotional data in an integrated manner, receive personalized advice, and provide support for maintaining long-term health and emotional balance through regular data collection, progress evaluation, and monitoring of potential risks.

[1765] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1766] Step 1:

[1767] The user's device collects health and emotional data from the user through a dedicated application, including sleep duration, exercise frequency, dietary intake, stress level, and mood. The user enters this data into an input form, or the application automatically collects the data from sensors and other devices.

[1768] Input: Health data, emotion data

[1769] Output: Collected data

[1770] Step 2:

[1771] The device encrypts the collected data using a method such as AES encryption, which ensures data security, and then sends the encrypted data to the server.

[1772] Input: Collected data

[1773] Output: Encrypted data

[1774] Step 3:

[1775] The server receives the encrypted data sent from the device and decrypts it. The decrypted data is stored in a database. The server prepares the stored data for analysis.

[1776] Input: Encrypted data

[1777] Output: Decrypted data

[1778] Step 4:

[1779] The server then feeds the decoded data into a generative AI model, which uses frameworks such as TensorFlow and PyTorch to analyze the data and assess the user's health and emotional state.

[1780] Input: Decrypted data

[1781] Output: Analysis results

[1782] Step 5:

[1783] The server generates personalized health and emotional care advice based on the analysis results, including specific instructions for action and suggestions for improvement for the user.

[1784] Input: Analysis results

[1785] Output: Personalized advice

[1786] Step 6:

[1787] The server transmits the generated advice to the user's terminal.

[1788] Input: Personalized advice

[1789] Output: Advice sent

[1790] Step 7:

[1791] The user's device displays the received advice, and the application presents the advice to the user in a visually easy-to-understand manner. The user can use this advice to adjust their daily activities.

[1792] Input: Submitted advice

[1793] Output: Advice displayed

[1794] Step 8:

[1795] At regular intervals, the server requests new data from the user. This request appears as a notification on the user's device. The user enters new health and emotional data, which is then sent back to the server. This process continues, periodically assessing the user's progress.

[1796] Input: New health and emotion data

[1797] Output: Continuous progress assessment

[1798] Step 9:

[1799] The server continuously monitors user data and evaluates the likelihood of potential health or emotional risks. If a risk is detected, the server immediately generates a warning message and instructions for response and sends them to the user's device. The user's device displays the warning and the user confirms the measures.

[1800] Input: Continuously collected data

[1801] Output: Warnings and actions to take

[1802] 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.

[1803] 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.

[1804] 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.

[1805] 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.

[1806] 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.

[1807] 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.

[1808] 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).

[1809] 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.

[1810] 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."

[1811] 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.

[1812] 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).

[1813] 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.

[1814] 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.

[1815] 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.

[1816] 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.

[1817] 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.

[1818] 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.

[1819] 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.

[1820] 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.

[1821] 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.

[1822] 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.

[1823] The following is further disclosed regarding the above embodiment.

[1824] (Claim 1)

[1825] means for collecting health data from a user;

[1826] a generative modeling means for analyzing the health data and evaluating the user's health condition;

[1827] means for generating personalized health advice based on the evaluation results of said generative model means;

[1828] means for transmitting the health advice to a user terminal;

[1829] A system including:

[1830] (Claim 2)

[1831] 10. The system of claim 1, further comprising means for periodically collecting health data of the user and assessing progress.

[1832] (Claim 3)

[1833] 10. The system of claim 1, further comprising means for assessing potential health risks based on the user's health data and generating alerts and countermeasures.

[1834] "Example 1"

[1835] (Claim 1)

[1836] means for collecting health data from a user;

[1837] means for encrypting the health data using a generated prompt and transmitting the encrypted health data to a server;

[1838] a server means for decrypting the encrypted health data and storing it in a database;

[1839] a generating AI model means for analyzing the health data and evaluating the user's health condition;

[1840] means for generating personalized health advice based on the evaluation results of the generating AI model means;

[1841] means for transmitting the generated health advice to a user terminal;

[1842] a terminal means for displaying the transmitted health advice to a user;

[1843] A system including:

[1844] (Claim 2)

[1845] 10. The system of claim 1, further comprising means for periodically collecting health data of the user and assessing progress.

[1846] (Claim 3)

[1847] 10. The system of claim 1, further comprising means for assessing potential health risks based on the user's health data and generating alerts and countermeasures.

[1848] "Application Example 1"

[1849] (Claim 1)

[1850] means for collecting health data from a user;

[1851] a generative modeling means for analyzing the health data and evaluating the user's health condition;

[1852] means for generating personalized health advice based on the evaluation results of said generative model means;

[1853] means for transmitting the health advice to a user terminal;

[1854] means for encrypting the health data and transmitting it to a server;

[1855] means for displaying the generated advice as a notification on a user's terminal;

[1856] A means to periodically request new data;

[1857] A system including:

[1858] (Claim 2)

[1859] 10. The system of claim 1, further comprising means for linking the system with health-related services in physical stores to provide personalized fitness plans and product recommendations in real time.

[1860] (Claim 3)

[1861] 10. The system of claim 1, further comprising means for assessing potential health risks based on the user's health data and generating alerts and countermeasures.

[1862] "Example 2: Combining Emotion Engines"

[1863] (Claim 1)

[1864] means for collecting health and emotional data from a user;

[1865] means for encrypting the health data and emotion data and transmitting the encrypted data to a server;

[1866] means for decrypting the health and emotion data stored on said server and storing it in a secure database;

[1867] a means for inputting the health data and emotion data into a generative AI model for analysis, and evaluating the user's health status and emotion status;

[1868] means for generating personalized health and emotional care advice based on the evaluation results of the generative AI model;

[1869] means for transmitting the generated advice to a user's terminal and displaying the advice;

[1870] A system including:

[1871] (Claim 2)

[1872] 10. The system of claim 1, further comprising means for periodically requesting new health and emotional data from the user and assessing progress.

[1873] (Claim 3)

[1874] 10. The system of claim 1, further comprising means for assessing potential health and emotional risks based on the user's health and emotional data and generating alerts and countermeasures.

[1875] "Application example 2 when combining emotion engines"

[1876] (Claim 1)

[1877] means for collecting health and emotional data from a user;

[1878] a generative modeling means for analyzing the health data and emotion data to evaluate the health state and emotion state of the user;

[1879] means for generating personalized health and emotional care advice based on the evaluation results of said generative model means;

[1880] means for transmitting said health and emotional care advice to a user's terminal;

[1881] A system including:

[1882] (Claim 2)

[1883] 10. The system of claim 1, further comprising means for periodically collecting health and emotional data of the user and assessing progress.

[1884] (Claim 3)

[1885] 10. The system of claim 1, further comprising means for assessing potential health and emotional risks based on the user's health and emotional data and generating alerts and countermeasures. [Explanation of symbols]

[1886] 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 health data from a user; a generative modeling means for analyzing the health data and evaluating the user's health condition; means for generating personalized health advice based on the evaluation results of said generative model means; means for transmitting the health advice to a user terminal; A system including:

2. 10. The system of claim 1, further comprising means for periodically collecting health data of the user and assessing progress.

3. The system of claim 1 , further comprising means for assessing potential health risks based on the user's health data and generating warnings and countermeasures.

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