System

Abstract

To provide a system.SOLUTION: This system comprises: means for collecting biological information of a user; means for analyzing the collected biological information; means for generating advice corresponding to changes in health stage and feeling of the user on the basis of the analysis result of the biological information using generative AI; and means for presenting the advice to 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] Conventional health management, training, and diet methods are generalized and do not take into account the effects of individual users' physical constitutions or medications. As a result, it is difficult to provide efficient health management and the best program for the user's health condition and physical condition. [Means for solving the problem]

[0005] This invention provides a system including means for streamlining health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, and means for providing a health management program suited to each user. Furthermore, it provides means for collecting the user's personal data and analyzing that data to generate a health management program suited to each user. It also provides means for adjusting the health management program in real time according to the user's health condition and changes in physical condition. This enables health management optimized for each individual user. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] The system of the present invention streamlines health management, training, and diet methods based on the personal data of each individual user. Specifically, it adjusts health management according to the user's constitution and medications, based on information input by the user and biometric information obtained from sensors (e.g., heart rate, blood pressure, body temperature, etc.). For example, it provides a health management program tailored to each individual user, such as proposing an exercise program to suppress blood pressure rise to a user taking medication for high blood pressure.

[0029] "Example 2"

[0030] Furthermore, the system of the present invention collects personal data of the user and analyzes that data to generate a health management program suited to the user. Specifically, personal data such as the user's age, sex, weight, height, lifestyle habits, and genetic information is collected, and the user's health condition and physical condition are analyzed based on this data. Based on the results of this analysis, a health management program suited to the user is generated and provided.

[0031] "Example 3"

[0032] The system of the present invention also adjusts the health management program in real time according to changes in the user's health condition and physical condition. Specifically, it monitors the user's biological information in real time and adjusts the health management program according to changes. For example, if the user's heart rate exceeds a certain threshold, the program can be adjusted to reduce exercise intensity, enabling appropriate health management according to the user's health condition.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: Collect input information from the user and biometric information obtained from sensors (e.g., heart rate, blood pressure, body temperature, etc.).

[0036] Step 2: Based on the collected information, adjust health management according to the user's constitution and medications.

[0037] Step 3: For example, for a user taking high blood pressure medication, an exercise program to suppress blood pressure rise is proposed, and a health management program suited to each individual user is provided. "Example 2"

[0038] Step 1: Collect personal data such as the user's age, gender, weight, height, lifestyle habits, and genetic information.

[0039] Step 2: Analyze the user's health and physical condition based on this data.

[0040] Step 3: Based on the results of this analysis, a health management program appropriate for the user is generated and provided.

[0041] "Example 3"

[0042] Step 1: Monitor the user's vital signs in real time.

[0043] Step 2: Adjust your health management program accordingly.

[0044] Step 3: For example, if the user's heart rate exceeds a certain threshold, the program can be adjusted to reduce exercise intensity, enabling appropriate health management according to the user's health condition.

[0045] Example 1

[0046] Next, a description will be given of Example 1 of Form 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."

[0047] Conventional health management systems have had difficulty providing appropriate health management programs tailored to individual users' physical constitutions and medications. Furthermore, they lack the ability to collect and analyze biometric information in real time, making it impossible to respond quickly to changes in the user's health status or physical condition. This has resulted in inefficient health management for users, making it difficult to recommend effective training and diet methods.

[0048] 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.

[0049] In this invention, the server includes means for streamlining health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suitable for each user, means for collecting the user's biological information in real time, means for analyzing the collected data and generating a health management program suitable for the user, means for proposing a health management program using a generative AI model, and means for the user to execute the proposed program. This makes it possible to provide an appropriate health management program according to the individual situation of each user, enabling real-time understanding of health conditions and rapid response.

[0050] "Individual user personal data" refers to personal information such as the user's age, gender, medical history, and medications being taken.

[0051] "Health Management" refers to activities and programs designed to maintain or improve a User's health.

[0052] "Training" refers to physical activity or exercise designed to improve a User's fitness or health.

[0053] "Diet method" refers to a meal plan or dietary restriction designed to improve a user's weight management and nutritional balance.

[0054] "User's constitution" refers to the user's genetic factors and physiological characteristics.

[0055] "Current medications" refers to medications that the user is currently taking.

[0056] "Health Management Program" means a set of activities or instructions designed to improve a user's health.

[0057] "Biometric information" refers to physiological data such as a user's heart rate, blood pressure, and body temperature.

[0058] "Real-time collection" refers to capturing a user's biometric information instantly and processing it without delay.

[0059] "Analyzing data" refers to assessing the user's health condition based on collected biometric information and personal data and generating an appropriate program.

[0060] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze data and generate a health management program that is appropriate for the user.

[0061] "Suggest" refers to notifying the user of the generated health management program and encouraging them to carry it out.

[0062] "Implementing" refers to the user acting in accordance with the proposed health management program.

[0063] MODE FOR CARRYING OUT THE INVENTION

[0064] This invention is a system that improves the efficiency of health management, training, and diet methods based on the personal data of each individual user. The system adjusts health management according to the user's constitution and medications, and provides a health management program that is suitable for each individual user.

[0065] Hardware and software used

[0066] Hardware: Sensors for capturing user biometric information (e.g., heart rate monitor, blood pressure monitor, thermometer)

[0067] Software: Dedicated applications for data collection and analysis, generative AI models (e.g., OpenAI's GPT-4)

[0068] Program processing procedure

[0069] 1. The user installs the dedicated application and enters personal information (age, gender, medical history, medications being taken, etc.).

[0070] 2. The device (smartphone or tablet) connects to sensors to collect the user's biometric information (heart rate, blood pressure, body temperature, etc.) in real time.

[0071] 3. The device sends the collected data to a dedicated application and stores it in a database.

[0072] 4. The server analyzes the stored data and generates a health management program based on the user's constitution and medications.

[0073] 5. The server uses a generative AI model (e.g., GPT-4) to suggest training and diet methods suitable for the user.

[0074] 6. The user reviews and executes the proposed program.

[0075] Specific examples

[0076] Let's say the user is a 50-year-old man who takes medication for high blood pressure.

[0077] The device uses a heart rate monitor and a blood pressure monitor to measure the user's heart rate and blood pressure.

[0078] The server analyzes the collected data and determines that the user's blood pressure is high.

[0079] The server uses the generative AI model to suggest an exercise program (e.g., walking or yoga) to lower blood pressure.

[0080] The user carries out the suggested exercise program and manages their health.

[0081] Prompt Sentence Examples

[0082] "For a 50-year-old male taking high blood pressure medication, please suggest an exercise program to lower his blood pressure."

[0083] In this way, the system provides a health management program tailored to the individual circumstances of each user.

[0084] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0085] Step 1:

[0086] The user installs a dedicated application and enters personal information (age, gender, medical history, medications being taken, etc.).

[0087] Input: User personal information

[0088] Output: User personal information stored in the application

[0089] What happens: A user opens an application on their smartphone and fills in the required information in the form on the screen.

[0090] Step 2:

[0091] The device connects to sensors (heart rate monitor, blood pressure monitor, thermometer, etc.) to collect the user's biometric information in real time.

[0092] Input: User's biometric information (heart rate, blood pressure, temperature, etc.)

[0093] Output: Collected biometric information

[0094] What happens: The user wears a heart rate monitor and the application collects the data via Bluetooth.

[0095] Step 3:

[0096] The biometric information collected by the device is sent to a dedicated application and stored in a database.

[0097] Input: Collected biometric information

[0098] Output: Biometric information stored in a database

[0099] What happens: The application sends data over the Internet to the server, which stores it in a database.

[0100] Step 4:

[0101] The server analyzes the stored data and generates a health management program based on the user's constitution and the medications they are taking.

[0102] Input: Biometric and personal information stored in a database

[0103] Output: Analysis results and generated health management program

[0104] How it works: The server retrieves the user's biometric and personal information from the database and analyzes it using an algorithm.

[0105] Step 5:

[0106] The server uses a generative AI model (e.g., GPT-4) to suggest training and diet methods suitable for the user.

[0107] Input: Analysis results and generated health management program

[0108] Output: Training and diet suggestions for the user

[0109] Specific operation: The server inputs a prompt sentence into the generative AI model and generates an appropriate health management program.

[0110] Step 6:

[0111] The user confirms and executes the proposed program.

[0112] Input: Proposed Health Care Program

[0113] Output: Executed health management program

[0114] What happens: The application notifies the user, who then confirms and runs the program within the application.

[0115] (Application example 1)

[0116] Next, a description will be given of Application Example 1 of Embodiment 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."

[0117] Conventional health management systems have not been able to fully utilize users' personal data and biometric information, making it difficult to provide optimal health management programs for each individual user. Furthermore, there was a lack of systems that could evaluate health status in real time or be used in physical stores, making it difficult to streamline user health management.

[0118] 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.

[0119] In this invention, the server includes means for streamlining health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suitable for each user, means for acquiring the user's biometric information in real time, means for evaluating the health condition based on the acquired biometric information, means for proposing an appropriate training program based on the evaluation results, and means including a smartphone application intended for use in physical stores. This makes it possible to evaluate the user's health condition in real time and provide the optimal health management program for each user.

[0120] "Personal data" is data, including personal and health information, about individual users.

[0121] "Health Management" refers to activities and programs designed to maintain or improve a User's health.

[0122] "Training" refers to physical activity or exercise designed to improve a User's fitness or health.

[0123] "Diet Method" refers to a diet or exercise method for managing or reducing a user's weight or body fat.

[0124] "Constitution" refers to the user's genetic or physiological characteristics.

[0125] "Current medications" refers to medications that the user is currently taking.

[0126] "Biometric information" refers to physical data such as a user's heart rate, blood pressure, and temperature.

[0127] "Health Management Program" means a set of activities and instructions designed to improve and maintain the health and well-being of a User.

[0128] "Real-time" refers to data being processed and evaluated as it is generated.

[0129] "Evaluation" refers to analyzing the user's health status and judging the results.

[0130] "Training Program" means a plan of exercise or physical activity designed to improve a User's fitness or health.

[0131] "Brick and mortar store" refers to a store that exists in a physical location.

[0132] "Smartphone application" refers to a software program that runs on a smartphone.

[0133] The system for carrying out this invention collects personal data and biometric information of a user, evaluates the health condition in real time, and provides an appropriate health management program. A specific embodiment of this system will be described below.

[0134] System Configuration

[0135] The system consists of the following main components:

[0136] 1. User devices: Devices such as smartphones and smartwatches collect users' personal data and biometric information.

[0137] 2. Server: Receives data sent from user devices and analyzes and evaluates them.

[0138] 3. Application: An application installed on a smartphone that provides the user interface and displays the health management program.

[0139] Data collection and analysis

[0140] The user device collects the user's personal data (age, weight, height, medications, etc.) and biometric information (heart rate, blood pressure, body temperature, etc.), which is then sent to a server via Bluetooth or Wi-Fi.

[0141] The server analyzes the received data and evaluates the user's health status using programming languages ​​such as Python, preprocessing the data, and using machine learning models.

[0142] Health management program generation

[0143] The server then generates an optimal health management program for the user based on the analysis results. For example, it might suggest low-intensity aerobic exercise for a user at risk of high blood pressure. The generated program is sent to the user's device and displayed to the user through the application.

[0144] Use in physical stores

[0145] The system is intended for use in brick-and-mortar fitness gyms and health food stores, where users can check their health status in real time through a smartphone application and optimize the services offered at the store.

[0146] Specific examples

[0147] User A: 40 years old, weighs 80 kg, is 180 cm tall, and is taking medication for high blood pressure. Heart rate: 90, blood pressure: 150, body temperature: 36.8°C.

[0148] Suggestion: Low-intensity aerobic exercise is recommended.

[0149] User B: 25 years old, weight 60 kg, height 165 cm, no medication. Heart rate 70, blood pressure 120, body temperature 36.5°C.

[0150] Suggestion: Continue with your regular training program.

[0151] Prompt Sentence Examples

[0152] Enter your age, weight, height, medications, heart rate, blood pressure and temperature, and we'll suggest the right training program for you.

[0153] In this way, users can understand their health status in real time and receive the optimal health management program.

[0154] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0155] Step 1:

[0156] The user starts the smartphone application and inputs personal data (age, weight, height, medications being taken, etc.) The input data is temporarily saved within the application.

[0157] Step 2:

[0158] Users wear sensors such as smartwatches to measure vital signs (heart rate, blood pressure, body temperature, etc.), and the measured data is sent to a smartphone via Bluetooth or Wi-Fi.

[0159] Step 3:

[0160] The device (smartphone) sends the collected personal data and biometric information to a server using the HTTPS protocol to ensure data security.

[0161] Step 4:

[0162] The server analyzes the received data using Python for data preprocessing (missing value imputation, data normalization, etc.) and machine learning models (e.g., random forests, neural networks). Based on the input data, the server evaluates the user's health status.

[0163] Step 5:

[0164] The server then generates an optimal health management program for the user based on the evaluation results. For example, it would suggest low-intensity aerobic exercise to a user at risk of high blood pressure.

[0165] Step 6:

[0166] The server sends the generated health management program to the terminal using the HTTPS protocol to ensure data security.

[0167] Step 7:

[0168] The device displays the received health management program to the user using the user interface of the smartphone application. The user then performs training and health management according to the proposed program.

[0169] Step 8:

[0170] Users can use smartphone applications at physical stores (fitness gyms and health food stores) to check their own health status and make optimal use of the services offered at the store.

[0171] Example 2

[0172] Next, a description will be given of Example 2 of Form 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."

[0173] Conventional health management systems have had difficulty efficiently collecting and analyzing users' personal data and providing optimal health management programs for each individual user. They also lacked the functionality to adjust health management programs in real time according to the user's health status and changes in physical condition. Furthermore, the technology to generate optimal health management programs for each user using generative AI models had not yet been fully established.

[0174] 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.

[0175] In this invention, the server includes: means for streamlining health management, training, and diet methods based on the personal data of each user; means for adjusting health management according to the user's constitution and medications; means for providing a health management program tailored to each user; means for collecting the user's personal data; means for storing the collected personal data in a database; means for analyzing the stored personal data; means for generating a health management program tailored to each user using a generative AI model based on the analysis results; and means for providing the generated health management program to the user. This makes it possible to efficiently collect and analyze the user's personal data and provide an optimal health management program for each user. Furthermore, the health management program can be adjusted in real time according to the user's health condition and changes in physical condition, and a health management program tailored to each user can be generated using the generative AI model.

[0176] "Individual user personal data" refers to information about an individual user, such as age, gender, weight, height, lifestyle habits, and genetic information.

[0177] "Health Management" refers to activities and programs designed to maintain or improve a user's health.

[0178] "Training" refers to exercises or physical activities designed to improve a user's physical strength or muscle power.

[0179] "Diet method" refers to a meal plan or dietary restriction designed to improve a user's weight management and nutritional balance.

[0180] "Constitution" refers to the user's genetic or physiological characteristics.

[0181] "Current medications" refers to medications that the user is currently taking.

[0182] "Health Management Program" means a set of activities or instructions designed to maintain or improve a user's health.

[0183] "Database" refers to a system for systematically storing users' personal data.

[0184] "Data analytics" refers to methods for using collected data to assess a user's health and physical condition.

[0185] "Generative AI model" refers to a model that uses artificial intelligence technology to generate a health management program suitable for the user.

[0186] A "prompt sentence" refers to an instruction sentence to be input into a generative AI model.

[0187] "Real-time" refers to responding immediately to changes in the user's health condition or physical condition.

[0188] This invention is a system that collects and analyzes personal data of users and uses a generative AI model to generate and provide a health management program suited to each individual user. Specific embodiments of this system are described below.

[0189] First, the user accesses a dedicated application or website using a device such as a smartphone or PC. The user enters personal data such as age, gender, weight, height, lifestyle habits (e.g., exercise frequency, smoking habits), and genetic information (e.g., family medical history) into the on-screen form. Once the input is complete, the user clicks the "Submit" button. The device then sends the entered data to the server in an appropriate format such as JSON.

[0190] The server receives the personal data sent from the terminal. The received data is saved in a relational database such as MySQL (registered trademark) or PostgreSQL using a database connection library (e.g., SQLAlchemy). A unique ID is assigned to each user in the database, and the personal data of each user is stored in the corresponding table.

[0191] The server then uses data analysis tools such as Python and R to analyze the stored personal data. Specifically, it preprocesses the data using libraries such as Pandas and NumPy, and then uses machine learning models and statistical methods to evaluate the user's health and physical condition. For example, it calculates the user's BMI (body mass index) and compares it with the standard value to evaluate health risks.

[0192] Based on the results of the data analysis, the server uses a generative AI model (e.g., GPT-4) to generate a health management program. The user's personal data and the analysis results are input into the generative AI model as a prompt. For example, the following prompt is input into the generative AI model:

[0193] The user is 30 years old, male, weighs 70 kg, and is 175 cm tall. He exercises three times a week and smokes daily. There is a family history of diabetes. Please generate a health management program suitable for this user.

[0194] The generative AI model outputs an appropriate health management program based on this prompt.

[0195] Finally, the server receives the health management program output from the generative AI model and sends it to the device. The user can then check the health management program that is best suited to them through the device. For example, the following health management program may be displayed on the screen of an application or website.

[0196] 1. Continue exercising three times a week and increase the time to 30 to 45 minutes.

[0197] 2. Reduce smoking and eventually aim to quit.

[0198] 3. When it comes to diet, limit your sugar intake and eat more vegetables and fruits.

[0199] 4. Check your blood sugar levels regularly and consult your doctor if any abnormalities occur.

[0200] The user can manage his / her health according to this program.

[0201] In this way, it is possible to efficiently collect and analyze users' personal data and provide the optimal health management program for each individual user.In addition, the health management program can be adjusted in real time according to the user's health condition and changes in physical condition, and a generative AI model can be used to generate a health management program that is suitable for the user.

[0202] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0203] Step 1:

[0204] A user accesses a dedicated application or website using a device such as a smartphone or PC. The user enters personal data such as age, gender, weight, height, lifestyle habits (e.g., exercise frequency, smoking habits), and genetic information (e.g., family medical history) into the on-screen form. Once the input is complete, the user clicks the "Submit" button. The device then sends the entered data to the server in an appropriate format such as JSON.

[0205] Input: User's personal data

[0206] Output: JSON format data

[0207] Step 2:

[0208] The server receives the personal data sent from the device. The received data is saved in a relational database such as MySQL or PostgreSQL using a database connection library (e.g., SQLAlchemy). A unique ID is assigned to each user in the database, and the personal data of each user is stored in the corresponding table.

[0209] Input: Personal data in JSON format

[0210] Output: Personal data stored in a database

[0211] Step 3:

[0212] The server uses data analysis tools such as Python and R to analyze the stored personal data. Specifically, it preprocesses the data using libraries such as Pandas and NumPy, and then uses machine learning models and statistical methods to evaluate the user's health and physical condition. For example, it calculates the user's BMI (body mass index) and compares it with the standard value to evaluate health risks.

[0213] Input: Personal data stored in a database

[0214] Output: Evaluation results of the user's health and physical condition

[0215] Step 4:

[0216] Based on the results of the data analysis, the server uses a generative AI model (e.g., GPT-4) to generate a health management program. The user's personal data and the analysis results are input into the generative AI model as a prompt. For example, the following prompt is input into the generative AI model:

[0217] The user is 30 years old, male, weighs 70 kg, and is 175 cm tall. He exercises three times a week and smokes daily. There is a family history of diabetes. Please generate a health management program suitable for this user.

[0218] The generative AI model outputs an appropriate health management program based on this prompt.

[0219] Input: User's personal data and analysis results

[0220] Output: Health management program based on generative AI model

[0221] Step 5:

[0222] The server receives the health management program output from the generative AI model and sends it to the device. The user can then check the health management program that is best suited to them through the device. For example, the following health management program may be displayed on the screen of an application or website.

[0223] 1. Continue exercising three times a week and increase the time to 30 to 45 minutes.

[0224] 2. Reduce smoking and eventually aim to quit.

[0225] 3. When it comes to diet, limit your sugar intake and eat more vegetables and fruits.

[0226] 4. Check your blood sugar levels regularly and consult your doctor if any abnormalities occur.

[0227] Input: Health management program using generative AI model

[0228] Output: Health management program provided to the user

[0229] (Application example 2)

[0230] Next, a description will be given of Application Example 2 of Form 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."

[0231] While conventional health management systems can collect and analyze users' personal data, it is difficult to provide optimal health management programs for each individual user. Additionally, they lack support for purchasing health foods and supplements in physical stores, or the ability to book health consultations and fitness programs in-store, making them unable to provide comprehensive support for users' health management.

[0232] 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.

[0233] In this invention, the server includes means for streamlining health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suited to each user, means for collecting and analyzing the user's personal data and scoring the user's health status, means for generating a health management program suited to the user based on the health score, means for supporting the purchase of health foods and supplements in physical stores, and means for providing a function for booking health consultations and fitness programs in stores, thereby enabling comprehensive health management based on the user's personal data.

[0234] "Personal data" refers to information about an individual, such as a user's age, gender, weight, height, lifestyle, and genetic information.

[0235] "Health Management Program" means a plan that includes specific guidance and advice for maintaining or improving a User's health.

[0236] A "health score" is a numerical indicator of a user's health status, calculated based on personal data.

[0237] A "physical store" is a store that exists in a physical location and where users can visit in person to purchase products or services.

[0238] "Health foods" are foods that are consumed for the purpose of maintaining or improving health.

[0239] "Supplements" are nutritional supplements taken to make up for nutrients that tend to be lacking in a normal diet.

[0240] "Health Consultation" is a service that allows users to ask questions and ask health-related questions to experts.

[0241] A "fitness program" is an exercise or training plan designed based on a user's physical strength and health condition.

[0242] The "reservation function" is a function that allows users to reserve a service or program by specifying the date and time of use in advance.

[0243] A system for implementing this invention includes a program for collecting and analyzing personal data of a user and scoring the user's health status. The system is realized using hardware such as a smartphone, a server, and a terminal in a brick-and-mortar store.

[0244] Hardware and Software

[0245] Hardware: Smartphones (iOS or ANDROID), servers, and physical store terminals

[0246] Software: Python 3.x, scikit-learn (library for data analysis), JSON (save and load data)

[0247] Data collection

[0248] Users enter personal information such as age, gender, weight, height, lifestyle habits, and genetic information through a smartphone application, which is then sent to a server and stored.

[0249] Data analysis

[0250] The server uses a machine learning model (Linear Regression) to score the user's health based on the collected personal data. Specifically, it calculates a health score using the user's age, gender, weight, height, lifestyle habits, and other features as input.

[0251] Health management program generation

[0252] The server generates a health management program suitable for the user based on the calculated health score, for example, providing a basic health management program if the health score is low and an advanced health management program if the health score is high.

[0253] In-store support

[0254] Through the smartphone application, users can access features that support the purchase of health foods and supplements in physical stores, as well as the ability to book health consultations and fitness programs in-store.

[0255] Specific examples

[0256] For example, if a 30-year-old male user weighs 70 kg, is 175 cm tall, and has an active lifestyle, the app will suggest an "intermediate health management program." The user can then purchase the appropriate supplements at a physical store and book a fitness program.

[0257] Prompt Sentence Examples

[0258] Enter your age, gender, weight, height, lifestyle, and genetic information. For example, if you are a 30-year-old male who weighs 70kg, is 175cm tall, and has an active lifestyle, enter the following:

[0259] Age: 30

[0260] Gender: Male

[0261] Weight: 70kg

[0262] Height: 175cm

[0263] Lifestyle: Active

[0264] Genetic Information: None

[0265] In this way, a system can be realized that provides a health management program suited to the user.

[0266] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0267] Step 1:

[0268] The user launches a smartphone application and enters personal data such as age, gender, weight, height, lifestyle habits, and genetic information.

[0269] Input: User's personal data (age, gender, weight, height, lifestyle habits, genetic information)

[0270] Output: Personal data is stored in the application and sent to the server.

[0271] Step 2:

[0272] The server saves the received personal data and stores it in a database.

[0273] Input: User's personal data

[0274] Output: Personal data stored in a database

[0275] Step 3:

[0276] The server uses a machine learning model (Linear Regression) to score the user's health status based on the stored personal data.

[0277] Input: Personal data stored in a database

[0278] Output: Health score

[0279] Specific operation: The server inputs personal data as features into a machine learning model and calculates a health score.

[0280] Step 4:

[0281] The server generates a health management program suitable for the user based on the health score.

[0282] Input: Health Score

[0283] Output: Health Management Program

[0284] Specific operation: The server evaluates the health score and selects a health management program (basic, intermediate, or advanced) according to the score.

[0285] Step 5:

[0286] The user checks the generated health management program through the smartphone application.

[0287] Enter: Health Management Program

[0288] Output: Health management program displayed on the user's smartphone

[0289] Specific operation: The application displays the health management program received from the server to the user.

[0290] Step 6:

[0291] Use the feature to help users purchase health foods and supplements in physical stores.

[0292] Enter: Health Management Program

[0293] Output: Purchase support information

[0294] What it does: The application displays a list of appropriate health foods and supplements based on your health management program and assists you in purchasing them.

[0295] Step 7:

[0296] Users can use the in-store health consultation and fitness program booking function.

[0297] Enter: Health Management Program

[0298] Output: Reservation information

[0299] Specific operation: The application provides a function for booking health consultations and fitness programs based on a health management program, and allows users to select the desired date and time to make a reservation.

[0300] In this way, comprehensive health management based on the user's personal data is realized.

[0301] Example 3

[0302] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0303] Conventional health management systems can provide health management programs based on the user's personal data, but they have the problem of difficulty in dynamically adjusting the program in response to changes in biological information in real time. Furthermore, individual adjustments based on the user's constitution or medications are insufficient, which can prevent optimal management of the user's health condition.

[0304] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes means for improving the efficiency of health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suitable for each user, means for collecting user's biological information in real time, means for transmitting the collected biological information to the server, means for analyzing the biological information received by the server, means for adjusting the health management program based on the analysis results, and means for notifying the user of the adjustment results. This enables dynamic health management according to the user's health condition and changes in physical condition.

[0305] "Personal data" refers to personal information and health information about individual users, including, specifically, age, gender, weight, height, exercise history, dietary history, and medications currently being taken.

[0306] "Health Management Program" means a series of activities and instructions designed to maintain or improve a User's health, including, but not limited to, exercise plans, dietary instructions, and rest instructions.

[0307] "Biometric information" refers to data obtained from the user's body, and specifically includes heart rate, blood pressure, body temperature, respiratory rate, etc.

[0308] "Real-time" refers to data collection and processing occurring immediately, meaning that it reflects the current state without delay.

[0309] "Server" refers to a computer system that collects, analyzes, stores, and transmits data over a network.

[0310] "Analysis" refers to processing collected data and extracting meaningful information, and specifically includes statistical analysis of data and the application of machine learning models.

[0311] "Notification" refers to the act of conveying information to users, and specifically includes push notifications from smartphone apps, emails, messages, etc.

[0312] "Adjustment" refers to changing and optimizing a health management program based on the analysis results, and specifically includes changing exercise intensity and modifying dietary content.

[0313] The present invention is a system for adjusting a health management program in real time in response to changes in a user's health condition and physical condition. Specific embodiments of this system will be described below.

[0314] Hardware and Software Configuration

[0315] Hardware

[0316] 1. Wearable devices: Used to collect biometric information from users. Specifically, devices equipped with sensors that measure heart rate, blood pressure, body temperature, etc. (e.g., smartwatches, fitness trackers) are used.

[0317] 2. Smartphone: Used as a relay device to send data collected from wearable devices to a server. The smartphone receives data via Bluetooth or Wi-Fi and sends it to the server via the Internet.

[0318] 3. Server: A computer system that collects, analyzes, stores, and transmits data. Use a server equipped with a high-performance processor and large memory capacity.

[0319] software

[0320] 1. Data analysis libraries: Use programming languages ​​such as Python and R and data analysis libraries such as Pandas and NumPy.

[0321] 2. Machine learning libraries: Use machine learning libraries such as TENSORFLOW (registered trademark) and scikit-learn to analyze data and make predictions.

[0322] 3. Smartphone app: An application for sending notifications to users, which works on iOS and Android platforms.

[0323] System Operation

[0324] Biometric data collection and transmission

[0325] The terminal (wearable device) collects the user's biometric information in real time. For example, when the user starts jogging, the heart rate sensor measures the user's heart rate and sends the data to a smartphone. The smartphone receives the data via Bluetooth and sends it to a server over the Internet.

[0326] Data analysis

[0327] The server analyzes the received vital signs. It uses Python to process the data using data analysis libraries such as Pandas and NumPy. For example, the server analyzes the user's heart rate data to see if it exceeds a certain threshold (e.g., 180 BPM).

[0328] Coordination of health management programs

[0329] The server then adjusts the health management program based on the analysis results. For example, if the user's heart rate exceeds 180 BPM, the server will change the program to reduce the intensity of the exercise. Specifically, it will instruct the user to slow down the pace of the exercise.

[0330] Notification of adjustment results

[0331] The server notifies the user of the adjustment results to their smartphone, which then uses a push notification to display a message to the user telling them to "slow down."

[0332] Examples of specific examples and prompts

[0333] As a concrete example, consider a scenario where a user is jogging: if the user's heart rate exceeds 180 BPM, the server will adjust the program to reduce the exercise intensity and notify the user to "slow down."

[0334] Example prompts to input to a generative AI model:

[0335] "Generate Python code that adjusts a health program to reduce exercise intensity if the user's heart rate exceeds 180 BPM."

[0336] In this way, the server, the terminal, and the user work together to realize the system for realizing appropriate health management according to the user's health condition. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0337] Step 1:

[0338] The device collects the user's biometric information in real time. Specifically, it measures data such as heart rate, blood pressure, and body temperature through sensors. For example, a smartwatch uses a heart rate sensor to measure the user's heart rate. The input is the user's biometric information, and the output is the measured biometric data.

[0339] Step 2:

[0340] The device sends the collected biometric information to a server via Bluetooth or Wi-Fi. For example, a smartwatch connects to the Internet via a smartphone and sends data to a server. The input is the measured biometric data, and the output is the data sent to the server.

[0341] Step 3:

[0342] The server analyzes the received biometric information. It uses Python to process the data using data analysis libraries such as Pandas and NumPy. Specifically, the server analyzes the user's heart rate data and checks whether it exceeds a certain threshold (e.g., 180 BPM). The input is the biometric information data sent to the server, and the output is the analysis result.

[0343] Step 4:

[0344] The server adjusts the health management program based on the analysis results. For example, if the user's heart rate exceeds 180 BPM, the server changes the program to reduce the exercise intensity. Specifically, it instructs the user to slow down the pace of exercise. The input is the analysis results, and the output is the adjusted health management program.

[0345] Step 5:

[0346] The server notifies the user of the adjustment results. It sends a message to the user via a smartphone app saying, "Please slow down." For example, an iOS app might display the message to the user using a push notification. The input is the adjusted health management program, and the output is a notification to the user.

[0347] (Application example 3)

[0348] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0349] Conventional health management systems provide health management programs based on users' personal data, but they have the problem of being unable to respond to changes in physical condition in real time. Also, when users are active in physical stores, they are unable to prompt them to take breaks at appropriate times, which can increase health risks.

[0350] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for improving the efficiency of health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suitable for each user, means for monitoring the user's biological information in real time, means for adjusting the health management program in real time according to the user's health condition and changes in physical condition, means for sending a notification encouraging the user to take a break based on the user's health condition, and means including an application installed on the smart device. This makes it possible to monitor the user's health condition in real time and encourage the user to take a break at the appropriate time.

[0351] "Personal data" is data, including personal and health information, about individual users.

[0352] "Health Management" refers to activities and programs designed to maintain or improve a User's health.

[0353] "Training" refers to exercises or physical activities designed to improve a user's physical strength or muscle power.

[0354] "Diet method" refers to a diet or exercise method for reducing a user's weight or body fat.

[0355] "Constitution" refers to the user's genetic or physiological characteristics.

[0356] "Current medications" refers to medications that the user is currently taking.

[0357] "Health Management Program" means a set of activities and instructions designed to maintain or improve a User's health.

[0358] "Biometric information" refers to physiological data such as a user's heart rate, blood pressure, and body temperature.

[0359] "Real-time" refers to data being processed and analyzed as it is generated.

[0360] "Monitoring" refers to the continuous observation and recording of a user's health condition.

[0361] "Notification" refers to a message that conveys information or instructions to the user.

[0362] "Smart device" refers to an electronic device with internet connectivity.

[0363] "Application" refers to a software program that provides a specific function or service.

[0364] A system for carrying out the present invention is for monitoring the health condition of a user in real time and providing an appropriate health management program. A specific embodiment of this system will be described below.

[0365] System Configuration

[0366] The system consists of the following major components:

[0367] 1. Smart devices (e.g., smart glasses)

[0368] 2. Heart rate sensor

[0369] 3. Server

[0370] 4. Application Software

[0371] Hardware and Software Use

[0372] Smart Devices

[0373] Smart devices are used to monitor users' biological information in real time, specifically smart glasses, which have a built-in heart rate sensor that continuously measures the user's heart rate.

[0374] server

[0375] The server receives biometric information sent from smart devices and processes the data in real time. The server is built using programming languages ​​such as Python. The server analyzes the user's health status and generates an appropriate health management program.

[0376] Application Software

[0377] The application software is installed on the smart device and is responsible for sending notifications to the user, such as displaying a notification encouraging the user to take a break if the user's heart rate exceeds a certain threshold.

[0378] Data processing and calculation

[0379] 1. Data collection: The heart rate sensor on the smart device collects the user's heart rate data.

[0380] 2. Data transmission: The collected data is transmitted to the server in real time.

[0381] 3. Data analysis: The server analyzes the received heart rate data and evaluates the user's health status.

[0382] 4. Program Adjustment: If the user's heart rate exceeds the threshold, the server adjusts the health management program and generates a notification to take a break.

[0383] 5. Send notifications: Notifications will be sent to your smart device based on the adjusted program.

[0384] Specific examples

[0385] As a user walks around the shopping mall, the heart rate sensor in the smart glasses measures the user's heart rate. If the heart rate exceeds 100, the server analyzes this data and sends a notification to the smart glasses saying, "Your heart rate is high. Please take a break."

[0386] Prompt Sentence Examples

[0387] "Generate a program that displays a notification on the smart glasses to encourage a break if the user's heart rate exceeds 100."

[0388] The above is a specific embodiment for carrying out the present invention. This system makes it possible to monitor the user's health condition in real time and encourage them to take a break at an appropriate time.

[0389] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0390] Step 1:

[0391] The heart rate sensor in the smart device (smart glasses) collects the user's heart rate data. The input is the user's real-time heart rate, and the output is the collected heart rate data. Specifically, the sensor comes into contact with the user's skin and measures the heart rate.

[0392] Step 2:

[0393] The smart device sends the collected heart rate data to the server. The input is the collected heart rate data, and the output is the data sent to the server. Specifically, the smart glasses send the data to the server using wireless communication.

[0394] Step 3:

[0395] The server analyzes the received heart rate data in real time. The input is the heart rate data sent to the server, and the output is the analysis result. Specifically, the server analyzes the data using a program such as Python to determine whether the heart rate exceeds a threshold.

[0396] Step 4:

[0397] The server adjusts the health management program based on the analysis results. The input is the analysis results, and the output is the adjusted health management program. Specifically, if the user's heart rate exceeds a threshold, the server will adjust the program, such as reducing the exercise intensity.

[0398] Step 5:

[0399] The server generates a notification encouraging a break based on the adjusted health management program. The input is the adjusted health management program, and the output is the generated notification. Specifically, the server generates a message saying, "Your heart rate is high. Please take a break."

[0400] Step 6:

[0401] The server sends the generated notification to the smart device. The input is the generated notification, and the output is the notification sent to the smart device. In concrete terms, the server sends the notification to the smart glasses via wireless communication.

[0402] Step 7:

[0403] The smart device displays the received notification to the user. The input is the notification sent from the server, and the output is the notification displayed to the user. The specific behavior is that the message "Your heart rate is high. Please take a break" is displayed on the smart glasses display.

[0404] 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.

[0405] "Example 1"

[0406] One embodiment of the present invention is a health management system that incorporates an emotion engine that recognizes the user's emotions. This system includes a means for streamlining health management, training, and diet methods based on the user's personal data, a means for adjusting health management according to the user's constitution and medications, and a means for providing a health management program tailored to each individual user. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions. This emotion engine infers emotions from the user's facial expressions, tone of voice, choice of words, and so on. For example, if it determines that the user is feeling stressed, it suggests exercise and diet programs that promote relaxation.

[0407] "Example 2"

[0408] Another embodiment is a system that collects personal data of a user, analyzes the collected personal data, and generates a health management program tailored to the user based on the analysis results. This system also incorporates an emotion engine that recognizes the user's emotions. This emotion engine grasps changes in the user's emotions in real time in their daily lives and uses this information to adjust the health management program. For example, when the user is feeling happy, the system recommends activities to maintain that emotion, and conversely, when the user is feeling sad or angry, the system recommends activities to alleviate those emotions.

[0409] "Example 3"

[0410] Another embodiment is a system that adjusts a health management program in real time according to changes in a user's health condition or physical condition. This system also incorporates an emotion engine that recognizes the user's emotions. This emotion engine grasps changes in the user's emotions in real time and uses that information to adjust the health management program. For example, if it determines that the user is feeling stressed, it will suggest exercise, diet, and relaxation methods to reduce stress.

[0411] The processing flow of each embodiment will be described below.

[0412] "Example 1"

[0413] Step 1: Collect personal data about the user, including their age, gender, weight, height, body fat percentage, exercise habits, and dietary habits.

[0414] Step 2: Analyze the collected personal data. This analysis will help understand the user's health, physical condition, lifestyle habits, etc.

[0415] Step 3: Recognize the user's emotions using the emotion engine. Infer emotions from the user's facial expressions, tone of voice, choice of words, etc.

[0416] Step 4: Based on the analysis and emotion recognition results, a health management program tailored to the user is generated. For example, if the system determines that the user is feeling stressed, it will suggest exercise and diet programs that promote relaxation.

[0417] "Example 2"

[0418] Step 1: Collect personal data of users and analyze the collected personal data.

[0419] Step 2: Use the emotion engine to understand the user's emotional changes in real time in their daily lives.

[0420] Step 3: Use the emotion recognition results to adjust the health management program. For example, if the user is feeling happy, activities to maintain that emotion are recommended. Conversely, if the user is feeling sad or angry, activities to alleviate those emotions are recommended.

[0421] "Example 3"

[0422] Step 1: Understand the user's health status and changes in physical condition in real time.

[0423] Step 2: The emotion engine is used to understand changes in the user's emotions in real time. Step 3: The health management program is adjusted in real time based on the health status, changes in physical condition, and emotion recognition results. For example, if it determines that the user is feeling stressed, it will suggest exercise, diet, and relaxation methods to reduce stress.

[0424] Example 1

[0425] Next, a description will be given of Example 1 of Form 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."

[0426] Conventional health management systems have the problem of being unable to fully utilize the personal data and biometric information of individual users, and can only provide general health management programs. Furthermore, because health management does not take into account the user's emotional state, it is difficult to provide appropriate programs that respond to stress and emotional changes. This results in inefficient health management for users, and the inability to provide optimal programs tailored to individual needs.

[0427] 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.

[0428] In this invention, the server includes means for streamlining health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suitable for each user, means for collecting the user's biometric information, means for analyzing the collected biometric information, means for generating a health management program based on the analysis results, means for recognizing the user's emotions, means for adjusting the health management program based on the emotional state, means for creating a program customized for each user using a generative AI model, and means for suggesting an optimal health management program for the user. This makes it possible to provide an optimal health management program that comprehensively takes into account the personal data, biometric information, and emotional state of each user.

[0429] "Personal data" refers to information about individual users, including their age, gender, medical history, and medications they are taking.

[0430] "Biometric information" is data that indicates the user's physical condition, including heart rate, blood pressure, body temperature, etc.

[0431] "Health Management Program" means a plan that includes specific instructions and suggestions for maintaining and improving the User's health.

[0432] "Emotion recognition" is a technology that estimates a user's emotional state from their facial expressions, tone of voice, choice of words, etc.

[0433] A "generative AI model" is an algorithm or system that uses artificial intelligence to analyze data and generate programs customized for individual users.

[0434] "Analysis" is the process of assessing a user's health and emotional state based on collected data.

[0435] "Suggestion" refers to the act of presenting the optimal health management program to the user based on the analysis results.

[0436] "Tuning" is the process of modifying and optimizing your health management program based on your constitution, medications, and emotional state.

[0437] "Collection" refers to the act of obtaining a user's personal data or biometric information.

[0438] "Efficiency" refers to the means of making health management, training, and diet methods more effective and faster.

[0439] This invention is a system that improves the efficiency of health management, training, and diet methods based on the personal data of each individual user. The system adjusts health management according to the user's constitution and medications, and provides a health management program that is suitable for each individual user.

[0440] Hardware and software used

[0441] Hardware:

[0442] Wearable devices (e.g., smartwatches for measuring heart rate and blood pressure)

[0443] Smartphone (used to collect and transmit data)

[0444] Server (used for analyzing data and generating programs)

[0445] software:

[0446] Data analysis tools (e.g., Python, R)

[0447] Emotion recognition engine (e.g., emotion recognition API)

[0448] Generative AI models (algorithms that create programs customized for each individual user)

[0449] Explanation of program processing

[0450] User personal data input:

[0451] Users enter their personal data (age, gender, medical history, medications they are taking, etc.) through a smartphone application, and the device sends this data to a server.

[0452] Biometric data collection:

[0453] The device (e.g., a wearable device) collects the user's biometric information, such as heart rate, blood pressure, and body temperature, in real time. The collected data is periodically sent to a server.

[0454] Data analysis:

[0455] The server analyzes the received personal data and biometric information, using data analysis tools such as Python and R to assess the user's health status, for example, to detect abnormalities in heart rate and blood pressure.

[0456] Health Management Program Generation:

[0457] The server generates an optimal health management program for each user based on the analysis results. For users taking high blood pressure medication, it suggests an exercise program to lower their blood pressure. Generative AI models are used to create customized programs for each individual user.

[0458] Emotion recognition:

[0459] The device uses an emotion recognition engine to analyze the user's facial expressions, tone of voice, choice of words, etc. The server then uses this data to estimate the user's emotional state.

[0460] Best program suggestions:

[0461] The server comprehensively evaluates the user's health and emotional state and suggests an optimal health management program. For example, if the user is feeling stressed, it will suggest an exercise or diet program that promotes relaxation. The suggested program is then notified to the user via their device.

[0462] Examples of specific examples and prompts

[0463] Examples:

[0464] User A opens the application and enters his / her personal data (age: 45, gender: male, medical history: high blood pressure, medications being taken: high blood pressure medication). The device (smartphone) sends this data to the server.

[0465] User A wears a smartwatch that measures his / her heart rate, blood pressure, and body temperature in real time. These data are sent to a smartphone via Bluetooth and then transferred to a server.

[0466] The server uses Python to analyze User A's heart rate and blood pressure data to detect signs of high blood pressure. Based on the analysis results, the server generates an exercise program suitable for User A (e.g., walking 30 minutes every morning).

[0467] Furthermore, to determine whether User A is feeling stressed, the device analyzes User A's facial expressions and tone of voice using an emotion recognition engine. If the server determines that User A is feeling stressed, it will suggest a yoga program to promote relaxation and a meal plan that is effective in reducing stress.

[0468] Example prompt sentence:

[0469] "User A is taking medication for high blood pressure and has been feeling stressed recently. Please suggest an appropriate health management program based on User A's heart rate, blood pressure, and body temperature data."

[0470] In this way, a system is realized in which the server, terminals, and users work together to provide an optimal health management program for each individual user.

[0471] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0472] Step 1:

[0473] User personal data entry

[0474] Users enter their personal data (age, gender, medical history, medications they are taking, etc.) through a smartphone application, and the device sends this data to a server.

[0475] Input: User's personal data

[0476] Output: Personal data sent to the server

[0477] Specific behavior: When a user enters data into an application's input form and presses the "Submit" button, the data is sent to the server.

[0478] Step 2:

[0479] Biometric data collection

[0480] The device (e.g., a wearable device) collects the user's biometric information, such as heart rate, blood pressure, and body temperature, in real time. The collected data is periodically sent to a server.

[0481] Input: User's biometric information

[0482] Output: Biometric information sent to the server

[0483] How it works: The wearable device measures the user's heart rate, blood pressure, and temperature and transmits the data via Bluetooth to a smartphone, which then forwards the data to a server.

[0484] Step 3:

[0485] Data analysis

[0486] The server analyzes the received personal data and biometric information, using data analysis tools such as Python and R to assess the user's health status, for example, to detect abnormalities in heart rate and blood pressure.

[0487] Input: Personal data, biometric information

[0488] Output: Health status assessment results

[0489] What it does: The server runs a Python script to analyze the data and detect outliers. The results are stored in a database.

[0490] Step 4:

[0491] Health management program generation

[0492] The server generates an optimal health management program for each user based on the analysis results. For users taking high blood pressure medication, it suggests an exercise program to lower their blood pressure. Generative AI models are used to create customized programs for each individual user.

[0493] Input: Health status assessment results

[0494] Output: Customized health management program

[0495] How it works: The server uses the generative AI model to generate exercise and meal plans based on the user's health status, and the generated programs are stored in a database.

[0496] Step 5:

[0497] emotion recognition

[0498] The device uses an emotion recognition engine to analyze the user's facial expressions, tone of voice, choice of words, etc. The server then uses this data to estimate the user's emotional state.

[0499] Input: Your facial expressions, tone of voice, and choice of words

[0500] Output: Estimated emotional state

[0501] Specific operation: The device uses a camera and microphone to collect the user's facial expressions and voice, and sends the data to an emotion recognition engine. The server receives the results of the emotion recognition engine and estimates the user's emotional state.

[0502] Step 6:

[0503] Proposal of the best program

[0504] The server comprehensively evaluates the user's health and emotional state and suggests an optimal health management program. For example, if the user is feeling stressed, it will suggest an exercise or diet program that promotes relaxation. The suggested program is then notified to the user via their device.

[0505] Input: Health status assessment results, emotional state estimation results

[0506] Output: Recommendations for optimal health management programs

[0507] How it works: The server selects the optimal program based on the user's health and emotional state and notifies the user through a smartphone application. The user can then review and implement the suggested program.

[0508] (Application example 1)

[0509] Next, a description will be given of Application Example 1 of Embodiment 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."

[0510] Conventional health management systems provide health management, training, and diet methods based on the user's personal data, but few systems take into account the user's emotional state and meal suggestions. There is also a need for more personalized health management by proposing meal menus based on the user's health and emotional state and linking with delivery services.

[0511] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for improving the efficiency of health management, training, and diet methods based on the personal data of each individual user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suitable for each individual user, means for combining an emotion engine that recognizes the user's emotions to provide a health management program according to the user's emotional state, and means for proposing an optimal meal menu for each individual user based on the user's health data and emotion data and providing a delivery service. This enables personalized health management and meal suggestions according to the user's health and emotional state.

[0512] "Personal data" refers to biometric information about individual users, such as heart rate, blood pressure, and body temperature, as well as information about dietary history and medications entered by users.

[0513] "Health Management" refers to activities and programs designed to maintain or improve a User's health.

[0514] "Training" refers to an exercise program designed to improve a user's physical strength and muscle power.

[0515] "Diet method" refers to dietary and exercise guidance to improve the user's weight management and nutritional balance.

[0516] An "emotion engine" is a system that infers emotions from a user's facial expressions, tone of voice, choice of words, etc.

[0517] "Health Management Program" refers to a specific plan for maintaining and improving health that is provided based on the user's personal and emotional data.

[0518] "Meal Menu" refers to specific meal suggestions based on the user's health and emotional state.

[0519] "Delivery service" refers to a service that delivers suggested meal menus to users.

[0520] The following system configuration will be described as an embodiment of the present invention.

[0521] System Configuration

[0522] Hardware

[0523] Smartphones: Equipped with a camera and microphone, they collect the user's facial expressions and tone of voice.

[0524] Smartwatches and fitness trackers: Collect biometric information such as heart rate, blood pressure, and temperature.

[0525] Server: Collects, analyzes, and stores data, and generates health management programs.

[0526] software

[0527] EmotionRecognizer: A library that recognizes emotions by analyzing the user's facial expressions and tone of voice.

[0528] HealthDataCollector: A library for collecting health data from smartwatches and fitness trackers.

[0529] requests: A Python library for sending HTTP requests.

[0530] Data processing and calculation

[0531] Health data collection

[0532] It collects biometric information such as heart rate, blood pressure, and temperature from the user's smartwatch or fitness tracker, and sends this data to a server using the HealthDataCollector library.

[0533] Collecting Emotional Data

[0534] It uses the smartphone's camera and microphone to collect the user's facial expressions and tone of voice, and uses the EmotionRecognizer library to recognize the user's emotions from this data.

[0535] Health management program generation

[0536] Based on the collected health and emotional data, the system generates a health management program that is optimal for each user. For example, it will suggest low-salt meals to a user with high blood pressure, or suggest relaxation exercises to a user experiencing stress.

[0537] Meal menu suggestions and delivery service

[0538] Based on the generated health management program, the system will suggest the optimal meal menu for the user, and the suggested meal menu will be ordered using the delivery service's API and delivered to the user.

[0539] Specific examples

[0540] User A's case

[0541] Health data: High blood pressure (140 / 90)

[0542] Emotional data: Feeling stressed

[0543] Suggested meals: Low-sodium menus using ingredients with a relaxing effect

[0544] User B's case

[0545] Health data: Normal blood pressure (120 / 80)

[0546] Emotional data: Relaxed

[0547] Suggested diet: Balanced diet

[0548] Prompt Sentence Examples

[0549] Design an application that suggests the optimal meal menu for each user based on their health data (heart rate, blood pressure, body temperature, etc.) and emotional data (facial expressions, tone of voice), and provides a delivery service. Specifically, the application will collect data from smartphones and smartwatches, suggest meal menus based on the user's health and emotional state, and confirm the order in cooperation with a delivery service.

[0550] In this way, personalized health management and dietary suggestions based on the user's health and emotional state become possible.

[0551] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0552] Step 1:

[0553] A user wears a smartwatch or fitness tracker to collect biometric information such as heart rate, blood pressure, and body temperature. This data is sent to a smartphone via Bluetooth or Wi-Fi. The smartphone then uses the HealthDataCollector library to send this data to a server. The input is the biometric information, and the output is the health data sent to the server.

[0554] Step 2:

[0555] The user uses the smartphone's camera and microphone to collect their facial expressions and tone of voice. The EmotionRecognizer library is used to recognize the user's emotions from this data. The input is facial expression and voice data, and the output is recognized emotion data. The smartphone sends this emotion data to the server.

[0556] Step 3:

[0557] The server integrates the received health data using the HealthDataCollector library and the received emotion data using the EmotionRecognizer library. This allows for unified management of the user's health and emotion states. The input is health data and emotion data, and the output is the integrated user data.

[0558] Step 4:

[0559] The server generates an optimal health management program for each user based on the integrated user data. For example, it suggests low-salt meals to a user with high blood pressure, and relaxation exercises to a user feeling stressed. The input is the integrated user data, and the output is the generated health management program.

[0560] Step 5:

[0561] The server proposes an optimal meal menu to the user based on the generated health management program. The proposed meal menu is then ordered using the delivery service's API. The input is the health management program, and the output is the proposed meal menu and the confirmed order.

[0562] Step 6:

[0563] The delivery service delivers suggested meals to the user based on the confirmed order. The input is the confirmed order and the output is the meal delivered to the user.

[0564] In this way, personalized health management and dietary suggestions are realized according to the user's health and emotional state.

[0565] Example 2

[0566] Next, a description will be given of Example 2 of Form 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."

[0567] Conventional health management systems provide health management programs based on the user's personal data, but they do not take into account the user's emotional state, which means they are unable to adequately support the user's psychological health. Furthermore, it is difficult to adjust the health management program in real time, which means they are unable to quickly respond to changes in the user's health or physical condition.

[0568] The specification processing by specification processing unit 290 of data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for improving the efficiency of health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suited to each user, means for collecting and analyzing the user's emotional data, and means for adjusting the health management program in real time based on the emotional data. This makes it possible to provide a health management program that takes the user's personal data and emotional data into consideration in a comprehensive manner.

[0569] "Personal data" refers to individual information about an individual user, such as the user's age, gender, weight, height, lifestyle habits, and genetic information.

[0570] A "health management program" is a plan for maintaining or improving health, such as a meal plan or exercise plan, that is generated based on the user's personal and emotional data.

[0571] "Emotion data" is information that indicates changes in the user's emotions in their daily lives, and includes emotional states such as joy, sadness, and anger.

[0572] "Real-time adjustment" means instantly responding to changes in the user's health and emotional state, changing and optimizing the health management program on the fly.

[0573] "Server" means a computer system that receives, stores, and analyzes users' personal data and emotional data, and generates and provides health management programs.

[0574] "Means for collection" refers to methods and devices for acquiring a user's personal data and emotional data, including, for example, input forms and sensors.

[0575] "Means for analyzing" refers to methods or devices for processing collected data and assessing a user's health or emotional state, including, for example, data analysis software and machine learning models.

[0576] The "means for providing" refers to a method or device for communicating the generated health management program to the user, and includes, for example, a display or notification function of the terminal.

[0577] MODE FOR CARRYING OUT THE INVENTION

[0578] This invention relates to a system that collects and analyzes personal data and emotional data of a user and provides an individually optimized health management program. Specific embodiments of this system are described below.

[0579] 1. Entering your personal data

[0580] The user uses the terminal to input their personal data, such as age, sex, weight, height, lifestyle habits, and genetic information, into an input form. The terminal then transmits this data to the server.

[0581] 2. Receipt and storage of data

[0582] The server receives the personal data sent from the device. The received data is stored in a database (e.g., MySQL or PostgreSQL). The server checks the integrity of the data and whether any data is missing.

[0583] 3. Data Preprocessing

[0584] The server preprocesses the stored data, specifically by imputing missing values ​​and normalizing the data. For example, it creates a data frame using the Python pandas library and imputes missing values ​​with the mean.

[0585] 4. Data Analysis

[0586] The server analyzes the preprocessed data using machine learning models (e.g., Random Forest or XGBoost from scikit-learn) and applies these models to predict the user's health and condition.

[0587] 5. Generating a Health Management Program

[0588] The server then generates a health management program tailored to the user based on the analysis results. Specifically, it creates a program that includes a meal plan and an exercise plan. For example, if the user is at high risk of diabetes, it will recommend a low-carb meal plan.

[0589] 6. Providing health management programs

[0590] The server transmits the generated health management program to the terminal, which displays the program to the user, who then performs health management according to the program provided.

[0591] 7. Emotion Data Collection (Alternative Embodiment)

[0592] Users input their emotional changes in daily life into the device, which then transmits the emotional data to the server in real time. For example, a user can select their current emotional state (e.g., joy, sadness, anger) using a smartphone app.

[0593] 8. Analyzing Emotional Data

[0594] The server analyzes the emotional data and uses an emotion engine (e.g., Microsoft® Azure® Emotion API) to understand the user's emotional state. The server then adjusts the health management program based on the emotional data.

[0595] 9. Emotion-Based Program Adjustments

[0596] The server then adjusts the health management program based on the user's emotional state based on the analysis results of the emotion engine. For example, if the user is feeling happy, it will recommend activities to maintain that emotion. Conversely, if the user is feeling sad or angry, it will recommend activities to alleviate those emotions.

[0597] Examples of specific examples and prompts

[0598] Specific examples

[0599] Assume the user is a 40-year-old man, weighs 80 kg, is 175 cm tall, and exercises three times a week. Genetic information indicates a family history of diabetes. When the user enters data into the system, the server receives the data and stores it in a database. The server then preprocesses the data and analyzes it using a machine learning model. Based on the analysis results, a health management program including a meal plan and exercise plan for diabetes prevention is generated and sent to the device. The user can then review and implement the program through the device.

[0600] Prompt Sentence Examples

[0601] "A 40-year-old male weighs 80 kg, is 175 cm tall, exercises three times a week, and has a family history of diabetes. Please generate a health management program appropriate for this user."

[0602] In this way, the system collects and analyzes the user's personal and emotional data to provide an individually optimized health management program.

[0603] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0604] Step 1: Enter your personal data

[0605] The user uses the terminal to input their own personal data. Specifically, they fill in an input form with data such as age, sex, weight, height, lifestyle habits, and genetic information. The input data is sent from the terminal to the server. The input data is the user's individual information, and the output is the personal data sent to the server.

[0606] Step 2: Receiving and storing data

[0607] The server receives the personal data sent from the terminal. The received data is stored in a database (e.g., MySQL or PostgreSQL). The server verifies the integrity of the data and checks whether any data is missing. The input is the personal data sent from the terminal, and the output is the consistent data stored in the database.

[0608] Step 3: Preprocessing the data

[0609] The server preprocesses the stored data. Specifically, it imputes missing values ​​and normalizes the data. For example, it creates a data frame using Python's pandas library and imputes missing values ​​with the mean value. The input is the personal data stored in the database, and the output is the preprocessed data.

[0610] Step 4: Analyze the data

[0611] The server analyzes the preprocessed data using machine learning models (e.g., Random Forest or XGBoost from scikit-learn). The server applies these models to predict the user's health and physical condition. The input is the preprocessed data, and the output is the analysis results.

[0612] Step 5: Generate a Health Management Program

[0613] The server generates a health management program suited to the user based on the analysis results. Specifically, it creates a program that includes a meal plan and an exercise plan. For example, if the user is at high risk of diabetes, it recommends a low-carbohydrate meal plan. The input is the analysis results, and the output is the generated health management program.

[0614] Step 6: Providing a health management program

[0615] The server sends the generated health management program to the terminal. The terminal displays the program to the user. The user performs health management according to the provided program. The input is the generated health management program, and the output is the program displayed on the terminal.

[0616] Step 7: Collecting Emotional Data (Alternative Embodiment)

[0617] The user inputs emotional changes in their daily life into the device. The device then transmits the emotional data to the server in real time. For example, a user uses a smartphone app to select their current emotional state (joy, sadness, anger, etc.). The input is the user's emotional data, and the output is the emotional data transmitted to the server.

[0618] Step 8: Analyze the sentiment data

[0619] The server analyzes the emotional data and uses an emotion engine (for example, Microsoft Azure's Emotion API) to understand the user's emotional state. The server then adjusts the health management program based on the emotional data. The input is the emotional data sent to the server, and the output is the analysis result of the emotional state.

[0620] Step 9: Adjust your program based on emotions

[0621] The server adjusts the health management program according to the user's emotional state based on the analysis results of the emotion engine. For example, when the user is feeling happy, it recommends activities to maintain that emotion. Conversely, when the user is feeling sad or angry, it recommends activities to alleviate those emotions. The input is the analysis result of the emotional state, and the output is the adjusted health management program.

[0622] (Application example 2)

[0623] Next, a description will be given of Application Example 2 of Form 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."

[0624] Conventional health management systems provide health management programs based on the user's personal data, but do not take the user's emotional state into account, which means they are unable to provide appropriate health management according to the user's psychological state. Furthermore, there is a lack of means to provide real-time feedback to users in physical stores, making it difficult to improve the user experience.

[0625] 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.

[0626] In this invention, the server includes means for streamlining health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suited to each user, means for recognizing the user's emotions in real time and adjusting the health management program based on the emotions, and means for collecting the user's personal data and emotional data in a physical store and providing feedback in real time. This enables appropriate health management that takes the user's emotional state into consideration, and makes it possible to provide feedback to the user in real time even in a physical store.

[0627] "Personal data" refers to information about an individual, such as a user's age, gender, weight, height, lifestyle, and genetic information.

[0628] "Health Management Program" means a plan that includes specific activities and instructions for maintaining and improving the health of a User.

[0629] The "emotion engine" is a system that recognizes the user's emotional state in real time and provides appropriate feedback and adjustments based on that information.

[0630] "Real-time feedback" refers to advice or information provided instantly to a user based on their current state or behavior.

[0631] A "brick and mortar store" is a physical store that users visit in person, such as a fitness gym or health food store.

[0632] "Collection means" refers to a method or device for acquiring personal data and emotional data of a user.

[0633] The "analysis means" refers to a method or device for analyzing the collected data and generating a health management program suited to the user.

[0634] The "adjustment means" refers to a method or device for appropriately modifying the health management program in response to the user's health and emotional state.

[0635] To implement the present invention, the following system configuration is required.

[0636] System Program

[0637] The system includes a program that collects and analyzes the user's personal and emotional data in real time to generate and adjust a health management program. Specifically, it uses the following hardware and software:

[0638] Hardware used

[0639] 1. Smartphone: Used to input user personal data and collect emotional data.

[0640] 2. Smart glasses: These are used to capture the user's facial expressions in real time and collect emotional data.

[0641] 3. Wearable devices: Used to collect user biometric data (e.g., heart rate, steps).

[0642] 4. Camera: Use the camera built into your smartphone or smart glasses to capture your facial expressions.

[0643] Software used

[0644] 1. OpenCV: An image processing library used to analyze the user's facial expressions.

[0645] 2. EmotionRecognizer: An emotion recognition library used to analyze the user's emotional state in real time.

[0646] 3. HealthProgramGenerator: A health management program generation library used to generate optimal health management programs based on collected personal data and emotional data.

[0647] Data processing and calculation

[0648] 1. Collection of Personal Data:

[0649] Through smartphones or smart glasses, the system collects information on the user's age, gender, weight, height, lifestyle habits, and genetic information.

[0650] It also integrates biometric data from wearable devices.

[0651] 2. Emotion Recognition:

[0652] The camera is used to capture the user's facial expressions and the emotions are analyzed using the EmotionRecognizer library.

[0653] Using voice recognition technology, it infers emotions from the user's tone of voice and choice of words.

[0654] 3. Generating Health Management Programs:

[0655] Based on the collected personal data and emotional data, the HealthProgramGenerator library generates an optimal health management program.

[0656] 4. Real-time feedback:

[0657] The generated health management program is displayed to the user in real time, and appropriate advice and information is provided.

[0658] Specific examples

[0659] Use at fitness gyms

[0660] When a user enters a fitness gym, the smart glasses analyze the user's facial expressions to detect stress, and the system recommends relaxation exercises and provides real-time feedback to the user.

[0661] Use in health food stores

[0662] While the user is walking through a health food store, their smartphone analyzes their personal data and recommends the most suitable supplements. It also takes into account the user's emotional state, suggesting products with a relaxing effect if the user is feeling stressed.

[0663] Prompt Sentence Examples

[0664] Collect user's age, gender, weight, height, lifestyle habits, and genetic information, and use an emotion engine to analyze the user's emotional state in real time. Based on this data, develop an application that generates the optimal health management program for the user and provides real-time feedback in fitness gyms and health food stores.

[0665] In this way, appropriate health management that takes into account the user's emotional state becomes possible, and feedback can be provided to the user in real time even in physical stores.

[0666] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0667] Step 1:

[0668] The user activates their smartphone or smart glasses and inputs their personal data, including age, gender, weight, height, lifestyle habits, and genetic information. The device collects this data and sends it to a server. The input data is used as the basis for analyzing the user's health condition.

[0669] Step 2:

[0670] The server receives the collected personal data and stores it in a database. The server then analyzes this data to assess the user's health and physical condition. Statistical methods and machine learning algorithms are used for the analysis. The output is an assessment of the user's health condition.

[0671] Step 3:

[0672] The user wears the smart glasses and the camera captures the user's facial expressions. The device inputs the captured image data into the EmotionRecognizer library, which analyzes the user's emotional state in real time. The analysis results in the user's emotional state (e.g., joy, sadness, anger).

[0673] Step 4:

[0674] The server receives the analyzed emotional data and integrates it with the personal data. It then uses the HealthProgramGenerator library to generate an optimal health management program for the user. This program is customized taking into account the user's health and emotional state. The output is a personalized health management program.

[0675] Step 5:

[0676] When a user visits a physical store (e.g., a fitness gym or health food store), the device continuously collects the user's personal and emotional data in real time. The server then adjusts the health management program in real time based on this data. The adjusted program includes advice and activities that are optimal for the user's current condition.

[0677] Step 6:

[0678] The device will then display a tailored health program to the user in real time. For example, a gym might advise on proper exercise form and intensity, or a health food store might recommend the best supplements. The user can then adjust their behavior based on this feedback.

[0679] Step 7:

[0680] Once the user takes action based on the feedback, the device again collects the results and sends them to the server, which then uses the collected data to further optimize the health management program, enabling continuous feedback and adjustments based on the user's health and emotional state.

[0681] Example 3

[0682] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0683] While conventional health management systems can provide health management programs based on users' personal data, they lack the ability to make real-time adjustments or make suggestions based on the user's emotions. As a result, they are unable to respond quickly to changes in the user's health condition or physical condition, making effective health management difficult.

[0684] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0685] In this invention, the server includes means for streamlining health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suited to each user, means for monitoring the user's biological information in real time, means for storing the monitored biological information in a database, means for analyzing the stored biological information, means for adjusting the health management program based on the analysis results, means for recognizing the user's emotions, means for generating suggestions for the user based on the recognized emotion data, and means for notifying the user of the generated suggestions. This enables quick response to changes in the user's health condition and physical condition, enabling effective health management.

[0686] "Personal data" refers to personal information about individual users, including their health status, physical constitution, medications they are taking, and lifestyle habits.

[0687] A "health management program" is a plan that includes a series of activities and instructions designed to maintain or improve a user's health.

[0688] "Biometric information" refers to data obtained from the user's body, and includes physiological information such as heart rate, blood pressure, and body temperature.

[0689] "Monitoring" is the process of observing and recording a user's biometric information in real time.

[0690] A "database" is a system for efficiently storing, managing, and searching collected data.

[0691] "Analysis" is the process of examining collected data in detail and extracting meaningful information.

[0692] "Emotion recognition" is a technology that determines a user's emotions from their facial expressions, tone of voice, etc.

[0693] "Suggestions" are specific actions or advice provided based on the user's health status and emotions.

[0694] A "notification" is a message or alert that conveys information to the user.

[0695] MODE FOR CARRYING OUT THE INVENTION

[0696] The present invention is a system for adjusting a health management program in real time in response to changes in a user's health condition and physical condition. Specific embodiments of this system will be described below.

[0697] server

[0698] The server uses dedicated software to monitor the user's biometric information in real time. This software collects biometric information from the user's wearable device (e.g., fitness tracker, smartwatch) and stores it in a database. The database uses a relational database such as MySQL or PostgreSQL. The collected data is analyzed using Python libraries such as Pandas and NumPy. Based on the analysis results, the server adjusts the health management program and notifies the device. Notifications are sent via a REST API.

[0699] Terminal

[0700] The device receives notifications from the server and makes appropriate suggestions to the user. The device is equipped with an emotion engine to recognize the user's emotions. This emotion engine uses libraries such as OpenCV and TensorFlow to analyze the user's facial expressions and tone of voice using a camera and microphone. Based on the recognized emotion data and notifications from the server, the device generates suggestions for the user and notifies the user via push notifications or voice assistants (e.g., voice recognition systems, virtual assistants).

[0701] User

[0702] The user wears a wearable device and goes about their daily life. The device measures biometric information such as heart rate and body temperature in real time and sends it to a server. The user checks the notifications from the device and implements a health management program according to the suggestions. For example, the user may take actions such as reducing exercise intensity or trying relaxation techniques.

[0703] Specific examples

[0704] Example 1: Heart rate monitoring

[0705] Server: Adjusts the health management program to reduce exercise intensity if the user's heart rate exceeds 120 beats per minute.

[0706] On your device: Notify the user when their heart rate is high and suggest reducing the intensity of their exercise.

[0707] User: Follow the device's suggestions and reduce the intensity of your exercise.

[0708] Example 2: Emotion recognition

[0709] Server: Collects the user's emotional data and suggests relaxation methods if it determines that the user's stress level is high.

[0710] Device: Determines if the user is feeling stressed and suggests relaxation methods.

[0711] User: Follow the device's suggestions and try relaxation techniques.

[0712] Prompt Sentence Examples

[0713] "Explain how to adjust the fitness program to reduce exercise intensity if the user's heart rate exceeds 120 beats per minute."

[0714] "Please explain how a system can suggest relaxation techniques if it determines that the user is stressed."

[0715] In this way, the roles of the server, terminal, and user are clarified, and specific methods for using the hardware and software are explained, making it easier to understand the operation of the system in natural language. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0716] Step 1:

[0717] The server collects biometric information from the user's wearable device.

[0718] Input: Biometric information such as heart rate, blood pressure, and temperature sent from a wearable device.

[0719] Data processing: The collected biometric information is stored in a database.

[0720] Output: Biometric information stored in a database.

[0721] Specific operation: The server receives data from the wearable device and stores it in a relational database such as MySQL or PostgreSQL.

[0722] Step 2:

[0723] The server analyzes the stored biometric information.

[0724] Input: Biometric information stored in a database.

[0725] Data computation: Analyze data using Python libraries such as Pandas and NumPy.

[0726] Output: Analysis results (e.g., warning if heart rate exceeds a certain threshold).

[0727] Specific operation: The server retrieves biometric information from the database and performs analysis to detect abnormal values.

[0728] Step 3:

[0729] The server adjusts the health management program based on the analysis results.

[0730] Input: Analysis results.

[0731] Data processing: Adjusting the content of health management programs.

[0732] Output: Coordinated health care program.

[0733] Specific operation: Based on the analysis results, the server makes adjustments such as reducing the exercise intensity and generates a new program.

[0734] Step 4:

[0735] The server notifies the terminal of the adjusted health management program.

[0736] Enter: coordinated health care programs.

[0737] Data processing: Generation of notification messages.

[0738] Output: Notification to terminal.

[0739] Specific operation: The server sends a notification to the device using the REST API.

[0740] Step 5:

[0741] The terminal receives the notification from the server.

[0742] Input: Notification from the server.

[0743] Data processing: Analysis of notification content.

[0744] Output: Prepare to notify the user.

[0745] Specific operation: The terminal receives the HTTP request and analyzes the notification content.

[0746] Step 6:

[0747] The device recognizes the user's emotions.

[0748] Input: Data from the camera or microphone.

[0749] Data Computation: Sentiment analysis using libraries such as OpenCV and TensorFlow.

[0750] Output: Recognized emotion data.

[0751] Specific operation: The device uses a camera and microphone to analyze the user's facial expressions and tone of voice to recognize emotions.

[0752] Step 7:

[0753] The device generates suggestions for the user based on the recognized emotion data and notifications from the server.

[0754] Input: Recognized emotion data and notification from the server.

[0755] Data processing: Proposal content generation.

[0756] Output: Suggestions to the user.

[0757] Specific operation: Based on the emotional data and notification content, the device generates suggestions such as relaxation methods and adjustments to exercise intensity.

[0758] Step 8:

[0759] The device notifies the user of the generated suggestions.

[0760] Input: The generated proposal.

[0761] Data processing: Generation of notification messages.

[0762] Output: Notification to the user.

[0763] What it does: The device notifies the user of the suggestion via push notification or voice assistant.

[0764] Step 9:

[0765] The user checks the notification from the terminal and executes the health management program according to the suggestion.

[0766] Input: Notifications from your device.

[0767] Data processing: Implementing the proposal.

[0768] Output: The health care program that was executed.

[0769] Specific actions: The user follows the device's suggestions and takes actions such as reducing exercise intensity or trying relaxation techniques.

[0770] (Application example 3)

[0771] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0772] Conventional health management systems can provide health management programs based on the user's personal data, but they have difficulty responding to changes in health status or emotions in real time. In addition, there is a lack of means to provide appropriate advice based on changes in the user's health status or emotions, which makes it difficult to adequately manage the user's health.

[0773] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for improving the efficiency of health management, training, and diet methods based on the personal data of each individual user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suitable for each individual user, means for adjusting the health management program in real time according to changes in the user's health condition and physical condition, means for recognizing the user's emotions and adjusting the health management program based on those changes, and means for providing appropriate advice based on changes in the user's health condition and emotions. This makes it possible to provide real-time health management and appropriate advice according to changes in the user's health condition and emotions.

[0774] "Personal Data" refers to personal information about an individual user, including information about their health, physical constitution, medications they are taking, training history, and dietary habits.

[0775] A "health management program" is a plan that includes a series of activities and instructions designed to maintain or improve a user's health.

[0776] "Real-time adjustment" means instantly reflecting changes in the user's health condition and emotions and making appropriate changes to the health management program on the spot.

[0777] The "emotion engine" is a technology that recognizes the user's emotions and grasps their changes in real time.

[0778] "Advice" is specific guidance or suggestions provided based on the user's health and emotional changes.

[0779] A "generative AI model" is an algorithm or system that uses artificial intelligence to analyze data and generate appropriate advice or programs.

[0780] A "smart device" is an electronic device that is connected to the Internet and has the ability to collect, process, and display user data.

[0781] The system for implementing this invention monitors changes in a user's health condition and emotions in real time and adjusts a health management program based on the results. A specific embodiment of this system will be described below.

[0782] System Configuration

[0783] The system consists of the following main components:

[0784] 1. Smart devices (e.g., smart glasses, smartphones)

[0785] 2. Server

[0786] 3. Generative AI Models

[0787] Hardware and Software

[0788] Smart devices are equipped with sensors that collect users' biometric information (heart rate, stress level, etc.) in real time, and these devices send the collected data to a server.

[0789] The server processes the received data and generates appropriate advice and health management programs using generative AI models. The server analyzes the data using Python and real-time data processing libraries (e.g., pandas, numpy).

[0790] The generative AI model includes algorithms for generating advice based on the user's health status and emotional changes. The generative AI model receives the user's personal data as input and outputs appropriate advice.

[0791] Data processing and calculation

[0792] The server receives the biometric information sent from the smart device and processes and analyzes the data in real time. Specifically, it performs the following processes.

[0793] 1. Data collection: Collecting biometric information such as heart rate and stress levels from smart devices.

[0794] 2. Data analysis: Analyze the collected data using Python and real-time data processing libraries.

[0795] 3. Advice generation: Using a generative AI model, advice is generated based on the user's health status and emotional changes.

[0796] Specific examples

[0797] For example, if an employee in a brick-and-mortar store is wearing smart glasses and their heart rate rises to 95, the server will generate the advice "Your heart rate is high. Please take a break" and display it on the smart glasses' display. If the employee's stress level reaches 8, the server will generate the advice "Your stress level is high. Please try relaxation techniques."

[0798] Prompt Sentence Examples

[0799] By inputting the following prompt sentences to the generative AI model, appropriate advice can be generated.

[0800] If the user's heart rate exceeds 90, generate an advice suggesting that they take a break.

[0801] If the user's stress level exceeds 7, generate a relaxation suggestion.

[0802] In this way, it becomes possible to provide real-time health management and appropriate advice in response to changes in the user's health condition and emotions.

[0803] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0804] Step 1:

[0805] Data collection

[0806] The server receives the user's biometric information (heart rate, stress level, etc.) from the smart device in real time. The input is the biometric information sent from the smart device, and the output is the biometric data stored on the server. Specifically, the smart device's sensors measure the user's heart rate and stress level and send the data to the server.

[0807] Step 2:

[0808] Data analysis

[0809] The server analyzes the received biometric data using Python or a real-time data processing library (e.g., pandas, numpy). The input is the biometric data collected in step 1, and the output is the analysis results. Specifically, the server calculates the average and fluctuations of heart rate and stress level, and detects abnormal values.

[0810] Step 3:

[0811] Advice Generation

[0812] The server uses the generative AI model to generate appropriate advice based on the analysis results. The input is the analysis result obtained in step 2, and the output is the generated advice. Specifically, the server inputs a prompt statement into the generative AI model to generate advice according to changes in the user's health condition and emotions. For example, the server uses the prompt statement, "If the user's heart rate exceeds 90, generate advice suggesting a break."

[0813] Step 4:

[0814] Providing advice

[0815] The server sends the generated advice to the smart device and provides it to the user. The input is the advice generated in step 3, and the output is the advice displayed on the smart device. Specifically, the server sends the generated advice to be displayed on the display of the smart glasses or smartphone. For example, the advice "Your heart rate is high. Please take a break" is displayed on the smart glasses.

[0816] Step 5:

[0817] Feedback collection

[0818] The user sends feedback on the advice provided to the server through the smart device. The input is the user's feedback, and the output is the feedback data stored on the server. Specifically, the user uses the interface of the smart device to evaluate whether the advice was helpful and then sends the information to the server.

[0819] Step 6:

[0820] Feedback Analysis

[0821] The server analyzes the collected feedback data and uses it to improve the accuracy of the generative AI model. The input is the feedback data collected in step 5, and the output is an improved generative AI model. Specifically, the server analyzes the feedback data and adjusts the parameters of the generative AI model so that it can generate more appropriate advice.

[0822] 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.

[0823] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) 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.

[0824] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0825] 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.

[0826] [Second embodiment]

[0827] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0828] 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.

[0829] 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).

[0830] 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.

[0831] 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.

[0832] 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).

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0839] "Example 1"

[0840] The system of the present invention streamlines health management, training, and diet methods based on the personal data of each individual user. Specifically, it adjusts health management according to the user's constitution and medications, based on information input by the user and biometric information obtained from sensors (e.g., heart rate, blood pressure, body temperature, etc.). For example, it provides a health management program tailored to each individual user, such as proposing an exercise program to suppress blood pressure rise to a user taking medication for high blood pressure.

[0841] "Example 2"

[0842] Furthermore, the system of the present invention collects personal data of the user and analyzes that data to generate a health management program suited to the user. Specifically, personal data such as the user's age, sex, weight, height, lifestyle habits, and genetic information is collected, and the user's health condition and physical condition are analyzed based on this data. Based on the results of this analysis, a health management program suited to the user is generated and provided.

[0843] "Example 3"

[0844] The system of the present invention also adjusts the health management program in real time according to changes in the user's health condition and physical condition. Specifically, it monitors the user's biological information in real time and adjusts the health management program according to changes. For example, if the user's heart rate exceeds a certain threshold, the program can be adjusted to reduce exercise intensity, enabling appropriate health management according to the user's health condition.

[0845] The processing flow of each embodiment will be described below.

[0846] "Example 1"

[0847] Step 1: Collect input information from the user and biometric information obtained from sensors (e.g., heart rate, blood pressure, body temperature, etc.).

[0848] Step 2: Based on the collected information, adjust health management according to the user's constitution and medications.

[0849] Step 3: For example, for a user taking high blood pressure medication, an exercise program to suppress blood pressure rise is proposed, and a health management program suited to each individual user is provided. "Example 2"

[0850] Step 1: Collect personal data such as the user's age, gender, weight, height, lifestyle habits, and genetic information.

[0851] Step 2: Analyze the user's health and physical condition based on this data.

[0852] Step 3: Based on the results of this analysis, a health management program appropriate for the user is generated and provided.

[0853] "Example 3"

[0854] Step 1: Monitor the user's vital signs in real time.

[0855] Step 2: Adjust your health management program accordingly.

[0856] Step 3: For example, if the user's heart rate exceeds a certain threshold, the program can be adjusted to reduce exercise intensity, enabling appropriate health management according to the user's health condition.

[0857] Example 1

[0858] Next, a description will be given of Example 1 of Form 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."

[0859] Conventional health management systems have had difficulty providing appropriate health management programs tailored to individual users' physical constitutions and medications. Furthermore, they lack the ability to collect and analyze biometric information in real time, making it impossible to respond quickly to changes in the user's health status or physical condition. This has resulted in inefficient health management for users, making it difficult to recommend effective training and diet methods.

[0860] 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.

[0861] In this invention, the server includes means for streamlining health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suitable for each user, means for collecting the user's biological information in real time, means for analyzing the collected data and generating a health management program suitable for the user, means for proposing a health management program using a generative AI model, and means for the user to execute the proposed program. This makes it possible to provide an appropriate health management program according to the individual situation of each user, enabling real-time understanding of health conditions and rapid response.

[0862] "Individual user personal data" refers to personal information such as the user's age, gender, medical history, and medications being taken.

[0863] "Health Management" refers to activities and programs designed to maintain or improve a User's health.

[0864] "Training" refers to physical activity or exercise designed to improve a User's fitness or health.

[0865] "Diet method" refers to a meal plan or dietary restriction designed to improve a user's weight management and nutritional balance.

[0866] "User's constitution" refers to the user's genetic factors and physiological characteristics.

[0867] "Current medications" refers to medications that the user is currently taking.

[0868] "Health Management Program" means a set of activities or instructions designed to improve a user's health.

[0869] "Biometric information" refers to physiological data such as a user's heart rate, blood pressure, and body temperature.

[0870] "Real-time collection" refers to capturing a user's biometric information instantly and processing it without delay.

[0871] "Analyzing data" refers to assessing the user's health condition based on collected biometric information and personal data and generating an appropriate program.

[0872] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze data and generate a health management program that is appropriate for the user.

[0873] "Suggest" refers to notifying the user of the generated health management program and encouraging them to carry it out.

[0874] "Implementing" refers to the user acting in accordance with the proposed health management program.

[0875] MODE FOR CARRYING OUT THE INVENTION

[0876] This invention is a system that improves the efficiency of health management, training, and diet methods based on the personal data of each individual user. The system adjusts health management according to the user's constitution and medications, and provides a health management program that is suitable for each individual user.

[0877] Hardware and software used

[0878] Hardware: Sensors for capturing user biometric information (e.g., heart rate monitor, blood pressure monitor, thermometer)

[0879] Software: Dedicated applications for data collection and analysis, generative AI models (e.g., OpenAI's GPT-4)

[0880] Program processing procedure

[0881] 1. The user installs the dedicated application and enters personal information (age, gender, medical history, medications being taken, etc.).

[0882] 2. The device (smartphone or tablet) connects to sensors to collect the user's biometric information (heart rate, blood pressure, body temperature, etc.) in real time.

[0883] 3. The device sends the collected data to a dedicated application and stores it in a database.

[0884] 4. The server analyzes the stored data and generates a health management program based on the user's constitution and medications.

[0885] 5. The server uses a generative AI model (e.g., GPT-4) to suggest training and diet methods suitable for the user.

[0886] 6. The user reviews and executes the proposed program.

[0887] Specific examples

[0888] Let's say the user is a 50-year-old man who takes medication for high blood pressure.

[0889] The device uses a heart rate monitor and a blood pressure monitor to measure the user's heart rate and blood pressure.

[0890] The server analyzes the collected data and determines that the user's blood pressure is high.

[0891] The server uses the generative AI model to suggest an exercise program (e.g., walking or yoga) to lower blood pressure.

[0892] The user carries out the suggested exercise program and manages their health.

[0893] Prompt Sentence Examples

[0894] "For a 50-year-old male taking high blood pressure medication, please suggest an exercise program to lower his blood pressure."

[0895] In this way, the system provides a health management program tailored to the individual circumstances of each user.

[0896] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0897] Step 1:

[0898] The user installs a dedicated application and enters personal information (age, gender, medical history, medications being taken, etc.).

[0899] Input: User personal information

[0900] Output: User personal information stored in the application

[0901] What happens: A user opens an application on their smartphone and fills in the required information in the form on the screen.

[0902] Step 2:

[0903] The device connects to sensors (heart rate monitor, blood pressure monitor, thermometer, etc.) to collect the user's biometric information in real time.

[0904] Input: User's biometric information (heart rate, blood pressure, temperature, etc.)

[0905] Output: Collected biometric information

[0906] What happens: The user wears a heart rate monitor and the application collects the data via Bluetooth.

[0907] Step 3:

[0908] The biometric information collected by the device is sent to a dedicated application and stored in a database.

[0909] Input: Collected biometric information

[0910] Output: Biometric information stored in a database

[0911] What happens: The application sends data over the Internet to the server, which stores it in a database.

[0912] Step 4:

[0913] The server analyzes the stored data and generates a health management program based on the user's constitution and the medications they are taking.

[0914] Input: Biometric and personal information stored in a database

[0915] Output: Analysis results and generated health management program

[0916] How it works: The server retrieves the user's biometric and personal information from the database and analyzes it using an algorithm.

[0917] Step 5:

[0918] The server uses a generative AI model (e.g., GPT-4) to suggest training and diet methods suitable for the user.

[0919] Input: Analysis results and generated health management program

[0920] Output: Training and diet suggestions for the user

[0921] Specific operation: The server inputs a prompt sentence into the generative AI model and generates an appropriate health management program.

[0922] Step 6:

[0923] The user confirms and executes the proposed program.

[0924] Input: Proposed Health Care Program

[0925] Output: Executed health management program

[0926] What happens: The application notifies the user, who then confirms and runs the program within the application.

[0927] (Application example 1)

[0928] Next, a description will be given of Application Example 1 of Form 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."

[0929] Conventional health management systems have not been able to fully utilize users' personal data and biometric information, making it difficult to provide optimal health management programs for each individual user. Furthermore, there was a lack of systems that could evaluate health status in real time or be used in physical stores, making it difficult to streamline user health management.

[0930] 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.

[0931] In this invention, the server includes means for streamlining health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suitable for each user, means for acquiring the user's biometric information in real time, means for evaluating the health condition based on the acquired biometric information, means for proposing an appropriate training program based on the evaluation results, and means including a smartphone application intended for use in physical stores. This makes it possible to evaluate the user's health condition in real time and provide the optimal health management program for each user.

[0932] "Personal data" is data, including personal and health information, about individual users.

[0933] "Health Management" refers to activities and programs designed to maintain or improve a User's health.

[0934] "Training" refers to physical activity or exercise designed to improve a User's fitness or health.

[0935] "Diet Method" refers to a diet or exercise method for managing or reducing a user's weight or body fat.

[0936] "Constitution" refers to the user's genetic or physiological characteristics.

[0937] "Current medications" refers to medications that the user is currently taking.

[0938] "Biometric information" refers to physical data such as a user's heart rate, blood pressure, and temperature.

[0939] "Health Management Program" means a set of activities and instructions designed to improve and maintain the health and well-being of a User.

[0940] "Real-time" refers to data being processed and evaluated as it is generated.

[0941] "Evaluation" refers to analyzing the user's health status and judging the results.

[0942] "Training Program" means a plan of exercise or physical activity designed to improve a User's fitness or health.

[0943] "Brick and mortar store" refers to a store that exists in a physical location.

[0944] "Smartphone application" refers to a software program that runs on a smartphone.

[0945] The system for carrying out this invention collects personal data and biometric information of a user, evaluates the health condition in real time, and provides an appropriate health management program. A specific embodiment of this system will be described below.

[0946] System Configuration

[0947] The system consists of the following main components:

[0948] 1. User devices: Devices such as smartphones and smartwatches collect users' personal data and biometric information.

[0949] 2. Server: Receives data sent from user devices and analyzes and evaluates them.

[0950] 3. Application: An application installed on a smartphone that provides the user interface and displays the health management program.

[0951] Data collection and analysis

[0952] The user device collects the user's personal data (age, weight, height, medications, etc.) and biometric information (heart rate, blood pressure, body temperature, etc.), which is then sent to a server via Bluetooth or Wi-Fi.

[0953] The server analyzes the received data and evaluates the user's health status using programming languages ​​such as Python, preprocessing the data, and using machine learning models.

[0954] Health management program generation

[0955] The server then generates an optimal health management program for the user based on the analysis results. For example, it might suggest low-intensity aerobic exercise for a user at risk of high blood pressure. The generated program is sent to the user's device and displayed to the user through the application.

[0956] Use in physical stores

[0957] The system is intended for use in brick-and-mortar fitness gyms and health food stores, where users can check their health status in real time through a smartphone application and optimize the services offered at the store.

[0958] Specific examples

[0959] User A: 40 years old, weighs 80 kg, is 180 cm tall, and is taking medication for high blood pressure. Heart rate: 90, blood pressure: 150, body temperature: 36.8°C.

[0960] Suggestion: Low-intensity aerobic exercise is recommended.

[0961] User B: 25 years old, weight 60 kg, height 165 cm, no medication. Heart rate 70, blood pressure 120, body temperature 36.5°C.

[0962] Suggestion: Continue with your regular training program.

[0963] Prompt Sentence Examples

[0964] Enter your age, weight, height, medications, heart rate, blood pressure and temperature, and we'll suggest the right training program for you.

[0965] In this way, users can understand their health status in real time and receive the optimal health management program.

[0966] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0967] Step 1:

[0968] The user starts the smartphone application and inputs personal data (age, weight, height, medications being taken, etc.) The input data is temporarily saved within the application.

[0969] Step 2:

[0970] Users wear sensors such as smartwatches to measure vital signs (heart rate, blood pressure, body temperature, etc.), and the measured data is sent to a smartphone via Bluetooth or Wi-Fi.

[0971] Step 3:

[0972] The device (smartphone) sends the collected personal data and biometric information to a server using the HTTPS protocol to ensure data security.

[0973] Step 4:

[0974] The server analyzes the received data using Python for data preprocessing (missing value imputation, data normalization, etc.) and machine learning models (e.g., random forests, neural networks). Based on the input data, the server evaluates the user's health status.

[0975] Step 5:

[0976] The server then generates an optimal health management program for the user based on the evaluation results. For example, it would suggest low-intensity aerobic exercise to a user at risk of high blood pressure.

[0977] Step 6:

[0978] The server sends the generated health management program to the terminal using the HTTPS protocol to ensure data security.

[0979] Step 7:

[0980] The device displays the received health management program to the user using the user interface of the smartphone application. The user then performs training and health management according to the proposed program.

[0981] Step 8:

[0982] Users can use smartphone applications at physical stores (fitness gyms and health food stores) to check their own health status and make optimal use of the services offered at the store.

[0983] Example 2

[0984] Next, a description will be given of Example 2 of Form 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."

[0985] Conventional health management systems have had difficulty efficiently collecting and analyzing users' personal data and providing optimal health management programs for each individual user. They also lacked the functionality to adjust health management programs in real time according to the user's health status and changes in physical condition. Furthermore, the technology to generate optimal health management programs for each user using generative AI models had not yet been fully established.

[0986] 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.

[0987] In this invention, the server includes: means for streamlining health management, training, and diet methods based on the personal data of each user; means for adjusting health management according to the user's constitution and medications; means for providing a health management program tailored to each user; means for collecting the user's personal data; means for storing the collected personal data in a database; means for analyzing the stored personal data; means for generating a health management program tailored to each user using a generative AI model based on the analysis results; and means for providing the generated health management program to the user. This makes it possible to efficiently collect and analyze the user's personal data and provide an optimal health management program for each user. Furthermore, the health management program can be adjusted in real time according to the user's health condition and changes in physical condition, and a health management program tailored to each user can be generated using the generative AI model.

[0988] "Individual user personal data" refers to information about an individual user, such as age, gender, weight, height, lifestyle habits, and genetic information.

[0989] "Health Management" refers to activities and programs designed to maintain or improve a user's health.

[0990] "Training" refers to exercises or physical activities designed to improve a user's physical strength or muscle power.

[0991] "Diet method" refers to a meal plan or dietary restriction designed to improve a user's weight management and nutritional balance.

[0992] "Constitution" refers to the user's genetic or physiological characteristics.

[0993] "Current medications" refers to medications that the user is currently taking.

[0994] "Health Management Program" means a set of activities or instructions designed to maintain or improve a user's health.

[0995] "Database" refers to a system for systematically storing users' personal data.

[0996] "Data analytics" refers to methods for using collected data to assess a user's health and physical condition.

[0997] "Generative AI model" refers to a model that uses artificial intelligence technology to generate a health management program suitable for the user.

[0998] A "prompt sentence" refers to an instruction sentence to be input into a generative AI model.

[0999] "Real-time" refers to responding immediately to changes in the user's health condition or physical condition.

[1000] This invention is a system that collects and analyzes personal data of users and uses a generative AI model to generate and provide a health management program suited to each individual user. Specific embodiments of this system are described below.

[1001] First, the user accesses a dedicated application or website using a device such as a smartphone or PC. The user enters personal data such as age, gender, weight, height, lifestyle habits (e.g., exercise frequency, smoking habits), and genetic information (e.g., family medical history) into the on-screen form. Once the input is complete, the user clicks the "Submit" button. The device then sends the entered data to the server in an appropriate format such as JSON.

[1002] The server receives the personal data sent from the device. The received data is saved in a relational database such as MySQL or PostgreSQL using a database connection library (e.g., SQLAlchemy). A unique ID is assigned to each user in the database, and the personal data of each user is stored in the corresponding table.

[1003] The server then uses data analysis tools such as Python and R to analyze the stored personal data. Specifically, it preprocesses the data using libraries such as Pandas and NumPy, and then uses machine learning models and statistical methods to evaluate the user's health and physical condition. For example, it calculates the user's BMI (body mass index) and compares it with the standard value to evaluate health risks.

[1004] Based on the results of the data analysis, the server uses a generative AI model (e.g., GPT-4) to generate a health management program. The user's personal data and the analysis results are input into the generative AI model as a prompt. For example, the following prompt is input into the generative AI model:

[1005] The user is 30 years old, male, weighs 70 kg, and is 175 cm tall. He exercises three times a week and smokes daily. There is a family history of diabetes. Please generate a health management program suitable for this user.

[1006] The generative AI model outputs an appropriate health management program based on this prompt.

[1007] Finally, the server receives the health management program output from the generative AI model and sends it to the device. The user can then check the health management program that is best suited to them through the device. For example, the following health management program may be displayed on the screen of an application or website.

[1008] 1. Continue exercising three times a week and increase the time to 30 to 45 minutes.

[1009] 2. Reduce smoking and eventually aim to quit.

[1010] 3. When it comes to diet, limit your sugar intake and eat more vegetables and fruits.

[1011] 4. Check your blood sugar levels regularly and consult your doctor if any abnormalities occur.

[1012] The user can manage his / her health according to this program.

[1013] In this way, it is possible to efficiently collect and analyze users' personal data and provide the optimal health management program for each individual user.In addition, the health management program can be adjusted in real time according to the user's health condition and changes in physical condition, and a generative AI model can be used to generate a health management program that is suitable for the user.

[1014] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1015] Step 1:

[1016] A user accesses a dedicated application or website using a device such as a smartphone or PC. The user enters personal data such as age, gender, weight, height, lifestyle habits (e.g., exercise frequency, smoking habits), and genetic information (e.g., family medical history) into the on-screen form. Once the input is complete, the user clicks the "Submit" button. The device then sends the entered data to the server in an appropriate format such as JSON.

[1017] Input: User's personal data

[1018] Output: JSON format data

[1019] Step 2:

[1020] The server receives the personal data sent from the device. The received data is saved in a relational database such as MySQL or PostgreSQL using a database connection library (e.g., SQLAlchemy). A unique ID is assigned to each user in the database, and the personal data of each user is stored in the corresponding table.

[1021] Input: Personal data in JSON format

[1022] Output: Personal data stored in a database

[1023] Step 3:

[1024] The server uses data analysis tools such as Python and R to analyze the stored personal data. Specifically, it preprocesses the data using libraries such as Pandas and NumPy, and then uses machine learning models and statistical methods to evaluate the user's health and physical condition. For example, it calculates the user's BMI (body mass index) and compares it with the standard value to evaluate health risks.

[1025] Input: Personal data stored in a database

[1026] Output: Evaluation results of the user's health and physical condition

[1027] Step 4:

[1028] Based on the results of the data analysis, the server uses a generative AI model (e.g., GPT-4) to generate a health management program. The user's personal data and the analysis results are input into the generative AI model as a prompt. For example, the following prompt is input into the generative AI model:

[1029] The user is 30 years old, male, weighs 70 kg, and is 175 cm tall. He exercises three times a week and smokes daily. There is a family history of diabetes. Please generate a health management program suitable for this user.

[1030] The generative AI model outputs an appropriate health management program based on this prompt.

[1031] Input: User's personal data and analysis results

[1032] Output: Health management program based on generative AI model

[1033] Step 5:

[1034] The server receives the health management program output from the generative AI model and sends it to the device. The user can then check the health management program that is best suited to them through the device. For example, the following health management program may be displayed on the screen of an application or website.

[1035] 1. Continue exercising three times a week and increase the time to 30 to 45 minutes.

[1036] 2. Reduce smoking and eventually aim to quit.

[1037] 3. When it comes to diet, limit your sugar intake and eat more vegetables and fruits.

[1038] 4. Check your blood sugar levels regularly and consult your doctor if any abnormalities occur.

[1039] Input: Health management program using generative AI model

[1040] Output: Health management program provided to the user

[1041] (Application example 2)

[1042] Next, a description will be given of Application Example 2 of Form 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."

[1043] While conventional health management systems can collect and analyze users' personal data, it is difficult to provide optimal health management programs for each individual user. Additionally, they lack support for purchasing health foods and supplements in physical stores, or the ability to book health consultations and fitness programs in-store, making them unable to provide comprehensive support for users' health management.

[1044] 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.

[1045] In this invention, the server includes means for streamlining health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suited to each user, means for collecting and analyzing the user's personal data and scoring the user's health status, means for generating a health management program suited to the user based on the health score, means for supporting the purchase of health foods and supplements in physical stores, and means for providing a function for booking health consultations and fitness programs in stores, thereby enabling comprehensive health management based on the user's personal data.

[1046] "Personal data" refers to information about an individual, such as a user's age, gender, weight, height, lifestyle, and genetic information.

[1047] "Health Management Program" means a plan that includes specific guidance and advice for maintaining or improving a User's health.

[1048] A "health score" is a numerical indicator of a user's health status, calculated based on personal data.

[1049] A "physical store" is a store that exists in a physical location and where users can visit in person to purchase products or services.

[1050] "Health foods" are foods that are consumed for the purpose of maintaining or improving health.

[1051] "Supplements" are nutritional supplements taken to make up for nutrients that tend to be lacking in a normal diet.

[1052] "Health Consultation" is a service that allows users to ask questions and ask health-related questions to experts.

[1053] A "fitness program" is an exercise or training plan designed based on a user's physical strength and health condition.

[1054] The "reservation function" is a function that allows users to reserve a service or program by specifying the date and time of use in advance.

[1055] A system for implementing this invention includes a program for collecting and analyzing personal data of a user and scoring the user's health status. The system is realized using hardware such as a smartphone, a server, and a terminal in a brick-and-mortar store.

[1056] Hardware and Software

[1057] Hardware: Smartphones (iOS or Android), servers, brick-and-mortar terminals

[1058] Software: Python 3.x, scikit-learn (library for data analysis), JSON (save and load data)

[1059] Data collection

[1060] Users enter personal information such as age, gender, weight, height, lifestyle habits, and genetic information through a smartphone application, which is then sent to a server and stored.

[1061] Data analysis

[1062] The server uses a machine learning model (Linear Regression) to score the user's health based on the collected personal data. Specifically, it calculates a health score using the user's age, gender, weight, height, lifestyle habits, and other features as input.

[1063] Health management program generation

[1064] The server generates a health management program suitable for the user based on the calculated health score, for example, providing a basic health management program if the health score is low and an advanced health management program if the health score is high.

[1065] In-store support

[1066] Through the smartphone application, users can access features that support the purchase of health foods and supplements in physical stores, as well as the ability to book health consultations and fitness programs in-store.

[1067] Specific examples

[1068] For example, if a 30-year-old male user weighs 70 kg, is 175 cm tall, and has an active lifestyle, the app will suggest an "intermediate health management program." The user can then purchase the appropriate supplements at a physical store and book a fitness program.

[1069] Prompt Sentence Examples

[1070] Enter your age, gender, weight, height, lifestyle, and genetic information. For example, if you are a 30-year-old male who weighs 70kg, is 175cm tall, and has an active lifestyle, enter the following:

[1071] Age: 30

[1072] Gender: Male

[1073] Weight: 70kg

[1074] Height: 175cm

[1075] Lifestyle: Active

[1076] Genetic Information: None

[1077] In this way, a system can be realized that provides a health management program suited to the user.

[1078] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1079] Step 1:

[1080] The user launches a smartphone application and enters personal data such as age, gender, weight, height, lifestyle habits, and genetic information.

[1081] Input: User's personal data (age, gender, weight, height, lifestyle habits, genetic information)

[1082] Output: Personal data is stored in the application and sent to the server.

[1083] Step 2:

[1084] The server saves the received personal data and stores it in a database.

[1085] Input: User's personal data

[1086] Output: Personal data stored in a database

[1087] Step 3:

[1088] The server uses a machine learning model (Linear Regression) to score the user's health status based on the stored personal data.

[1089] Input: Personal data stored in a database

[1090] Output: Health score

[1091] Specific operation: The server inputs personal data as features into a machine learning model and calculates a health score.

[1092] Step 4:

[1093] The server generates a health management program suitable for the user based on the health score.

[1094] Input: Health Score

[1095] Output: Health Management Program

[1096] Specific operation: The server evaluates the health score and selects a health management program (basic, intermediate, or advanced) according to the score.

[1097] Step 5:

[1098] The user checks the generated health management program through the smartphone application.

[1099] Enter: Health Management Program

[1100] Output: Health management program displayed on the user's smartphone

[1101] Specific operation: The application displays the health management program received from the server to the user.

[1102] Step 6:

[1103] Use the feature to help users purchase health foods and supplements in physical stores.

[1104] Enter: Health Management Program

[1105] Output: Purchase support information

[1106] What it does: The application displays a list of appropriate health foods and supplements based on your health management program and assists you in purchasing them.

[1107] Step 7:

[1108] Users can use the in-store health consultation and fitness program booking function.

[1109] Enter: Health Management Program

[1110] Output: Reservation information

[1111] Specific operation: The application provides a function for booking health consultations and fitness programs based on a health management program, and allows users to select the desired date and time to make a reservation.

[1112] In this way, comprehensive health management based on the user's personal data is realized.

[1113] Example 3

[1114] Next, a description will be given of Example 3 of Form Example 3. 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."

[1115] Conventional health management systems can provide health management programs based on the user's personal data, but they have the problem of difficulty in dynamically adjusting the program in response to changes in biological information in real time. Furthermore, individual adjustments based on the user's constitution or medications are insufficient, which can prevent optimal management of the user's health condition.

[1116] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes means for improving the efficiency of health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suitable for each user, means for collecting user's biological information in real time, means for transmitting the collected biological information to the server, means for analyzing the biological information received by the server, means for adjusting the health management program based on the analysis results, and means for notifying the user of the adjustment results. This enables dynamic health management according to the user's health condition and changes in physical condition.

[1117] "Personal data" refers to personal information and health information about individual users, including, specifically, age, gender, weight, height, exercise history, dietary history, and medications currently being taken.

[1118] "Health Management Program" means a series of activities and instructions designed to maintain or improve a User's health, including, but not limited to, exercise plans, dietary instructions, and rest instructions.

[1119] "Biometric information" refers to data obtained from the user's body, and specifically includes heart rate, blood pressure, body temperature, respiratory rate, etc.

[1120] "Real-time" refers to data collection and processing occurring immediately, meaning that it reflects the current state without delay.

[1121] "Server" refers to a computer system that collects, analyzes, stores, and transmits data over a network.

[1122] "Analysis" refers to processing collected data and extracting meaningful information, and specifically includes statistical analysis of data and the application of machine learning models.

[1123] "Notification" refers to the act of conveying information to users, and specifically includes push notifications from smartphone apps, emails, messages, etc.

[1124] "Adjustment" refers to changing and optimizing a health management program based on the analysis results, and specifically includes changing exercise intensity and modifying dietary content.

[1125] The present invention is a system for adjusting a health management program in real time in response to changes in a user's health condition and physical condition. Specific embodiments of this system will be described below.

[1126] Hardware and Software Configuration

[1127] Hardware

[1128] 1. Wearable devices: Used to collect biometric information from users. Specifically, devices equipped with sensors that measure heart rate, blood pressure, body temperature, etc. (e.g., smartwatches, fitness trackers) are used.

[1129] 2. Smartphone: Used as a relay device to send data collected from wearable devices to a server. The smartphone receives data via Bluetooth or Wi-Fi and sends it to the server via the Internet.

[1130] 3. Server: A computer system that collects, analyzes, stores, and transmits data. Use a server equipped with a high-performance processor and large memory capacity.

[1131] software

[1132] 1. Data analysis libraries: Use programming languages ​​such as Python and R and data analysis libraries such as Pandas and NumPy.

[1133] 2. Machine learning libraries: Use machine learning libraries such as TensorFlow and scikit-learn to analyze data and make predictions.

[1134] 3. Smartphone app: An application for sending notifications to users, which works on iOS and Android platforms.

[1135] System Operation

[1136] Biometric data collection and transmission

[1137] The terminal (wearable device) collects the user's biometric information in real time. For example, when the user starts jogging, the heart rate sensor measures the user's heart rate and sends the data to a smartphone. The smartphone receives the data via Bluetooth and sends it to a server over the Internet.

[1138] Data analysis

[1139] The server analyzes the received vital signs. It uses Python to process the data using data analysis libraries such as Pandas and NumPy. For example, the server analyzes the user's heart rate data to see if it exceeds a certain threshold (e.g., 180 BPM).

[1140] Coordination of health management programs

[1141] The server then adjusts the health management program based on the analysis results. For example, if the user's heart rate exceeds 180 BPM, the server will change the program to reduce the intensity of the exercise. Specifically, it will instruct the user to slow down the pace of the exercise.

[1142] Notification of adjustment results

[1143] The server notifies the user of the adjustment results to their smartphone, which then uses a push notification to display a message to the user telling them to "slow down."

[1144] Examples of specific examples and prompts

[1145] As a concrete example, consider a scenario where a user is jogging: if the user's heart rate exceeds 180 BPM, the server will adjust the program to reduce the exercise intensity and notify the user to "slow down."

[1146] Example prompts to input to a generative AI model:

[1147] "Generate Python code that adjusts a health program to reduce exercise intensity if the user's heart rate exceeds 180 BPM."

[1148] In this way, the server, the terminal, and the user work together to realize the system for realizing appropriate health management according to the user's health condition. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1149] Step 1:

[1150] The device collects the user's biometric information in real time. Specifically, it measures data such as heart rate, blood pressure, and body temperature through sensors. For example, a smartwatch uses a heart rate sensor to measure the user's heart rate. The input is the user's biometric information, and the output is the measured biometric data.

[1151] Step 2:

[1152] The device sends the collected biometric information to a server via Bluetooth or Wi-Fi. For example, a smartwatch connects to the Internet via a smartphone and sends data to a server. The input is the measured biometric data, and the output is the data sent to the server.

[1153] Step 3:

[1154] The server analyzes the received biometric information. It uses Python to process the data using data analysis libraries such as Pandas and NumPy. Specifically, the server analyzes the user's heart rate data and checks whether it exceeds a certain threshold (e.g., 180 BPM). The input is the biometric information data sent to the server, and the output is the analysis result.

[1155] Step 4:

[1156] The server adjusts the health management program based on the analysis results. For example, if the user's heart rate exceeds 180 BPM, the server changes the program to reduce the exercise intensity. Specifically, it instructs the user to slow down the pace of exercise. The input is the analysis results, and the output is the adjusted health management program.

[1157] Step 5:

[1158] The server notifies the user of the adjustment results. It sends a message to the user via a smartphone app saying, "Please slow down." For example, an iOS app might display the message to the user using a push notification. The input is the adjusted health management program, and the output is a notification to the user.

[1159] (Application example 3)

[1160] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1161] Conventional health management systems provide health management programs based on users' personal data, but they have the problem of being unable to respond to changes in physical condition in real time. Also, when users are active in physical stores, they are unable to prompt them to take breaks at appropriate times, which can increase health risks.

[1162] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for improving the efficiency of health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suitable for each user, means for monitoring the user's biological information in real time, means for adjusting the health management program in real time according to the user's health condition and changes in physical condition, means for sending a notification encouraging the user to take a break based on the user's health condition, and means including an application installed on the smart device. This makes it possible to monitor the user's health condition in real time and encourage the user to take a break at the appropriate time.

[1163] "Personal data" is data, including personal and health information, about individual users.

[1164] "Health Management" refers to activities and programs designed to maintain or improve a User's health.

[1165] "Training" refers to exercises or physical activities designed to improve a user's physical strength or muscle power.

[1166] "Diet method" refers to a diet or exercise method for reducing a user's weight or body fat.

[1167] "Constitution" refers to the user's genetic or physiological characteristics.

[1168] "Current medications" refers to medications that the user is currently taking.

[1169] "Health Management Program" means a set of activities and instructions designed to maintain or improve a User's health.

[1170] "Biometric information" refers to physiological data such as a user's heart rate, blood pressure, and body temperature.

[1171] "Real-time" refers to data being processed and analyzed as it is generated.

[1172] "Monitoring" refers to the continuous observation and recording of a user's health condition.

[1173] "Notification" refers to a message that conveys information or instructions to the user.

[1174] "Smart device" refers to an electronic device with internet connectivity.

[1175] "Application" refers to a software program that provides a specific function or service.

[1176] A system for carrying out the present invention is for monitoring the health condition of a user in real time and providing an appropriate health management program. A specific embodiment of this system will be described below.

[1177] System Configuration

[1178] The system consists of the following major components:

[1179] 1. Smart devices (e.g., smart glasses)

[1180] 2. Heart rate sensor

[1181] 3. Server

[1182] 4. Application Software

[1183] Hardware and Software Use

[1184] Smart Devices

[1185] Smart devices are used to monitor users' biological information in real time, specifically smart glasses, which have a built-in heart rate sensor that continuously measures the user's heart rate.

[1186] server

[1187] The server receives biometric information sent from smart devices and processes the data in real time. The server is built using programming languages ​​such as Python. The server analyzes the user's health status and generates an appropriate health management program.

[1188] Application Software

[1189] The application software is installed on the smart device and is responsible for sending notifications to the user, such as displaying a notification encouraging the user to take a break if the user's heart rate exceeds a certain threshold.

[1190] Data processing and calculation

[1191] 1. Data collection: The heart rate sensor on the smart device collects the user's heart rate data.

[1192] 2. Data transmission: The collected data is transmitted to the server in real time.

[1193] 3. Data analysis: The server analyzes the received heart rate data and evaluates the user's health status.

[1194] 4. Program Adjustment: If the user's heart rate exceeds the threshold, the server adjusts the health management program and generates a notification to take a break.

[1195] 5. Send notifications: Notifications will be sent to your smart device based on the adjusted program.

[1196] Specific examples

[1197] As a user walks around the shopping mall, the heart rate sensor in the smart glasses measures the user's heart rate. If the heart rate exceeds 100, the server analyzes this data and sends a notification to the smart glasses saying, "Your heart rate is high. Please take a break."

[1198] Prompt Sentence Examples

[1199] "Generate a program that displays a notification on the smart glasses to encourage a break if the user's heart rate exceeds 100."

[1200] The above is a specific embodiment for carrying out the present invention. This system makes it possible to monitor the user's health condition in real time and encourage them to take a break at an appropriate time.

[1201] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1202] Step 1:

[1203] The heart rate sensor in the smart device (smart glasses) collects the user's heart rate data. The input is the user's real-time heart rate, and the output is the collected heart rate data. Specifically, the sensor comes into contact with the user's skin and measures the heart rate.

[1204] Step 2:

[1205] The smart device sends the collected heart rate data to the server. The input is the collected heart rate data, and the output is the data sent to the server. Specifically, the smart glasses send the data to the server using wireless communication.

[1206] Step 3:

[1207] The server analyzes the received heart rate data in real time. The input is the heart rate data sent to the server, and the output is the analysis result. Specifically, the server analyzes the data using a program such as Python to determine whether the heart rate exceeds a threshold.

[1208] Step 4:

[1209] The server adjusts the health management program based on the analysis results. The input is the analysis results, and the output is the adjusted health management program. Specifically, if the user's heart rate exceeds a threshold, the server will adjust the program, such as reducing the exercise intensity.

[1210] Step 5:

[1211] The server generates a notification encouraging a break based on the adjusted health management program. The input is the adjusted health management program, and the output is the generated notification. Specifically, the server generates a message saying, "Your heart rate is high. Please take a break."

[1212] Step 6:

[1213] The server sends the generated notification to the smart device. The input is the generated notification, and the output is the notification sent to the smart device. In concrete terms, the server sends the notification to the smart glasses via wireless communication.

[1214] Step 7:

[1215] The smart device displays the received notification to the user. The input is the notification sent from the server, and the output is the notification displayed to the user. The specific behavior is that the message "Your heart rate is high. Please take a break" is displayed on the smart glasses display.

[1216] 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.

[1217] "Example 1"

[1218] One embodiment of the present invention is a health management system that incorporates an emotion engine that recognizes the user's emotions. This system includes a means for streamlining health management, training, and diet methods based on the user's personal data, a means for adjusting health management according to the user's constitution and medications, and a means for providing a health management program tailored to each individual user. Furthermore, the system incorporates an emotion engine that recognizes the user's emotions. This emotion engine infers emotions from the user's facial expressions, tone of voice, choice of words, and so on. For example, if it determines that the user is feeling stressed, it suggests exercise and diet programs that promote relaxation.

[1219] "Example 2"

[1220] Another embodiment is a system that collects personal data of a user, analyzes the collected personal data, and generates a health management program tailored to the user based on the analysis results. This system also incorporates an emotion engine that recognizes the user's emotions. This emotion engine grasps changes in the user's emotions in real time in their daily lives and uses this information to adjust the health management program. For example, when the user is feeling happy, the system recommends activities to maintain that emotion, and conversely, when the user is feeling sad or angry, the system recommends activities to alleviate those emotions.

[1221] "Example 3"

[1222] Another embodiment is a system that adjusts a health management program in real time according to changes in a user's health condition or physical condition. This system also incorporates an emotion engine that recognizes the user's emotions. This emotion engine grasps changes in the user's emotions in real time and uses that information to adjust the health management program. For example, if it determines that the user is feeling stressed, it will suggest exercise, diet, and relaxation methods to reduce stress.

[1223] The processing flow of each embodiment will be described below.

[1224] "Example 1"

[1225] Step 1: Collect personal data about the user, including their age, gender, weight, height, body fat percentage, exercise habits, and dietary habits.

[1226] Step 2: Analyze the collected personal data. This analysis will help understand the user's health, physical condition, lifestyle habits, etc.

[1227] Step 3: Recognize the user's emotions using the emotion engine. Infer emotions from the user's facial expressions, tone of voice, choice of words, etc.

[1228] Step 4: Based on the analysis and emotion recognition results, a health management program tailored to the user is generated. For example, if the system determines that the user is feeling stressed, it will suggest exercise and diet programs that promote relaxation.

[1229] "Example 2"

[1230] Step 1: Collect personal data of users and analyze the collected personal data.

[1231] Step 2: Use the emotion engine to understand the user's emotional changes in real time in their daily lives.

[1232] Step 3: Use the emotion recognition results to adjust the health management program. For example, if the user is feeling happy, activities to maintain that emotion are recommended. Conversely, if the user is feeling sad or angry, activities to alleviate those emotions are recommended.

[1233] "Example 3"

[1234] Step 1: Understand the user's health status and changes in physical condition in real time.

[1235] Step 2: The emotion engine is used to understand changes in the user's emotions in real time. Step 3: The health management program is adjusted in real time based on the health status, changes in physical condition, and emotion recognition results. For example, if it determines that the user is feeling stressed, it will suggest exercise, diet, and relaxation methods to reduce stress.

[1236] Example 1

[1237] Next, a description will be given of Example 1 of Form 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."

[1238] Conventional health management systems have the problem of being unable to fully utilize the personal data and biometric information of individual users, and can only provide general health management programs. Furthermore, because health management does not take into account the user's emotional state, it is difficult to provide appropriate programs that respond to stress and emotional changes. This results in inefficient health management for users, and the inability to provide optimal programs tailored to individual needs.

[1239] 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.

[1240] In this invention, the server includes means for streamlining health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suitable for each user, means for collecting the user's biometric information, means for analyzing the collected biometric information, means for generating a health management program based on the analysis results, means for recognizing the user's emotions, means for adjusting the health management program based on the emotional state, means for creating a program customized for each user using a generative AI model, and means for suggesting an optimal health management program for the user. This makes it possible to provide an optimal health management program that comprehensively takes into account the personal data, biometric information, and emotional state of each user.

[1241] "Personal data" refers to information about individual users, including their age, gender, medical history, and medications they are taking.

[1242] "Biometric information" is data that indicates the user's physical condition, including heart rate, blood pressure, body temperature, etc.

[1243] "Health Management Program" means a plan that includes specific instructions and suggestions for maintaining and improving the User's health.

[1244] "Emotion recognition" is a technology that estimates a user's emotional state from their facial expressions, tone of voice, choice of words, etc.

[1245] A "generative AI model" is an algorithm or system that uses artificial intelligence to analyze data and generate programs customized for individual users.

[1246] "Analysis" is the process of assessing a user's health and emotional state based on collected data.

[1247] "Suggestion" refers to the act of presenting the optimal health management program to the user based on the analysis results.

[1248] "Tuning" is the process of modifying and optimizing your health management program based on your constitution, medications, and emotional state.

[1249] "Collection" refers to the act of obtaining a user's personal data or biometric information.

[1250] "Efficiency" refers to the means of making health management, training, and diet methods more effective and faster.

[1251] This invention is a system that improves the efficiency of health management, training, and diet methods based on the personal data of each individual user. The system adjusts health management according to the user's constitution and medications, and provides a health management program that is suitable for each individual user.

[1252] Hardware and software used

[1253] Hardware:

[1254] Wearable devices (e.g., smartwatches for measuring heart rate and blood pressure)

[1255] Smartphone (used to collect and transmit data)

[1256] Server (used for analyzing data and generating programs)

[1257] software:

[1258] Data analysis tools (e.g., Python, R)

[1259] Emotion recognition engine (e.g., emotion recognition API)

[1260] Generative AI models (algorithms that create programs customized for each individual user)

[1261] Explanation of program processing

[1262] User personal data input:

[1263] Users enter their personal data (age, gender, medical history, medications they are taking, etc.) through a smartphone application, and the device sends this data to a server.

[1264] Biometric data collection:

[1265] The device (e.g., a wearable device) collects the user's biometric information, such as heart rate, blood pressure, and body temperature, in real time. The collected data is periodically sent to a server.

[1266] Data analysis:

[1267] The server analyzes the received personal data and biometric information, using data analysis tools such as Python and R to assess the user's health status, for example, to detect abnormalities in heart rate and blood pressure.

[1268] Health Management Program Generation:

[1269] The server generates an optimal health management program for each user based on the analysis results. For users taking high blood pressure medication, it suggests an exercise program to lower their blood pressure. Generative AI models are used to create customized programs for each individual user.

[1270] Emotion recognition:

[1271] The device uses an emotion recognition engine to analyze the user's facial expressions, tone of voice, choice of words, etc. The server then uses this data to estimate the user's emotional state.

[1272] Best program suggestions:

[1273] The server comprehensively evaluates the user's health and emotional state and suggests an optimal health management program. For example, if the user is feeling stressed, it will suggest an exercise or diet program that promotes relaxation. The suggested program is then notified to the user via their device.

[1274] Examples of specific examples and prompts

[1275] Examples:

[1276] User A opens the application and enters his / her personal data (age: 45, gender: male, medical history: high blood pressure, medications being taken: high blood pressure medication). The device (smartphone) sends this data to the server.

[1277] User A wears a smartwatch that measures his / her heart rate, blood pressure, and body temperature in real time. These data are sent to a smartphone via Bluetooth and then transferred to a server.

[1278] The server uses Python to analyze User A's heart rate and blood pressure data to detect signs of high blood pressure. Based on the analysis results, the server generates an exercise program suitable for User A (e.g., walking 30 minutes every morning).

[1279] Furthermore, to determine whether User A is feeling stressed, the device analyzes User A's facial expressions and tone of voice using an emotion recognition engine. If the server determines that User A is feeling stressed, it will suggest a yoga program to promote relaxation and a meal plan that is effective in reducing stress.

[1280] Example prompt sentence:

[1281] "User A is taking medication for high blood pressure and has been feeling stressed recently. Please suggest an appropriate health management program based on User A's heart rate, blood pressure, and body temperature data."

[1282] In this way, a system is realized in which the server, terminals, and users work together to provide an optimal health management program for each individual user.

[1283] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1284] Step 1:

[1285] User personal data entry

[1286] Users enter their personal data (age, gender, medical history, medications they are taking, etc.) through a smartphone application, and the device sends this data to a server.

[1287] Input: User's personal data

[1288] Output: Personal data sent to the server

[1289] Specific behavior: When a user enters data into an application's input form and presses the "Submit" button, the data is sent to the server.

[1290] Step 2:

[1291] Biometric data collection

[1292] The device (e.g., a wearable device) collects the user's biometric information, such as heart rate, blood pressure, and body temperature, in real time. The collected data is periodically sent to a server.

[1293] Input: User's biometric information

[1294] Output: Biometric information sent to the server

[1295] How it works: The wearable device measures the user's heart rate, blood pressure, and temperature and transmits the data via Bluetooth to a smartphone, which then forwards the data to a server.

[1296] Step 3:

[1297] Data analysis

[1298] The server analyzes the received personal data and biometric information, using data analysis tools such as Python and R to assess the user's health status, for example, to detect abnormalities in heart rate and blood pressure.

[1299] Input: Personal data, biometric information

[1300] Output: Health status assessment results

[1301] What it does: The server runs a Python script to analyze the data and detect outliers. The results are stored in a database.

[1302] Step 4:

[1303] Health management program generation

[1304] The server generates an optimal health management program for each user based on the analysis results. For users taking high blood pressure medication, it suggests an exercise program to lower their blood pressure. Generative AI models are used to create customized programs for each individual user.

[1305] Input: Health status assessment results

[1306] Output: Customized health management program

[1307] How it works: The server uses the generative AI model to generate exercise and meal plans based on the user's health status, and the generated programs are stored in a database.

[1308] Step 5:

[1309] emotion recognition

[1310] The device uses an emotion recognition engine to analyze the user's facial expressions, tone of voice, choice of words, etc. The server then uses this data to estimate the user's emotional state.

[1311] Input: Your facial expressions, tone of voice, and choice of words

[1312] Output: Estimated emotional state

[1313] Specific operation: The device uses a camera and microphone to collect the user's facial expressions and voice, and sends the data to an emotion recognition engine. The server receives the results of the emotion recognition engine and estimates the user's emotional state.

[1314] Step 6:

[1315] Proposal of the best program

[1316] The server comprehensively evaluates the user's health and emotional state and suggests an optimal health management program. For example, if the user is feeling stressed, it will suggest an exercise or diet program that promotes relaxation. The suggested program is then notified to the user via their device.

[1317] Input: Health status assessment results, emotional state estimation results

[1318] Output: Recommendations for optimal health management programs

[1319] How it works: The server selects the optimal program based on the user's health and emotional state and notifies the user through a smartphone application. The user can then review and implement the suggested program.

[1320] (Application example 1)

[1321] Next, a description will be given of Application Example 1 of Form 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."

[1322] Conventional health management systems provide health management, training, and diet methods based on the user's personal data, but few systems take into account the user's emotional state and meal suggestions. There is also a need for more personalized health management by proposing meal menus based on the user's health and emotional state and linking with delivery services.

[1323] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for improving the efficiency of health management, training, and diet methods based on the personal data of each individual user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suitable for each individual user, means for combining an emotion engine that recognizes the user's emotions to provide a health management program according to the user's emotional state, and means for proposing an optimal meal menu for each individual user based on the user's health data and emotion data and providing a delivery service. This enables personalized health management and meal suggestions according to the user's health and emotional state.

[1324] "Personal data" refers to biometric information about individual users, such as heart rate, blood pressure, and body temperature, as well as information about dietary history and medications entered by users.

[1325] "Health Management" refers to activities and programs designed to maintain or improve a User's health.

[1326] "Training" refers to an exercise program designed to improve a user's physical strength and muscle power.

[1327] "Diet method" refers to dietary and exercise guidance to improve the user's weight management and nutritional balance.

[1328] An "emotion engine" is a system that infers emotions from a user's facial expressions, tone of voice, choice of words, etc.

[1329] "Health Management Program" refers to a specific plan for maintaining and improving health that is provided based on the user's personal and emotional data.

[1330] "Meal Menu" refers to specific meal suggestions based on the user's health and emotional state.

[1331] "Delivery service" refers to a service that delivers suggested meal menus to users.

[1332] The following system configuration will be described as an embodiment of the present invention.

[1333] System Configuration

[1334] Hardware

[1335] Smartphones: Equipped with a camera and microphone, they collect the user's facial expressions and tone of voice.

[1336] Smartwatches and fitness trackers: Collect biometric information such as heart rate, blood pressure, and temperature.

[1337] Server: Collects, analyzes, and stores data, and generates health management programs.

[1338] software

[1339] EmotionRecognizer: A library that recognizes emotions by analyzing the user's facial expressions and tone of voice.

[1340] HealthDataCollector: A library for collecting health data from smartwatches and fitness trackers.

[1341] requests: A Python library for sending HTTP requests.

[1342] Data processing and calculation

[1343] Health data collection

[1344] It collects biometric information such as heart rate, blood pressure, and temperature from the user's smartwatch or fitness tracker, and sends this data to a server using the HealthDataCollector library.

[1345] Collecting Emotional Data

[1346] It uses the smartphone's camera and microphone to collect the user's facial expressions and tone of voice, and uses the EmotionRecognizer library to recognize the user's emotions from this data.

[1347] Health management program generation

[1348] Based on the collected health and emotional data, the system generates a health management program that is optimal for each user. For example, it will suggest low-salt meals to a user with high blood pressure, or suggest relaxation exercises to a user experiencing stress.

[1349] Meal menu suggestions and delivery service

[1350] Based on the generated health management program, the system will suggest the optimal meal menu for the user, and the suggested meal menu will be ordered using the delivery service's API and delivered to the user.

[1351] Specific examples

[1352] User A's case

[1353] Health data: High blood pressure (140 / 90)

[1354] Emotional data: Feeling stressed

[1355] Suggested meals: Low-sodium menus using ingredients with a relaxing effect

[1356] User B's case

[1357] Health data: Normal blood pressure (120 / 80)

[1358] Emotional data: Relaxed

[1359] Suggested diet: Balanced diet

[1360] Prompt Sentence Examples

[1361] Design an application that suggests the optimal meal menu for each user based on their health data (heart rate, blood pressure, body temperature, etc.) and emotional data (facial expressions, tone of voice), and provides a delivery service. Specifically, the application will collect data from smartphones and smartwatches, suggest meal menus based on the user's health and emotional state, and confirm the order in cooperation with a delivery service.

[1362] In this way, personalized health management and dietary suggestions based on the user's health and emotional state become possible.

[1363] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1364] Step 1:

[1365] A user wears a smartwatch or fitness tracker to collect biometric information such as heart rate, blood pressure, and body temperature. This data is sent to a smartphone via Bluetooth or Wi-Fi. The smartphone then uses the HealthDataCollector library to send this data to a server. The input is the biometric information, and the output is the health data sent to the server.

[1366] Step 2:

[1367] The user uses the smartphone's camera and microphone to collect their facial expressions and tone of voice. The EmotionRecognizer library is used to recognize the user's emotions from this data. The input is facial expression and voice data, and the output is recognized emotion data. The smartphone sends this emotion data to the server.

[1368] Step 3:

[1369] The server integrates the received health data using the HealthDataCollector library and the received emotion data using the EmotionRecognizer library. This allows for unified management of the user's health and emotion states. The input is health data and emotion data, and the output is the integrated user data.

[1370] Step 4:

[1371] The server generates an optimal health management program for each user based on the integrated user data. For example, it suggests low-salt meals to a user with high blood pressure, and relaxation exercises to a user feeling stressed. The input is the integrated user data, and the output is the generated health management program.

[1372] Step 5:

[1373] The server proposes an optimal meal menu to the user based on the generated health management program. The proposed meal menu is then ordered using the delivery service's API. The input is the health management program, and the output is the proposed meal menu and the confirmed order.

[1374] Step 6:

[1375] The delivery service delivers suggested meals to the user based on the confirmed order. The input is the confirmed order and the output is the meal delivered to the user.

[1376] In this way, personalized health management and dietary suggestions are realized according to the user's health and emotional state.

[1377] Example 2

[1378] Next, a description will be given of Example 2 of Form 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."

[1379] Conventional health management systems provide health management programs based on the user's personal data, but they do not take into account the user's emotional state, which means they are unable to adequately support the user's psychological health. Furthermore, it is difficult to adjust the health management program in real time, which means they are unable to quickly respond to changes in the user's health or physical condition.

[1380] The specification processing by specification processing unit 290 of data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for improving the efficiency of health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suited to each user, means for collecting and analyzing the user's emotional data, and means for adjusting the health management program in real time based on the emotional data. This makes it possible to provide a health management program that takes the user's personal data and emotional data into consideration in a comprehensive manner.

[1381] "Personal data" refers to individual information about an individual user, such as the user's age, gender, weight, height, lifestyle habits, and genetic information.

[1382] A "health management program" is a plan for maintaining or improving health, such as a meal plan or exercise plan, that is generated based on the user's personal and emotional data.

[1383] "Emotion data" is information that indicates changes in the user's emotions in their daily lives, and includes emotional states such as joy, sadness, and anger.

[1384] "Real-time adjustment" means instantly responding to changes in the user's health and emotional state, changing and optimizing the health management program on the fly.

[1385] "Server" means a computer system that receives, stores, and analyzes users' personal data and emotional data, and generates and provides health management programs.

[1386] "Means for collection" refers to methods and devices for acquiring a user's personal data and emotional data, including, for example, input forms and sensors.

[1387] "Means for analyzing" refers to methods or devices for processing collected data and assessing a user's health or emotional state, including, for example, data analysis software and machine learning models.

[1388] The "means for providing" refers to a method or device for communicating the generated health management program to the user, and includes, for example, a display or notification function of the terminal.

[1389] MODE FOR CARRYING OUT THE INVENTION

[1390] This invention relates to a system that collects and analyzes personal data and emotional data of a user and provides an individually optimized health management program. Specific embodiments of this system are described below.

[1391] 1. Entering your personal data

[1392] The user uses the terminal to input their personal data, such as age, sex, weight, height, lifestyle habits, and genetic information, into an input form. The terminal then transmits this data to the server.

[1393] 2. Receipt and storage of data

[1394] The server receives the personal data sent from the device. The received data is stored in a database (e.g., MySQL or PostgreSQL). The server checks the integrity of the data and whether any data is missing.

[1395] 3. Data Preprocessing

[1396] The server preprocesses the stored data, specifically by imputing missing values ​​and normalizing the data. For example, it creates a data frame using the Python pandas library and imputes missing values ​​with the mean.

[1397] 4. Data Analysis

[1398] The server analyzes the preprocessed data using machine learning models (e.g., Random Forest or XGBoost from scikit-learn) and applies these models to predict the user's health and condition.

[1399] 5. Generating a Health Management Program

[1400] The server then generates a health management program tailored to the user based on the analysis results. Specifically, it creates a program that includes a meal plan and an exercise plan. For example, if the user is at high risk of diabetes, it will recommend a low-carb meal plan.

[1401] 6. Providing health management programs

[1402] The server transmits the generated health management program to the terminal, which displays the program to the user, who then performs health management according to the program provided.

[1403] 7. Emotion Data Collection (Alternative Embodiment)

[1404] Users input their emotional changes in daily life into the device, which then transmits the emotional data to the server in real time. For example, a user can select their current emotional state (e.g., joy, sadness, anger) using a smartphone app.

[1405] 8. Analyzing Emotional Data

[1406] The server analyzes the emotional data and uses an emotion engine (e.g., Microsoft Azure's Emotion API) to understand the user's emotional state. The server then adjusts the health management program based on the emotional data.

[1407] 9. Emotion-Based Program Adjustments

[1408] The server then adjusts the health management program based on the user's emotional state based on the analysis results of the emotion engine. For example, if the user is feeling happy, it will recommend activities to maintain that emotion. Conversely, if the user is feeling sad or angry, it will recommend activities to alleviate those emotions.

[1409] Examples of specific examples and prompts

[1410] Specific examples

[1411] Assume the user is a 40-year-old man, weighs 80 kg, is 175 cm tall, and exercises three times a week. Genetic information indicates a family history of diabetes. When the user enters data into the system, the server receives the data and stores it in a database. The server then preprocesses the data and analyzes it using a machine learning model. Based on the analysis results, a health management program including a meal plan and exercise plan for diabetes prevention is generated and sent to the device. The user can then review and implement the program through the device.

[1412] Prompt Sentence Examples

[1413] "A 40-year-old male weighs 80 kg, is 175 cm tall, exercises three times a week, and has a family history of diabetes. Please generate a health management program appropriate for this user."

[1414] In this way, the system collects and analyzes the user's personal and emotional data to provide an individually optimized health management program.

[1415] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1416] Step 1: Enter your personal data

[1417] The user uses the terminal to input their own personal data. Specifically, they fill in an input form with data such as age, sex, weight, height, lifestyle habits, and genetic information. The input data is sent from the terminal to the server. The input data is the user's individual information, and the output is the personal data sent to the server.

[1418] Step 2: Receiving and storing data

[1419] The server receives the personal data sent from the terminal. The received data is stored in a database (e.g., MySQL or PostgreSQL). The server verifies the integrity of the data and checks whether any data is missing. The input is the personal data sent from the terminal, and the output is the consistent data stored in the database.

[1420] Step 3: Preprocessing the data

[1421] The server preprocesses the stored data. Specifically, it imputes missing values ​​and normalizes the data. For example, it creates a data frame using Python's pandas library and imputes missing values ​​with the mean value. The input is the personal data stored in the database, and the output is the preprocessed data.

[1422] Step 4: Analyze the data

[1423] The server analyzes the preprocessed data using machine learning models (e.g., Random Forest or XGBoost from scikit-learn). The server applies these models to predict the user's health and physical condition. The input is the preprocessed data, and the output is the analysis results.

[1424] Step 5: Generate a Health Management Program

[1425] The server generates a health management program suited to the user based on the analysis results. Specifically, it creates a program that includes a meal plan and an exercise plan. For example, if the user is at high risk of diabetes, it recommends a low-carbohydrate meal plan. The input is the analysis results, and the output is the generated health management program.

[1426] Step 6: Providing a health management program

[1427] The server sends the generated health management program to the terminal. The terminal displays the program to the user. The user performs health management according to the provided program. The input is the generated health management program, and the output is the program displayed on the terminal.

[1428] Step 7: Collecting Emotional Data (Alternative Embodiment)

[1429] The user inputs emotional changes in their daily life into the device. The device then transmits the emotional data to the server in real time. For example, a user uses a smartphone app to select their current emotional state (joy, sadness, anger, etc.). The input is the user's emotional data, and the output is the emotional data transmitted to the server.

[1430] Step 8: Analyze the sentiment data

[1431] The server analyzes the emotional data and uses an emotion engine (for example, Microsoft Azure's Emotion API) to understand the user's emotional state. The server then adjusts the health management program based on the emotional data. The input is the emotional data sent to the server, and the output is the analysis result of the emotional state.

[1432] Step 9: Adjust your program based on emotions

[1433] The server adjusts the health management program according to the user's emotional state based on the analysis results of the emotion engine. For example, when the user is feeling happy, it recommends activities to maintain that emotion. Conversely, when the user is feeling sad or angry, it recommends activities to alleviate those emotions. The input is the analysis result of the emotional state, and the output is the adjusted health management program.

[1434] (Application example 2)

[1435] Next, a description will be given of Application Example 2 of Form 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."

[1436] Conventional health management systems provide health management programs based on the user's personal data, but do not take the user's emotional state into account, which means they are unable to provide appropriate health management according to the user's psychological state. Furthermore, there is a lack of means to provide real-time feedback to users in physical stores, making it difficult to improve the user experience.

[1437] 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.

[1438] In this invention, the server includes means for streamlining health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suited to each user, means for recognizing the user's emotions in real time and adjusting the health management program based on the emotions, and means for collecting the user's personal data and emotional data in a physical store and providing feedback in real time. This enables appropriate health management that takes the user's emotional state into consideration, and makes it possible to provide feedback to the user in real time even in a physical store.

[1439] "Personal data" refers to information about an individual, such as a user's age, gender, weight, height, lifestyle, and genetic information.

[1440] "Health Management Program" means a plan that includes specific activities and instructions for maintaining and improving the health of a User.

[1441] The "emotion engine" is a system that recognizes the user's emotional state in real time and provides appropriate feedback and adjustments based on that information.

[1442] "Real-time feedback" refers to advice or information provided instantly to a user based on their current state or behavior.

[1443] A "brick and mortar store" is a physical store that users visit in person, such as a fitness gym or health food store.

[1444] "Collection means" refers to a method or device for acquiring personal data and emotional data of a user.

[1445] The "analysis means" refers to a method or device for analyzing the collected data and generating a health management program suited to the user.

[1446] The "adjustment means" refers to a method or device for appropriately modifying the health management program in response to the user's health and emotional state.

[1447] To implement the present invention, the following system configuration is required.

[1448] System Program

[1449] The system includes a program that collects and analyzes the user's personal and emotional data in real time to generate and adjust a health management program. Specifically, it uses the following hardware and software:

[1450] Hardware used

[1451] 1. Smartphone: Used to input user personal data and collect emotional data.

[1452] 2. Smart glasses: These are used to capture the user's facial expressions in real time and collect emotional data.

[1453] 3. Wearable devices: Used to collect user biometric data (e.g., heart rate, steps).

[1454] 4. Camera: Use the camera built into your smartphone or smart glasses to capture your facial expressions.

[1455] Software used

[1456] 1. OpenCV: An image processing library used to analyze the user's facial expressions.

[1457] 2. EmotionRecognizer: An emotion recognition library used to analyze the user's emotional state in real time.

[1458] 3. HealthProgramGenerator: A health management program generation library used to generate optimal health management programs based on collected personal data and emotional data.

[1459] Data processing and calculation

[1460] 1. Collection of Personal Data:

[1461] Through smartphones or smart glasses, the system collects information on the user's age, gender, weight, height, lifestyle habits, and genetic information.

[1462] It also integrates biometric data from wearable devices.

[1463] 2. Emotion Recognition:

[1464] The camera is used to capture the user's facial expressions and the emotions are analyzed using the EmotionRecognizer library.

[1465] Using voice recognition technology, it infers emotions from the user's tone of voice and choice of words.

[1466] 3. Generating Health Management Programs:

[1467] Based on the collected personal data and emotional data, the HealthProgramGenerator library generates an optimal health management program.

[1468] 4. Real-time feedback:

[1469] The generated health management program is displayed to the user in real time, and appropriate advice and information is provided.

[1470] Specific examples

[1471] Use at fitness gyms

[1472] When a user enters a fitness gym, the smart glasses analyze the user's facial expressions to detect stress, and the system recommends relaxation exercises and provides real-time feedback to the user.

[1473] Use in health food stores

[1474] While the user is walking through a health food store, their smartphone analyzes their personal data and recommends the most suitable supplements. It also takes into account the user's emotional state, suggesting products with a relaxing effect if the user is feeling stressed.

[1475] Prompt Sentence Examples

[1476] Collect user's age, gender, weight, height, lifestyle habits, and genetic information, and use an emotion engine to analyze the user's emotional state in real time. Based on this data, develop an application that generates the optimal health management program for the user and provides real-time feedback in fitness gyms and health food stores.

[1477] In this way, appropriate health management that takes into account the user's emotional state becomes possible, and feedback can be provided to the user in real time even in physical stores.

[1478] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1479] Step 1:

[1480] The user activates their smartphone or smart glasses and inputs their personal data, including age, gender, weight, height, lifestyle habits, and genetic information. The device collects this data and sends it to a server. The input data is used as the basis for analyzing the user's health condition.

[1481] Step 2:

[1482] The server receives the collected personal data and stores it in a database. The server then analyzes this data to assess the user's health and physical condition. Statistical methods and machine learning algorithms are used for the analysis. The output is an assessment of the user's health condition.

[1483] Step 3:

[1484] The user wears the smart glasses and the camera captures the user's facial expressions. The device inputs the captured image data into the EmotionRecognizer library, which analyzes the user's emotional state in real time. The analysis results in the user's emotional state (e.g., joy, sadness, anger).

[1485] Step 4:

[1486] The server receives the analyzed emotional data and integrates it with the personal data. It then uses the HealthProgramGenerator library to generate an optimal health management program for the user. This program is customized taking into account the user's health and emotional state. The output is a personalized health management program.

[1487] Step 5:

[1488] When a user visits a physical store (e.g., a fitness gym or health food store), the device continuously collects the user's personal and emotional data in real time. The server then adjusts the health management program in real time based on this data. The adjusted program includes advice and activities that are optimal for the user's current condition.

[1489] Step 6:

[1490] The device will then display a tailored health program to the user in real time. For example, a gym might advise on proper exercise form and intensity, or a health food store might recommend the best supplements. The user can then adjust their behavior based on this feedback.

[1491] Step 7:

[1492] Once the user takes action based on the feedback, the device again collects the results and sends them to the server, which then uses the collected data to further optimize the health management program, enabling continuous feedback and adjustments based on the user's health and emotional state.

[1493] Example 3

[1494] Next, a description will be given of Example 3 of Form Example 3. 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."

[1495] While conventional health management systems can provide health management programs based on users' personal data, they lack the ability to make real-time adjustments or make suggestions based on the user's emotions. As a result, they are unable to respond quickly to changes in the user's health condition or physical condition, making effective health management difficult.

[1496] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1497] In this invention, the server includes means for streamlining health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suited to each user, means for monitoring the user's biological information in real time, means for storing the monitored biological information in a database, means for analyzing the stored biological information, means for adjusting the health management program based on the analysis results, means for recognizing the user's emotions, means for generating suggestions for the user based on the recognized emotion data, and means for notifying the user of the generated suggestions. This enables quick response to changes in the user's health condition and physical condition, enabling effective health management.

[1498] "Personal data" refers to personal information about individual users, including their health status, physical constitution, medications they are taking, and lifestyle habits.

[1499] A "health management program" is a plan that includes a series of activities and instructions designed to maintain or improve a user's health.

[1500] "Biometric information" refers to data obtained from the user's body, and includes physiological information such as heart rate, blood pressure, and body temperature.

[1501] "Monitoring" is the process of observing and recording a user's biometric information in real time.

[1502] A "database" is a system for efficiently storing, managing, and searching collected data.

[1503] "Analysis" is the process of examining collected data in detail and extracting meaningful information.

[1504] "Emotion recognition" is a technology that determines a user's emotions from their facial expressions, tone of voice, etc.

[1505] "Suggestions" are specific actions or advice provided based on the user's health status and emotions.

[1506] A "notification" is a message or alert that conveys information to the user.

[1507] MODE FOR CARRYING OUT THE INVENTION

[1508] The present invention is a system for adjusting a health management program in real time in response to changes in a user's health condition and physical condition. Specific embodiments of this system will be described below.

[1509] server

[1510] The server uses dedicated software to monitor the user's biometric information in real time. This software collects biometric information from the user's wearable device (e.g., fitness tracker, smartwatch) and stores it in a database. The database uses a relational database such as MySQL or PostgreSQL. The collected data is analyzed using Python libraries such as Pandas and NumPy. Based on the analysis results, the server adjusts the health management program and notifies the device. Notifications are sent via a REST API.

[1511] Terminal

[1512] The device receives notifications from the server and makes appropriate suggestions to the user. The device is equipped with an emotion engine to recognize the user's emotions. This emotion engine uses libraries such as OpenCV and TensorFlow to analyze the user's facial expressions and tone of voice using a camera and microphone. Based on the recognized emotion data and notifications from the server, the device generates suggestions for the user and notifies the user via push notifications or voice assistants (e.g., voice recognition systems, virtual assistants).

[1513] User

[1514] The user wears a wearable device and goes about their daily life. The device measures biometric information such as heart rate and body temperature in real time and sends it to a server. The user checks the notifications from the device and implements a health management program according to the suggestions. For example, the user may take actions such as reducing exercise intensity or trying relaxation techniques.

[1515] Specific examples

[1516] Example 1: Heart rate monitoring

[1517] Server: Adjusts the health management program to reduce exercise intensity if the user's heart rate exceeds 120 beats per minute.

[1518] On your device: Notify the user when their heart rate is high and suggest reducing the intensity of their exercise.

[1519] User: Follow the device's suggestions and reduce the intensity of your exercise.

[1520] Example 2: Emotion recognition

[1521] Server: Collects the user's emotional data and suggests relaxation methods if it determines that the user's stress level is high.

[1522] Device: Determines if the user is feeling stressed and suggests relaxation methods.

[1523] User: Follow the device's suggestions and try relaxation techniques.

[1524] Prompt Sentence Examples

[1525] "Explain how to adjust the fitness program to reduce exercise intensity if the user's heart rate exceeds 120 beats per minute."

[1526] "Please explain how a system can suggest relaxation techniques if it determines that the user is stressed."

[1527] In this way, the roles of the server, terminal, and user are clarified, and specific methods for using the hardware and software are explained, making it easier to understand the operation of the system in natural language. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1528] Step 1:

[1529] The server collects biometric information from the user's wearable device.

[1530] Input: Biometric information such as heart rate, blood pressure, and temperature sent from a wearable device.

[1531] Data processing: The collected biometric information is stored in a database.

[1532] Output: Biometric information stored in a database.

[1533] Specific operation: The server receives data from the wearable device and stores it in a relational database such as MySQL or PostgreSQL.

[1534] Step 2:

[1535] The server analyzes the stored biometric information.

[1536] Input: Biometric information stored in a database.

[1537] Data computation: Analyze data using Python libraries such as Pandas and NumPy.

[1538] Output: Analysis results (e.g., warning if heart rate exceeds a certain threshold).

[1539] Specific operation: The server retrieves biometric information from the database and performs analysis to detect abnormal values.

[1540] Step 3:

[1541] The server adjusts the health management program based on the analysis results.

[1542] Input: Analysis results.

[1543] Data processing: Adjusting the content of health management programs.

[1544] Output: Coordinated health care program.

[1545] Specific operation: Based on the analysis results, the server makes adjustments such as reducing the exercise intensity and generates a new program.

[1546] Step 4:

[1547] The server notifies the terminal of the adjusted health management program.

[1548] Enter: coordinated health care programs.

[1549] Data processing: Generation of notification messages.

[1550] Output: Notification to terminal.

[1551] Specific operation: The server sends a notification to the device using the REST API.

[1552] Step 5:

[1553] The terminal receives the notification from the server.

[1554] Input: Notification from the server.

[1555] Data processing: Analysis of notification content.

[1556] Output: Prepare to notify the user.

[1557] Specific operation: The terminal receives the HTTP request and analyzes the notification content.

[1558] Step 6:

[1559] The device recognizes the user's emotions.

[1560] Input: Data from the camera or microphone.

[1561] Data Computation: Sentiment analysis using libraries such as OpenCV and TensorFlow.

[1562] Output: Recognized emotion data.

[1563] Specific operation: The device uses a camera and microphone to analyze the user's facial expressions and tone of voice to recognize emotions.

[1564] Step 7:

[1565] The device generates suggestions for the user based on the recognized emotion data and notifications from the server.

[1566] Input: Recognized emotion data and notification from the server.

[1567] Data processing: Proposal content generation.

[1568] Output: Suggestions to the user.

[1569] Specific operation: Based on the emotional data and notification content, the device generates suggestions such as relaxation methods and adjustments to exercise intensity.

[1570] Step 8:

[1571] The device notifies the user of the generated suggestions.

[1572] Input: The generated proposal.

[1573] Data processing: Generation of notification messages.

[1574] Output: Notification to the user.

[1575] What it does: The device notifies the user of the suggestion via push notification or voice assistant.

[1576] Step 9:

[1577] The user checks the notification from the terminal and executes the health management program according to the suggestion.

[1578] Input: Notifications from your device.

[1579] Data processing: Implementing the proposal.

[1580] Output: The health care program that was executed.

[1581] Specific actions: The user follows the device's suggestions and takes actions such as reducing exercise intensity or trying relaxation techniques.

[1582] (Application example 3)

[1583] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1584] Conventional health management systems can provide health management programs based on the user's personal data, but they have difficulty responding to changes in health status or emotions in real time. In addition, there is a lack of means to provide appropriate advice based on changes in the user's health status or emotions, which makes it difficult to adequately manage the user's health.

[1585] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for improving the efficiency of health management, training, and diet methods based on the personal data of each individual user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suitable for each individual user, means for adjusting the health management program in real time according to changes in the user's health condition and physical condition, means for recognizing the user's emotions and adjusting the health management program based on those changes, and means for providing appropriate advice based on changes in the user's health condition and emotions. This makes it possible to provide real-time health management and appropriate advice according to changes in the user's health condition and emotions.

[1586] "Personal Data" refers to personal information about an individual user, including information about their health, physical constitution, medications they are taking, training history, and dietary habits.

[1587] A "health management program" is a plan that includes a series of activities and instructions designed to maintain or improve a user's health.

[1588] "Real-time adjustment" means instantly reflecting changes in the user's health condition and emotions and making appropriate changes to the health management program on the spot.

[1589] The "emotion engine" is a technology that recognizes the user's emotions and grasps their changes in real time.

[1590] "Advice" is specific guidance or suggestions provided based on the user's health and emotional changes.

[1591] A "generative AI model" is an algorithm or system that uses artificial intelligence to analyze data and generate appropriate advice or programs.

[1592] A "smart device" is an electronic device that is connected to the Internet and has the ability to collect, process, and display user data.

[1593] The system for implementing this invention monitors changes in a user's health condition and emotions in real time and adjusts a health management program based on the results. A specific embodiment of this system will be described below.

[1594] System Configuration

[1595] The system consists of the following main components:

[1596] 1. Smart devices (e.g., smart glasses, smartphones)

[1597] 2. Server

[1598] 3. Generative AI Models

[1599] Hardware and Software

[1600] Smart devices are equipped with sensors that collect users' biometric information (heart rate, stress level, etc.) in real time, and these devices send the collected data to a server.

[1601] The server processes the received data and generates appropriate advice and health management programs using generative AI models. The server analyzes the data using Python and real-time data processing libraries (e.g., pandas, numpy).

[1602] The generative AI model includes algorithms for generating advice based on the user's health status and emotional changes. The generative AI model receives the user's personal data as input and outputs appropriate advice.

[1603] Data processing and calculation

[1604] The server receives the biometric information sent from the smart device and processes and analyzes the data in real time. Specifically, it performs the following processes.

[1605] 1. Data collection: Collecting biometric information such as heart rate and stress levels from smart devices.

[1606] 2. Data analysis: Analyze the collected data using Python and real-time data processing libraries.

[1607] 3. Advice generation: Using a generative AI model, advice is generated based on the user's health status and emotional changes.

[1608] Specific examples

[1609] For example, if an employee in a brick-and-mortar store is wearing smart glasses and their heart rate rises to 95, the server will generate the advice "Your heart rate is high. Please take a break" and display it on the smart glasses' display. If the employee's stress level reaches 8, the server will generate the advice "Your stress level is high. Please try relaxation techniques."

[1610] Prompt Sentence Examples

[1611] By inputting the following prompt sentences to the generative AI model, appropriate advice can be generated.

[1612] If the user's heart rate exceeds 90, generate an advice suggesting that they take a break.

[1613] If the user's stress level exceeds 7, generate a relaxation suggestion.

[1614] In this way, it becomes possible to provide real-time health management and appropriate advice in response to changes in the user's health condition and emotions.

[1615] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1616] Step 1:

[1617] Data collection

[1618] The server receives the user's biometric information (heart rate, stress level, etc.) from the smart device in real time. The input is the biometric information sent from the smart device, and the output is the biometric data stored on the server. Specifically, the smart device's sensors measure the user's heart rate and stress level and send the data to the server.

[1619] Step 2:

[1620] Data analysis

[1621] The server analyzes the received biometric data using Python or a real-time data processing library (e.g., pandas, numpy). The input is the biometric data collected in step 1, and the output is the analysis results. Specifically, the server calculates the average and fluctuations of heart rate and stress level, and detects abnormal values.

[1622] Step 3:

[1623] Advice Generation

[1624] The server uses the generative AI model to generate appropriate advice based on the analysis results. The input is the analysis result obtained in step 2, and the output is the generated advice. Specifically, the server inputs a prompt statement into the generative AI model to generate advice according to changes in the user's health condition and emotions. For example, the server uses the prompt statement, "If the user's heart rate exceeds 90, generate advice suggesting a break."

[1625] Step 4:

[1626] Providing advice

[1627] The server sends the generated advice to the smart device and provides it to the user. The input is the advice generated in step 3, and the output is the advice displayed on the smart device. Specifically, the server sends the generated advice to be displayed on the display of the smart glasses or smartphone. For example, the advice "Your heart rate is high. Please take a break" is displayed on the smart glasses.

[1628] Step 5:

[1629] Feedback collection

[1630] The user sends feedback on the advice provided to the server through the smart device. The input is the user's feedback, and the output is the feedback data stored on the server. Specifically, the user uses the interface of the smart device to evaluate whether the advice was helpful and then sends the information to the server.

[1631] Step 6:

[1632] Feedback Analysis

[1633] The server analyzes the collected feedback data and uses it to improve the accuracy of the generative AI model. The input is the feedback data collected in step 5, and the output is an improved generative AI model. Specifically, the server analyzes the feedback data and adjusts the parameters of the generative AI model so that it can generate more appropriate advice.

[1634] 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.

[1635] 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> ) 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.

[1636] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1637] 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.

[1638] [Third embodiment]

[1639] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1640] 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.

[1641] 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).

[1642] 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.

[1643] 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.

[1644] 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).

[1645] 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.

[1646] 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.

[1647] 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.

[1648] 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.

[1649] 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.

[1650] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1651] "Example 1"

[1652] The system of the present invention streamlines health management, training, and diet methods based on the personal data of each individual user. Specifically, it adjusts health management according to the user's constitution and medications, based on information input by the user and biometric information obtained from sensors (e.g., heart rate, blood pressure, body temperature, etc.). For example, it provides a health management program tailored to each individual user, such as proposing an exercise program to suppress blood pressure rise to a user taking medication for high blood pressure.

[1653] "Example 2"

[1654] Furthermore, the system of the present invention collects personal data of the user and analyzes that data to generate a health management program suited to the user. Specifically, personal data such as the user's age, sex, weight, height, lifestyle habits, and genetic information is collected, and the user's health condition and physical condition are analyzed based on this data. Based on the results of this analysis, a health management program suited to the user is generated and provided.

[1655] "Example 3"

[1656] The system of the present invention also adjusts the health management program in real time according to changes in the user's health condition and physical condition. Specifically, it monitors the user's biological information in real time and adjusts the health management program according to changes. For example, if the user's heart rate exceeds a certain threshold, the program can be adjusted to reduce exercise intensity, enabling appropriate health management according to the user's health condition.

[1657] The processing flow of each embodiment will be described below.

[1658] "Example 1"

[1659] Step 1: Collect input information from the user and biometric information obtained from sensors (e.g., heart rate, blood pressure, body temperature, etc.).

[1660] Step 2: Based on the collected information, adjust health management according to the user's constitution and medications.

[1661] Step 3: For example, for a user taking high blood pressure medication, an exercise program to suppress blood pressure rise is proposed, and a health management program suited to each individual user is provided. "Example 2"

[1662] Step 1: Collect personal data such as the user's age, gender, weight, height, lifestyle habits, and genetic information.

[1663] Step 2: Analyze the user's health and physical condition based on this data.

[1664] Step 3: Based on the results of this analysis, a health management program appropriate for the user is generated and provided.

[1665] "Example 3"

[1666] Step 1: Monitor the user's vital signs in real time.

[1667] Step 2: Adjust your health management program accordingly.

[1668] Step 3: For example, if the user's heart rate exceeds a certain threshold, the program can be adjusted to reduce exercise intensity, enabling appropriate health management according to the user's health condition.

[1669] Example 1

[1670] Next, a description will be given of Example 1 of Form 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."

[1671] Conventional health management systems have had difficulty providing appropriate health management programs tailored to individual users' physical constitutions and medications. Furthermore, they lack the ability to collect and analyze biometric information in real time, making it impossible to respond quickly to changes in the user's health status or physical condition. This has resulted in inefficient health management for users, making it difficult to recommend effective training and diet methods.

[1672] 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.

[1673] In this invention, the server includes means for streamlining health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suitable for each user, means for collecting the user's biological information in real time, means for analyzing the collected data and generating a health management program suitable for the user, means for proposing a health management program using a generative AI model, and means for the user to execute the proposed program. This makes it possible to provide an appropriate health management program according to the individual situation of each user, enabling real-time understanding of health conditions and rapid response.

[1674] "Individual user personal data" refers to personal information such as the user's age, gender, medical history, and medications being taken.

[1675] "Health Management" refers to activities and programs designed to maintain or improve a User's health.

[1676] "Training" refers to physical activity or exercise designed to improve a User's fitness or health.

[1677] "Diet method" refers to a meal plan or dietary restriction designed to improve a user's weight management and nutritional balance.

[1678] "User's constitution" refers to the user's genetic factors and physiological characteristics.

[1679] "Current medications" refers to medications that the user is currently taking.

[1680] "Health Management Program" means a set of activities or instructions designed to improve a user's health.

[1681] "Biometric information" refers to physiological data such as a user's heart rate, blood pressure, and body temperature.

[1682] "Real-time collection" refers to capturing a user's biometric information instantly and processing it without delay.

[1683] "Analyzing data" refers to assessing the user's health condition based on collected biometric information and personal data and generating an appropriate program.

[1684] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze data and generate a health management program that is appropriate for the user.

[1685] "Suggest" refers to notifying the user of the generated health management program and encouraging them to carry it out.

[1686] "Implementing" refers to the user acting in accordance with the proposed health management program.

[1687] MODE FOR CARRYING OUT THE INVENTION

[1688] This invention is a system that improves the efficiency of health management, training, and diet methods based on the personal data of each individual user. The system adjusts health management according to the user's constitution and medications, and provides a health management program that is suitable for each individual user.

[1689] Hardware and software used

[1690] Hardware: Sensors for capturing user biometric information (e.g., heart rate monitor, blood pressure monitor, thermometer)

[1691] Software: Dedicated applications for data collection and analysis, generative AI models (e.g., OpenAI's GPT-4)

[1692] Program processing procedure

[1693] 1. The user installs the dedicated application and enters personal information (age, gender, medical history, medications being taken, etc.).

[1694] 2. The device (smartphone or tablet) connects to sensors to collect the user's biometric information (heart rate, blood pressure, body temperature, etc.) in real time.

[1695] 3. The device sends the collected data to a dedicated application and stores it in a database.

[1696] 4. The server analyzes the stored data and generates a health management program based on the user's constitution and medications.

[1697] 5. The server uses a generative AI model (e.g., GPT-4) to suggest training and diet methods suitable for the user.

[1698] 6. The user reviews and executes the proposed program.

[1699] Specific examples

[1700] Let's say the user is a 50-year-old man who takes medication for high blood pressure.

[1701] The device uses a heart rate monitor and a blood pressure monitor to measure the user's heart rate and blood pressure.

[1702] The server analyzes the collected data and determines that the user's blood pressure is high.

[1703] The server uses the generative AI model to suggest an exercise program (e.g., walking or yoga) to lower blood pressure.

[1704] The user carries out the suggested exercise program and manages their health.

[1705] Prompt Sentence Examples

[1706] "For a 50-year-old male taking high blood pressure medication, please suggest an exercise program to lower his blood pressure."

[1707] In this way, the system provides a health management program tailored to the individual circumstances of each user.

[1708] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1709] Step 1:

[1710] The user installs a dedicated application and enters personal information (age, gender, medical history, medications being taken, etc.).

[1711] Input: User personal information

[1712] Output: User personal information stored in the application

[1713] What happens: A user opens an application on their smartphone and fills in the required information in the form on the screen.

[1714] Step 2:

[1715] The device connects to sensors (heart rate monitor, blood pressure monitor, thermometer, etc.) to collect the user's biometric information in real time.

[1716] Input: User's biometric information (heart rate, blood pressure, temperature, etc.)

[1717] Output: Collected biometric information

[1718] What happens: The user wears a heart rate monitor and the application collects the data via Bluetooth.

[1719] Step 3:

[1720] The biometric information collected by the device is sent to a dedicated application and stored in a database.

[1721] Input: Collected biometric information

[1722] Output: Biometric information stored in a database

[1723] What happens: The application sends data over the Internet to the server, which stores it in a database.

[1724] Step 4:

[1725] The server analyzes the stored data and generates a health management program based on the user's constitution and the medications they are taking.

[1726] Input: Biometric and personal information stored in a database

[1727] Output: Analysis results and generated health management program

[1728] How it works: The server retrieves the user's biometric and personal information from the database and analyzes it using an algorithm.

[1729] Step 5:

[1730] The server uses a generative AI model (e.g., GPT-4) to suggest training and diet methods suitable for the user.

[1731] Input: Analysis results and generated health management program

[1732] Output: Training and diet suggestions for the user

[1733] Specific operation: The server inputs a prompt sentence into the generative AI model and generates an appropriate health management program.

[1734] Step 6:

[1735] The user confirms and executes the proposed program.

[1736] Input: Proposed Health Care Program

[1737] Output: Executed health management program

[1738] What happens: The application notifies the user, who then confirms and runs the program within the application.

[1739] (Application example 1)

[1740] Next, a description will be given of Application Example 1 of Form 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."

[1741] Conventional health management systems have not been able to fully utilize users' personal data and biometric information, making it difficult to provide optimal health management programs for each individual user. Furthermore, there was a lack of systems that could evaluate health status in real time or be used in physical stores, making it difficult to streamline user health management.

[1742] 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.

[1743] In this invention, the server includes means for streamlining health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suitable for each user, means for acquiring the user's biometric information in real time, means for evaluating the health condition based on the acquired biometric information, means for proposing an appropriate training program based on the evaluation results, and means including a smartphone application intended for use in physical stores. This makes it possible to evaluate the user's health condition in real time and provide the optimal health management program for each user.

[1744] "Personal data" is data, including personal and health information, about individual users.

[1745] "Health Management" refers to activities and programs designed to maintain or improve a User's health.

[1746] "Training" refers to physical activity or exercise designed to improve a User's fitness or health.

[1747] "Diet Method" refers to a diet or exercise method for managing or reducing a user's weight or body fat.

[1748] "Constitution" refers to the user's genetic or physiological characteristics.

[1749] "Current medications" refers to medications that the user is currently taking.

[1750] "Biometric information" refers to physical data such as a user's heart rate, blood pressure, and temperature.

[1751] "Health Management Program" means a set of activities and instructions designed to improve and maintain the health and well-being of a User.

[1752] "Real-time" refers to data being processed and evaluated as it is generated.

[1753] "Evaluation" refers to analyzing the user's health status and judging the results.

[1754] "Training Program" means a plan of exercise or physical activity designed to improve a User's fitness or health.

[1755] "Brick and mortar store" refers to a store that exists in a physical location.

[1756] "Smartphone application" refers to a software program that runs on a smartphone.

[1757] The system for carrying out this invention collects personal data and biometric information of a user, evaluates the health condition in real time, and provides an appropriate health management program. A specific embodiment of this system will be described below.

[1758] System Configuration

[1759] The system consists of the following main components:

[1760] 1. User devices: Devices such as smartphones and smartwatches collect users' personal data and biometric information.

[1761] 2. Server: Receives data sent from user devices and analyzes and evaluates them.

[1762] 3. Application: An application installed on a smartphone that provides the user interface and displays the health management program.

[1763] Data collection and analysis

[1764] The user device collects the user's personal data (age, weight, height, medications, etc.) and biometric information (heart rate, blood pressure, body temperature, etc.), which is then sent to a server via Bluetooth or Wi-Fi.

[1765] The server analyzes the received data and evaluates the user's health status using programming languages ​​such as Python, preprocessing the data, and using machine learning models.

[1766] Health management program generation

[1767] The server then generates an optimal health management program for the user based on the analysis results. For example, it might suggest low-intensity aerobic exercise for a user at risk of high blood pressure. The generated program is sent to the user's device and displayed to the user through the application.

[1768] Use in physical stores

[1769] The system is intended for use in brick-and-mortar fitness gyms and health food stores, where users can check their health status in real time through a smartphone application and optimize the services offered at the store.

[1770] Specific examples

[1771] User A: 40 years old, weighs 80 kg, is 180 cm tall, and is taking medication for high blood pressure. Heart rate: 90, blood pressure: 150, body temperature: 36.8°C.

[1772] Suggestion: Low-intensity aerobic exercise is recommended.

[1773] User B: 25 years old, weight 60 kg, height 165 cm, no medication. Heart rate 70, blood pressure 120, body temperature 36.5°C.

[1774] Suggestion: Continue with your regular training program.

[1775] Prompt Sentence Examples

[1776] Enter your age, weight, height, medications, heart rate, blood pressure and temperature, and we'll suggest the right training program for you.

[1777] In this way, users can understand their health status in real time and receive the optimal health management program.

[1778] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1779] Step 1:

[1780] The user starts the smartphone application and inputs personal data (age, weight, height, medications being taken, etc.) The input data is temporarily saved within the application.

[1781] Step 2:

[1782] Users wear sensors such as smartwatches to measure vital signs (heart rate, blood pressure, body temperature, etc.), and the measured data is sent to a smartphone via Bluetooth or Wi-Fi.

[1783] Step 3:

[1784] The device (smartphone) sends the collected personal data and biometric information to a server using the HTTPS protocol to ensure data security.

[1785] Step 4:

[1786] The server analyzes the received data using Python for data preprocessing (missing value imputation, data normalization, etc.) and machine learning models (e.g., random forests, neural networks). Based on the input data, the server evaluates the user's health status.

[1787] Step 5:

[1788] The server then generates an optimal health management program for the user based on the evaluation results. For example, it would suggest low-intensity aerobic exercise to a user at risk of high blood pressure.

[1789] Step 6:

[1790] The server sends the generated health management program to the terminal using the HTTPS protocol to ensure data security.

[1791] Step 7:

[1792] The device displays the received health management program to the user using the user interface of the smartphone application. The user then performs training and health management according to the proposed program.

[1793] Step 8:

[1794] Users can use smartphone applications at physical stores (fitness gyms and health food stores) to check their own health status and make optimal use of the services offered at the store.

[1795] Example 2

[1796] Next, a description will be given of Example 2 of Form 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."

[1797] Conventional health management systems have had difficulty efficiently collecting and analyzing users' personal data and providing optimal health management programs for each individual user. They also lacked the functionality to adjust health management programs in real time according to the user's health status and changes in physical condition. Furthermore, the technology to generate optimal health management programs for each user using generative AI models had not yet been fully established.

[1798] 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.

[1799] In this invention, the server includes: means for streamlining health management, training, and diet methods based on the personal data of each user; means for adjusting health management according to the user's constitution and medications; means for providing a health management program tailored to each user; means for collecting the user's personal data; means for storing the collected personal data in a database; means for analyzing the stored personal data; means for generating a health management program tailored to each user using a generative AI model based on the analysis results; and means for providing the generated health management program to the user. This makes it possible to efficiently collect and analyze the user's personal data and provide an optimal health management program for each user. Furthermore, the health management program can be adjusted in real time according to the user's health condition and changes in physical condition, and a health management program tailored to each user can be generated using the generative AI model.

[1800] "Individual user personal data" refers to information about an individual user, such as age, gender, weight, height, lifestyle habits, and genetic information.

[1801] "Health Management" refers to activities and programs designed to maintain or improve a user's health.

[1802] "Training" refers to exercises or physical activities designed to improve a user's physical strength or muscle power.

[1803] "Diet method" refers to a meal plan or dietary restriction designed to improve a user's weight management and nutritional balance.

[1804] "Constitution" refers to the user's genetic or physiological characteristics.

[1805] "Current medications" refers to medications that the user is currently taking.

[1806] "Health Management Program" means a set of activities or instructions designed to maintain or improve a user's health.

[1807] "Database" refers to a system for systematically storing users' personal data.

[1808] "Data analytics" refers to methods for using collected data to assess a user's health and physical condition.

[1809] "Generative AI model" refers to a model that uses artificial intelligence technology to generate a health management program suitable for the user.

[1810] A "prompt sentence" refers to an instruction sentence to be input into a generative AI model.

[1811] "Real-time" refers to responding immediately to changes in the user's health condition or physical condition.

[1812] This invention is a system that collects and analyzes personal data of users and uses a generative AI model to generate and provide a health management program suited to each individual user. Specific embodiments of this system are described below.

[1813] First, the user accesses a dedicated application or website using a device such as a smartphone or PC. The user enters personal data such as age, gender, weight, height, lifestyle habits (e.g., exercise frequency, smoking habits), and genetic information (e.g., family medical history) into the on-screen form. Once the input is complete, the user clicks the "Submit" button. The device then sends the entered data to the server in an appropriate format such as JSON.

[1814] The server receives the personal data sent from the device. The received data is saved in a relational database such as MySQL or PostgreSQL using a database connection library (e.g., SQLAlchemy). A unique ID is assigned to each user in the database, and the personal data of each user is stored in the corresponding table.

[1815] The server then uses data analysis tools such as Python and R to analyze the stored personal data. Specifically, it preprocesses the data using libraries such as Pandas and NumPy, and then uses machine learning models and statistical methods to evaluate the user's health and physical condition. For example, it calculates the user's BMI (body mass index) and compares it with the standard value to evaluate health risks.

[1816] Based on the results of the data analysis, the server uses a generative AI model (e.g., GPT-4) to generate a health management program. The user's personal data and the analysis results are input into the generative AI model as a prompt. For example, the following prompt is input into the generative AI model:

[1817] The user is 30 years old, male, weighs 70 kg, and is 175 cm tall. He exercises three times a week and smokes daily. There is a family history of diabetes. Please generate a health management program suitable for this user.

[1818] The generative AI model outputs an appropriate health management program based on this prompt.

[1819] Finally, the server receives the health management program output from the generative AI model and sends it to the device. The user can then check the health management program that is best suited to them through the device. For example, the following health management program may be displayed on the screen of an application or website.

[1820] 1. Continue exercising three times a week and increase the time to 30 to 45 minutes.

[1821] 2. Reduce smoking and eventually aim to quit.

[1822] 3. When it comes to diet, limit your sugar intake and eat more vegetables and fruits.

[1823] 4. Check your blood sugar levels regularly and consult your doctor if any abnormalities occur.

[1824] The user can manage his / her health according to this program.

[1825] In this way, it is possible to efficiently collect and analyze users' personal data and provide the optimal health management program for each individual user.In addition, the health management program can be adjusted in real time according to the user's health condition and changes in physical condition, and a generative AI model can be used to generate a health management program that is suitable for the user.

[1826] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1827] Step 1:

[1828] A user accesses a dedicated application or website using a device such as a smartphone or PC. The user enters personal data such as age, gender, weight, height, lifestyle habits (e.g., exercise frequency, smoking habits), and genetic information (e.g., family medical history) into the on-screen form. Once the input is complete, the user clicks the "Submit" button. The device then sends the entered data to the server in an appropriate format such as JSON.

[1829] Input: User's personal data

[1830] Output: JSON format data

[1831] Step 2:

[1832] The server receives the personal data sent from the device. The received data is saved in a relational database such as MySQL or PostgreSQL using a database connection library (e.g., SQLAlchemy). A unique ID is assigned to each user in the database, and the personal data of each user is stored in the corresponding table.

[1833] Input: Personal data in JSON format

[1834] Output: Personal data stored in a database

[1835] Step 3:

[1836] The server uses data analysis tools such as Python and R to analyze the stored personal data. Specifically, it preprocesses the data using libraries such as Pandas and NumPy, and then uses machine learning models and statistical methods to evaluate the user's health and physical condition. For example, it calculates the user's BMI (body mass index) and compares it with the standard value to evaluate health risks.

[1837] Input: Personal data stored in a database

[1838] Output: Evaluation results of the user's health and physical condition

[1839] Step 4:

[1840] Based on the results of the data analysis, the server uses a generative AI model (e.g., GPT-4) to generate a health management program. The user's personal data and the analysis results are input into the generative AI model as a prompt. For example, the following prompt is input into the generative AI model:

[1841] The user is 30 years old, male, weighs 70 kg, and is 175 cm tall. He exercises three times a week and smokes daily. There is a family history of diabetes. Please generate a health management program suitable for this user.

[1842] The generative AI model outputs an appropriate health management program based on this prompt.

[1843] Input: User's personal data and analysis results

[1844] Output: Health management program based on generative AI model

[1845] Step 5:

[1846] The server receives the health management program output from the generative AI model and sends it to the device. The user can then check the health management program that is best suited to them through the device. For example, the following health management program may be displayed on the screen of an application or website.

[1847] 1. Continue exercising three times a week and increase the time to 30 to 45 minutes.

[1848] 2. Reduce smoking and eventually aim to quit.

[1849] 3. When it comes to diet, limit your sugar intake and eat more vegetables and fruits.

[1850] 4. Check your blood sugar levels regularly and consult your doctor if any abnormalities occur.

[1851] Input: Health management program using generative AI model

[1852] Output: Health management program provided to the user

[1853] (Application example 2)

[1854] Next, a description will be given of Application Example 2 of Form 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."

[1855] While conventional health management systems can collect and analyze users' personal data, it is difficult to provide optimal health management programs for each individual user. Additionally, they lack support for purchasing health foods and supplements in physical stores, or the ability to book health consultations and fitness programs in-store, making them unable to provide comprehensive support for users' health management.

[1856] 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.

[1857] In this invention, the server includes means for streamlining health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suited to each user, means for collecting and analyzing the user's personal data and scoring the user's health status, means for generating a health management program suited to the user based on the health score, means for supporting the purchase of health foods and supplements in physical stores, and means for providing a function for booking health consultations and fitness programs in stores, thereby enabling comprehensive health management based on the user's personal data.

[1858] "Personal data" refers to information about an individual, such as a user's age, gender, weight, height, lifestyle, and genetic information.

[1859] "Health Management Program" means a plan that includes specific guidance and advice for maintaining or improving a User's health.

[1860] A "health score" is a numerical indicator of a user's health status, calculated based on personal data.

[1861] A "physical store" is a store that exists in a physical location and where users can visit in person to purchase products or services.

[1862] "Health foods" are foods that are consumed for the purpose of maintaining or improving health.

[1863] "Supplements" are nutritional supplements taken to make up for nutrients that tend to be lacking in a normal diet.

[1864] "Health Consultation" is a service that allows users to ask questions and ask health-related questions to experts.

[1865] A "fitness program" is an exercise or training plan designed based on a user's physical strength and health condition.

[1866] The "reservation function" is a function that allows users to reserve a service or program by specifying the date and time of use in advance.

[1867] A system for implementing this invention includes a program for collecting and analyzing personal data of a user and scoring the user's health status. The system is realized using hardware such as a smartphone, a server, and a terminal in a brick-and-mortar store.

[1868] Hardware and Software

[1869] Hardware: Smartphones (iOS or Android), servers, brick-and-mortar terminals

[1870] Software: Python 3.x, scikit-learn (library for data analysis), JSON (save and load data)

[1871] Data collection

[1872] Users enter personal information such as age, gender, weight, height, lifestyle habits, and genetic information through a smartphone application, which is then sent to a server and stored.

[1873] Data analysis

[1874] The server uses a machine learning model (Linear Regression) to score the user's health based on the collected personal data. Specifically, it calculates a health score using the user's age, gender, weight, height, lifestyle habits, and other features as input.

[1875] Health management program generation

[1876] The server generates a health management program suitable for the user based on the calculated health score, for example, providing a basic health management program if the health score is low and an advanced health management program if the health score is high.

[1877] In-store support

[1878] Through the smartphone application, users can access features that support the purchase of health foods and supplements in physical stores, as well as the ability to book health consultations and fitness programs in-store.

[1879] Specific examples

[1880] For example, if a 30-year-old male user weighs 70 kg, is 175 cm tall, and has an active lifestyle, the app will suggest an "intermediate health management program." The user can then purchase the appropriate supplements at a physical store and book a fitness program.

[1881] Prompt Sentence Examples

[1882] Enter your age, gender, weight, height, lifestyle, and genetic information. For example, if you are a 30-year-old male who weighs 70kg, is 175cm tall, and has an active lifestyle, enter the following:

[1883] Age: 30

[1884] Gender: Male

[1885] Weight: 70kg

[1886] Height: 175cm

[1887] Lifestyle: Active

[1888] Genetic Information: None

[1889] In this way, a system can be realized that provides a health management program suited to the user.

[1890] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1891] Step 1:

[1892] The user launches a smartphone application and enters personal data such as age, gender, weight, height, lifestyle habits, and genetic information.

[1893] Input: User's personal data (age, gender, weight, height, lifestyle habits, genetic information)

[1894] Output: Personal data is stored in the application and sent to the server.

[1895] Step 2:

[1896] The server saves the received personal data and stores it in a database.

[1897] Input: User's personal data

[1898] Output: Personal data stored in a database

[1899] Step 3:

[1900] The server uses a machine learning model (Linear Regression) to score the user's health status based on the stored personal data.

[1901] Input: Personal data stored in a database

[1902] Output: Health score

[1903] Specific operation: The server inputs personal data as features into a machine learning model and calculates a health score.

[1904] Step 4:

[1905] The server generates a health management program suitable for the user based on the health score.

[1906] Input: Health Score

[1907] Output: Health Management Program

[1908] Specific operation: The server evaluates the health score and selects a health management program (basic, intermediate, or advanced) according to the score.

[1909] Step 5:

[1910] The user checks the generated health management program through the smartphone application.

[1911] Enter: Health Management Program

[1912] Output: Health management program displayed on the user's smartphone

[1913] Specific operation: The application displays the health management program received from the server to the user.

[1914] Step 6:

[1915] Use the feature to help users purchase health foods and supplements in physical stores.

[1916] Enter: Health Management Program

[1917] Output: Purchase support information

[1918] What it does: The application displays a list of appropriate health foods and supplements based on your health management program and assists you in purchasing them.

[1919] Step 7:

[1920] Users can use the in-store health consultation and fitness program booking function.

[1921] Enter: Health Management Program

[1922] Output: Reservation information

[1923] Specific operation: The application provides a function for booking health consultations and fitness programs based on a health management program, and allows users to select the desired date and time to make a reservation.

[1924] In this way, comprehensive health management based on the user's personal data is realized.

[1925] Example 3

[1926] Next, a third embodiment of the third embodiment will be described. 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."

[1927] Conventional health management systems can provide health management programs based on the user's personal data, but they have the problem of difficulty in dynamically adjusting the program in response to changes in biological information in real time. Furthermore, individual adjustments based on the user's constitution or medications are insufficient, which can prevent optimal management of the user's health condition.

[1928] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes means for improving the efficiency of health management, training, and diet methods based on the personal data of each user, means for adjusting health management according to the user's constitution and medications, means for providing a health management program suitable for each user, means for collecting user's biological information in real time, means for transmitting the collected biological information to the server, means for analyzing the biological information received by the server, means for adjusting the health management program based on the analysis results, and means for notifying the user of the adjustment results. This enables dynamic health management according to the user's health condition and changes in physical condition.

[1929] "Personal data" refers to personal information and health information about individual users, including, specifically, age, gender, weight, height, exercise history, dietary history, and medications currently being taken.

[1930] "Health Management Program" means a series of activities and instructions designed to maintain or improve a User's health, including, but not limited to, exercise plans, dietary instructions, and rest instructions.

[1931] "Biometric information" refers to data obtained from the user's body, and specifically includes heart rate, blood pressure, body temperature, respiratory rate, etc.

[1932] "Real-time" refers to data collection and processing occurring immediately, meaning that it reflects the current state without delay.

[1933] "Server" refers to a computer system that collects, analyzes, stores, and transmits data over a network.

[1934] "Analysis" refers to processing collected data and extracting meaningful information, and specifically includes statistical analysis of data and the application of machine learning models.

[1935] "Notification" refers to the act of conveying information to users, and specifically includes push notifications from smartphone apps, emails, messages, etc.

[1936] "Adjustment" refers to changing and optimizing a health management program based on the analysis results, and specifically includes changing exercise intensity and modifying dietary co...

Claims

1. A means for collecting biometric information of a user; A means for analyzing the collected biometric information; A means for generating advice according to changes in the health condition and emotions of the user based on an analysis result of the biometric information using a generation AI; means for providing said advice to a user; A system including:

2. means for collecting feedback on said advice; The system of claim 1 , further comprising: means for analyzing the collected feedback and improving accuracy of the generative AI.

3. The system of claim 1 , wherein the means for providing advice provides the advice in real time via a smart device.

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