System

Abstract

A system is provided.SOLUTION: A system comprising: means for allowing a user to input his / her height, weight, eating / drinking pattern, and exercise amount; means for collecting the inputted data and storing the collected data in a database; means for evaluating a health risk using the stored data; means for predicting a disease that may occur in the future based on the health risk specified by the analyzing means; means for generating a preventive measure or advice for improving a lifestyle habit for the predicted disease; and means for receiving a question or consultation about health from a user and generating a corresponding answer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, the risk of lifestyle-related and chronic diseases is increasing, making it necessary for individuals to understand their own health status in real time and take appropriate preventative measures. However, traditional health management methods rely on regular health checkups and self-management, making it difficult to identify risks in a timely manner or provide appropriate advice. Furthermore, when users have questions or concerns about their health, they often lack the specialized knowledge to provide appropriate responses. [Means for solving the problem]

[0005] The present invention provides a system that provides a means for users to input health data such as their height, weight, eating and drinking patterns, and amount of exercise, and collects and stores that data in a database. It also includes an analysis means for assessing health risks using the stored data and a means for predicting future illnesses based on the analysis results. It also includes a means for generating and providing advice on preventive measures and lifestyle improvements for predicted illnesses. The system also includes a means for accepting health-related questions and inquiries from users and generating corresponding answers, allowing users to understand their own health status in real time and receive appropriate preventive measures and advice.

[0006] A "user" is an individual who uses the system to input their own health data and manage their health.

[0007] "Height" refers to the height of the user from the ground to the top of the head, and is basic biometric data for assessing health status.

[0008] "Weight" refers to the user's overall mass, and together with height, is an important piece of data for calculating BMI.

[0009] "Eating and drinking patterns" refers to a user's dietary and drinking habits, and is data used to evaluate nutritional balance and calorie intake.

[0010] "Amount of exercise" indicates the degree of daily physical activity of the user, and includes specific exercise data such as frequency of pedometer use or gym visits.

[0011] "Means" refers to a device or method for realizing a specific function, and is an element that performs a necessary function in a system.

[0012] A "database" is an electronic storage device for effectively storing, managing, and retrieving collected data.

[0013] "Analytical tools" refer to specific algorithms or analytical methods that assess health risks and derive results based on collected data.

[0014] "Health risk" refers to the risk of health problems or illnesses that may occur in the future based on current lifestyle habits and health status.

[0015] "Preventive measures" refer to behavioral or lifestyle changes recommended to reduce or avoid specific health risks.

[0016] "Advice for improving lifestyle habits" refers to information that suggests specific lifestyle changes or new behaviors that a user should take to maintain their health.

[0017] "Means for accepting questions and inquiries" refers to the interface that users use to ask the system questions or ask for advice about their health.

[0018] "Answer generation means" refers to a processing device or algorithm that provides appropriate information or solutions to questions or inquiries submitted by users. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] The system of the present invention collects and analyzes health data of a user and provides advice on preventive measures and lifestyle improvements based on health risks. The following describes in detail an embodiment of the present invention.

[0041] User data entry and collection

[0042] Users log in to an application displayed on a device such as a smartphone or PC and enter their health data, such as their height, weight, eating and drinking patterns, and amount of exercise. For example, a user might enter data such as height 170 cm, weight 80 kg, daily alcohol consumption, a diet lacking in vegetables, 5,000 steps per day, and going to the gym twice a week.

[0043] The terminal sends the entered data to the server, which receives the data and stores it in a database. It also checks the data for blank spaces and abnormal values, and if any abnormalities are found, prompts the user to re-enter the data.

[0044] Data analysis and health risk assessment

[0045] The server uses the stored data to perform analysis, such as calculating the user's BMI, analyzing their eating and drinking patterns (to assess whether they are consuming too many calories or are lacking in nutrients), and checking their physical activity (comparing it to the recommended number of steps). For example, the BMI is calculated as follows:

[0046] BMI = Weight (kg) / (Height (m) Height (m))

[0047] Example: 80kg / (1.7m 1.7m) = 27.7

[0048] This allows the system to assess the user's health risks and predict specific diseases they are likely to develop. For example, if a user's BMI is 27.7, the system will assess that the user is at risk of obesity, high blood pressure, and diabetes.

[0049] Providing advice on preventative measures and lifestyle changes

[0050] The server generates specific preventive measures and lifestyle improvement advice for the user based on the evaluation results, for example:

[0051] Reduce your alcohol intake

[0052] Add 200g of vegetables to each meal

[0053] Walk more than 10,000 steps a day

[0054] This advice is sent to users' devices such as smartphones and computers, and related articles and information are also provided for easy reference.

[0055] Receiving and responding to health-related inquiries from users

[0056] The system also accepts health-related questions and consultations from users. For example, a user can input into the application a question about how tired they have been feeling recently when they wake up in the morning.

[0057] The device sends the user's inquiry to a server, which analyzes the information and generates an appropriate response using AI and a rule-based engine. For example, if the user feels tired in the morning, it may be due to a lack of exercise or poor sleep quality. The server may suggest daily stretching and improving the sleep environment, and recommend a doctor's visit if necessary.

[0058] As described above, the system of the present invention provides a system that allows users to understand their own health condition in real time and receive appropriate preventive measures and advice.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] Users log in to the application and enter their health data, such as height, weight, eating and drinking patterns, and exercise amount. For example, a user might be 170 cm tall, weigh 80 kg, drink alcohol daily, have a diet lacking in vegetables, walk 5,000 steps a day, and go to the gym twice a week.

[0062] Step 2:

[0063] The device sends the entered data to the server, which transfers the data using a secure communication protocol such as HTTPS.

[0064] Step 3:

[0065] The server stores the received data in a database. Relational databases such as PostgreSQL or MySQL (registered trademarks) are commonly used here. When the data is stored, it is linked to identifying information such as the user ID.

[0066] Step 4:

[0067] The server performs preprocessing for analyzing the collected data. During this process, it checks for blank spaces and outliers, and performs data completion and filtering as necessary. If an outlier is detected, it prompts the user to re-enter the data.

[0068] Step 5:

[0069] The server will analyze the collected health data, including the following:

[0070] Calculate BMI: BMI = weight (kg) / (height (m) height (m)). For example, 80kg / (1.7m 1.7m) = 27.7.

[0071] Eating pattern analysis: Evaluate calorie surpluses and nutritional deficiencies, for example, by comparing alcohol intake or vegetable intake with baseline thresholds.

[0072] Check your physical activity: Analyze your steps and gym visit frequency and compare them with the recommended standard (e.g. 10,000 steps per day).

[0073] Step 6:

[0074] The server evaluates health risks based on the analysis results and predicts future illnesses. For example, if a BMI is 27.7, it is determined that the individual is at risk of obesity, diabetes, or high blood pressure.

[0075] Step 7:

[0076] The server generates specific preventive measures and lifestyle improvement advice for predicted illnesses. For example:

[0077] Reduce your alcohol intake to one drink a day

[0078] "Add 200g of vegetables to each meal"

[0079] "Walk more than 10,000 steps a day"

[0080] Step 8:

[0081] The device notifies the user of the generated advice via push notification, email, in-app notification, etc. Related articles and information are also provided.

[0082] Step 9:

[0083] When a user wishes to input a question or consultation about health, the user inputs the question into the application. For example, the user may input a question such as, "Recently, I've been feeling very tired when I wake up in the morning."

[0084] Step 10:

[0085] The terminal transmits the contents of the user's consultation to the server.

[0086] Step 11:

[0087] The server then launches an AI engine and rule-based system to analyze the customer's inquiry and generate an appropriate response. For example, if the customer feels tired in the morning, the server will consider the quality of their sleep and lack of exercise and suggest measures such as "daily stretching" and "reviewing their sleeping environment."

[0088] Step 12:

[0089] The terminal notifies the user of the generated answer, and in some cases may also notify the user of a recommendation to see a doctor.

[0090] These steps allow users to understand their health status in real time and receive appropriate preventive measures and advice.

[0091] Example 1

[0092] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0093] In today's world, lifestyle-related diseases and health risks are on the rise, requiring individual users to constantly monitor their own health status and take preventative measures and improve their lifestyles. However, many existing systems struggle to accurately manage and analyze users' health data and provide appropriate advice. Furthermore, they are not adequately responsive to users' health-related questions or inquiries, making it difficult to obtain appropriate advice immediately.

[0094] 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.

[0095] In this invention, the server includes: a means for a user to input their height, weight, eating and drinking patterns, and amount of exercise; a means for collecting the input data and storing it in a database; a means for checking the stored data for abnormal values ​​and prompting the user to re-enter the data if abnormalities are found; a data analysis means for assessing the user's health risk using the stored data, including a BMI calculation means, an eating and drinking pattern analysis means, and an exercise amount check means; a means for generating advice on preventive measures and lifestyle improvements using a generative AI model based on the assessed health risk; a means for notifying the user of the generated advice; a means for accepting health-related questions and inquiries from the user and generating corresponding answers using an AI or rule-based engine; and an encryption means for securely communicating all of the above means. This allows users to accurately manage their health data, assess their health risks in real time, and receive appropriate advice on preventive measures and lifestyle improvements. Health-related questions and inquiries can also be responded to promptly.

[0096] "User" refers to an individual who uses the system to manage their own health data and receive advice on preventive measures and lifestyle improvements.

[0097] A "terminal" is a device through which a user inputs their own health data and communicates with the server, and specifically includes smartphones and personal computers.

[0098] A "server" is a central computer system that receives health data sent by users, stores, analyzes, and evaluates the data, and generates advice and answers to notify the users.

[0099] "Health data" refers to information entered to understand a user's health condition, such as their height, weight, eating and drinking patterns, and amount of exercise.

[0100] "Database" refers to a system for structuring and storing health data entered and submitted by users.

[0101] An "outlier" is a value in the health data entered by the user that is outside the normal range, including incorrect data entry or extreme values.

[0102] "Data analysis" refers to the process of calculations and analysis to assess a user's health status and risks based on stored health data.

[0103] "BMI calculation means" refers to a means for calculating a user's BMI (Body Mass Index) based on their weight and height, and assessing their health risk.

[0104] The "eating and drinking pattern analysis means" is a means for analyzing the eating and drinking data input by the user and evaluating whether there is an excess of calories or a nutritional deficiency.

[0105] The "exercise amount check means" refers to a means for checking the user's exercise amount and comparing it with the recommended exercise amount.

[0106] A "generative AI model" is an artificial intelligence model that generates advice on preventive measures and lifestyle improvements based on collected data.

[0107] "Advice" refers to specific suggestions and improvements to reduce health risks based on the user's health data and analysis results.

[0108] "Encryption methods" refers to the techniques and protocols used to encrypt data so that user data is communicated securely.

[0109] "AI and rule-based engines" refer to engines based on artificial intelligence and defined rules that generate appropriate answers to user inquiries and questions.

[0110] These definitions clearly show the functions of the system and the role of each means.

[0111] The system of the present invention collects and analyzes a user's health data and provides advice on preventive measures and lifestyle improvements based on health risks. The main components of the system and their operation are described in detail below.

[0112] User data entry and collection

[0113] Users log in to an application displayed on a device such as a smartphone or PC and enter their health data, such as their height, weight, eating and drinking patterns, and amount of exercise. For example, a user might enter data such as height 170 cm, weight 80 kg, daily alcohol consumption, a diet lacking in vegetables, 5,000 steps per day, and going to the gym twice a week.

[0114] The device temporarily stores the entered data in memory and then sends it to the server using a security protocol (e.g., HTTPS). The data is encrypted with AES256 and transmitted securely.

[0115] Data transmission and storage

[0116] The server decodes the received user data and stores it in a database. The stored data is checked for blanks and abnormal values. If an abnormality is detected, a message is sent to the terminal prompting the user to re-enter the data.

[0117] Data analysis and health risk assessment

[0118] The server retrieves the user data stored in the database and runs the analysis program. Specifically, it performs the following calculations to calculate the user's BMI:

[0119] BMI = Weight (kg) / (Height (m) Height (m))

[0120] Example: If you are 1.7m tall and weigh 80kg, then 80 / (1.7 1.7) = 27.7

[0121] It also analyzes eating and drinking patterns and checks the amount of exercise, which can help assess the user's health risks and determine whether they are at risk of obesity, high blood pressure, or diabetes.

[0122] Providing advice on preventative measures and lifestyle changes

[0123] The server uses a generative AI model based on the data analysis results to automatically generate advice for users on preventive measures and lifestyle improvements. For example, the following advice may be generated:

[0124] Reduce your alcohol intake

[0125] Add 200g of vegetables to each meal

[0126] Walk more than 10,000 steps a day

[0127] The generated advice is sent to the terminal and notified to the user. In addition, related information and articles are also provided to support the user in easily implementing the advice.

[0128] Receiving and responding to health-related inquiries from users

[0129] The user inputs health-related questions or concerns through the application. For example, they input a question such as, "I feel extremely tired when I wake up in the morning." The device encodes the question and sends it to the server.

[0130] The server analyzes the received consultation content and generates an appropriate response using AI and a rule-based engine. For example, if the user feels tired in the morning, it predicts that they are not getting enough exercise or their sleep quality is poor, and suggests advice such as daily stretching and reviewing their sleep environment, and recommends seeing a doctor if necessary. The generated response is sent to the device and notified to the user.

[0131] Examples of concrete examples and prompts

[0132] Specific working example:

[0133] The user inputs their daily health data and the data is sent to a server.

[0134] The server analyzes the stored data, assesses health risks, and notifies users of preventive measures.

[0135] Users can enter their health questions and receive instant advice.

[0136] Example prompt sentence:

[0137] "Enter the user's health data and generate appropriate lifestyle improvement advice."

[0138] "Please provide a risk assessment for a user with a BMI of 27.7 and suggest preventative measures for diabetes."

[0139] This system allows users to accurately manage their own health data and receive advice on appropriate preventive measures and lifestyle improvements. Furthermore, health-related questions and inquiries are quickly responded to, improving the overall quality of health management.

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Step 1:

[0142] Users log in to the application and enter health data such as height, weight, eating and drinking patterns, and amount of exercise. Examples of data entered by users include "height 170 cm, weight 80 kg, daily alcohol consumption, insufficient vegetable intake, 5,000 steps per day, and gym visits twice a week." The device temporarily stores the entered data in its memory.

[0143] Input: User's health data (height, weight, eating and drinking patterns, amount of exercise, etc.)

[0144] Output: Health data stored in the device memory

[0145] Specific operation: When a user enters health data into the application form and presses the input button, the data is temporarily saved in memory.

[0146] Step 2:

[0147] The device encodes the entered data and sends it to the server using a security protocol (e.g., HTTPS). The data is then encrypted with AES256 for secure communication. If the transmission is successful, the device receives a confirmation message from the server.

[0148] Input: Health data stored in the device memory

[0149] Output: Health data sent to the server and a confirmation message

[0150] Specific operation: Encodes the data into JSON format and sends it to the server using a secure communication protocol.

[0151] Step 3:

[0152] The server decodes the received health data and stores it in a database. The stored data is checked for blank spaces and abnormal values, and if an abnormality is detected, a message is sent to the device prompting re-entry.

[0153] Input: Health data received by the server

[0154] Output: Health data stored in the database and a message prompting you to re-enter (if there are abnormal values)

[0155] Specific behavior: Decodes and performs data integrity and range checks before saving to the database.

[0156] Step 4:

[0157] The server retrieves user data stored in a database and runs a data analysis program to calculate BMI, assess eating and drinking patterns, and check physical activity.

[0158] Input: Health data stored in a database

[0159] Output: Analysis results (BMI value, dietary pattern evaluation, exercise amount evaluation)

[0160] Specific operation: Based on the user's health data, calculate BMI as follows: 80kg / (1.7m 1.7m) = 27.7. Also calculates and evaluates eating and drinking patterns and exercise volume.

[0161] Step 5:

[0162] The server uses a generative AI model based on the analysis results to automatically generate advice for users on preventive measures and lifestyle improvements.

[0163] Input: Analysis results (BMI value, dietary pattern evaluation, exercise amount evaluation)

[0164] Output: Generated advice (e.g., reduce alcohol intake, add vegetables to each meal, walk at least 10,000 steps per day)

[0165] How it works: The analysis results are fed into an AI model to generate appropriate advice, which is customized to each individual user's situation.

[0166] Step 6:

[0167] The server sends the generated advice to the terminal and notifies the user, along with related articles and information.

[0168] Input: Generated advice

[0169] Output: Advice and related information posted to the terminal

[0170] Specific operation: The server notifies the user of advice from the generated AI model and sets reminders as needed.

[0171] Step 7:

[0172] Users can input their health-related questions or concerns into the application, and when the consultation is submitted, the device encodes the information and sends it to the server.

[0173] Input: User's health consultation details

[0174] Output: Consultation details sent from the device to the server

[0175] Specific operation: The content entered into the consultation box is encoded and sent again to the server via a secure protocol.

[0176] Step 8:

[0177] The server analyzes the received inquiry and generates an appropriate response using AI and a rule-based engine. For example, if a customer says, "I feel extremely tired when I wake up in the morning," the server will generate suggestions for improving their exercise and sleep quality.

[0178] Input: Consultation content received by the server

[0179] Output: The generated answer

[0180] Specific operation: The consultation content is passed to the AI ​​engine, which extracts and generates an appropriate answer based on related data.

[0181] Step 9:

[0182] The server sends the generated answer to the terminal and notifies the user.

[0183] Input: Generated Answer

[0184] Output: Answer reported to the terminal

[0185] Specific operation: The generated answer is notified to the device and instructions are provided so that the user can take immediate action.

[0186] Through the above processing steps, users can manage their own health conditions in real time and receive appropriate advice, while also being able to quickly respond to any health-related questions or inquiries.

[0187] (Application example 1)

[0188] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0189] The main purpose of conventional health management systems is to assess health risks based on users' health data and provide advice on preventative measures and lifestyle improvements. However, a user's health condition can also affect security awareness and risks, and no system has been available to address this. There is a need for a system that can address situations where a deterioration in health condition poses a security risk.

[0190] 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.

[0191] In this invention, the server includes means for a user to input their height, weight, eating and drinking patterns, and amount of exercise, means for collecting the input data and storing it in a database, means for evaluating health risks using the stored data, means for predicting future illnesses based on the health risks identified by the analysis means, means for generating advice on preventive measures and lifestyle improvements for the predicted illnesses, means for receiving health-related questions and inquiries from the user and generating corresponding answers, means for evaluating security risks in relation to the evaluated health risks and notifying the user, and means for generating warnings and advice according to the security risks, thereby enabling users to deal with both health risks and security risks.

[0192] "User" refers to an individual who uses this system and is responsible for managing health data and security risks.

[0193] "Height" refers to the length of the user's entire body, and is one of the basic physical data required for health analysis.

[0194] "Weight" is data for measuring the mass of the user's body, and is basic data used for health risk assessment.

[0195] "Eating and drinking patterns" are data that represent the user's food and drink intake status, and are important information for analyzing the user's health condition.

[0196] "Amount of exercise" refers to the degree of physical activity a user engages in in daily life or specific exercise, and is data used for health risk assessment.

[0197] A "means" refers to a method or device used to achieve a particular purpose.

[0198] A "database" is an electronic storage device for organizing, storing, and analyzing health data collected from users.

[0199] "Analytical tools" refers to the processes and tools used to analyze collected health data and assess health and security risks.

[0200] "Health risk" refers to the likelihood of future health problems or illnesses occurring based on the user's health status.

[0201] "Disease prediction means" refers to a method or tool for predicting future illnesses based on a user's health data.

[0202] "Preventive measures" refer to specific measures or steps taken to prevent predicted future diseases or health risks.

[0203] "Lifestyle advice" refers to specific instructions or advice regarding recommended habits or behaviors to improve a user's health.

[0204] The "health consultation receiving means" refers to a function or method for receiving health-related questions or inquiries from users and providing measures or advice depending on the content of those inquiries.

[0205] "Security risk" refers to the possibility of a security-related risk occurring based on the user's health condition.

[0206] "Security risk assessment means" refers to a method or device for analyzing a user's health data and assessing security risks.

[0207] "Warning" refers to a notification or message that alerts the user to a potential problem.

[0208] "Advice generation means" refers to the process or tool for generating specific measures or advice according to the user's situation.

[0209] This invention is a system that collects and analyzes a user's health data, evaluates health risks, and provides advice on preventive measures and lifestyle improvements. This system also evaluates security risks based on the user's health status and provides warnings and advice.

[0210] composition

[0211] The system includes the following means:

[0212] 1. A means for users to input their height, weight, eating and drinking patterns, and activity levels:

[0213] This is an application that is displayed on devices such as smartphones and PCs. Users input data such as their height (170 cm), weight (80 kg), daily alcohol intake, and walking 5,000 steps per day.

[0214] 2. A means of collecting the entered data and storing it in a database:

[0215] The terminal sends the entered data to the server, which receives the data and stores it in a database. If there are any abnormal values, the user is prompted to re-enter the data.

[0216] 3. Analytical tools to assess health risks:

[0217] The server uses the stored data to assess health risks, specifically by calculating the user's BMI and analyzing their eating and drinking patterns and physical activity.

[0218] 4. A method to predict future illnesses based on identified health risks:

[0219] The server then predicts illnesses based on the assessment results, for example predicting that a user with a high BMI is at risk of obesity or diabetes.

[0220] 5. Means of generating preventative and lifestyle advice:

[0221] For predicted illnesses, specific preventive measures and advice on lifestyle improvements are generated and sent to the user's device. For example, advice such as reducing alcohol intake or walking at least 10,000 steps per day could be considered.

[0222] 6. A means of accepting health-related questions and inquiries from users and generating corresponding answers:

[0223] The device sends the user's inquiry to a server, which uses AI and a rule-based engine to generate an appropriate response, such as advice on improving sleep quality or lack of exercise to combat morning fatigue.

[0224] 7. Means of assessing and informing users of security risks in relation to health risks:

[0225] The server evaluates the user's security risk based on the health risk assessment results. If the stress or fatigue level is high, the server notifies the user that their security awareness has decreased.

[0226] 8. Means of generating warnings and advice based on security risks:

[0227] The server generates specific warnings and advice based on the security risk and notifies the user. For example, if the stress level is high, the server provides advice such as "avoid making big decisions and get adequate rest."

[0228] Specific examples

[0229] As a concrete example, consider a scenario in which a user enters the following data into an application:

[0230] Height: 170cm

[0231] Weight: 80kg

[0232] Alcohol consumption: 30ml / day

[0233] Steps: 5,000 steps / day

[0234] Gym visits: 2 times / week

[0235] Stress level: 50

[0236] Fatigue level: 40

[0237] The server receives this data, assesses health risks (e.g., risk of high blood pressure or diabetes based on a BMI of 27.7), and provides specific advice to the user (e.g., reduce alcohol intake or add 200g of vegetables to each meal).

[0238] Additionally, based on the same data, security risks (for example, security advice such as "take regular breaks" due to moderate stress and fatigue levels) are also provided.

[0239] Prompt Sentence Examples

[0240] A possible prompt for a generative AI model might look something like this:

[0241] "Generate a Python program that evaluates security risks based on the health data of user ID 1 (height: 170cm, weight: 80kg, alcohol intake: 30ml / day, steps: 5000 / day, gym visits: 2 times / week, stress level: 50, fatigue level: 40) and provides appropriate advice."

[0242] By using this prompt, the appropriate AI model can be used to analyze individual health conditions and security risks, and specific advice can be provided to the user.

[0243] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0244] Step 1:

[0245] Users enter their health data (height, weight, eating and drinking patterns, amount of exercise, stress level, and fatigue level) into a smartphone or PC application. For example, input data might be: height 170 cm, weight 80 kg, alcohol intake 30 ml / day, steps taken 5,000 / day, gym visits 2 times / week, stress level 50, and fatigue level 40. This data will be used to assess future health and security risks.

[0246] Step 2:

[0247] The terminal sends the entered data to the server. At this time, the data is checked for abnormal values ​​and blank spaces, and if there are any abnormalities, the user is prompted to re-enter the data. The data is checked for abnormal values ​​by, for example, checking the range based on BMI calculations.

[0248] Step 3:

[0249] The server stores the received data in a database, formatting the data before storing it and preprocessing it as needed, so that subsequent analysis can proceed smoothly.

[0250] Step 4:

[0251] The server uses the stored data to assess health risks. For example, it calculates BMI and analyzes eating and drinking patterns and exercise volume. Specifically, it calculates BMI using the user's weight and height, and assesses health risks based on the results. The results of this assessment are accumulated as, for example, the risk of obesity or diabetes.

[0252] Step 5:

[0253] The server then predicts future illnesses based on the identified health risks. For example, a BMI of 27.7 predicts a high risk of obesity, high blood pressure, and diabetes. This prediction is saved as an analysis result and serves as the basis for notifications to the user.

[0254] Step 6:

[0255] The server generates advice on preventive measures and lifestyle improvements for predicted illnesses. For example, specific advice such as reducing alcohol intake or walking at least 10,000 steps per day is generated. This advice is sent to the user's device to promote health improvement.

[0256] Step 7:

[0257] When a user inputs a health-related question into the application, the device sends the content to the server. The server then analyzes the question using AI and a rule-based engine and generates a response appropriate to the question. For example, if a user comments that they are "unable to get rid of morning fatigue," the server generates advice such as "stretching every day and improving the quality of their sleep."

[0258] Step 8:

[0259] The server evaluates security risks based on the health risk assessment results. If the user's stress or fatigue level is high, it determines that the user's security awareness is declining and issues a warning. This evaluation is performed taking into account the relationship between the user's health state and security awareness.

[0260] Step 9:

[0261] The server generates warnings and advice based on security risks. For example, if the user's stress level is high, specific advice such as "avoid making big decisions and get adequate rest" is sent to the user's device. This notification helps users to increase their security awareness.

[0262] This series of steps will create a system that allows users to receive comprehensive health management and security risk assessment based on their health data, thereby improving both the quality of life and safety of users.

[0263] 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.

[0264] The system of the present invention not only collects and analyzes a user's health data and provides advice on preventive measures and lifestyle improvements based on health risks, but also recognizes the user's emotions and performs comprehensive health management. An embodiment of the present invention will be described in detail below.

[0265] User data entry and collection

[0266] Users log in to the application using a device such as a smartphone or PC and enter their health data, such as their height, weight, eating and drinking patterns, and amount of exercise. They can also enter questions or concerns about their health. For example, a user might enter data such as height 170 cm, weight 80 kg, daily alcohol consumption, a diet lacking in vegetables, 5,000 steps per day, and going to the gym twice a week.

[0267] The terminal transmits the input data to the server, where the transmitted data is communicated using a secure protocol.

[0268] Data storage and preprocessing

[0269] The server saves the received data in a database. After saving, it checks for blanks and outliers, and performs completion and filtering as necessary. If an outlier is detected, the server prompts the user to re-enter the data.

[0270] Data analysis and health risk assessment

[0271] The server analyzes the collected data, including the following specific analysis items:

[0272] Calculate BMI: BMI = weight (kg) / (height (m) height (m)). For example, 80kg / (1.7m 1.7m) = 27.7.

[0273] Analysis of dietary patterns: Assessment of calorie surpluses and nutritional deficiencies. For example, alcohol intake and vegetable intake are compared with baseline values.

[0274] Physical activity assessment: Analyze the number of steps taken and frequency of gym visits and compare them with the recommended standard (e.g., 10,000 steps per day).

[0275] This allows the user to assess their health risks and predict future illnesses. A BMI of 27.7 indicates a risk of obesity, diabetes, and high blood pressure.

[0276] Emotion recognition by emotion engine

[0277] The server is equipped with an emotion engine that estimates the user's emotional state based on the user's input data and behavioral data. For example, when a user inputs detailed records of their diet and exercise, the server infers the user's emotional state from the frequency and content of these records. The server can also extract the user's emotions from the content of questions and inquiries.

[0278] Comprehensive health assessment and prevention

[0279] The server integrates health data and emotional data to perform a comprehensive health risk assessment and generate specific advice on preventative measures and lifestyle improvements. For example, based on the analysis of the emotional engine, it generates specific advice such as, "You've been feeling stressed recently, so try incorporating relaxation techniques."

[0280] Providing advice

[0281] The device will notify the user of the generated advice via push notification, email, or in-app notification, and will also provide related articles and information, making it easier for users to obtain the knowledge they need.

[0282] Responding to inquiries from users

[0283] When a user wants to input a question or inquiry about their health, the user inputs the question via the application. For example, a question such as "I've been feeling very tired when I wake up in the morning lately" may be input.

[0284] The device sends the consultation details to the server, which uses an emotion engine to analyze the details and generates an appropriate response using an AI engine and rule-based system. For example, if the cause is thought to be lack of exercise or poor sleep quality, the server will suggest daily stretching and improvements to the sleep environment, and recommend seeing a doctor.

[0285] As described above, the system of the present invention realizes a system that grasps the user's health condition in real time and provides comprehensive preventive measures and advice that also take into account the user's emotional state.

[0286] The processing flow will be explained below.

[0287] Step 1:

[0288] Users log in to the application and enter their health data, such as their height, weight, eating and drinking patterns, and exercise volume. They can also enter questions and concerns about their health. For example, a user might enter data such as height 170 cm, weight 80 kg, daily alcohol consumption, lack of vegetable intake, 5,000 steps per day, and gym visits twice a week.

[0289] Step 2:

[0290] The terminal sends the data entered by the user to the server using a secure communication protocol such as HTTPS.

[0291] Step 3:

[0292] The server saves the received data in a database. After saving, it checks whether there are any blank spaces or abnormal values ​​in the data, and if there are any abnormalities, it prompts the user to re-enter the data.

[0293] Step 4:

[0294] The server analyzes the stored data. The specific analysis items are as follows:

[0295] Calculating BMI: BMI = weight (kg) / (height (m) height (m))

[0296] For example, 80kg / (1.7m 1.7m) = 27.7

[0297] Analysis of eating and drinking patterns: Assessment of calorie surplus and nutritional deficiencies

[0298] Check your exercise volume: Analyze your steps and gym visit frequency and compare them with the recommended standard (e.g., 10,000 steps per day)

[0299] Step 5:

[0300] The server uses an emotion engine to infer the user's emotional state from input data and behavioral data. For example, it infers emotions from dietary habits and exercise records, and incorporates these into the analysis results.

[0301] Step 6:

[0302] The server integrates health and emotional data to perform a comprehensive health risk assessment. For example, if a BMI of 27.7 indicates a high risk of obesity, diabetes, and high blood pressure, and predicts a high stress level, the server takes this into account when conducting a risk assessment.

[0303] Step 7:

[0304] The server generates specific preventive measures and lifestyle improvement advice based on the assessment results, for example:

[0305] Reduce your alcohol intake to one drink a day

[0306] "Add 200g of vegetables to each meal"

[0307] "Walk more than 10,000 steps a day"

[0308] Implement relaxation techniques to reduce stress

[0309] Step 8:

[0310] The device notifies the user of the generated advice via push notification, email, or in-app notification, and also provides related articles and information.

[0311] Step 9:

[0312] When a user inputs a question or question about health into the application, the user may input, for example, "I've been feeling very tired when I wake up in the morning lately."

[0313] Step 10:

[0314] The terminal transmits the contents of the user's consultation to the server.

[0315] Step 11:

[0316] The server analyzes the consultation using an emotion engine and generates appropriate responses using an AI engine and rule-based system. For example, if the user feels tired in the morning, the server may suggest daily stretching and improvements to the sleep environment, and recommend a doctor's visit, as this may be due to a lack of exercise or poor sleep quality.

[0317] Step 12:

[0318] The terminal notifies the user of the generated answer, allowing the user to take appropriate action.

[0319] In this way, the system of the present invention can grasp the user's health condition in real time and provide comprehensive preventive measures and advice that also take into account the user's emotional state.

[0320] Example 2

[0321] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0322] Conventional health management systems are limited to predicting health risks based on a user's basic health data (height, weight, eating and drinking patterns, and amount of exercise) and providing advice on preventive measures and lifestyle improvements. However, these systems lack comprehensive health risk assessments that take into account the user's emotional state, and the provision of advice that includes the user's emotional state. Taking into account the user's emotional state is expected to enable more appropriate and effective health management, so this issue needs to be resolved.

[0323] 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.

[0324] In this invention, the server includes means for inputting a user's height, weight, eating and drinking patterns, and amount of exercise, means for collecting the input data and storing it in a database, means for evaluating health risks using the stored data, means for predicting future illnesses based on the health risks identified by the analysis means, means for generating advice on preventive measures and lifestyle improvements for the predicted illnesses, means for estimating the user's emotional state based on the input data and behavioral data, means for integrating the health data and emotional data to evaluate overall health risks and generating specific advice on preventive measures and lifestyle improvements, and means for receiving health-related questions and inquiries from the user and generating corresponding answers, thereby enabling comprehensive health management that also takes the user's emotional state into consideration.

[0325] "User data" refers to health-related information such as height, weight, eating and drinking patterns, and amount of exercise input by the user.

[0326] The "collection means" is a method or device for collecting data entered by a user and transmitting it to a server.

[0327] A "database" is a repository of information for storing collected user data.

[0328] "Analysis means" refers to methods and devices for analyzing collected data and assessing health risks.

[0329] "Health risks" are predictions of diseases or symptoms that may occur in the future based on the user's health condition.

[0330] A "prediction means" is a method or device for predicting future diseases or symptoms based on the health risks identified by the analysis means.

[0331] The "advice generation means" refers to a method or device for generating advice on preventive measures and lifestyle improvements based on the results of health risk assessment and disease prediction.

[0332] The "emotional state" refers to the user's emotional or mental state estimated based on the user's input data and behavioral data.

[0333] The "emotion estimation means" is a method or device for estimating an emotional state based on input data and behavioral data of a user.

[0334] An "integrated assessment means" is a method or device for integrating health data and emotional data to perform a comprehensive health risk assessment.

[0335] The "inquiry response means" is a method or device for receiving health-related questions or inquiries from users and generating corresponding answers.

[0336] The system of the present invention not only collects and analyzes a user's health data and provides advice on preventive measures and lifestyle improvements based on health risks, but also recognizes the user's emotions to provide comprehensive health management. An embodiment of this system is described in detail below.

[0337] User data entry and collection

[0338] Users log in to the application using a device such as a smartphone or PC. They input health data such as their height, weight, eating and drinking patterns, and amount of exercise. Specifically, they input data such as "height 170 cm, weight 80 kg, daily alcohol consumption, diet lacking in vegetables, 5,000 steps per day, gym visits twice a week." Users can also input health-related questions and concerns.

[0339] The terminal collects the data entered by the user in real time and transmits it to a server using a secure protocol (e.g., HTTPS).

[0340] Data storage and preprocessing

[0341] The server stores the received data in a dedicated database. After storing the data, the server checks for blanks and abnormal values. For example, it can detect abnormally high height or extremely low weight.

[0342] If an abnormal value is detected, the server generates a message to prompt the user to re-enter the data and sends it to the terminal. The user receives the message and re-enters the correct data.

[0343] Data analysis and health risk assessment

[0344] The server analyzes the user data stored in the database, including:

[0345] 1. Calculating BMI: The server calculates BMI based on weight and height data using the following formula:

[0346] BMI = Weight (kg) / (Height (m) Height (m))

[0347] For example, if you weigh 80 kg and are 1.7 m tall, your BMI = 80 / (1.7 1.7) = 27.7.

[0348] 2. Analysis of eating and drinking patterns: The server analyzes the user's eating and drinking data and evaluates calories and nutritional balance. For example, "daily alcohol consumption" can be judged as excessive calories, and "a diet lacking in vegetables" can be judged as a risk of nutritional deficiency.

[0349] 3. Exercise assessment: The server analyzes step count data and gym visit frequency and compares it with the recommended standard (e.g., 10,000 steps per day). For example, if a user only walks 5,000 steps per day, it can assess that the user is not getting enough exercise.

[0350] Emotion recognition by emotion engine

[0351] The server is equipped with an emotion engine that infers the user's emotional state based on their input data and behavioral data. For example, if a user frequently enters detailed records of their diet and exercise, the server can infer their emotions from the frequency of their input and the use of words.

[0352] Comprehensive health assessment and prevention

[0353] The server integrates health and emotional data to perform a comprehensive health risk assessment and generate specific advice on preventative measures and lifestyle improvements. For example, based on the analysis results of the emotion engine, it generates specific advice such as, "You've been feeling stressed recently, so try incorporating relaxation techniques."

[0354] Providing advice

[0355] The device will notify the user of the advice sent from the server via push notification, email, or in-app notification, and will also provide related articles and information, making it easy for users to obtain the knowledge they need.

[0356] Responding to inquiries from users

[0357] The user inputs questions or concerns about their health via the application. For example, they may input a question such as, "Recently, I've been feeling extremely tired when I wake up in the morning."

[0358] The device sends the user's consultation details to the server. The server uses an emotion engine to analyze the consultation details and utilizes an AI engine and rule-based system to generate an appropriate response. For example, if the consultation details are thought to be caused by a lack of exercise or poor quality sleep, the server will suggest "daily stretching and improving your sleep environment" and recommend seeing a doctor.

[0359] Examples of prompt statements

[0360] This system can understand the user's health condition in real time and provide comprehensive preventive measures and advice that take into account their emotional state. As a specific example, by inputting the following prompt sentences into the generative AI model, more detailed advice can be obtained:

[0361] "If a user reports feeling stressed recently, what relaxation techniques should we suggest?"

[0362] This prompt allows the AI ​​model to provide specific relaxation advice.

[0363] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0364] Step 1:

[0365] Users log in to the application using a device such as a smartphone or PC. In the application, they enter their own health data, such as their height, weight, eating and drinking patterns, and amount of exercise. For example, they might enter data such as "height 170 cm, weight 80 kg, daily alcohol consumption, diet lacking in vegetables, 5,000 steps per day, and going to the gym twice a week." The entered data is temporarily stored in the device's memory.

[0366] Step 2:

[0367] The device sends the data entered by the user to the server using a secure protocol (e.g., HTTPS). The sent data is temporarily stored in the server's memory. At this stage, the input is the health data entered by the user, and the output is the health data sent to the server.

[0368] Step 3:

[0369] The server stores the received data in a dedicated database. Once the stored data is confirmed, it moves on to the next step. In this step, the input is the health data sent from the device, and the output is the health data stored in the database.

[0370] Step 4:

[0371] The server checks the data stored in the database to see if there are any blank spaces or abnormal values. For example, it checks to see if any abnormal values ​​have been entered for height or weight. If an abnormal value is detected during this process, the server generates a message prompting the user to re-enter the data and sends it to the terminal. The message requesting re-entry is displayed to the user. The input in this step is the health data stored in the database, and the output is the result of the abnormal value check and a message requesting the user to re-enter the data.

[0372] Step 5:

[0373] The user receives a re-entry request message from the server and re-enters the correct data. The re-entered data is again sent from the terminal to the server and stored in the database. The input in this step is the re-entry request message, and the output is the corrected health data.

[0374] Step 6:

[0375] The server uses the available data stored in the database to assess the health risk as follows:

[0376] 1. Calculate BMI: Calculate BMI from user's weight and height. For example, if user weighs 80kg and is 1.7m tall, BMI = 80 / (1.7 1.7) = 27.7.

[0377] 2. Analysis of eating and drinking patterns: Evaluate the calorie intake and nutritional balance of the user. For example, if the user consumes alcohol every day, it will be determined that the user is consuming too many calories.

[0378] 3. Exercise assessment: Analyzes step count data and gym visit frequency and compares it with the recommended standard (e.g., 10,000 steps per day). For example, if a user only walks 5,000 steps per day, it is assessed as not getting enough exercise.

[0379] The input in this step is the health data stored in the database, and the output is the result of the health risk assessment.

[0380] Step 7:

[0381] The server is equipped with an emotion engine that estimates the user's emotional state based on the user's input data and behavioral data. For example, if a user frequently enters detailed records of their diet and exercise, the engine can infer the user's emotional state from the frequency of their input and the use of words. The input in this step is the user's health and behavioral data, and the output is an estimate of the user's emotional state.

[0382] Step 8:

[0383] The server integrates health data and emotional data to perform a comprehensive health risk assessment and generate specific preventive measures and advice for improving lifestyle habits. For example, it generates advice such as, "Because your BMI is high and you're under stress, improve your diet and adopt relaxation techniques." The input in this step is health data and emotional data, and the output is specific preventive measures and advice.

[0384] Step 9:

[0385] The device notifies the user of the advice sent from the server. Notification methods include push notifications, emails, and in-app notifications. Related articles and information are also provided to help users obtain the necessary knowledge. The input in this step is the advice sent from the server, and the output is the notification sent to the user.

[0386] (Application example 2)

[0387] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0388] Conventional health management systems assess health risks by collecting and analyzing users' physical and lifestyle information, but lack the functionality to provide advice that takes emotional state into account or to suggest individually optimal meal plans. As a result, they often fail to adequately manage users' overall health and end up only providing incomplete, partial advice. Furthermore, the lack of a system that proposes customized meal plans based on health risks and emotional state makes it difficult to effectively support users in improving their health.

[0389] 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.

[0390] In this invention, the server includes means for a user to input their own physical information and lifestyle data, means for collecting and storing the input data, means for evaluating health risks using the stored data, means for predicting future diseases based on the health risks identified by the analysis means, means for generating advice on preventive measures and lifestyle improvements for the predicted diseases, means for receiving health-related questions and inquiries from the user and generating corresponding answers, means for analyzing the user's emotional state and providing advice based on the emotional state, and means for individually suggesting an optimal meal menu based on the health risks and emotional data. This enables comprehensive health management for the user and realizes individually optimized advice on preventive measures and lifestyle improvements and suggestions for a meal menu.

[0391] "User" refers to an individual who utilizes the System to enter and collect their own health care information.

[0392] "Physical information" refers to basic physical data such as a user's height, weight, and body mass index (BMI).

[0393] "Lifestyle data" refers to information related to a user's daily life, such as their eating and drinking patterns, amount of exercise, sleep time, smoking and drinking habits, etc.

[0394] "Data collection means" refers to a device or system that has the function of collecting physical information and lifestyle habit data entered by the user and incorporating it into the system.

[0395] "Storage means" refers to a database within the system that has the functionality to safely and effectively store collected data.

[0396] "Analysis Tools" refers to algorithms or programs that utilize collected and stored data to assess a user's health risks and emotional state.

[0397] "Health risk" refers to the possibility of a user contracting a disease or experiencing a worsening health condition in the future, as analyzed based on collected data.

[0398] "Preventive measures" refer to specific behavioral or habit changes recommended based on the analysis results to reduce the risk of developing a disease.

[0399] "Lifestyle improvement advice" refers to guidance or suggestions provided to a user to change their lifestyle to a healthier one.

[0400] "Emotional state" refers to information analyzed by the emotion engine that indicates the user's psychological health and mood.

[0401] "Answer generation means" refers to the algorithms and rule-based systems within the system that generate appropriate answers to user doubts and questions.

[0402] The "meal menu suggestion means" refers to a part of the system that has the function of providing an optimal meal plan based on the user's health and emotional state.

[0403] The system of the present invention collects physical information and lifestyle data of a user, performs health risk assessment and emotion analysis, and proposes specific meal menus. This system is realized with the following configuration and processing.

[0404] User data entry and collection

[0405] Users log in to the application using a device such as a smartphone, PC, or wearable device and enter their physical and lifestyle information, such as height, weight, eating and drinking patterns, and amount of exercise. They can also enter health-related questions and concerns. For example, if a user enters their height as 170 cm, weight as 80 kg, daily alcohol consumption, a diet lacking in vegetables, 5,000 steps per day, and gym visits twice a week, the device sends this data to the server. The transmitted data is communicated using a secure protocol.

[0406] Data storage and preprocessing

[0407] The server saves the received data in a database. After saving, it checks for blanks and outliers, and performs completion and filtering as necessary. If an outlier is detected, the server prompts the user to re-enter the data.

[0408] Data analysis and health risk assessment

[0409] The server analyzes the collected data, including the following specific analysis items:

[0410] Calculate BMI: BMI = weight (kg) / (height (m) height (m)). For example, 80kg / (1.7m 1.7m) = 27.7.

[0411] Analysis of dietary patterns: Assessment of calorie surpluses and nutritional deficiencies. For example, alcohol intake and vegetable intake are compared with baseline values.

[0412] Physical activity assessment: Analyze the number of steps taken and frequency of gym visits and compare them with the recommended standard (e.g., 10,000 steps per day).

[0413] This allows the server to assess the user's health risks and predict future illnesses. For example, if the BMI is 27.7, it is determined that the user is at risk of obesity, diabetes, and high blood pressure.

[0414] Emotion recognition by emotion engine

[0415] The server is equipped with an emotion engine that estimates the user's emotional state based on the user's input data and behavioral data. For example, when a user inputs detailed records of their diet and exercise, the server infers the user's emotional state from the frequency and content of these records. The server can also extract the user's emotions from the content of questions and inquiries.

[0416] Comprehensive health assessment and preventative care

[0417] The server integrates health data and emotional data to perform a comprehensive health risk assessment and generate specific advice on preventative measures and lifestyle improvements. For example, based on the analysis of the emotional engine, it generates specific advice such as, "You've been feeling stressed recently, so try incorporating relaxation techniques."

[0418] Meal menu suggestions

[0419] The server then proposes an optimal meal plan based on the individual's health risk and emotional data. For example, if the individual is at high risk, it will propose a low-calorie, high-nutrition menu, and if the individual is at low risk, it will propose a balanced, regular menu.

[0420] Examples of specific prompts are:

[0421] The user's height is 170cm, weight is 80kg, eating habits are high in fat, and exercise habits are low. The user also says, "I've been feeling a lot of stress lately..." Based on this data, assess health risks and suggest an appropriate meal plan.

[0422] This will enable comprehensive health management for users and provide individually optimized preventative measures and meal menus.

[0423] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0424] Step 1:

[0425] A user logs in to the application using a device such as a smartphone or computer.

[0426] Input: User's height, weight, eating and drinking patterns, amount of exercise, health questions and concerns.

[0427] How it works: The user enters this data into the application's input form.

[0428] Output: The entered data is recorded on the terminal.

[0429] Step 2:

[0430] The terminal transmits the input data to the server.

[0431] Input: User data entered in step 1.

[0432] How it works: The device sends data to the server using a secure protocol.

[0433] Output: The data is passed to the server.

[0434] Step 3:

[0435] The server stores the received data in a database.

[0436] Input: User data received in step 2.

[0437] Operation: The server saves data to the database and checks whether the saved contents are normal.

[0438] Output: The data is stored in a database, and outlier detection and imputation are performed as needed.

[0439] Step 4:

[0440] The server analyzes the stored data and assesses health risks.

[0441] Input: User data stored in the database.

[0442] How it works: The server calculates BMI, analyzes eating patterns (assessing calorie surpluses and nutritional deficiencies), and assesses physical activity.

[0443] Output: Health risk assessment results. For example, if your BMI is 27.7, you are at high risk for obesity, diabetes, and high blood pressure.

[0444] Step 5:

[0445] The server uses an emotion engine to analyze the user's emotional state.

[0446] Input: Consultation details and behavioral data stored in the database.

[0447] How it works: The server uses an emotion engine to extract emotions from text data, for example identifying stress states from input such as "I've been feeling stressed lately."

[0448] Output: The result of the emotion analysis is obtained. For example, the emotional state is analyzed as "high stress."

[0449] Step 6:

[0450] The server integrates the health risk assessment results and emotion analysis results to perform a comprehensive health risk assessment.

[0451] Input: Health risk assessment results from step 4 and sentiment analysis results from step 5.

[0452] How it works: The server combines these results to perform a comprehensive risk assessment.

[0453] Output: A comprehensive health risk assessment result is obtained. For example, if a person has a high risk of obesity and high stress, the overall risk will be assessed as very high.

[0454] Step 7:

[0455] Based on the overall evaluation results, the server generates specific advice on preventive measures and lifestyle improvements.

[0456] Input: Overall health risk assessment results obtained in Step 6.

[0457] How it works: The server uses the generative AI model to generate specific advice to address the risk, such as "You've been feeling stressed lately, so try some relaxation techniques."

[0458] Output: Advice on preventive measures and lifestyle changes.

[0459] Step 8:

[0460] The server will suggest optimal meal plans for each individual based on health risks and emotional data.

[0461] Input: Health risk assessment results and emotion data analyzed in Step 4 and Step 5.

[0462] Operation: The server generates and proposes a meal menu tailored to the user's health condition. For example, if the user is at high risk, it proposes a low-calorie, high-nutrition menu.

[0463] Output: Personalized optimized meal menu suggestions.

[0464] Step 9:

[0465] The device will notify the user of suggested preventative measures, advice on lifestyle changes, and meal plans.

[0466] Input: Step 7 advice and step 8 meal menu.

[0467] How it works: The device provides information to the user via push notifications, emails, and in-app notifications.

[0468] Output: The user checks the advice and meal menu through the app.

[0469] 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.

[0470] 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> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0471] 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.

[0472] [Second embodiment]

[0473] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0474] 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.

[0475] 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).

[0476] 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.

[0477] 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.

[0478] 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).

[0479] 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.

[0480] 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.

[0481] 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.

[0482] 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.

[0483] 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.

[0484] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0485] The system of the present invention collects and analyzes health data of a user and provides advice on preventive measures and lifestyle improvements based on health risks. The following describes in detail an embodiment of the present invention.

[0486] User data entry and collection

[0487] Users log in to an application displayed on a device such as a smartphone or PC and enter their health data, such as their height, weight, eating and drinking patterns, and amount of exercise. For example, a user might enter data such as height 170 cm, weight 80 kg, daily alcohol consumption, a diet lacking in vegetables, 5,000 steps per day, and going to the gym twice a week.

[0488] The terminal sends the entered data to the server, which receives the data and stores it in a database. It also checks the data for blank spaces and abnormal values, and if any abnormalities are found, prompts the user to re-enter the data.

[0489] Data analysis and health risk assessment

[0490] The server uses the stored data to perform analysis, such as calculating the user's BMI, analyzing their eating and drinking patterns (to assess whether they are consuming too many calories or are lacking in nutrients), and checking their physical activity (comparing it to the recommended number of steps). For example, the BMI is calculated as follows:

[0491] BMI = Weight (kg) / (Height (m) Height (m))

[0492] Example: 80kg / (1.7m 1.7m) = 27.7

[0493] This allows the system to assess the user's health risks and predict specific diseases they are likely to develop. For example, if a user's BMI is 27.7, the system will assess that the user is at risk of obesity, high blood pressure, and diabetes.

[0494] Providing advice on preventative measures and lifestyle changes

[0495] The server generates specific preventive measures and lifestyle improvement advice for the user based on the evaluation results, for example:

[0496] Reduce your alcohol intake

[0497] Add 200g of vegetables to each meal

[0498] Walk more than 10,000 steps a day

[0499] This advice is sent to users' devices such as smartphones and computers, and related articles and information are also provided for easy reference.

[0500] Receiving and responding to health-related inquiries from users

[0501] The system also accepts health-related questions and consultations from users. For example, a user can input into the application a question about how tired they have been feeling recently when they wake up in the morning.

[0502] The device sends the user's inquiry to a server, which analyzes the information and generates an appropriate response using AI and a rule-based engine. For example, if the user feels tired in the morning, it may be due to a lack of exercise or poor sleep quality. The server may suggest daily stretching and improving the sleep environment, and recommend a doctor's visit if necessary.

[0503] As described above, the system of the present invention provides a system that allows users to understand their own health condition in real time and receive appropriate preventive measures and advice.

[0504] The processing flow will be explained below.

[0505] Step 1:

[0506] Users log in to the application and enter their health data, such as height, weight, eating and drinking patterns, and exercise amount. For example, a user might be 170 cm tall, weigh 80 kg, drink alcohol daily, have a diet lacking in vegetables, walk 5,000 steps a day, and go to the gym twice a week.

[0507] Step 2:

[0508] The device sends the entered data to the server, which transfers the data using a secure communication protocol such as HTTPS.

[0509] Step 3:

[0510] The server stores the received data in a database, typically a relational database such as PostgreSQL or MySQL. When the data is stored, it is associated with identifying information such as the user ID.

[0511] Step 4:

[0512] The server performs preprocessing for analyzing the collected data. During this process, it checks for blank spaces and outliers, and performs data completion and filtering as necessary. If an outlier is detected, it prompts the user to re-enter the data.

[0513] Step 5:

[0514] The server will analyze the collected health data, including the following:

[0515] Calculate BMI: BMI = weight (kg) / (height (m) height (m)). For example, 80kg / (1.7m 1.7m) = 27.7.

[0516] Eating pattern analysis: Evaluate calorie surpluses and nutritional deficiencies, for example, by comparing alcohol intake or vegetable intake with baseline thresholds.

[0517] Check your physical activity: Analyze your steps and gym visit frequency and compare them with the recommended standard (e.g. 10,000 steps per day).

[0518] Step 6:

[0519] The server evaluates health risks based on the analysis results and predicts future illnesses. For example, if a BMI is 27.7, it is determined that the individual is at risk of obesity, diabetes, or high blood pressure.

[0520] Step 7:

[0521] The server generates specific preventive measures and lifestyle improvement advice for predicted illnesses. For example:

[0522] Reduce your alcohol intake to one drink a day

[0523] "Add 200g of vegetables to each meal"

[0524] "Walk more than 10,000 steps a day"

[0525] Step 8:

[0526] The device notifies the user of the generated advice via push notification, email, in-app notification, etc. Related articles and information are also provided.

[0527] Step 9:

[0528] When a user wishes to input a question or consultation about health, the user inputs the question into the application. For example, the user may input a question such as, "Recently, I've been feeling very tired when I wake up in the morning."

[0529] Step 10:

[0530] The terminal transmits the contents of the user's consultation to the server.

[0531] Step 11:

[0532] The server then launches an AI engine and rule-based system to analyze the customer's inquiry and generate an appropriate response. For example, if the customer feels tired in the morning, the server will consider the quality of their sleep and lack of exercise and suggest measures such as "daily stretching" and "reviewing their sleeping environment."

[0533] Step 12:

[0534] The terminal notifies the user of the generated answer, and in some cases may also notify the user of a recommendation to see a doctor.

[0535] These steps allow users to understand their health status in real time and receive appropriate preventive measures and advice.

[0536] Example 1

[0537] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0538] In today's world, lifestyle-related diseases and health risks are on the rise, requiring individual users to constantly monitor their own health status and take preventative measures and improve their lifestyles. However, many existing systems struggle to accurately manage and analyze users' health data and provide appropriate advice. Furthermore, they are not adequately responsive to users' health-related questions or inquiries, making it difficult to obtain appropriate advice immediately.

[0539] 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.

[0540] In this invention, the server includes: a means for a user to input their height, weight, eating and drinking patterns, and amount of exercise; a means for collecting the input data and storing it in a database; a means for checking the stored data for abnormal values ​​and prompting the user to re-enter the data if abnormalities are found; a data analysis means for assessing the user's health risk using the stored data, including a BMI calculation means, an eating and drinking pattern analysis means, and an exercise amount check means; a means for generating advice on preventive measures and lifestyle improvements using a generative AI model based on the assessed health risk; a means for notifying the user of the generated advice; a means for accepting health-related questions and inquiries from the user and generating corresponding answers using an AI or rule-based engine; and an encryption means for securely communicating all of the above means. This allows users to accurately manage their health data, assess their health risks in real time, and receive appropriate advice on preventive measures and lifestyle improvements. Health-related questions and inquiries can also be responded to promptly.

[0541] "User" refers to an individual who uses the system to manage their own health data and receive advice on preventive measures and lifestyle improvements.

[0542] A "terminal" is a device through which a user inputs their own health data and communicates with the server, and specifically includes smartphones and personal computers.

[0543] A "server" is a central computer system that receives health data sent by users, stores, analyzes, and evaluates the data, and generates advice and answers to notify the users.

[0544] "Health data" refers to information entered to understand a user's health condition, such as their height, weight, eating and drinking patterns, and amount of exercise.

[0545] "Database" refers to a system for structuring and storing health data entered and submitted by users.

[0546] An "outlier" is a value in the health data entered by the user that is outside the normal range, including incorrect data entry or extreme values.

[0547] "Data analysis" refers to the process of calculations and analysis to assess a user's health status and risks based on stored health data.

[0548] "BMI calculation means" refers to a means for calculating a user's BMI (Body Mass Index) based on their weight and height, and assessing their health risk.

[0549] The "eating and drinking pattern analysis means" is a means for analyzing the eating and drinking data input by the user and evaluating whether there is an excess of calories or a nutritional deficiency.

[0550] The "exercise amount check means" refers to a means for checking the user's exercise amount and comparing it with the recommended exercise amount.

[0551] A "generative AI model" is an artificial intelligence model that generates advice on preventive measures and lifestyle improvements based on collected data.

[0552] "Advice" refers to specific suggestions and improvements to reduce health risks based on the user's health data and analysis results.

[0553] "Encryption methods" refers to the techniques and protocols used to encrypt data so that user data is communicated securely.

[0554] "AI and rule-based engines" refer to engines based on artificial intelligence and defined rules that generate appropriate answers to user inquiries and questions.

[0555] These definitions clearly show the functions of the system and the role of each means.

[0556] The system of the present invention collects and analyzes a user's health data and provides advice on preventive measures and lifestyle improvements based on health risks. The main components of the system and their operation are described in detail below.

[0557] User data entry and collection

[0558] Users log in to an application displayed on a device such as a smartphone or PC and enter their health data, such as their height, weight, eating and drinking patterns, and amount of exercise. For example, a user might enter data such as height 170 cm, weight 80 kg, daily alcohol consumption, a diet lacking in vegetables, 5,000 steps per day, and going to the gym twice a week.

[0559] The device temporarily stores the entered data in memory and then sends it to the server using a security protocol (e.g., HTTPS). The data is encrypted with AES256 and transmitted securely.

[0560] Data transmission and storage

[0561] The server decodes the received user data and stores it in a database. The stored data is checked for blanks and abnormal values. If an abnormality is detected, a message is sent to the terminal prompting the user to re-enter the data.

[0562] Data analysis and health risk assessment

[0563] The server retrieves the user data stored in the database and runs the analysis program. Specifically, it performs the following calculations to calculate the user's BMI:

[0564] BMI = Weight (kg) / (Height (m) Height (m))

[0565] Example: If you are 1.7m tall and weigh 80kg, then 80 / (1.7 1.7) = 27.7

[0566] It also analyzes eating and drinking patterns and checks the amount of exercise, which can help assess the user's health risks and determine whether they are at risk of obesity, high blood pressure, or diabetes.

[0567] Providing advice on preventative measures and lifestyle changes

[0568] The server uses a generative AI model based on the data analysis results to automatically generate advice for users on preventive measures and lifestyle improvements. For example, the following advice may be generated:

[0569] Reduce your alcohol intake

[0570] Add 200g of vegetables to each meal

[0571] Walk more than 10,000 steps a day

[0572] The generated advice is sent to the terminal and notified to the user. In addition, related information and articles are also provided to support the user in easily implementing the advice.

[0573] Receiving and responding to health-related inquiries from users

[0574] The user inputs health-related questions or concerns through the application. For example, they input a question such as, "I feel extremely tired when I wake up in the morning." The device encodes the question and sends it to the server.

[0575] The server analyzes the received consultation content and generates an appropriate response using AI and a rule-based engine. For example, if the user feels tired in the morning, it predicts that they are not getting enough exercise or their sleep quality is poor, and suggests advice such as daily stretching and reviewing their sleep environment, and recommends seeing a doctor if necessary. The generated response is sent to the device and notified to the user.

[0576] Examples of concrete examples and prompts

[0577] Specific working example:

[0578] The user inputs their daily health data and the data is sent to a server.

[0579] The server analyzes the stored data, assesses health risks, and notifies users of preventive measures.

[0580] Users can enter their health questions and receive instant advice.

[0581] Example prompt sentence:

[0582] "Enter the user's health data and generate appropriate lifestyle improvement advice."

[0583] "Please provide a risk assessment for a user with a BMI of 27.7 and suggest preventative measures for diabetes."

[0584] This system allows users to accurately manage their own health data and receive advice on appropriate preventive measures and lifestyle improvements. Furthermore, health-related questions and inquiries are quickly responded to, improving the overall quality of health management.

[0585] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0586] Step 1:

[0587] Users log in to the application and enter health data such as height, weight, eating and drinking patterns, and amount of exercise. Examples of data entered by users include "height 170 cm, weight 80 kg, daily alcohol consumption, insufficient vegetable intake, 5,000 steps per day, and gym visits twice a week." The device temporarily stores the entered data in its memory.

[0588] Input: User's health data (height, weight, eating and drinking patterns, amount of exercise, etc.)

[0589] Output: Health data stored in the device memory

[0590] Specific operation: When a user enters health data into the application form and presses the input button, the data is temporarily saved in memory.

[0591] Step 2:

[0592] The device encodes the entered data and sends it to the server using a security protocol (e.g., HTTPS). The data is then encrypted with AES256 for secure communication. If the transmission is successful, the device receives a confirmation message from the server.

[0593] Input: Health data stored in the device memory

[0594] Output: Health data sent to the server and a confirmation message

[0595] Specific operation: Encodes the data into JSON format and sends it to the server using a secure communication protocol.

[0596] Step 3:

[0597] The server decodes the received health data and stores it in a database. The stored data is checked for blank spaces and abnormal values, and if an abnormality is detected, a message is sent to the device prompting re-entry.

[0598] Input: Health data received by the server

[0599] Output: Health data stored in the database and a message prompting you to re-enter (if there are abnormal values)

[0600] Specific behavior: Decodes and performs data integrity and range checks before saving to the database.

[0601] Step 4:

[0602] The server retrieves user data stored in a database and runs a data analysis program to calculate BMI, assess eating and drinking patterns, and check physical activity.

[0603] Input: Health data stored in a database

[0604] Output: Analysis results (BMI value, dietary pattern evaluation, exercise amount evaluation)

[0605] Specific operation: Based on the user's health data, calculate BMI as follows: 80kg / (1.7m 1.7m) = 27.7. Also calculates and evaluates eating and drinking patterns and exercise volume.

[0606] Step 5:

[0607] The server uses a generative AI model based on the analysis results to automatically generate advice for users on preventive measures and lifestyle improvements.

[0608] Input: Analysis results (BMI value, dietary pattern evaluation, exercise amount evaluation)

[0609] Output: Generated advice (e.g., reduce alcohol intake, add vegetables to each meal, walk at least 10,000 steps per day)

[0610] How it works: The analysis results are fed into an AI model to generate appropriate advice, which is customized to each individual user's situation.

[0611] Step 6:

[0612] The server sends the generated advice to the terminal and notifies the user, along with related articles and information.

[0613] Input: Generated advice

[0614] Output: Advice and related information posted to the terminal

[0615] Specific operation: The server notifies the user of advice from the generated AI model and sets reminders as needed.

[0616] Step 7:

[0617] Users can input their health-related questions or concerns into the application, and when the consultation is submitted, the device encodes the information and sends it to the server.

[0618] Input: User's health consultation details

[0619] Output: Consultation details sent from the device to the server

[0620] Specific operation: The content entered into the consultation box is encoded and sent again to the server via a secure protocol.

[0621] Step 8:

[0622] The server analyzes the received inquiry and generates an appropriate response using AI and a rule-based engine. For example, if a customer says, "I feel extremely tired when I wake up in the morning," the server will generate suggestions for improving their exercise and sleep quality.

[0623] Input: Consultation content received by the server

[0624] Output: The generated answer

[0625] Specific operation: The consultation content is passed to the AI ​​engine, which extracts and generates an appropriate answer based on related data.

[0626] Step 9:

[0627] The server sends the generated answer to the terminal and notifies the user.

[0628] Input: Generated Answer

[0629] Output: Answer reported to the terminal

[0630] Specific operation: The generated answer is notified to the device and instructions are provided so that the user can take immediate action.

[0631] Through the above processing steps, users can manage their own health conditions in real time and receive appropriate advice, while also being able to quickly respond to any health-related questions or inquiries.

[0632] (Application example 1)

[0633] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0634] The main purpose of conventional health management systems is to assess health risks based on users' health data and provide advice on preventative measures and lifestyle improvements. However, a user's health condition can also affect security awareness and risks, and no system has been available to address this. There is a need for a system that can address situations where a deterioration in health condition poses a security risk.

[0635] 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.

[0636] In this invention, the server includes means for a user to input their height, weight, eating and drinking patterns, and amount of exercise, means for collecting the input data and storing it in a database, means for evaluating health risks using the stored data, means for predicting future illnesses based on the health risks identified by the analysis means, means for generating advice on preventive measures and lifestyle improvements for the predicted illnesses, means for receiving health-related questions and inquiries from the user and generating corresponding answers, means for evaluating security risks in relation to the evaluated health risks and notifying the user, and means for generating warnings and advice according to the security risks, thereby enabling users to deal with both health risks and security risks.

[0637] "User" refers to an individual who uses this system and is responsible for managing health data and security risks.

[0638] "Height" refers to the length of the user's entire body, and is one of the basic physical data required for health analysis.

[0639] "Weight" is data for measuring the mass of the user's body, and is basic data used for health risk assessment.

[0640] "Eating and drinking patterns" are data that represent the user's food and drink intake status, and are important information for analyzing the user's health condition.

[0641] "Amount of exercise" refers to the degree of physical activity a user engages in in daily life or specific exercise, and is data used for health risk assessment.

[0642] A "means" refers to a method or device used to achieve a particular purpose.

[0643] A "database" is an electronic storage device for organizing, storing, and analyzing health data collected from users.

[0644] "Analytical tools" refers to the processes and tools used to analyze collected health data and assess health and security risks.

[0645] "Health risk" refers to the likelihood of future health problems or illnesses occurring based on the user's health status.

[0646] "Disease prediction means" refers to a method or tool for predicting future illnesses based on a user's health data.

[0647] "Preventive measures" refer to specific measures or steps taken to prevent predicted future diseases or health risks.

[0648] "Lifestyle advice" refers to specific instructions or advice regarding recommended habits or behaviors to improve a user's health.

[0649] The "health consultation receiving means" refers to a function or method for receiving health-related questions or inquiries from users and providing measures or advice depending on the content of those inquiries.

[0650] "Security risk" refers to the possibility of a security-related risk occurring based on the user's health condition.

[0651] "Security risk assessment means" refers to a method or device for analyzing a user's health data and assessing security risks.

[0652] "Warning" refers to a notification or message that alerts the user to a potential problem.

[0653] "Advice generation means" refers to the process or tool for generating specific measures or advice according to the user's situation.

[0654] This invention is a system that collects and analyzes a user's health data, evaluates health risks, and provides advice on preventive measures and lifestyle improvements. This system also evaluates security risks based on the user's health status and provides warnings and advice.

[0655] composition

[0656] The system includes the following means:

[0657] 1. A means for users to input their height, weight, eating and drinking patterns, and activity levels:

[0658] This is an application that is displayed on devices such as smartphones and PCs. Users input data such as their height (170 cm), weight (80 kg), daily alcohol intake, and walking 5,000 steps per day.

[0659] 2. A means of collecting the entered data and storing it in a database:

[0660] The terminal sends the entered data to the server, which receives the data and stores it in a database. If there are any abnormal values, the user is prompted to re-enter the data.

[0661] 3. Analytical tools to assess health risks:

[0662] The server uses the stored data to assess health risks, specifically by calculating the user's BMI and analyzing their eating and drinking patterns and physical activity.

[0663] 4. A method to predict future illnesses based on identified health risks:

[0664] The server then predicts illnesses based on the assessment results, for example predicting that a user with a high BMI is at risk of obesity or diabetes.

[0665] 5. Means of generating preventative and lifestyle advice:

[0666] For predicted illnesses, specific preventive measures and advice on lifestyle improvements are generated and sent to the user's device. For example, advice such as reducing alcohol intake or walking at least 10,000 steps per day could be considered.

[0667] 6. A means of accepting health-related questions and inquiries from users and generating corresponding answers:

[0668] The device sends the user's inquiry to a server, which uses AI and a rule-based engine to generate an appropriate response, such as advice on improving sleep quality or lack of exercise to combat morning fatigue.

[0669] 7. Means of assessing and informing users of security risks in relation to health risks:

[0670] The server evaluates the user's security risk based on the health risk assessment results. If the stress or fatigue level is high, the server notifies the user that their security awareness has decreased.

[0671] 8. Means of generating warnings and advice based on security risks:

[0672] The server generates specific warnings and advice based on the security risk and notifies the user. For example, if the stress level is high, the server provides advice such as "avoid making big decisions and get adequate rest."

[0673] Specific examples

[0674] As a concrete example, consider a scenario in which a user enters the following data into an application:

[0675] Height: 170cm

[0676] Weight: 80kg

[0677] Alcohol consumption: 30ml / day

[0678] Steps: 5,000 steps / day

[0679] Gym visits: 2 times / week

[0680] Stress level: 50

[0681] Fatigue level: 40

[0682] The server receives this data, assesses health risks (e.g., risk of high blood pressure or diabetes based on a BMI of 27.7), and provides specific advice to the user (e.g., reduce alcohol intake or add 200g of vegetables to each meal).

[0683] Additionally, based on the same data, security risks (for example, security advice such as "take regular breaks" due to moderate stress and fatigue levels) are also provided.

[0684] Prompt Sentence Examples

[0685] A possible prompt for a generative AI model might look something like this:

[0686] "Generate a Python program that evaluates security risks based on the health data of user ID 1 (height: 170cm, weight: 80kg, alcohol intake: 30ml / day, steps: 5000 / day, gym visits: 2 times / week, stress level: 50, fatigue level: 40) and provides appropriate advice."

[0687] By using this prompt, the appropriate AI model can be used to analyze individual health conditions and security risks, and specific advice can be provided to the user.

[0688] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0689] Step 1:

[0690] Users enter their health data (height, weight, eating and drinking patterns, amount of exercise, stress level, and fatigue level) into a smartphone or PC application. For example, input data might be: height 170 cm, weight 80 kg, alcohol intake 30 ml / day, steps taken 5,000 / day, gym visits 2 times / week, stress level 50, and fatigue level 40. This data will be used to assess future health and security risks.

[0691] Step 2:

[0692] The terminal sends the entered data to the server. At this time, the data is checked for abnormal values ​​and blank spaces, and if there are any abnormalities, the user is prompted to re-enter the data. The data is checked for abnormal values ​​by, for example, checking the range based on BMI calculations.

[0693] Step 3:

[0694] The server stores the received data in a database, formatting the data before storing it and preprocessing it as needed, so that subsequent analysis can proceed smoothly.

[0695] Step 4:

[0696] The server uses the stored data to assess health risks. For example, it calculates BMI and analyzes eating and drinking patterns and exercise volume. Specifically, it calculates BMI using the user's weight and height, and assesses health risks based on the results. The results of this assessment are accumulated as, for example, the risk of obesity or diabetes.

[0697] Step 5:

[0698] The server then predicts future illnesses based on the identified health risks. For example, a BMI of 27.7 predicts a high risk of obesity, high blood pressure, and diabetes. This prediction is saved as an analysis result and serves as the basis for notifications to the user.

[0699] Step 6:

[0700] The server generates advice on preventive measures and lifestyle improvements for predicted illnesses. For example, specific advice such as reducing alcohol intake or walking at least 10,000 steps per day is generated. This advice is sent to the user's device to promote health improvement.

[0701] Step 7:

[0702] When a user inputs a health-related question into the application, the device sends the content to the server. The server then analyzes the question using AI and a rule-based engine and generates a response appropriate to the question. For example, if a user comments that they are "unable to get rid of morning fatigue," the server generates advice such as "stretching every day and improving the quality of their sleep."

[0703] Step 8:

[0704] The server evaluates security risks based on the health risk assessment results. If the user's stress or fatigue level is high, it determines that the user's security awareness is declining and issues a warning. This evaluation is performed taking into account the relationship between the user's health state and security awareness.

[0705] Step 9:

[0706] The server generates warnings and advice based on security risks. For example, if the user's stress level is high, specific advice such as "avoid making big decisions and get adequate rest" is sent to the user's device. This notification helps users to increase their security awareness.

[0707] This series of steps will create a system that allows users to receive comprehensive health management and security risk assessment based on their health data, thereby improving both the quality of life and safety of users.

[0708] 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.

[0709] The system of the present invention not only collects and analyzes a user's health data and provides advice on preventive measures and lifestyle improvements based on health risks, but also recognizes the user's emotions and performs comprehensive health management. An embodiment of the present invention will be described in detail below.

[0710] User data entry and collection

[0711] Users log in to the application using a device such as a smartphone or PC and enter their health data, such as their height, weight, eating and drinking patterns, and amount of exercise. They can also enter questions or concerns about their health. For example, a user might enter data such as height 170 cm, weight 80 kg, daily alcohol consumption, a diet lacking in vegetables, 5,000 steps per day, and going to the gym twice a week.

[0712] The terminal transmits the input data to the server, where the transmitted data is communicated using a secure protocol.

[0713] Data storage and preprocessing

[0714] The server saves the received data in a database. After saving, it checks for blanks and outliers, and performs completion and filtering as necessary. If an outlier is detected, the server prompts the user to re-enter the data.

[0715] Data analysis and health risk assessment

[0716] The server analyzes the collected data, including the following specific analysis items:

[0717] Calculate BMI: BMI = weight (kg) / (height (m) height (m)). For example, 80kg / (1.7m 1.7m) = 27.7.

[0718] Analysis of dietary patterns: Assessment of calorie surpluses and nutritional deficiencies. For example, alcohol intake and vegetable intake are compared with baseline values.

[0719] Physical activity assessment: Analyze the number of steps taken and frequency of gym visits and compare them with the recommended standard (e.g., 10,000 steps per day).

[0720] This allows the user to assess their health risks and predict future illnesses. A BMI of 27.7 indicates a risk of obesity, diabetes, and high blood pressure.

[0721] Emotion recognition by emotion engine

[0722] The server is equipped with an emotion engine that estimates the user's emotional state based on the user's input data and behavioral data. For example, when a user inputs detailed records of their diet and exercise, the server infers the user's emotional state from the frequency and content of these records. The server can also extract the user's emotions from the content of questions and inquiries.

[0723] Comprehensive health assessment and prevention

[0724] The server integrates health data and emotional data to perform a comprehensive health risk assessment and generate specific advice on preventative measures and lifestyle improvements. For example, based on the analysis of the emotional engine, it generates specific advice such as, "You've been feeling stressed recently, so try incorporating relaxation techniques."

[0725] Providing advice

[0726] The device will notify the user of the generated advice via push notification, email, or in-app notification, and will also provide related articles and information, making it easier for users to obtain the knowledge they need.

[0727] Responding to inquiries from users

[0728] When a user wants to input a question or inquiry about their health, the user inputs the question via the application. For example, a question such as "I've been feeling very tired when I wake up in the morning lately" may be input.

[0729] The device sends the consultation details to the server, which uses an emotion engine to analyze the details and generates an appropriate response using an AI engine and rule-based system. For example, if the cause is thought to be lack of exercise or poor sleep quality, the server will suggest daily stretching and improvements to the sleep environment, and recommend seeing a doctor.

[0730] As described above, the system of the present invention realizes a system that grasps the user's health condition in real time and provides comprehensive preventive measures and advice that also take into account the user's emotional state.

[0731] The processing flow will be explained below.

[0732] Step 1:

[0733] Users log in to the application and enter their health data, such as their height, weight, eating and drinking patterns, and exercise volume. They can also enter questions and concerns about their health. For example, a user might enter data such as height 170 cm, weight 80 kg, daily alcohol consumption, lack of vegetable intake, 5,000 steps per day, and gym visits twice a week.

[0734] Step 2:

[0735] The terminal sends the data entered by the user to the server using a secure communication protocol such as HTTPS.

[0736] Step 3:

[0737] The server saves the received data in a database. After saving, it checks whether there are any blank spaces or abnormal values ​​in the data, and if there are any abnormalities, it prompts the user to re-enter the data.

[0738] Step 4:

[0739] The server analyzes the stored data. The specific analysis items are as follows:

[0740] Calculating BMI: BMI = weight (kg) / (height (m) height (m))

[0741] For example, 80kg / (1.7m 1.7m) = 27.7

[0742] Analysis of eating and drinking patterns: Assessment of calorie surplus and nutritional deficiencies

[0743] Check your exercise volume: Analyze your steps and gym visit frequency and compare them with the recommended standard (e.g., 10,000 steps per day)

[0744] Step 5:

[0745] The server uses an emotion engine to infer the user's emotional state from input data and behavioral data. For example, it infers emotions from dietary habits and exercise records, and incorporates these into the analysis results.

[0746] Step 6:

[0747] The server integrates health and emotional data to perform a comprehensive health risk assessment. For example, if a BMI of 27.7 indicates a high risk of obesity, diabetes, and high blood pressure, and predicts a high stress level, the server takes this into account when conducting a risk assessment.

[0748] Step 7:

[0749] The server generates specific preventive measures and lifestyle improvement advice based on the assessment results, for example:

[0750] Reduce your alcohol intake to one drink a day

[0751] "Add 200g of vegetables to each meal"

[0752] "Walk more than 10,000 steps a day"

[0753] Implement relaxation techniques to reduce stress

[0754] Step 8:

[0755] The device notifies the user of the generated advice via push notification, email, or in-app notification, and also provides related articles and information.

[0756] Step 9:

[0757] When a user inputs a question or question about health into the application, the user may input, for example, "I've been feeling very tired when I wake up in the morning lately."

[0758] Step 10:

[0759] The terminal transmits the contents of the user's consultation to the server.

[0760] Step 11:

[0761] The server analyzes the consultation using an emotion engine and generates appropriate responses using an AI engine and rule-based system. For example, if the user feels tired in the morning, the server may suggest daily stretching and improvements to the sleep environment, and recommend a doctor's visit, as this may be due to a lack of exercise or poor sleep quality.

[0762] Step 12:

[0763] The terminal notifies the user of the generated answer, allowing the user to take appropriate action.

[0764] In this way, the system of the present invention can grasp the user's health condition in real time and provide comprehensive preventive measures and advice that also take into account the user's emotional state.

[0765] Example 2

[0766] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0767] Conventional health management systems are limited to predicting health risks based on a user's basic health data (height, weight, eating and drinking patterns, and amount of exercise) and providing advice on preventive measures and lifestyle improvements. However, these systems lack comprehensive health risk assessments that take into account the user's emotional state, and the provision of advice that includes the user's emotional state. Taking into account the user's emotional state is expected to enable more appropriate and effective health management, so this issue needs to be resolved.

[0768] 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.

[0769] In this invention, the server includes means for inputting a user's height, weight, eating and drinking patterns, and amount of exercise, means for collecting the input data and storing it in a database, means for evaluating health risks using the stored data, means for predicting future illnesses based on the health risks identified by the analysis means, means for generating advice on preventive measures and lifestyle improvements for the predicted illnesses, means for estimating the user's emotional state based on the input data and behavioral data, means for integrating the health data and emotional data to evaluate overall health risks and generating specific advice on preventive measures and lifestyle improvements, and means for receiving health-related questions and inquiries from the user and generating corresponding answers, thereby enabling comprehensive health management that also takes the user's emotional state into consideration.

[0770] "User data" refers to health-related information such as height, weight, eating and drinking patterns, and amount of exercise input by the user.

[0771] The "collection means" is a method or device for collecting data entered by a user and transmitting it to a server.

[0772] A "database" is a repository of information for storing collected user data.

[0773] "Analysis means" refers to methods and devices for analyzing collected data and assessing health risks.

[0774] "Health risks" are predictions of diseases or symptoms that may occur in the future based on the user's health condition.

[0775] A "prediction means" is a method or device for predicting future diseases or symptoms based on the health risks identified by the analysis means.

[0776] The "advice generation means" refers to a method or device for generating advice on preventive measures and lifestyle improvements based on the results of health risk assessment and disease prediction.

[0777] The "emotional state" refers to the user's emotional or mental state estimated based on the user's input data and behavioral data.

[0778] The "emotion estimation means" is a method or device for estimating an emotional state based on input data and behavioral data of a user.

[0779] An "integrated assessment means" is a method or device for integrating health data and emotional data to perform a comprehensive health risk assessment.

[0780] The "inquiry response means" is a method or device for receiving health-related questions or inquiries from users and generating corresponding answers.

[0781] The system of the present invention not only collects and analyzes a user's health data and provides advice on preventive measures and lifestyle improvements based on health risks, but also recognizes the user's emotions to provide comprehensive health management. An embodiment of this system is described in detail below.

[0782] User data entry and collection

[0783] Users log in to the application using a device such as a smartphone or PC. They input health data such as their height, weight, eating and drinking patterns, and amount of exercise. Specifically, they input data such as "height 170 cm, weight 80 kg, daily alcohol consumption, diet lacking in vegetables, 5,000 steps per day, gym visits twice a week." Users can also input health-related questions and concerns.

[0784] The terminal collects the data entered by the user in real time and transmits it to a server using a secure protocol (e.g., HTTPS).

[0785] Data storage and preprocessing

[0786] The server stores the received data in a dedicated database. After storing the data, the server checks for blanks and abnormal values. For example, it can detect abnormally high height or extremely low weight.

[0787] If an abnormal value is detected, the server generates a message to prompt the user to re-enter the data and sends it to the terminal. The user receives the message and re-enters the correct data.

[0788] Data analysis and health risk assessment

[0789] The server analyzes the user data stored in the database, including:

[0790] 1. Calculating BMI: The server calculates BMI based on weight and height data using the following formula:

[0791] BMI = Weight (kg) / (Height (m) Height (m))

[0792] For example, if you weigh 80 kg and are 1.7 m tall, your BMI = 80 / (1.7 1.7) = 27.7.

[0793] 2. Analysis of eating and drinking patterns: The server analyzes the user's eating and drinking data and evaluates calories and nutritional balance. For example, "daily alcohol consumption" can be judged as excessive calories, and "a diet lacking in vegetables" can be judged as a risk of nutritional deficiency.

[0794] 3. Exercise assessment: The server analyzes step count data and gym visit frequency and compares it with the recommended standard (e.g., 10,000 steps per day). For example, if a user only walks 5,000 steps per day, it can assess that the user is not getting enough exercise.

[0795] Emotion recognition by emotion engine

[0796] The server is equipped with an emotion engine that infers the user's emotional state based on their input data and behavioral data. For example, if a user frequently enters detailed records of their diet and exercise, the server can infer their emotions from the frequency of their input and the use of words.

[0797] Comprehensive health assessment and prevention

[0798] The server integrates health and emotional data to perform a comprehensive health risk assessment and generate specific advice on preventative measures and lifestyle improvements. For example, based on the analysis results of the emotion engine, it generates specific advice such as, "You've been feeling stressed recently, so try incorporating relaxation techniques."

[0799] Providing advice

[0800] The device will notify the user of the advice sent from the server via push notification, email, or in-app notification, and will also provide related articles and information, making it easy for users to obtain the knowledge they need.

[0801] Responding to inquiries from users

[0802] The user inputs questions or concerns about their health via the application. For example, they may input a question such as, "Recently, I've been feeling extremely tired when I wake up in the morning."

[0803] The device sends the user's consultation details to the server. The server uses an emotion engine to analyze the consultation details and utilizes an AI engine and rule-based system to generate an appropriate response. For example, if the consultation details are thought to be caused by a lack of exercise or poor quality sleep, the server will suggest "daily stretching and improving your sleep environment" and recommend seeing a doctor.

[0804] Examples of prompt statements

[0805] This system can understand the user's health condition in real time and provide comprehensive preventive measures and advice that take into account their emotional state. As a specific example, by inputting the following prompt sentences into the generative AI model, more detailed advice can be obtained:

[0806] "If a user reports feeling stressed recently, what relaxation techniques should we suggest?"

[0807] This prompt allows the AI ​​model to provide specific relaxation advice.

[0808] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0809] Step 1:

[0810] Users log in to the application using a device such as a smartphone or PC. In the application, they enter their own health data, such as their height, weight, eating and drinking patterns, and amount of exercise. For example, they might enter data such as "height 170 cm, weight 80 kg, daily alcohol consumption, diet lacking in vegetables, 5,000 steps per day, and going to the gym twice a week." The entered data is temporarily stored in the device's memory.

[0811] Step 2:

[0812] The device sends the data entered by the user to the server using a secure protocol (e.g., HTTPS). The sent data is temporarily stored in the server's memory. At this stage, the input is the health data entered by the user, and the output is the health data sent to the server.

[0813] Step 3:

[0814] The server stores the received data in a dedicated database. Once the stored data is confirmed, it moves on to the next step. In this step, the input is the health data sent from the device, and the output is the health data stored in the database.

[0815] Step 4:

[0816] The server checks the data stored in the database to see if there are any blank spaces or abnormal values. For example, it checks to see if any abnormal values ​​have been entered for height or weight. If an abnormal value is detected during this process, the server generates a message prompting the user to re-enter the data and sends it to the terminal. The message requesting re-entry is displayed to the user. The input in this step is the health data stored in the database, and the output is the result of the abnormal value check and a message requesting the user to re-enter the data.

[0817] Step 5:

[0818] The user receives a re-entry request message from the server and re-enters the correct data. The re-entered data is again sent from the terminal to the server and stored in the database. The input in this step is the re-entry request message, and the output is the corrected health data.

[0819] Step 6:

[0820] The server uses the available data stored in the database to assess the health risk as follows:

[0821] 1. Calculate BMI: Calculate BMI from user's weight and height. For example, if user weighs 80kg and is 1.7m tall, BMI = 80 / (1.7 1.7) = 27.7.

[0822] 2. Analysis of eating and drinking patterns: Evaluate the calorie intake and nutritional balance of the user. For example, if the user consumes alcohol every day, it will be determined that the user is consuming too many calories.

[0823] 3. Exercise assessment: Analyzes step count data and gym visit frequency and compares it with the recommended standard (e.g., 10,000 steps per day). For example, if a user only walks 5,000 steps per day, it is assessed as not getting enough exercise.

[0824] The input in this step is the health data stored in the database, and the output is the result of the health risk assessment.

[0825] Step 7:

[0826] The server is equipped with an emotion engine that estimates the user's emotional state based on the user's input data and behavioral data. For example, if a user frequently enters detailed records of their diet and exercise, the engine can infer the user's emotional state from the frequency of their input and the use of words. The input in this step is the user's health and behavioral data, and the output is an estimate of the user's emotional state.

[0827] Step 8:

[0828] The server integrates health data and emotional data to perform a comprehensive health risk assessment and generate specific preventive measures and advice for improving lifestyle habits. For example, it generates advice such as, "Because your BMI is high and you're under stress, improve your diet and adopt relaxation techniques." The input in this step is health data and emotional data, and the output is specific preventive measures and advice.

[0829] Step 9:

[0830] The device notifies the user of the advice sent from the server. Notification methods include push notifications, emails, and in-app notifications. Related articles and information are also provided to help users obtain the necessary knowledge. The input in this step is the advice sent from the server, and the output is the notification sent to the user.

[0831] (Application example 2)

[0832] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0833] Conventional health management systems assess health risks by collecting and analyzing users' physical and lifestyle information, but lack the functionality to provide advice that takes emotional state into account or to suggest individually optimal meal plans. As a result, they often fail to adequately manage users' overall health and end up only providing incomplete, partial advice. Furthermore, the lack of a system that proposes customized meal plans based on health risks and emotional state makes it difficult to effectively support users in improving their health.

[0834] 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.

[0835] In this invention, the server includes means for a user to input their own physical information and lifestyle data, means for collecting and storing the input data, means for evaluating health risks using the stored data, means for predicting future diseases based on the health risks identified by the analysis means, means for generating advice on preventive measures and lifestyle improvements for the predicted diseases, means for receiving health-related questions and inquiries from the user and generating corresponding answers, means for analyzing the user's emotional state and providing advice based on the emotional state, and means for individually suggesting an optimal meal menu based on the health risks and emotional data. This enables comprehensive health management for the user and realizes individually optimized advice on preventive measures and lifestyle improvements and suggestions for a meal menu.

[0836] "User" refers to an individual who utilizes the System to enter and collect their own health care information.

[0837] "Physical information" refers to basic physical data such as a user's height, weight, and body mass index (BMI).

[0838] "Lifestyle data" refers to information related to a user's daily life, such as their eating and drinking patterns, amount of exercise, sleep time, smoking and drinking habits, etc.

[0839] "Data collection means" refers to a device or system that has the function of collecting physical information and lifestyle habit data entered by the user and incorporating it into the system.

[0840] "Storage means" refers to a database within the system that has the functionality to safely and effectively store collected data.

[0841] "Analysis Tools" refers to algorithms or programs that utilize collected and stored data to assess a user's health risks and emotional state.

[0842] "Health risk" refers to the possibility of a user contracting a disease or experiencing a worsening health condition in the future, as analyzed based on collected data.

[0843] "Preventive measures" refer to specific behavioral or habit changes recommended based on the analysis results to reduce the risk of developing a disease.

[0844] "Lifestyle improvement advice" refers to guidance or suggestions provided to a user to change their lifestyle to a healthier one.

[0845] "Emotional state" refers to information analyzed by the emotion engine that indicates the user's psychological health and mood.

[0846] "Answer generation means" refers to the algorithms and rule-based systems within the system that generate appropriate answers to user doubts and questions.

[0847] The "meal menu suggestion means" refers to a part of the system that has the function of providing an optimal meal plan based on the user's health and emotional state.

[0848] The system of the present invention collects physical information and lifestyle data of a user, performs health risk assessment and emotion analysis, and proposes specific meal menus. This system is realized with the following configuration and processing.

[0849] User data entry and collection

[0850] Users log in to the application using a device such as a smartphone, PC, or wearable device and enter their physical and lifestyle information, such as height, weight, eating and drinking patterns, and amount of exercise. They can also enter health-related questions and concerns. For example, if a user enters their height as 170 cm, weight as 80 kg, daily alcohol consumption, a diet lacking in vegetables, 5,000 steps per day, and gym visits twice a week, the device sends this data to the server. The transmitted data is communicated using a secure protocol.

[0851] Data storage and preprocessing

[0852] The server saves the received data in a database. After saving, it checks for blanks and outliers, and performs completion and filtering as necessary. If an outlier is detected, the server prompts the user to re-enter the data.

[0853] Data analysis and health risk assessment

[0854] The server analyzes the collected data, including the following specific analysis items:

[0855] Calculate BMI: BMI = weight (kg) / (height (m) height (m)). For example, 80kg / (1.7m 1.7m) = 27.7.

[0856] Analysis of dietary patterns: Assessment of calorie surpluses and nutritional deficiencies. For example, alcohol intake and vegetable intake are compared with baseline values.

[0857] Physical activity assessment: Analyze the number of steps taken and frequency of gym visits and compare them with the recommended standard (e.g., 10,000 steps per day).

[0858] This allows the server to assess the user's health risks and predict future illnesses. For example, if the BMI is 27.7, it is determined that the user is at risk of obesity, diabetes, and high blood pressure.

[0859] Emotion recognition by emotion engine

[0860] The server is equipped with an emotion engine that estimates the user's emotional state based on the user's input data and behavioral data. For example, when a user inputs detailed records of their diet and exercise, the server infers the user's emotional state from the frequency and content of these records. The server can also extract the user's emotions from the content of questions and inquiries.

[0861] Comprehensive health assessment and preventative care

[0862] The server integrates health data and emotional data to perform a comprehensive health risk assessment and generate specific advice on preventative measures and lifestyle improvements. For example, based on the analysis of the emotional engine, it generates specific advice such as, "You've been feeling stressed recently, so try incorporating relaxation techniques."

[0863] Meal menu suggestions

[0864] The server then proposes an optimal meal plan based on the individual's health risk and emotional data. For example, if the individual is at high risk, it will propose a low-calorie, high-nutrition menu, and if the individual is at low risk, it will propose a balanced, regular menu.

[0865] Examples of specific prompts are:

[0866] The user's height is 170cm, weight is 80kg, eating habits are high in fat, and exercise habits are low. The user also says, "I've been feeling a lot of stress lately..." Based on this data, assess health risks and suggest an appropriate meal plan.

[0867] This will enable comprehensive health management for users and provide individually optimized preventative measures and meal menus.

[0868] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0869] Step 1:

[0870] A user logs in to the application using a device such as a smartphone or computer.

[0871] Input: User's height, weight, eating and drinking patterns, amount of exercise, health questions and concerns.

[0872] How it works: The user enters this data into the application's input form.

[0873] Output: The entered data is recorded on the terminal.

[0874] Step 2:

[0875] The terminal transmits the input data to the server.

[0876] Input: User data entered in step 1.

[0877] How it works: The device sends data to the server using a secure protocol.

[0878] Output: The data is passed to the server.

[0879] Step 3:

[0880] The server stores the received data in a database.

[0881] Input: User data received in step 2.

[0882] Operation: The server saves data to the database and checks whether the saved contents are normal.

[0883] Output: The data is stored in a database, and outlier detection and imputation are performed as needed.

[0884] Step 4:

[0885] The server analyzes the stored data and assesses health risks.

[0886] Input: User data stored in the database.

[0887] How it works: The server calculates BMI, analyzes eating patterns (assessing calorie surpluses and nutritional deficiencies), and assesses physical activity.

[0888] Output: Health risk assessment results. For example, if your BMI is 27.7, you are at high risk for obesity, diabetes, and high blood pressure.

[0889] Step 5:

[0890] The server uses an emotion engine to analyze the user's emotional state.

[0891] Input: Consultation details and behavioral data stored in the database.

[0892] How it works: The server uses an emotion engine to extract emotions from text data, for example identifying stress states from input such as "I've been feeling stressed lately."

[0893] Output: The result of the emotion analysis is obtained. For example, the emotional state is analyzed as "high stress."

[0894] Step 6:

[0895] The server integrates the health risk assessment results and emotion analysis results to perform a comprehensive health risk assessment.

[0896] Input: Health risk assessment results from step 4 and sentiment analysis results from step 5.

[0897] How it works: The server combines these results to perform a comprehensive risk assessment.

[0898] Output: A comprehensive health risk assessment result is obtained. For example, if a person has a high risk of obesity and high stress, the overall risk will be assessed as very high.

[0899] Step 7:

[0900] Based on the overall evaluation results, the server generates specific advice on preventive measures and lifestyle improvements.

[0901] Input: Overall health risk assessment results obtained in Step 6.

[0902] How it works: The server uses the generative AI model to generate specific advice to address the risk, such as "You've been feeling stressed lately, so try some relaxation techniques."

[0903] Output: Advice on preventive measures and lifestyle changes.

[0904] Step 8:

[0905] The server will suggest optimal meal plans for each individual based on health risks and emotional data.

[0906] Input: Health risk assessment results and emotion data analyzed in Step 4 and Step 5.

[0907] Operation: The server generates and proposes a meal menu tailored to the user's health condition. For example, if the user is at high risk, it proposes a low-calorie, high-nutrition menu.

[0908] Output: Personalized optimized meal menu suggestions.

[0909] Step 9:

[0910] The device will notify the user of suggested preventative measures, advice on lifestyle changes, and meal plans.

[0911] Input: Step 7 advice and step 8 meal menu.

[0912] How it works: The device provides information to the user via push notifications, emails, and in-app notifications.

[0913] Output: The user checks the advice and meal menu through the app.

[0914] 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.

[0915] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0916] 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.

[0917] [Third embodiment]

[0918] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0919] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0920] 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).

[0921] 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.

[0922] 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.

[0923] 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).

[0924] 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.

[0925] 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.

[0926] 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.

[0927] 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.

[0928] 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.

[0929] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0930] The system of the present invention collects and analyzes health data of a user and provides advice on preventive measures and lifestyle improvements based on health risks. The following describes in detail an embodiment of the present invention.

[0931] User data entry and collection

[0932] Users log in to an application displayed on a device such as a smartphone or PC and enter their health data, such as their height, weight, eating and drinking patterns, and amount of exercise. For example, a user might enter data such as height 170 cm, weight 80 kg, daily alcohol consumption, a diet lacking in vegetables, 5,000 steps per day, and going to the gym twice a week.

[0933] The terminal sends the entered data to the server, which receives the data and stores it in a database. It also checks the data for blank spaces and abnormal values, and if any abnormalities are found, prompts the user to re-enter the data.

[0934] Data analysis and health risk assessment

[0935] The server uses the stored data to perform analysis, such as calculating the user's BMI, analyzing their eating and drinking patterns (to assess whether they are consuming too many calories or are lacking in nutrients), and checking their physical activity (comparing it to the recommended number of steps). For example, the BMI is calculated as follows:

[0936] BMI = Weight (kg) / (Height (m) Height (m))

[0937] Example: 80kg / (1.7m 1.7m) = 27.7

[0938] This allows the system to assess the user's health risks and predict specific diseases they are likely to develop. For example, if a user's BMI is 27.7, the system will assess that the user is at risk of obesity, high blood pressure, and diabetes.

[0939] Providing advice on preventative measures and lifestyle changes

[0940] The server generates specific preventive measures and lifestyle improvement advice for the user based on the evaluation results, for example:

[0941] Reduce your alcohol intake

[0942] Add 200g of vegetables to each meal

[0943] Walk more than 10,000 steps a day

[0944] This advice is sent to users' devices such as smartphones and computers, and related articles and information are also provided for easy reference.

[0945] Receiving and responding to health-related inquiries from users

[0946] The system also accepts health-related questions and consultations from users. For example, a user can input into the application a question about how tired they have been feeling recently when they wake up in the morning.

[0947] The device sends the user's inquiry to a server, which analyzes the information and generates an appropriate response using AI and a rule-based engine. For example, if the user feels tired in the morning, it may be due to a lack of exercise or poor sleep quality. The server may suggest daily stretching and improving the sleep environment, and recommend a doctor's visit if necessary.

[0948] As described above, the system of the present invention provides a system that allows users to understand their own health condition in real time and receive appropriate preventive measures and advice.

[0949] The processing flow will be explained below.

[0950] Step 1:

[0951] Users log in to the application and enter their health data, such as height, weight, eating and drinking patterns, and exercise amount. For example, a user might be 170 cm tall, weigh 80 kg, drink alcohol daily, have a diet lacking in vegetables, walk 5,000 steps a day, and go to the gym twice a week.

[0952] Step 2:

[0953] The device sends the entered data to the server, which transfers the data using a secure communication protocol such as HTTPS.

[0954] Step 3:

[0955] The server stores the received data in a database, typically a relational database such as PostgreSQL or MySQL. When the data is stored, it is associated with identifying information such as the user ID.

[0956] Step 4:

[0957] The server performs preprocessing for analyzing the collected data. During this process, it checks for blank spaces and outliers, and performs data completion and filtering as necessary. If an outlier is detected, it prompts the user to re-enter the data.

[0958] Step 5:

[0959] The server will analyze the collected health data, including the following:

[0960] Calculate BMI: BMI = weight (kg) / (height (m) height (m)). For example, 80kg / (1.7m 1.7m) = 27.7.

[0961] Eating pattern analysis: Evaluate calorie surpluses and nutritional deficiencies, for example, by comparing alcohol intake or vegetable intake with baseline thresholds.

[0962] Check your physical activity: Analyze your steps and gym visit frequency and compare them with the recommended standard (e.g. 10,000 steps per day).

[0963] Step 6:

[0964] The server evaluates health risks based on the analysis results and predicts future illnesses. For example, if a BMI is 27.7, it is determined that the individual is at risk of obesity, diabetes, or high blood pressure.

[0965] Step 7:

[0966] The server generates specific preventive measures and lifestyle improvement advice for predicted illnesses. For example:

[0967] Reduce your alcohol intake to one drink a day

[0968] "Add 200g of vegetables to each meal"

[0969] "Walk more than 10,000 steps a day"

[0970] Step 8:

[0971] The device notifies the user of the generated advice via push notification, email, in-app notification, etc. Related articles and information are also provided.

[0972] Step 9:

[0973] When a user wishes to input a question or consultation about health, the user inputs the question into the application. For example, the user may input a question such as, "Recently, I've been feeling very tired when I wake up in the morning."

[0974] Step 10:

[0975] The terminal transmits the contents of the user's consultation to the server.

[0976] Step 11:

[0977] The server then launches an AI engine and rule-based system to analyze the customer's inquiry and generate an appropriate response. For example, if the customer feels tired in the morning, the server will consider the quality of their sleep and lack of exercise and suggest measures such as "daily stretching" and "reviewing their sleeping environment."

[0978] Step 12:

[0979] The terminal notifies the user of the generated answer, and in some cases may also notify the user of a recommendation to see a doctor.

[0980] These steps allow users to understand their health status in real time and receive appropriate preventive measures and advice.

[0981] Example 1

[0982] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0983] In today's world, lifestyle-related diseases and health risks are on the rise, requiring individual users to constantly monitor their own health status and take preventative measures and improve their lifestyles. However, many existing systems struggle to accurately manage and analyze users' health data and provide appropriate advice. Furthermore, they are not adequately responsive to users' health-related questions or inquiries, making it difficult to obtain appropriate advice immediately.

[0984] 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.

[0985] In this invention, the server includes: a means for a user to input their height, weight, eating and drinking patterns, and amount of exercise; a means for collecting the input data and storing it in a database; a means for checking the stored data for abnormal values ​​and prompting the user to re-enter the data if abnormalities are found; a data analysis means for assessing the user's health risk using the stored data, including a BMI calculation means, an eating and drinking pattern analysis means, and an exercise amount check means; a means for generating advice on preventive measures and lifestyle improvements using a generative AI model based on the assessed health risk; a means for notifying the user of the generated advice; a means for accepting health-related questions and inquiries from the user and generating corresponding answers using an AI or rule-based engine; and an encryption means for securely communicating all of the above means. This allows users to accurately manage their health data, assess their health risks in real time, and receive appropriate advice on preventive measures and lifestyle improvements. Health-related questions and inquiries can also be responded to promptly.

[0986] "User" refers to an individual who uses the system to manage their own health data and receive advice on preventive measures and lifestyle improvements.

[0987] A "terminal" is a device through which a user inputs their own health data and communicates with the server, and specifically includes smartphones and personal computers.

[0988] A "server" is a central computer system that receives health data sent by users, stores, analyzes, and evaluates the data, and generates advice and answers to notify the users.

[0989] "Health data" refers to information entered to understand a user's health condition, such as their height, weight, eating and drinking patterns, and amount of exercise.

[0990] "Database" refers to a system for structuring and storing health data entered and submitted by users.

[0991] An "outlier" is a value in the health data entered by the user that is outside the normal range, including incorrect data entry or extreme values.

[0992] "Data analysis" refers to the process of calculations and analysis to assess a user's health status and risks based on stored health data.

[0993] "BMI calculation means" refers to a means for calculating a user's BMI (Body Mass Index) based on their weight and height, and assessing their health risk.

[0994] The "eating and drinking pattern analysis means" is a means for analyzing the eating and drinking data input by the user and evaluating whether there is an excess of calories or a nutritional deficiency.

[0995] The "exercise amount check means" refers to a means for checking the user's exercise amount and comparing it with the recommended exercise amount.

[0996] A "generative AI model" is an artificial intelligence model that generates advice on preventive measures and lifestyle improvements based on collected data.

[0997] "Advice" refers to specific suggestions and improvements to reduce health risks based on the user's health data and analysis results.

[0998] "Encryption methods" refers to the techniques and protocols used to encrypt data so that user data is communicated securely.

[0999] "AI and rule-based engines" refer to engines based on artificial intelligence and defined rules that generate appropriate answers to user inquiries and questions.

[1000] These definitions clearly show the functions of the system and the role of each means.

[1001] The system of the present invention collects and analyzes a user's health data and provides advice on preventive measures and lifestyle improvements based on health risks. The main components of the system and their operation are described in detail below.

[1002] User data entry and collection

[1003] Users log in to an application displayed on a device such as a smartphone or PC and enter their health data, such as their height, weight, eating and drinking patterns, and amount of exercise. For example, a user might enter data such as height 170 cm, weight 80 kg, daily alcohol consumption, a diet lacking in vegetables, 5,000 steps per day, and going to the gym twice a week.

[1004] The device temporarily stores the entered data in memory and then sends it to the server using a security protocol (e.g., HTTPS). The data is encrypted with AES256 and transmitted securely.

[1005] Data transmission and storage

[1006] The server decodes the received user data and stores it in a database. The stored data is checked for blanks and abnormal values. If an abnormality is detected, a message is sent to the terminal prompting the user to re-enter the data.

[1007] Data analysis and health risk assessment

[1008] The server retrieves the user data stored in the database and runs the analysis program. Specifically, it performs the following calculations to calculate the user's BMI:

[1009] BMI = Weight (kg) / (Height (m) Height (m))

[1010] Example: If you are 1.7m tall and weigh 80kg, then 80 / (1.7 1.7) = 27.7

[1011] It also analyzes eating and drinking patterns and checks the amount of exercise, which can help assess the user's health risks and determine whether they are at risk of obesity, high blood pressure, or diabetes.

[1012] Providing advice on preventative measures and lifestyle changes

[1013] The server uses a generative AI model based on the data analysis results to automatically generate advice for users on preventive measures and lifestyle improvements. For example, the following advice may be generated:

[1014] Reduce your alcohol intake

[1015] Add 200g of vegetables to each meal

[1016] Walk more than 10,000 steps a day

[1017] The generated advice is sent to the terminal and notified to the user. In addition, related information and articles are also provided to support the user in easily implementing the advice.

[1018] Receiving and responding to health-related inquiries from users

[1019] The user inputs health-related questions or concerns through the application. For example, they input a question such as, "I feel extremely tired when I wake up in the morning." The device encodes the question and sends it to the server.

[1020] The server analyzes the received consultation content and generates an appropriate response using AI and a rule-based engine. For example, if the user feels tired in the morning, it predicts that they are not getting enough exercise or their sleep quality is poor, and suggests advice such as daily stretching and reviewing their sleep environment, and recommends seeing a doctor if necessary. The generated response is sent to the device and notified to the user.

[1021] Examples of concrete examples and prompts

[1022] Specific working example:

[1023] The user inputs their daily health data and the data is sent to a server.

[1024] The server analyzes the stored data, assesses health risks, and notifies users of preventive measures.

[1025] Users can enter their health questions and receive instant advice.

[1026] Example prompt sentence:

[1027] "Enter the user's health data and generate appropriate lifestyle improvement advice."

[1028] "Please provide a risk assessment for a user with a BMI of 27.7 and suggest preventative measures for diabetes."

[1029] This system allows users to accurately manage their own health data and receive advice on appropriate preventive measures and lifestyle improvements. Furthermore, health-related questions and inquiries are quickly responded to, improving the overall quality of health management.

[1030] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1031] Step 1:

[1032] Users log in to the application and enter health data such as height, weight, eating and drinking patterns, and amount of exercise. Examples of data entered by users include "height 170 cm, weight 80 kg, daily alcohol consumption, insufficient vegetable intake, 5,000 steps per day, and gym visits twice a week." The device temporarily stores the entered data in its memory.

[1033] Input: User's health data (height, weight, eating and drinking patterns, amount of exercise, etc.)

[1034] Output: Health data stored in the device memory

[1035] Specific operation: When a user enters health data into the application form and presses the input button, the data is temporarily saved in memory.

[1036] Step 2:

[1037] The device encodes the entered data and sends it to the server using a security protocol (e.g., HTTPS). The data is then encrypted with AES256 for secure communication. If the transmission is successful, the device receives a confirmation message from the server.

[1038] Input: Health data stored in the device memory

[1039] Output: Health data sent to the server and a confirmation message

[1040] Specific operation: Encodes the data into JSON format and sends it to the server using a secure communication protocol.

[1041] Step 3:

[1042] The server decodes the received health data and stores it in a database. The stored data is checked for blank spaces and abnormal values, and if an abnormality is detected, a message is sent to the device prompting re-entry.

[1043] Input: Health data received by the server

[1044] Output: Health data stored in the database and a message prompting you to re-enter (if there are abnormal values)

[1045] Specific behavior: Decodes and performs data integrity and range checks before saving to the database.

[1046] Step 4:

[1047] The server retrieves user data stored in a database and runs a data analysis program to calculate BMI, assess eating and drinking patterns, and check physical activity.

[1048] Input: Health data stored in a database

[1049] Output: Analysis results (BMI value, dietary pattern evaluation, exercise amount evaluation)

[1050] Specific operation: Based on the user's health data, calculate BMI as follows: 80kg / (1.7m 1.7m) = 27.7. Also calculates and evaluates eating and drinking patterns and exercise volume.

[1051] Step 5:

[1052] The server uses a generative AI model based on the analysis results to automatically generate advice for users on preventive measures and lifestyle improvements.

[1053] Input: Analysis results (BMI value, dietary pattern evaluation, exercise amount evaluation)

[1054] Output: Generated advice (e.g., reduce alcohol intake, add vegetables to each meal, walk at least 10,000 steps per day)

[1055] How it works: The analysis results are fed into an AI model to generate appropriate advice, which is customized to each individual user's situation.

[1056] Step 6:

[1057] The server sends the generated advice to the terminal and notifies the user, along with related articles and information.

[1058] Input: Generated advice

[1059] Output: Advice and related information posted to the terminal

[1060] Specific operation: The server notifies the user of advice from the generated AI model and sets reminders as needed.

[1061] Step 7:

[1062] Users can input their health-related questions or concerns into the application, and when the consultation is submitted, the device encodes the information and sends it to the server.

[1063] Input: User's health consultation details

[1064] Output: Consultation details sent from the device to the server

[1065] Specific operation: The content entered into the consultation box is encoded and sent again to the server via a secure protocol.

[1066] Step 8:

[1067] The server analyzes the received inquiry and generates an appropriate response using AI and a rule-based engine. For example, if a customer says, "I feel extremely tired when I wake up in the morning," the server will generate suggestions for improving their exercise and sleep quality.

[1068] Input: Consultation content received by the server

[1069] Output: The generated answer

[1070] Specific operation: The consultation content is passed to the AI ​​engine, which extracts and generates an appropriate answer based on related data.

[1071] Step 9:

[1072] The server sends the generated answer to the terminal and notifies the user.

[1073] Input: Generated Answer

[1074] Output: Answer reported to the terminal

[1075] Specific operation: The generated answer is notified to the device and instructions are provided so that the user can take immediate action.

[1076] Through the above processing steps, users can manage their own health conditions in real time and receive appropriate advice, while also being able to quickly respond to any health-related questions or inquiries.

[1077] (Application example 1)

[1078] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1079] The main purpose of conventional health management systems is to assess health risks based on users' health data and provide advice on preventative measures and lifestyle improvements. However, a user's health condition can also affect security awareness and risks, and no system has been available to address this. There is a need for a system that can address situations where a deterioration in health condition poses a security risk.

[1080] 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.

[1081] In this invention, the server includes means for a user to input their height, weight, eating and drinking patterns, and amount of exercise, means for collecting the input data and storing it in a database, means for evaluating health risks using the stored data, means for predicting future illnesses based on the health risks identified by the analysis means, means for generating advice on preventive measures and lifestyle improvements for the predicted illnesses, means for receiving health-related questions and inquiries from the user and generating corresponding answers, means for evaluating security risks in relation to the evaluated health risks and notifying the user, and means for generating warnings and advice according to the security risks, thereby enabling users to deal with both health risks and security risks.

[1082] "User" refers to an individual who uses this system and is responsible for managing health data and security risks.

[1083] "Height" refers to the length of the user's entire body, and is one of the basic physical data required for health analysis.

[1084] "Weight" is data for measuring the mass of the user's body, and is basic data used for health risk assessment.

[1085] "Eating and drinking patterns" are data that represent the user's food and drink intake status, and are important information for analyzing the user's health condition.

[1086] "Amount of exercise" refers to the degree of physical activity a user engages in in daily life or specific exercise, and is data used for health risk assessment.

[1087] A "means" refers to a method or device used to achieve a particular purpose.

[1088] A "database" is an electronic storage device for organizing, storing, and analyzing health data collected from users.

[1089] "Analytical tools" refers to the processes and tools used to analyze collected health data and assess health and security risks.

[1090] "Health risk" refers to the likelihood of future health problems or illnesses occurring based on the user's health status.

[1091] "Disease prediction means" refers to a method or tool for predicting future illnesses based on a user's health data.

[1092] "Preventive measures" refer to specific measures or steps taken to prevent predicted future diseases or health risks.

[1093] "Lifestyle advice" refers to specific instructions or advice regarding recommended habits or behaviors to improve a user's health.

[1094] The "health consultation receiving means" refers to a function or method for receiving health-related questions or inquiries from users and providing measures or advice depending on the content of those inquiries.

[1095] "Security risk" refers to the possibility of a security-related risk occurring based on the user's health condition.

[1096] "Security risk assessment means" refers to a method or device for analyzing a user's health data and assessing security risks.

[1097] "Warning" refers to a notification or message that alerts the user to a potential problem.

[1098] "Advice generation means" refers to the process or tool for generating specific measures or advice according to the user's situation.

[1099] This invention is a system that collects and analyzes a user's health data, evaluates health risks, and provides advice on preventive measures and lifestyle improvements. This system also evaluates security risks based on the user's health status and provides warnings and advice.

[1100] composition

[1101] The system includes the following means:

[1102] 1. A means for users to input their height, weight, eating and drinking patterns, and activity levels:

[1103] This is an application that is displayed on devices such as smartphones and PCs. Users input data such as their height (170 cm), weight (80 kg), daily alcohol intake, and walking 5,000 steps per day.

[1104] 2. A means of collecting the entered data and storing it in a database:

[1105] The terminal sends the entered data to the server, which receives the data and stores it in a database. If there are any abnormal values, the user is prompted to re-enter the data.

[1106] 3. Analytical tools to assess health risks:

[1107] The server uses the stored data to assess health risks, specifically by calculating the user's BMI and analyzing their eating and drinking patterns and physical activity.

[1108] 4. A method to predict future illnesses based on identified health risks:

[1109] The server then predicts illnesses based on the assessment results, for example predicting that a user with a high BMI is at risk of obesity or diabetes.

[1110] 5. Means of generating preventative and lifestyle advice:

[1111] For predicted illnesses, specific preventive measures and advice on lifestyle improvements are generated and sent to the user's device. For example, advice such as reducing alcohol intake or walking at least 10,000 steps per day could be considered.

[1112] 6. A means of accepting health-related questions and inquiries from users and generating corresponding answers:

[1113] The device sends the user's inquiry to a server, which uses AI and a rule-based engine to generate an appropriate response, such as advice on improving sleep quality or lack of exercise to combat morning fatigue.

[1114] 7. Means of assessing and informing users of security risks in relation to health risks:

[1115] The server evaluates the user's security risk based on the health risk assessment results. If the stress or fatigue level is high, the server notifies the user that their security awareness has decreased.

[1116] 8. Means of generating warnings and advice based on security risks:

[1117] The server generates specific warnings and advice based on the security risk and notifies the user. For example, if the stress level is high, the server provides advice such as "avoid making big decisions and get adequate rest."

[1118] Specific examples

[1119] As a concrete example, consider a scenario in which a user enters the following data into an application:

[1120] Height: 170cm

[1121] Weight: 80kg

[1122] Alcohol consumption: 30ml / day

[1123] Steps: 5,000 steps / day

[1124] Gym visits: 2 times / week

[1125] Stress level: 50

[1126] Fatigue level: 40

[1127] The server receives this data, assesses health risks (e.g., risk of high blood pressure or diabetes based on a BMI of 27.7), and provides specific advice to the user (e.g., reduce alcohol intake or add 200g of vegetables to each meal).

[1128] Additionally, based on the same data, security risks (for example, security advice such as "take regular breaks" due to moderate stress and fatigue levels) are also provided.

[1129] Prompt Sentence Examples

[1130] A possible prompt for a generative AI model might look something like this:

[1131] "Generate a Python program that evaluates security risks based on the health data of user ID 1 (height: 170cm, weight: 80kg, alcohol intake: 30ml / day, steps: 5000 / day, gym visits: 2 times / week, stress level: 50, fatigue level: 40) and provides appropriate advice."

[1132] By using this prompt, the appropriate AI model can be used to analyze individual health conditions and security risks, and specific advice can be provided to the user.

[1133] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1134] Step 1:

[1135] Users enter their health data (height, weight, eating and drinking patterns, amount of exercise, stress level, and fatigue level) into a smartphone or PC application. For example, input data might be: height 170 cm, weight 80 kg, alcohol intake 30 ml / day, steps taken 5,000 / day, gym visits 2 times / week, stress level 50, and fatigue level 40. This data will be used to assess future health and security risks.

[1136] Step 2:

[1137] The terminal sends the entered data to the server. At this time, the data is checked for abnormal values ​​and blank spaces, and if there are any abnormalities, the user is prompted to re-enter the data. The data is checked for abnormal values ​​by, for example, checking the range based on BMI calculations.

[1138] Step 3:

[1139] The server stores the received data in a database, formatting the data before storing it and preprocessing it as needed, so that subsequent analysis can proceed smoothly.

[1140] Step 4:

[1141] The server uses the stored data to assess health risks. For example, it calculates BMI and analyzes eating and drinking patterns and exercise volume. Specifically, it calculates BMI using the user's weight and height, and assesses health risks based on the results. The results of this assessment are accumulated as, for example, the risk of obesity or diabetes.

[1142] Step 5:

[1143] The server then predicts future illnesses based on the identified health risks. For example, a BMI of 27.7 predicts a high risk of obesity, high blood pressure, and diabetes. This prediction is saved as an analysis result and serves as the basis for notifications to the user.

[1144] Step 6:

[1145] The server generates advice on preventive measures and lifestyle improvements for predicted illnesses. For example, specific advice such as reducing alcohol intake or walking at least 10,000 steps per day is generated. This advice is sent to the user's device to promote health improvement.

[1146] Step 7:

[1147] When a user inputs a health-related question into the application, the device sends the content to the server. The server then analyzes the question using AI and a rule-based engine and generates a response appropriate to the question. For example, if a user comments that they are "unable to get rid of morning fatigue," the server generates advice such as "stretching every day and improving the quality of their sleep."

[1148] Step 8:

[1149] The server evaluates security risks based on the health risk assessment results. If the user's stress or fatigue level is high, it determines that the user's security awareness is declining and issues a warning. This evaluation is performed taking into account the relationship between the user's health state and security awareness.

[1150] Step 9:

[1151] The server generates warnings and advice based on security risks. For example, if the user's stress level is high, specific advice such as "avoid making big decisions and get adequate rest" is sent to the user's device. This notification helps users to increase their security awareness.

[1152] This series of steps will create a system that allows users to receive comprehensive health management and security risk assessment based on their health data, thereby improving both the quality of life and safety of users.

[1153] 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.

[1154] The system of the present invention not only collects and analyzes a user's health data and provides advice on preventive measures and lifestyle improvements based on health risks, but also recognizes the user's emotions and performs comprehensive health management. An embodiment of the present invention will be described in detail below.

[1155] User data entry and collection

[1156] Users log in to the application using a device such as a smartphone or PC and enter their health data, such as their height, weight, eating and drinking patterns, and amount of exercise. They can also enter questions or concerns about their health. For example, a user might enter data such as height 170 cm, weight 80 kg, daily alcohol consumption, a diet lacking in vegetables, 5,000 steps per day, and going to the gym twice a week.

[1157] The terminal transmits the input data to the server, where the transmitted data is communicated using a secure protocol.

[1158] Data storage and preprocessing

[1159] The server saves the received data in a database. After saving, it checks for blanks and outliers, and performs completion and filtering as necessary. If an outlier is detected, the server prompts the user to re-enter the data.

[1160] Data analysis and health risk assessment

[1161] The server analyzes the collected data, including the following specific analysis items:

[1162] Calculate BMI: BMI = weight (kg) / (height (m) height (m)). For example, 80kg / (1.7m 1.7m) = 27.7.

[1163] Analysis of dietary patterns: Assessment of calorie surpluses and nutritional deficiencies. For example, alcohol intake and vegetable intake are compared with baseline values.

[1164] Physical activity assessment: Analyze the number of steps taken and frequency of gym visits and compare them with the recommended standard (e.g., 10,000 steps per day).

[1165] This allows the user to assess their health risks and predict future illnesses. A BMI of 27.7 indicates a risk of obesity, diabetes, and high blood pressure.

[1166] Emotion recognition by emotion engine

[1167] The server is equipped with an emotion engine that estimates the user's emotional state based on the user's input data and behavioral data. For example, when a user inputs detailed records of their diet and exercise, the server infers the user's emotional state from the frequency and content of these records. The server can also extract the user's emotions from the content of questions and inquiries.

[1168] Comprehensive health assessment and prevention

[1169] The server integrates health data and emotional data to perform a comprehensive health risk assessment and generate specific advice on preventative measures and lifestyle improvements. For example, based on the analysis of the emotional engine, it generates specific advice such as, "You've been feeling stressed recently, so try incorporating relaxation techniques."

[1170] Providing advice

[1171] The device will notify the user of the generated advice via push notification, email, or in-app notification, and will also provide related articles and information, making it easier for users to obtain the knowledge they need.

[1172] Responding to inquiries from users

[1173] When a user wants to input a question or inquiry about their health, the user inputs the question via the application. For example, a question such as "I've been feeling very tired when I wake up in the morning lately" may be input.

[1174] The device sends the consultation details to the server, which uses an emotion engine to analyze the details and generates an appropriate response using an AI engine and rule-based system. For example, if the cause is thought to be lack of exercise or poor sleep quality, the server will suggest daily stretching and improvements to the sleep environment, and recommend seeing a doctor.

[1175] As described above, the system of the present invention realizes a system that grasps the user's health condition in real time and provides comprehensive preventive measures and advice that also take into account the user's emotional state.

[1176] The processing flow will be explained below.

[1177] Step 1:

[1178] Users log in to the application and enter their health data, such as their height, weight, eating and drinking patterns, and exercise volume. They can also enter questions and concerns about their health. For example, a user might enter data such as height 170 cm, weight 80 kg, daily alcohol consumption, lack of vegetable intake, 5,000 steps per day, and gym visits twice a week.

[1179] Step 2:

[1180] The terminal sends the data entered by the user to the server using a secure communication protocol such as HTTPS.

[1181] Step 3:

[1182] The server saves the received data in a database. After saving, it checks whether there are any blank spaces or abnormal values ​​in the data, and if there are any abnormalities, it prompts the user to re-enter the data.

[1183] Step 4:

[1184] The server analyzes the stored data. The specific analysis items are as follows:

[1185] Calculating BMI: BMI = weight (kg) / (height (m) height (m))

[1186] For example, 80kg / (1.7m 1.7m) = 27.7

[1187] Analysis of eating and drinking patterns: Assessment of calorie surplus and nutritional deficiencies

[1188] Check your exercise volume: Analyze your steps and gym visit frequency and compare them with the recommended standard (e.g., 10,000 steps per day)

[1189] Step 5:

[1190] The server uses an emotion engine to infer the user's emotional state from input data and behavioral data. For example, it infers emotions from dietary habits and exercise records, and incorporates these into the analysis results.

[1191] Step 6:

[1192] The server integrates health and emotional data to perform a comprehensive health risk assessment. For example, if a BMI of 27.7 indicates a high risk of obesity, diabetes, and high blood pressure, and predicts a high stress level, the server takes this into account when conducting a risk assessment.

[1193] Step 7:

[1194] The server generates specific preventive measures and lifestyle improvement advice based on the assessment results, for example:

[1195] Reduce your alcohol intake to one drink a day

[1196] "Add 200g of vegetables to each meal"

[1197] "Walk more than 10,000 steps a day"

[1198] Implement relaxation techniques to reduce stress

[1199] Step 8:

[1200] The device notifies the user of the generated advice via push notification, email, or in-app notification, and also provides related articles and information.

[1201] Step 9:

[1202] When a user inputs a question or question about health into the application, the user may input, for example, "I've been feeling very tired when I wake up in the morning lately."

[1203] Step 10:

[1204] The terminal transmits the contents of the user's consultation to the server.

[1205] Step 11:

[1206] The server analyzes the consultation using an emotion engine and generates appropriate responses using an AI engine and rule-based system. For example, if the user feels tired in the morning, the server may suggest daily stretching and improvements to the sleep environment, and recommend a doctor's visit, as this may be due to a lack of exercise or poor sleep quality.

[1207] Step 12:

[1208] The terminal notifies the user of the generated answer, allowing the user to take appropriate action.

[1209] In this way, the system of the present invention can grasp the user's health condition in real time and provide comprehensive preventive measures and advice that also take into account the user's emotional state.

[1210] Example 2

[1211] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1212] Conventional health management systems are limited to predicting health risks based on a user's basic health data (height, weight, eating and drinking patterns, and amount of exercise) and providing advice on preventive measures and lifestyle improvements. However, these systems lack comprehensive health risk assessments that take into account the user's emotional state, and the provision of advice that includes the user's emotional state. Taking into account the user's emotional state is expected to enable more appropriate and effective health management, so this issue needs to be resolved.

[1213] 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.

[1214] In this invention, the server includes means for inputting a user's height, weight, eating and drinking patterns, and amount of exercise, means for collecting the input data and storing it in a database, means for evaluating health risks using the stored data, means for predicting future illnesses based on the health risks identified by the analysis means, means for generating advice on preventive measures and lifestyle improvements for the predicted illnesses, means for estimating the user's emotional state based on the input data and behavioral data, means for integrating the health data and emotional data to evaluate overall health risks and generating specific advice on preventive measures and lifestyle improvements, and means for receiving health-related questions and inquiries from the user and generating corresponding answers, thereby enabling comprehensive health management that also takes the user's emotional state into consideration.

[1215] "User data" refers to health-related information such as height, weight, eating and drinking patterns, and amount of exercise input by the user.

[1216] The "collection means" is a method or device for collecting data entered by a user and transmitting it to a server.

[1217] A "database" is a repository of information for storing collected user data.

[1218] "Analysis means" refers to methods and devices for analyzing collected data and assessing health risks.

[1219] "Health risks" are predictions of diseases or symptoms that may occur in the future based on the user's health condition.

[1220] A "prediction means" is a method or device for predicting future diseases or symptoms based on the health risks identified by the analysis means.

[1221] The "advice generation means" refers to a method or device for generating advice on preventive measures and lifestyle improvements based on the results of health risk assessment and disease prediction.

[1222] The "emotional state" refers to the user's emotional or mental state estimated based on the user's input data and behavioral data.

[1223] The "emotion estimation means" is a method or device for estimating an emotional state based on input data and behavioral data of a user.

[1224] An "integrated assessment means" is a method or device for integrating health data and emotional data to perform a comprehensive health risk assessment.

[1225] The "inquiry response means" is a method or device for receiving health-related questions or inquiries from users and generating corresponding answers.

[1226] The system of the present invention not only collects and analyzes a user's health data and provides advice on preventive measures and lifestyle improvements based on health risks, but also recognizes the user's emotions to provide comprehensive health management. An embodiment of this system is described in detail below.

[1227] User data entry and collection

[1228] Users log in to the application using a device such as a smartphone or PC. They input health data such as their height, weight, eating and drinking patterns, and amount of exercise. Specifically, they input data such as "height 170 cm, weight 80 kg, daily alcohol consumption, diet lacking in vegetables, 5,000 steps per day, gym visits twice a week." Users can also input health-related questions and concerns.

[1229] The terminal collects the data entered by the user in real time and transmits it to a server using a secure protocol (e.g., HTTPS).

[1230] Data storage and preprocessing

[1231] The server stores the received data in a dedicated database. After storing the data, the server checks for blanks and abnormal values. For example, it can detect abnormally high height or extremely low weight.

[1232] If an abnormal value is detected, the server generates a message to prompt the user to re-enter the data and sends it to the terminal. The user receives the message and re-enters the correct data.

[1233] Data analysis and health risk assessment

[1234] The server analyzes the user data stored in the database, including:

[1235] 1. Calculating BMI: The server calculates BMI based on weight and height data using the following formula:

[1236] BMI = Weight (kg) / (Height (m) Height (m))

[1237] For example, if you weigh 80 kg and are 1.7 m tall, your BMI = 80 / (1.7 1.7) = 27.7.

[1238] 2. Analysis of eating and drinking patterns: The server analyzes the user's eating and drinking data and evaluates calories and nutritional balance. For example, "daily alcohol consumption" can be judged as excessive calories, and "a diet lacking in vegetables" can be judged as a risk of nutritional deficiency.

[1239] 3. Exercise assessment: The server analyzes step count data and gym visit frequency and compares it with the recommended standard (e.g., 10,000 steps per day). For example, if a user only walks 5,000 steps per day, it can assess that the user is not getting enough exercise.

[1240] Emotion recognition by emotion engine

[1241] The server is equipped with an emotion engine that infers the user's emotional state based on their input data and behavioral data. For example, if a user frequently enters detailed records of their diet and exercise, the server can infer their emotions from the frequency of their input and the use of words.

[1242] Comprehensive health assessment and prevention

[1243] The server integrates health and emotional data to perform a comprehensive health risk assessment and generate specific advice on preventative measures and lifestyle improvements. For example, based on the analysis results of the emotion engine, it generates specific advice such as, "You've been feeling stressed recently, so try incorporating relaxation techniques."

[1244] Providing advice

[1245] The device will notify the user of the advice sent from the server via push notification, email, or in-app notification, and will also provide related articles and information, making it easy for users to obtain the knowledge they need.

[1246] Responding to inquiries from users

[1247] The user inputs questions or concerns about their health via the application. For example, they may input a question such as, "Recently, I've been feeling extremely tired when I wake up in the morning."

[1248] The device sends the user's consultation details to the server. The server uses an emotion engine to analyze the consultation details and utilizes an AI engine and rule-based system to generate an appropriate response. For example, if the consultation details are thought to be caused by a lack of exercise or poor quality sleep, the server will suggest "daily stretching and improving your sleep environment" and recommend seeing a doctor.

[1249] Examples of prompt statements

[1250] This system can understand the user's health condition in real time and provide comprehensive preventive measures and advice that take into account their emotional state. As a specific example, by inputting the following prompt sentences into the generative AI model, more detailed advice can be obtained:

[1251] "If a user reports feeling stressed recently, what relaxation techniques should we suggest?"

[1252] This prompt allows the AI ​​model to provide specific relaxation advice.

[1253] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1254] Step 1:

[1255] Users log in to the application using a device such as a smartphone or PC. In the application, they enter their own health data, such as their height, weight, eating and drinking patterns, and amount of exercise. For example, they might enter data such as "height 170 cm, weight 80 kg, daily alcohol consumption, diet lacking in vegetables, 5,000 steps per day, and going to the gym twice a week." The entered data is temporarily stored in the device's memory.

[1256] Step 2:

[1257] The device sends the data entered by the user to the server using a secure protocol (e.g., HTTPS). The sent data is temporarily stored in the server's memory. At this stage, the input is the health data entered by the user, and the output is the health data sent to the server.

[1258] Step 3:

[1259] The server stores the received data in a dedicated database. Once the stored data is confirmed, it moves on to the next step. In this step, the input is the health data sent from the device, and the output is the health data stored in the database.

[1260] Step 4:

[1261] The server checks the data stored in the database to see if there are any blank spaces or abnormal values. For example, it checks to see if any abnormal values ​​have been entered for height or weight. If an abnormal value is detected during this process, the server generates a message prompting the user to re-enter the data and sends it to the terminal. The message requesting re-entry is displayed to the user. The input in this step is the health data stored in the database, and the output is the result of the abnormal value check and a message requesting the user to re-enter the data.

[1262] Step 5:

[1263] The user receives a re-entry request message from the server and re-enters the correct data. The re-entered data is again sent from the terminal to the server and stored in the database. The input in this step is the re-entry request message, and the output is the corrected health data.

[1264] Step 6:

[1265] The server uses the available data stored in the database to assess the health risk as follows:

[1266] 1. Calculate BMI: Calculate BMI from user's weight and height. For example, if user weighs 80kg and is 1.7m tall, BMI = 80 / (1.7 1.7) = 27.7.

[1267] 2. Analysis of eating and drinking patterns: Evaluate the calorie intake and nutritional balance of the user. For example, if the user consumes alcohol every day, it will be determined that the user is consuming too many calories.

[1268] 3. Exercise assessment: Analyzes step count data and gym visit frequency and compares it with the recommended standard (e.g., 10,000 steps per day). For example, if a user only walks 5,000 steps per day, it is assessed as not getting enough exercise.

[1269] The input in this step is the health data stored in the database, and the output is the result of the health risk assessment.

[1270] Step 7:

[1271] The server is equipped with an emotion engine that estimates the user's emotional state based on the user's input data and behavioral data. For example, if a user frequently enters detailed records of their diet and exercise, the engine can infer the user's emotional state from the frequency of their input and the use of words. The input in this step is the user's health and behavioral data, and the output is an estimate of the user's emotional state.

[1272] Step 8:

[1273] The server integrates health data and emotional data to perform a comprehensive health risk assessment and generate specific preventive measures and advice for improving lifestyle habits. For example, it generates advice such as, "Because your BMI is high and you're under stress, improve your diet and adopt relaxation techniques." The input in this step is health data and emotional data, and the output is specific preventive measures and advice.

[1274] Step 9:

[1275] The device notifies the user of the advice sent from the server. Notification methods include push notifications, emails, and in-app notifications. Related articles and information are also provided to help users obtain the necessary knowledge. The input in this step is the advice sent from the server, and the output is the notification sent to the user.

[1276] (Application example 2)

[1277] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1278] Conventional health management systems assess health risks by collecting and analyzing users' physical and lifestyle information, but lack the functionality to provide advice that takes emotional state into account or to suggest individually optimal meal plans. As a result, they often fail to adequately manage users' overall health and end up only providing incomplete, partial advice. Furthermore, the lack of a system that proposes customized meal plans based on health risks and emotional state makes it difficult to effectively support users in improving their health.

[1279] 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.

[1280] In this invention, the server includes means for a user to input their own physical information and lifestyle data, means for collecting and storing the input data, means for evaluating health risks using the stored data, means for predicting future diseases based on the health risks identified by the analysis means, means for generating advice on preventive measures and lifestyle improvements for the predicted diseases, means for receiving health-related questions and inquiries from the user and generating corresponding answers, means for analyzing the user's emotional state and providing advice based on the emotional state, and means for individually suggesting an optimal meal menu based on the health risks and emotional data. This enables comprehensive health management for the user and realizes individually optimized advice on preventive measures and lifestyle improvements and suggestions for a meal menu.

[1281] "User" refers to an individual who utilizes the System to enter and collect their own health care information.

[1282] "Physical information" refers to basic physical data such as a user's height, weight, and body mass index (BMI).

[1283] "Lifestyle data" refers to information related to a user's daily life, such as their eating and drinking patterns, amount of exercise, sleep time, smoking and drinking habits, etc.

[1284] "Data collection means" refers to a device or system that has the function of collecting physical information and lifestyle habit data entered by the user and incorporating it into the system.

[1285] "Storage means" refers to a database within the system that has the functionality to safely and effectively store collected data.

[1286] "Analysis Tools" refers to algorithms or programs that utilize collected and stored data to assess a user's health risks and emotional state.

[1287] "Health risk" refers to the possibility of a user contracting a disease or experiencing a worsening health condition in the future, as analyzed based on collected data.

[1288] "Preventive measures" refer to specific behavioral or habit changes recommended based on the analysis results to reduce the risk of developing a disease.

[1289] "Lifestyle improvement advice" refers to guidance or suggestions provided to a user to change their lifestyle to a healthier one.

[1290] "Emotional state" refers to information analyzed by the emotion engine that indicates the user's psychological health and mood.

[1291] "Answer generation means" refers to the algorithms and rule-based systems within the system that generate appropriate answers to user doubts and questions.

[1292] The "meal menu suggestion means" refers to a part of the system that has the function of providing an optimal meal plan based on the user's health and emotional state.

[1293] The system of the present invention collects physical information and lifestyle data of a user, performs health risk assessment and emotion analysis, and proposes specific meal menus. This system is realized with the following configuration and processing.

[1294] User data entry and collection

[1295] Users log in to the application using a device such as a smartphone, PC, or wearable device and enter their physical and lifestyle information, such as height, weight, eating and drinking patterns, and amount of exercise. They can also enter health-related questions and concerns. For example, if a user enters their height as 170 cm, weight as 80 kg, daily alcohol consumption, a diet lacking in vegetables, 5,000 steps per day, and gym visits twice a week, the device sends this data to the server. The transmitted data is communicated using a secure protocol.

[1296] Data storage and preprocessing

[1297] The server saves the received data in a database. After saving, it checks for blanks and outliers, and performs completion and filtering as necessary. If an outlier is detected, the server prompts the user to re-enter the data.

[1298] Data analysis and health risk assessment

[1299] The server analyzes the collected data, including the following specific analysis items:

[1300] Calculate BMI: BMI = weight (kg) / (height (m) height (m)). For example, 80kg / (1.7m 1.7m) = 27.7.

[1301] Analysis of dietary patterns: Assessment of calorie surpluses and nutritional deficiencies. For example, alcohol intake and vegetable intake are compared with baseline values.

[1302] Physical activity assessment: Analyze the number of steps taken and frequency of gym visits and compare them with the recommended standard (e.g., 10,000 steps per day).

[1303] This allows the server to assess the user's health risks and predict future illnesses. For example, if the BMI is 27.7, it is determined that the user is at risk of obesity, diabetes, and high blood pressure.

[1304] Emotion recognition by emotion engine

[1305] The server is equipped with an emotion engine that estimates the user's emotional state based on the user's input data and behavioral data. For example, when a user inputs detailed records of their diet and exercise, the server infers the user's emotional state from the frequency and content of these records. The server can also extract the user's emotions from the content of questions and inquiries.

[1306] Comprehensive health assessment and preventative care

[1307] The server integrates health data and emotional data to perform a comprehensive health risk assessment and generate specific advice on preventative measures and lifestyle improvements. For example, based on the analysis of the emotional engine, it generates specific advice such as, "You've been feeling stressed recently, so try incorporating relaxation techniques."

[1308] Meal menu suggestions

[1309] The server then proposes an optimal meal plan based on the individual's health risk and emotional data. For example, if the individual is at high risk, it will propose a low-calorie, high-nutrition menu, and if the individual is at low risk, it will propose a balanced, regular menu.

[1310] Examples of specific prompts are:

[1311] The user's height is 170cm, weight is 80kg, eating habits are high in fat, and exercise habits are low. The user also says, "I've been feeling a lot of stress lately..." Based on this data, assess health risks and suggest an appropriate meal plan.

[1312] This will enable comprehensive health management for users and provide individually optimized preventative measures and meal menus.

[1313] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1314] Step 1:

[1315] A user logs in to the application using a device such as a smartphone or computer.

[1316] Input: User's height, weight, eating and drinking patterns, amount of exercise, health questions and concerns.

[1317] How it works: The user enters this data into the application's input form.

[1318] Output: The entered data is recorded on the terminal.

[1319] Step 2:

[1320] The terminal transmits the input data to the server.

[1321] Input: User data entered in step 1.

[1322] How it works: The device sends data to the server using a secure protocol.

[1323] Output: The data is passed to the server.

[1324] Step 3:

[1325] The server stores the received data in a database.

[1326] Input: User data received in step 2.

[1327] Operation: The server saves data to the database and checks whether the saved contents are normal.

[1328] Output: The data is stored in a database, and outlier detection and imputation are performed as needed.

[1329] Step 4:

[1330] The server analyzes the stored data and assesses health risks.

[1331] Input: User data stored in the database.

[1332] How it works: The server calculates BMI, analyzes eating patterns (assessing calorie surpluses and nutritional deficiencies), and assesses physical activity.

[1333] Output: Health risk assessment results. For example, if your BMI is 27.7, you are at high risk for obesity, diabetes, and high blood pressure.

[1334] Step 5:

[1335] The server uses an emotion engine to analyze the user's emotional state.

[1336] Input: Consultation details and behavioral data stored in the database.

[1337] How it works: The server uses an emotion engine to extract emotions from text data, for example identifying stress states from input such as "I've been feeling stressed lately."

[1338] Output: The result of the emotion analysis is obtained. For example, the emotional state is analyzed as "high stress."

[1339] Step 6:

[1340] The server integrates the health risk assessment results and emotion analysis results to perform a comprehensive health risk assessment.

[1341] Input: Health risk assessment results from step 4 and sentiment analysis results from step 5.

[1342] How it works: The server combines these results to perform a comprehensive risk assessment.

[1343] Output: A comprehensive health risk assessment result is obtained. For example, if a person has a high risk of obesity and high stress, the overall risk will be assessed as very high.

[1344] Step 7:

[1345] Based on the overall evaluation results, the server generates specific advice on preventive measures and lifestyle improvements.

[1346] Input: Overall health risk assessment results obtained in Step 6.

[1347] How it works: The server uses the generative AI model to generate specific advice to address the risk, such as "You've been feeling stressed lately, so try some relaxation techniques."

[1348] Output: Advice on preventive measures and lifestyle changes.

[1349] Step 8:

[1350] The server will suggest optimal meal plans for each individual based on health risks and emotional data.

[1351] Input: Health risk assessment results and emotion data analyzed in Step 4 and Step 5.

[1352] Operation: The server generates and proposes a meal menu tailored to the user's health condition. For example, if the user is at high risk, it proposes a low-calorie, high-nutrition menu.

[1353] Output: Personalized optimized meal menu suggestions.

[1354] Step 9:

[1355] The device will notify the user of suggested preventative measures, advice on lifestyle changes, and meal plans.

[1356] Input: Step 7 advice and step 8 meal menu.

[1357] How it works: The device provides information to the user via push notifications, emails, and in-app notifications.

[1358] Output: The user checks the advice and meal menu through the app.

[1359] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1360] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1361] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1362] [Fourth embodiment]

[1363] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1364] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1365] 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).

[1366] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1367] 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.

[1368] 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).

[1369] 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.

[1370] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1371] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1372] 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.

[1373] 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.

[1374] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1375] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1376] The system of the present invention collects and analyzes health data of a user and provides advice on preventive measures and lifestyle improvements based on health risks. The following describes in detail an embodiment of the present invention.

[1377] User data entry and collection

[1378] Users log in to an application displayed on a device such as a smartphone or PC and enter their health data, such as their height, weight, eating and drinking patterns, and amount of exercise. For example, a user might enter data such as height 170 cm, weight 80 kg, daily alcohol consumption, a diet lacking in vegetables, 5,000 steps per day, and going to the gym twice a week.

[1379] The terminal sends the entered data to the server, which receives the data and stores it in a database. It also checks the data for blank spaces and abnormal values, and if any abnormalities are found, prompts the user to re-enter the data.

[1380] Data analysis and health risk assessment

[1381] The server uses the stored data to perform analysis, such as calculating the user's BMI, analyzing their eating and drinking patterns (to assess whether they are consuming too many calories or are lacking in nutrients), and checking their physical activity (comparing it to the recommended number of steps). For example, the BMI is calculated as follows:

[1382] BMI = Weight (kg) / (Height (m) Height (m))

[1383] Example: 80kg / (1.7m 1.7m) = 27.7

[1384] This allows the system to assess the user's health risks and predict specific diseases they are likely to develop. For example, if a user's BMI is 27.7, the system will assess that the user is at risk of obesity, high blood pressure, and diabetes.

[1385] Providing advice on preventative measures and lifestyle changes

[1386] The server generates specific preventive measures and lifestyle improvement advice for the user based on the evaluation results, for example:

[1387] Reduce your alcohol intake

[1388] Add 200g of vegetables to each meal

[1389] Walk more than 10,000 steps a day

[1390] This advice is sent to users' devices such as smartphones and computers, and related articles and information are also provided for easy reference.

[1391] Receiving and responding to health-related inquiries from users

[1392] The system also accepts health-related questions and consultations from users. For example, a user can input into the application a question about how tired they have been feeling recently when they wake up in the morning.

[1393] The device sends the user's inquiry to a server, which analyzes the information and generates an appropriate response using AI and a rule-based engine. For example, if the user feels tired in the morning, it may be due to a lack of exercise or poor sleep quality. The server may suggest daily stretching and improving the sleep environment, and recommend a doctor's visit if necessary.

[1394] As described above, the system of the present invention provides a system that allows users to understand their own health condition in real time and receive appropriate preventive measures and advice.

[1395] The processing flow will be explained below.

[1396] Step 1:

[1397] Users log in to the application and enter their health data, such as height, weight, eating and drinking patterns, and exercise amount. For example, a user might be 170 cm tall, weigh 80 kg, drink alcohol daily, have a diet lacking in vegetables, walk 5,000 steps a day, and go to the gym twice a week.

[1398] Step 2:

[1399] The device sends the entered data to the server, which transfers the data using a secure communication protocol such as HTTPS.

[1400] Step 3:

[1401] The server stores the received data in a database, typically a relational database such as PostgreSQL or MySQL. When the data is stored, it is associated with identifying information such as the user ID.

[1402] Step 4:

[1403] The server performs preprocessing for analyzing the collected data. During this process, it checks for blank spaces and outliers, and performs data completion and filtering as necessary. If an outlier is detected, it prompts the user to re-enter the data.

[1404] Step 5:

[1405] The server will analyze the collected health data, including the following:

[1406] Calculate BMI: BMI = weight (kg) / (height (m) height (m)). For example, 80kg / (1.7m 1.7m) = 27.7.

[1407] Eating pattern analysis: Evaluate calorie surpluses and nutritional deficiencies, for example, by comparing alcohol intake or vegetable intake with baseline thresholds.

[1408] Check your physical activity: Analyze your steps and gym visit frequency and compare them with the recommended standard (e.g. 10,000 steps per day).

[1409] Step 6:

[1410] The server evaluates health risks based on the analysis results and predicts future illnesses. For example, if a BMI is 27.7, it is determined that the individual is at risk of obesity, diabetes, or high blood pressure.

[1411] Step 7:

[1412] The server generates specific preventive measures and lifestyle improvement advice for predicted illnesses. For example:

[1413] Reduce your alcohol intake to one drink a day

[1414] "Add 200g of vegetables to each meal"

[1415] "Walk more than 10,000 steps a day"

[1416] Step 8:

[1417] The device notifies the user of the generated advice via push notification, email, in-app notification, etc. Related articles and information are also provided.

[1418] Step 9:

[1419] When a user wishes to input a question or consultation about health, the user inputs the question into the application. For example, the user may input a question such as, "Recently, I've been feeling very tired when I wake up in the morning."

[1420] Step 10:

[1421] The terminal transmits the contents of the user's consultation to the server.

[1422] Step 11:

[1423] The server then launches an AI engine and rule-based system to analyze the customer's inquiry and generate an appropriate response. For example, if the customer feels tired in the morning, the server will consider the quality of their sleep and lack of exercise and suggest measures such as "daily stretching" and "reviewing their sleeping environment."

[1424] Step 12:

[1425] The terminal notifies the user of the generated answer, and in some cases may also notify the user of a recommendation to see a doctor.

[1426] These steps allow users to understand their health status in real time and receive appropriate preventive measures and advice.

[1427] Example 1

[1428] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1429] In today's world, lifestyle-related diseases and health risks are on the rise, requiring individual users to constantly monitor their own health status and take preventative measures and improve their lifestyles. However, many existing systems struggle to accurately manage and analyze users' health data and provide appropriate advice. Furthermore, they are not adequately responsive to users' health-related questions or inquiries, making it difficult to obtain appropriate advice immediately.

[1430] 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.

[1431] In this invention, the server includes: a means for a user to input their height, weight, eating and drinking patterns, and amount of exercise; a means for collecting the input data and storing it in a database; a means for checking the stored data for abnormal values ​​and prompting the user to re-enter the data if abnormalities are found; a data analysis means for assessing the user's health risk using the stored data, including a BMI calculation means, an eating and drinking pattern analysis means, and an exercise amount check means; a means for generating advice on preventive measures and lifestyle improvements using a generative AI model based on the assessed health risk; a means for notifying the user of the generated advice; a means for accepting health-related questions and inquiries from the user and generating corresponding answers using an AI or rule-based engine; and an encryption means for securely communicating all of the above means. This allows users to accurately manage their health data, assess their health risks in real time, and receive appropriate advice on preventive measures and lifestyle improvements. Health-related questions and inquiries can also be responded to promptly.

[1432] "User" refers to an individual who uses the system to manage their own health data and receive advice on preventive measures and lifestyle improvements.

[1433] A "terminal" is a device through which a user inputs their own health data and communicates with the server, and specifically includes smartphones and personal computers.

[1434] A "server" is a central computer system that receives health data sent by users, stores, analyzes, and evaluates the data, and generates advice and answers to notify the users.

[1435] "Health data" refers to information entered to understand a user's health condition, such as their height, weight, eating and drinking patterns, and amount of exercise.

[1436] "Database" refers to a system for structuring and storing health data entered and submitted by users.

[1437] An "outlier" is a value in the health data entered by the user that is outside the normal range, including incorrect data entry or extreme values.

[1438] "Data analysis" refers to the process of calculations and analysis to assess a user's health status and risks based on stored health data.

[1439] "BMI calculation means" refers to a means for calculating a user's BMI (Body Mass Index) based on their weight and height, and assessing their health risk.

[1440] The "eating and drinking pattern analysis means" is a means for analyzing the eating and drinking data input by the user and evaluating whether there is an excess of calories or a nutritional deficiency.

[1441] The "exercise amount check means" refers to a means for checking the user's exercise amount and comparing it with the recommended exercise amount.

[1442] A "generative AI model" is an artificial intelligence model that generates advice on preventive measures and lifestyle improvements based on collected data.

[1443] "Advice" refers to specific suggestions and improvements to reduce health risks based on the user's health data and analysis results.

[1444] "Encryption methods" refers to the techniques and protocols used to encrypt data so that user data is communicated securely.

[1445] "AI and rule-based engines" refer to engines based on artificial intelligence and defined rules that generate appropriate answers to user inquiries and questions.

[1446] These definitions clearly show the functions of the system and the role of each means.

[1447] The system of the present invention collects and analyzes a user's health data and provides advice on preventive measures and lifestyle improvements based on health risks. The main components of the system and their operation are described in detail below.

[1448] User data entry and collection

[1449] Users log in to an application displayed on a device such as a smartphone or PC and enter their health data, such as their height, weight, eating and drinking patterns, and amount of exercise. For example, a user might enter data such as height 170 cm, weight 80 kg, daily alcohol consumption, a diet lacking in vegetables, 5,000 steps per day, and going to the gym twice a week.

[1450] The device temporarily stores the entered data in memory and then sends it to the server using a security protocol (e.g., HTTPS). The data is encrypted with AES256 and transmitted securely.

[1451] Data transmission and storage

[1452] The server decodes the received user data and stores it in a database. The stored data is checked for blanks and abnormal values. If an abnormality is detected, a message is sent to the terminal prompting the user to re-enter the data.

[1453] Data analysis and health risk assessment

[1454] The server retrieves the user data stored in the database and runs the analysis program. Specifically, it performs the following calculations to calculate the user's BMI:

[1455] BMI = Weight (kg) / (Height (m) Height (m))

[1456] Example: If you are 1.7m tall and weigh 80kg, then 80 / (1.7 1.7) = 27.7

[1457] It also analyzes eating and drinking patterns and checks the amount of exercise, which can help assess the user's health risks and determine whether they are at risk of obesity, high blood pressure, or diabetes.

[1458] Providing advice on preventative measures and lifestyle changes

[1459] The server uses a generative AI model based on the data analysis results to automatically generate advice for users on preventive measures and lifestyle improvements. For example, the following advice may be generated:

[1460] Reduce your alcohol intake

[1461] Add 200g of vegetables to each meal

[1462] Walk more than 10,000 steps a day

[1463] The generated advice is sent to the terminal and notified to the user. In addition, related information and articles are also provided to support the user in easily implementing the advice.

[1464] Receiving and responding to health-related inquiries from users

[1465] The user inputs health-related questions or concerns through the application. For example, they input a question such as, "I feel extremely tired when I wake up in the morning." The device encodes the question and sends it to the server.

[1466] The server analyzes the received consultation content and generates an appropriate response using AI and a rule-based engine. For example, if the user feels tired in the morning, it predicts that they are not getting enough exercise or their sleep quality is poor, and suggests advice such as daily stretching and reviewing their sleep environment, and recommends seeing a doctor if necessary. The generated response is sent to the device and notified to the user.

[1467] Examples of concrete examples and prompts

[1468] Specific working example:

[1469] The user inputs their daily health data and the data is sent to a server.

[1470] The server analyzes the stored data, assesses health risks, and notifies users of preventive measures.

[1471] Users can enter their health questions and receive instant advice.

[1472] Example prompt sentence:

[1473] "Enter the user's health data and generate appropriate lifestyle improvement advice."

[1474] "Please provide a risk assessment for a user with a BMI of 27.7 and suggest preventative measures for diabetes."

[1475] This system allows users to accurately manage their own health data and receive advice on appropriate preventive measures and lifestyle improvements. Furthermore, health-related questions and inquiries are quickly responded to, improving the overall quality of health management.

[1476] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1477] Step 1:

[1478] Users log in to the application and enter health data such as height, weight, eating and drinking patterns, and amount of exercise. Examples of data entered by users include "height 170 cm, weight 80 kg, daily alcohol consumption, insufficient vegetable intake, 5,000 steps per day, and gym visits twice a week." The device temporarily stores the entered data in its memory.

[1479] Input: User's health data (height, weight, eating and drinking patterns, amount of exercise, etc.)

[1480] Output: Health data stored in the device memory

[1481] Specific operation: When a user enters health data into the application form and presses the input button, the data is temporarily saved in memory.

[1482] Step 2:

[1483] The device encodes the entered data and sends it to the server using a security protocol (e.g., HTTPS). The data is then encrypted with AES256 for secure communication. If the transmission is successful, the device receives a confirmation message from the server.

[1484] Input: Health data stored in the device memory

[1485] Output: Health data sent to the server and a confirmation message

[1486] Specific operation: Encodes the data into JSON format and sends it to the server using a secure communication protocol.

[1487] Step 3:

[1488] The server decodes the received health data and stores it in a database. The stored data is checked for blank spaces and abnormal values, and if an abnormality is detected, a message is sent to the device prompting re-entry.

[1489] Input: Health data received by the server

[1490] Output: Health data stored in the database and a message prompting you to re-enter (if there are abnormal values)

[1491] Specific behavior: Decodes and performs data integrity and range checks before saving to the database.

[1492] Step 4:

[1493] The server retrieves user data stored in a database and runs a data analysis program to calculate BMI, assess eating and drinking patterns, and check physical activity.

[1494] Input: Health data stored in a database

[1495] Output: Analysis results (BMI value, dietary pattern evaluation, exercise amount evaluation)

[1496] Specific operation: Based on the user's health data, calculate BMI as follows: 80kg / (1.7m 1.7m) = 27.7. Also calculates and evaluates eating and drinking patterns and exercise volume.

[1497] Step 5:

[1498] The server uses a generative AI model based on the analysis results to automatically generate advice for users on preventive measures and lifestyle improvements.

[1499] Input: Analysis results (BMI value, dietary pattern evaluation, exercise amount evaluation)

[1500] Output: Generated advice (e.g., reduce alcohol intake, add vegetables to each meal, walk at least 10,000 steps per day)

[1501] How it works: The analysis results are fed into an AI model to generate appropriate advice, which is customized to each individual user's situation.

[1502] Step 6:

[1503] The server sends the generated advice to the terminal and notifies the user, along with related articles and information.

[1504] Input: Generated advice

[1505] Output: Advice and related information posted to the terminal

[1506] Specific operation: The server notifies the user of advice from the generated AI model and sets reminders as needed.

[1507] Step 7:

[1508] Users can input their health-related questions or concerns into the application, and when the consultation is submitted, the device encodes the information and sends it to the server.

[1509] Input: User's health consultation details

[1510] Output: Consultation details sent from the device to the server

[1511] Specific operation: The content entered into the consultation box is encoded and sent again to the server via a secure protocol.

[1512] Step 8:

[1513] The server analyzes the received inquiry and generates an appropriate response using AI and a rule-based engine. For example, if a customer says, "I feel extremely tired when I wake up in the morning," the server will generate suggestions for improving their exercise and sleep quality.

[1514] Input: Consultation content received by the server

[1515] Output: The generated answer

[1516] Specific operation: The consultation content is passed to the AI ​​engine, which extracts and generates an appropriate answer based on related data.

[1517] Step 9:

[1518] The server sends the generated answer to the terminal and notifies the user.

[1519] Input: Generated Answer

[1520] Output: Answer reported to the terminal

[1521] Specific operation: The generated answer is notified to the device and instructions are provided so that the user can take immediate action.

[1522] Through the above processing steps, users can manage their own health conditions in real time and receive appropriate advice, while also being able to quickly respond to any health-related questions or inquiries.

[1523] (Application example 1)

[1524] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1525] The main purpose of conventional health management systems is to assess health risks based on users' health data and provide advice on preventative measures and lifestyle improvements. However, a user's health condition can also affect security awareness and risks, and no system has been available to address this. There is a need for a system that can address situations where a deterioration in health condition poses a security risk.

[1526] 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.

[1527] In this invention, the server includes means for a user to input their height, weight, eating and drinking patterns, and amount of exercise, means for collecting the input data and storing it in a database, means for evaluating health risks using the stored data, means for predicting future illnesses based on the health risks identified by the analysis means, means for generating advice on preventive measures and lifestyle improvements for the predicted illnesses, means for receiving health-related questions and inquiries from the user and generating corresponding answers, means for evaluating security risks in relation to the evaluated health risks and notifying the user, and means for generating warnings and advice according to the security risks, thereby enabling users to deal with both health risks and security risks.

[1528] "User" refers to an individual who uses this system and is responsible for managing health data and security risks.

[1529] "Height" refers to the length of the user's entire body, and is one of the basic physical data required for health analysis.

[1530] "Weight" is data for measuring the mass of the user's body, and is basic data used for health risk assessment.

[1531] "Eating and drinking patterns" are data that represent the user's food and drink intake status, and are important information for analyzing the user's health condition.

[1532] "Amount of exercise" refers to the degree of physical activity a user engages in in daily life or specific exercise, and is data used for health risk assessment.

[1533] A "means" refers to a method or device used to achieve a particular purpose.

[1534] A "database" is an electronic storage device for organizing, storing, and analyzing health data collected from users.

[1535] "Analytical tools" refers to the processes and tools used to analyze collected health data and assess health and security risks.

[1536] "Health risk" refers to the likelihood of future health problems or illnesses occurring based on the user's health status.

[1537] "Disease prediction means" refers to a method or tool for predicting future illnesses based on a user's health data.

[1538] "Preventive measures" refer to specific measures or steps taken to prevent predicted future diseases or health risks.

[1539] "Lifestyle advice" refers to specific instructions or advice regarding recommended habits or behaviors to improve a user's health.

[1540] The "health consultation receiving means" refers to a function or method for receiving health-related questions or inquiries from users and providing measures or advice depending on the content of those inquiries.

[1541] "Security risk" refers to the possibility of a security-related risk occurring based on the user's health condition.

[1542] "Security risk assessment means" refers to a method or device for analyzing a user's health data and assessing security risks.

[1543] "Warning" refers to a notification or message that alerts the user to a potential problem.

[1544] "Advice generation means" refers to the process or tool for generating specific measures or advice according to the user's situation.

[1545] This invention is a system that collects and analyzes a user's health data, evaluates health risks, and provides advice on preventive measures and lifestyle improvements. This system also evaluates security risks based on the user's health status and provides warnings and advice.

[1546] composition

[1547] The system includes the following means:

[1548] 1. A means for users to input their height, weight, eating and drinking patterns, and activity levels:

[1549] This is an application that is displayed on devices such as smartphones and PCs. Users input data such as their height (170 cm), weight (80 kg), daily alcohol intake, and walking 5,000 steps per day.

[1550] 2. A means of collecting the entered data and storing it in a database:

[1551] The terminal sends the entered data to the server, which receives the data and stores it in a database. If there are any abnormal values, the user is prompted to re-enter the data.

[1552] 3. Analytical tools to assess health risks:

[1553] The server uses the stored data to assess health risks, specifically by calculating the user's BMI and analyzing their eating and drinking patterns and physical activity.

[1554] 4. A method to predict future illnesses based on identified health risks:

[1555] The server then predicts illnesses based on the assessment results, for example predicting that a user with a high BMI is at risk of obesity or diabetes.

[1556] 5. Means of generating preventative and lifestyle advice:

[1557] For predicted illnesses, specific preventive measures and advice on lifestyle improvements are generated and sent to the user's device. For example, advice such as reducing alcohol intake or walking at least 10,000 steps per day could be considered.

[1558] 6. A means of accepting health-related questions and inquiries from users and generating corresponding answers:

[1559] The device sends the user's inquiry to a server, which uses AI and a rule-based engine to generate an appropriate response, such as advice on improving sleep quality or lack of exercise to combat morning fatigue.

[1560] 7. Means of assessing and informing users of security risks in relation to health risks:

[1561] The server evaluates the user's security risk based on the health risk assessment results. If the stress or fatigue level is high, the server notifies the user that their security awareness has decreased.

[1562] 8. Means of generating warnings and advice based on security risks:

[1563] The server generates specific warnings and advice based on the security risk and notifies the user. For example, if the stress level is high, the server provides advice such as "avoid making big decisions and get adequate rest."

[1564] Specific examples

[1565] As a concrete example, consider a scenario in which a user enters the following data into an application:

[1566] Height: 170cm

[1567] Weight: 80kg

[1568] Alcohol consumption: 30ml / day

[1569] Steps: 5,000 steps / day

[1570] Gym visits: 2 times / week

[1571] Stress level: 50

[1572] Fatigue level: 40

[1573] The server receives this data, assesses health risks (e.g., risk of high blood pressure or diabetes based on a BMI of 27.7), and provides specific advice to the user (e.g., reduce alcohol intake or add 200g of vegetables to each meal).

[1574] Additionally, based on the same data, security risks (for example, security advice such as "take regular breaks" due to moderate stress and fatigue levels) are also provided.

[1575] Prompt Sentence Examples

[1576] A possible prompt for a generative AI model might look something like this:

[1577] "Generate a Python program that evaluates security risks based on the health data of user ID 1 (height: 170cm, weight: 80kg, alcohol intake: 30ml / day, steps: 5000 / day, gym visits: 2 times / week, stress level: 50, fatigue level: 40) and provides appropriate advice."

[1578] By using this prompt, the appropriate AI model can be used to analyze individual health conditions and security risks, and specific advice can be provided to the user.

[1579] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1580] Step 1:

[1581] Users enter their health data (height, weight, eating and drinking patterns, amount of exercise, stress level, and fatigue level) into a smartphone or PC application. For example, input data might be: height 170 cm, weight 80 kg, alcohol intake 30 ml / day, steps taken 5,000 / day, gym visits 2 times / week, stress level 50, and fatigue level 40. This data will be used to assess future health and security risks.

[1582] Step 2:

[1583] The terminal sends the entered data to the server. At this time, the data is checked for abnormal values ​​and blank spaces, and if there are any abnormalities, the user is prompted to re-enter the data. The data is checked for abnormal values ​​by, for example, checking the range based on BMI calculations.

[1584] Step 3:

[1585] The server stores the received data in a database, formatting the data before storing it and preprocessing it as needed, so that subsequent analysis can proceed smoothly.

[1586] Step 4:

[1587] The server uses the stored data to assess health risks. For example, it calculates BMI and analyzes eating and drinking patterns and exercise volume. Specifically, it calculates BMI using the user's weight and height, and assesses health risks based on the results. The results of this assessment are accumulated as, for example, the risk of obesity or diabetes.

[1588] Step 5:

[1589] The server then predicts future illnesses based on the identified health risks. For example, a BMI of 27.7 predicts a high risk of obesity, high blood pressure, and diabetes. This prediction is saved as an analysis result and serves as the basis for notifications to the user.

[1590] Step 6:

[1591] The server generates advice on preventive measures and lifestyle improvements for predicted illnesses. For example, specific advice such as reducing alcohol intake or walking at least 10,000 steps per day is generated. This advice is sent to the user's device to promote health improvement.

[1592] Step 7:

[1593] When a user inputs a health-related question into the application, the device sends the content to the server. The server then analyzes the question using AI and a rule-based engine and generates a response appropriate to the question. For example, if a user comments that they are "unable to get rid of morning fatigue," the server generates advice such as "stretching every day and improving the quality of their sleep."

[1594] Step 8:

[1595] The server evaluates security risks based on the health risk assessment results. If the user's stress or fatigue level is high, it determines that the user's security awareness is declining and issues a warning. This evaluation is performed taking into account the relationship between the user's health state and security awareness.

[1596] Step 9:

[1597] The server generates warnings and advice based on security risks. For example, if the user's stress level is high, specific advice such as "avoid making big decisions and get adequate rest" is sent to the user's device. This notification helps users to increase their security awareness.

[1598] This series of steps will create a system that allows users to receive comprehensive health management and security risk assessment based on their health data, thereby improving both the quality of life and safety of users.

[1599] 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.

[1600] The system of the present invention not only collects and analyzes a user's health data and provides advice on preventive measures and lifestyle improvements based on health risks, but also recognizes the user's emotions and performs comprehensive health management. An embodiment of the present invention will be described in detail below.

[1601] User data entry and collection

[1602] Users log in to the application using a device such as a smartphone or PC and enter their health data, such as their height, weight, eating and drinking patterns, and amount of exercise. They can also enter questions or concerns about their health. For example, a user might enter data such as height 170 cm, weight 80 kg, daily alcohol consumption, a diet lacking in vegetables, 5,000 steps per day, and going to the gym twice a week.

[1603] The terminal transmits the input data to the server, where the transmitted data is communicated using a secure protocol.

[1604] Data storage and preprocessing

[1605] The server saves the received data in a database. After saving, it checks for blanks and outliers, and performs completion and filtering as necessary. If an outlier is detected, the server prompts the user to re-enter the data.

[1606] Data analysis and health risk assessment

[1607] The server analyzes the collected data, including the following specific analysis items:

[1608] Calculate BMI: BMI = weight (kg) / (height (m) height (m)). For example, 80kg / (1.7m 1.7m) = 27.7.

[1609] Analysis of dietary patterns: Assessment of calorie surpluses and nutritional deficiencies. For example, alcohol intake and vegetable intake are compared with baseline values.

[1610] Physical activity assessment: Analyze the number of steps taken and frequency of gym visits and compare them with the recommended standard (e.g., 10,000 steps per day).

[1611] This allows the user to assess their health risks and predict future illnesses. A BMI of 27.7 indicates a risk of obesity, diabetes, and high blood pressure.

[1612] Emotion recognition by emotion engine

[1613] The server is equipped with an emotion engine that estimates the user's emotional state based on the user's input data and behavioral data. For example, when a user inputs detailed records of their diet and exercise, the server infers the user's emotional state from the frequency and content of these records. The server can also extract the user's emotions from the content of questions and inquiries.

[1614] Comprehensive health assessment and prevention

[1615] The server integrates health data and emotional data to perform a comprehensive health risk assessment and generate specific advice on preventative measures and lifestyle improvements. For example, based on the analysis of the emotional engine, it generates specific advice such as, "You've been feeling stressed recently, so try incorporating relaxation techniques."

[1616] Providing advice

[1617] The device will notify the user of the generated advice via push notification, email, or in-app notification, and will also provide related articles and information, making it easier for users to obtain the knowledge they need.

[1618] Responding to inquiries from users

[1619] When a user wants to input a question or inquiry about their health, the user inputs the question via the application. For example, a question such as "I've been feeling very tired when I wake up in the morning lately" may be input.

[1620] The device sends the consultation details to the server, which uses an emotion engine to analyze the details and generates an appropriate response using an AI engine and rule-based system. For example, if the cause is thought to be lack of exercise or poor sleep quality, the server will suggest daily stretching and improvements to the sleep environment, and recommend seeing a doctor.

[1621] As described above, the system of the present invention realizes a system that grasps the user's health condition in real time and provides comprehensive preventive measures and advice that also take into account the user's emotional state.

[1622] The processing flow will be explained below.

[1623] Step 1:

[1624] Users log in to the application and enter their health data, such as their height, weight, eating and drinking patterns, and exercise volume. They can also enter questions and concerns about their health. For example, a user might enter data such as height 170 cm, weight 80 kg, daily alcohol consumption, lack of vegetable intake, 5,000 steps per day, and gym visits twice a week.

[1625] Step 2:

[1626] The terminal sends the data entered by the user to the server using a secure communication protocol such as HTTPS.

[1627] Step 3:

[1628] The server saves the received data in a database. After saving, it checks whether there are any blank spaces or abnormal values ​​in the data, and if there are any abnormalities, it prompts the user to re-enter the data.

[1629] Step 4:

[1630] The server analyzes the stored data. The specific analysis items are as follows:

[1631] Calculating BMI: BMI = weight (kg) / (height (m) height (m))

[1632] For example, 80kg / (1.7m 1.7m) = 27.7

[1633] Analysis of eating and drinking patterns: Assessment of calorie surplus and nutritional deficiencies

[1634] Check your exercise volume: Analyze your steps and gym visit frequency and compare them with the recommended standard (e.g., 10,000 steps per day)

[1635] Step 5:

[1636] The server uses an emotion engine to infer the user's emotional state from input data and behavioral data. For example, it infers emotions from dietary habits and exercise records, and incorporates these into the analysis results.

[1637] Step 6:

[1638] The server integrates health and emotional data to perform a comprehensive health risk assessment. For example, if a BMI of 27.7 indicates a high risk of obesity, diabetes, and high blood pressure, and predicts a high stress level, the server takes this into account when conducting a risk assessment.

[1639] Step 7:

[1640] The server generates specific preventive measures and lifestyle improvement advice based on the assessment results, for example:

[1641] Reduce your alcohol intake to one drink a day

[1642] "Add 200g of vegetables to each meal"

[1643] "Walk more than 10,000 steps a day"

[1644] Implement relaxation techniques to reduce stress

[1645] Step 8:

[1646] The device notifies the user of the generated advice via push notification, email, or in-app notification, and also provides related articles and information.

[1647] Step 9:

[1648] When a user inputs a question or question about health into the application, the user may input, for example, "I've been feeling very tired when I wake up in the morning lately."

[1649] Step 10:

[1650] The terminal transmits the contents of the user's consultation to the server.

[1651] Step 11:

[1652] The server analyzes the consultation using an emotion engine and generates appropriate responses using an AI engine and rule-based system. For example, if the user feels tired in the morning, the server may suggest daily stretching and improvements to the sleep environment, and recommend a doctor's visit, as this may be due to a lack of exercise or poor sleep quality.

[1653] Step 12:

[1654] The terminal notifies the user of the generated answer, allowing the user to take appropriate action.

[1655] In this way, the system of the present invention can grasp the user's health condition in real time and provide comprehensive preventive measures and advice that also take into account the user's emotional state.

[1656] Example 2

[1657] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1658] Conventional health management systems are limited to predicting health risks based on a user's basic health data (height, weight, eating and drinking patterns, and amount of exercise) and providing advice on preventive measures and lifestyle improvements. However, these systems lack comprehensive health risk assessments that take into account the user's emotional state, and the provision of advice that includes the user's emotional state. Taking into account the user's emotional state is expected to enable more appropriate and effective health management, so this issue needs to be resolved.

[1659] 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.

[1660] In this invention, the server includes means for inputting a user's height, weight, eating and drinking patterns, and amount of exercise, means for collecting the input data and storing it in a database, means for evaluating health risks using the stored data, means for predicting future illnesses based on the health risks identified by the analysis means, means for generating advice on preventive measures and lifestyle improvements for the predicted illnesses, means for estimating the user's emotional state based on the input data and behavioral data, means for integrating the health data and emotional data to evaluate overall health risks and generating specific advice on preventive measures and lifestyle improvements, and means for receiving health-related questions and inquiries from the user and generating corresponding answers, thereby enabling comprehensive health management that also takes the user's emotional state into consideration.

[1661] "User data" refers to health-related information such as height, weight, eating and drinking patterns, and amount of exercise input by the user.

[1662] The "collection means" is a method or device for collecting data entered by a user and transmitting it to a server.

[1663] A "database" is a repository of information for storing collected user data.

[1664] "Analysis means" refers to methods and devices for analyzing collected data and assessing health risks.

[1665] "Health risks" are predictions of diseases or symptoms that may occur in the future based on the user's health condition.

[1666] A "prediction means" is a method or device for predicting future diseases or symptoms based on the health risks identified by the analysis means.

[1667] The "advice generation means" refers to a method or device for generating advice on preventive measures and lifestyle improvements based on the results of health risk assessment and disease prediction.

[1668] The "emotional state" refers to the user's emotional or mental state estimated based on the user's input data and behavioral data.

[1669] The "emotion estimation means" is a method or device for estimating an emotional state based on input data and behavioral data of a user.

[1670] An "integrated assessment means" is a method or device for integrating health data and emotional data to perform a comprehensive health risk assessment.

[1671] The "inquiry response means" is a method or device for receiving health-related questions or inquiries from users and generating corresponding answers.

[1672] The system of the present invention not only collects and analyzes a user's health data and provides advice on preventive measures and lifestyle improvements based on health risks, but also recognizes the user's emotions to provide comprehensive health management. An embodiment of this system is described in detail below.

[1673] User data entry and collection

[1674] Users log in to the application using a device such as a smartphone or PC. They input health data such as their height, weight, eating and drinking patterns, and amount of exercise. Specifically, they input data such as "height 170 cm, weight 80 kg, daily alcohol consumption, diet lacking in vegetables, 5,000 steps per day, gym visits twice a week." Users can also input health-related questions and concerns.

[1675] The terminal collects the data entered by the user in real time and transmits it to a server using a secure protocol (e.g., HTTPS).

[1676] Data storage and preprocessing

[1677] The server stores the received data in a dedicated database. After storing the data, the server checks for blanks and abnormal values. For example, it can detect abnormally high height or extremely low weight.

[1678] If an abnormal value is detected, the server generates a message to prompt the user to re-enter the data and sends it to the terminal. The user receives the message and re-enters the correct data.

[1679] Data analysis and health risk assessment

[1680] The server analyzes the user data stored in the database, including:

[1681] 1. Calculating BMI: The server calculates BMI based on weight and height data using the following formula:

[1682] BMI = Weight (kg) / (Height (m) Height (m))

[1683] For example, if you weigh 80 kg and are 1.7 m tall, your BMI = 80 / (1.7 1.7) = 27.7.

[1684] 2. Analysis of eating and drinking patterns: The server analyzes the user's eating and drinking data and evaluates calories and nutritional balance. For example, "daily alcohol consumption" can be judged as excessive calories, and "a diet lacking in vegetables" can be judged as a risk of nutritional deficiency.

[1685] 3. Exercise assessment: The server analyzes step count data and gym visit frequency and compares it with the recommended standard (e.g., 10,000 steps per day). For example, if a user only walks 5,000 steps per day, it can assess that the user is not getting enough exercise.

[1686] Emotion recognition by emotion engine

[1687] The server is equipped with an emotion engine that infers the user's emotional state based on their input data and behavioral data. For example, if a user frequently enters detailed records of their diet and exercise, the server can infer their emotions from the frequency of their input and the use of words.

[1688] Comprehensive health assessment and prevention

[1689] The server integrates health and emotional data to perform a comprehensive health risk assessment and generate specific advice on preventative measures and lifestyle improvements. For example, based on the analysis results of the emotion engine, it generates specific advice such as, "You've been feeling stressed recently, so try incorporating relaxation techniques."

[1690] Providing advice

[1691] The device will notify the user of the advice sent from the server via push notification, email, or in-app notification, and will also provide related articles and information, making it easy for users to obtain the knowledge they need.

[1692] Responding to inquiries from users

[1693] The user inputs questions or concerns about their health via the application. For example, they may input a question such as, "Recently, I've been feeling extremely tired when I wake up in the morning."

[1694] The device sends the user's consultation details to the server. The server uses an emotion engine to analyze the consultation details and utilizes an AI engine and rule-based system to generate an appropriate response. For example, if the consultation details are thought to be caused by a lack of exercise or poor quality sleep, the server will suggest "daily stretching and improving your sleep environment" and recommend seeing a doctor.

[1695] Examples of prompt statements

[1696] This system can understand the user's health condition in real time and provide comprehensive preventive measures and advice that take into account their emotional state. As a specific example, by inputting the following prompt sentences into the generative AI model, more detailed advice can be obtained:

[1697] "If a user reports feeling stressed recently, what relaxation techniques should we suggest?"

[1698] This prompt allows the AI ​​model to provide specific relaxation advice.

[1699] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1700] Step 1:

[1701] Users log in to the application using a device such as a smartphone or PC. In the application, they enter their own health data, such as their height, weight, eating and drinking patterns, and amount of exercise. For example, they might enter data such as "height 170 cm, weight 80 kg, daily alcohol consumption, diet lacking in vegetables, 5,000 steps per day, and going to the gym twice a week." The entered data is temporarily stored in the device's memory.

[1702] Step 2:

[1703] The device sends the data entered by the user to the server using a secure protocol (e.g., HTTPS). The sent data is temporarily stored in the server's memory. At this stage, the input is the health data entered by the user, and the output is the health data sent to the server.

[1704] Step 3:

[1705] The server stores the received data in a dedicated database. Once the stored data is confirmed, it moves on to the next step. In this step, the input is the health data sent from the device, and the output is the health data stored in the database.

[1706] Step 4:

[1707] The server checks the data stored in the database to see if there are any blank spaces or abnormal values. For example, it checks to see if any abnormal values ​​have been entered for height or weight. If an abnormal value is detected during this process, the server generates a message prompting the user to re-enter the data and sends it to the terminal. The message requesting re-entry is displayed to the user. The input in this step is the health data stored in the database, and the output is the result of the abnormal value check and a message requesting the user to re-enter the data.

[1708] Step 5:

[1709] The user receives a re-entry request message from the server and re-enters the correct data. The re-entered data is again sent from the terminal to the server and stored in the database. The input in this step is the re-entry request message, and the output is the corrected health data.

[1710] Step 6:

[1711] The server uses the available data stored in the database to assess the health risk as follows:

[1712] 1. Calculate BMI: Calculate BMI from user's weight and height. For example, if user weighs 80kg and is 1.7m tall, BMI = 80 / (1.7 1.7) = 27.7.

[1713] 2. Analysis of eating and drinking patterns: Evaluate the calorie intake and nutritional balance of the user. For example, if the user consumes alcohol every day, it will be determined that the user is consuming too many calories.

[1714] 3. Exercise assessment: Analyzes step count data and gym visit frequency and compares it with the recommended standard (e.g., 10,000 steps per day). For example, if a user only walks 5,000 steps per day, it is assessed as not getting enough exercise.

[1715] The input in this step is the health data stored in the database, and the output is the result of the health risk assessment.

[1716] Step 7:

[1717] The server is equipped with an emotion engine that estimates the user's emotional state based on the user's input data and behavioral data. For example, if a user frequently enters detailed records of their diet and exercise, the engine can infer the user's emotional state from the frequency of their input and the use of words. The input in this step is the user's health and behavioral data, and the output is an estimate of the user's emotional state.

[1718] Step 8:

[1719] The server integrates health data and emotional data to perform a comprehensive health risk assessment and generate specific preventive measures and advice for improving lifestyle habits. For example, it generates advice such as, "Because your BMI is high and you're under stress, improve your diet and adopt relaxation techniques." The input in this step is health data and emotional data, and the output is specific preventive measures and advice.

[1720] Step 9:

[1721] The device notifies the user of the advice sent from the server. Notification methods include push notifications, emails, and in-app notifications. Related articles and information are also provided to help users obtain the necessary knowledge. The input in this step is the advice sent from the server, and the output is the notification sent to the user.

[1722] (Application example 2)

[1723] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1724] Conventional health management systems assess health risks by collecting and analyzing users' physical and lifestyle information, but lack the functionality to provide advice that takes emotional state into account or to suggest individually optimal meal plans. As a result, they often fail to adequately manage users' overall health and end up only providing incomplete, partial advice. Furthermore, the lack of a system that proposes customized meal plans based on health risks and emotional state makes it difficult to effectively support users in improving their health.

[1725] 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.

[1726] In this invention, the server includes means for a user to input their own physical information and lifestyle data, means for collecting and storing the input data, means for evaluating health risks using the stored data, means for predicting future diseases based on the health risks identified by the analysis means, means for generating advice on preventive measures and lifestyle improvements for the predicted diseases, means for receiving health-related questions and inquiries from the user and generating corresponding answers, means for analyzing the user's emotional state and providing advice based on the emotional state, and means for individually suggesting an optimal meal menu based on the health risks and emotional data. This enables comprehensive health management for the user and realizes individually optimized advice on preventive measures and lifestyle improvements and suggestions for a meal menu.

[1727] "User" refers to an individual who utilizes the System to enter and collect their own health care information.

[1728] "Physical information" refers to basic physical data such as a user's height, weight, and body mass index (BMI).

[1729] "Lifestyle data" refers to information related to a user's daily life, such as their eating and drinking patterns, amount of exercise, sleep time, smoking and drinking habits, etc.

[1730] "Data collection means" refers to a device or system that has the function of collecting physical information and lifestyle habit data entered by the user and incorporating it into the system.

[1731] "Storage means" refers to a database within the system that has the functionality to safely and effectively store collected data.

[1732] "Analysis Tools" refers to algorithms or programs that utilize collected and stored data to assess a user's health risks and emotional state.

[1733] "Health risk" refers to the possibility of a user contracting a disease or experiencing a worsening health condition in the future, as analyzed based on collected data.

[1734] "Preventive measures" refer to specific behavioral or habit changes recommended based on the analysis results to reduce the risk of developing a disease.

[1735] "Lifestyle improvement advice" refers to guidance or suggestions provided to a user to change their lifestyle to a healthier one.

[1736] "Emotional state" refers to information analyzed by the emotion engine that indicates the user's psychological health and mood.

[1737] "Answer generation means" refers to the algorithms and rule-based systems within the system that generate appropriate answers to user doubts and questions.

[1738] The "meal menu suggestion means" refers to a part of the system that has the function of providing an optimal meal plan based on the user's health and emotional state.

[1739] The system of the present invention collects physical information and lifestyle data of a user, performs health risk assessment and emotion analysis, and proposes specific meal menus. This system is realized with the following configuration and processing.

[1740] User data entry and collection

[1741] Users log in to the application using a device such as a smartphone, PC, or wearable device and enter their physical and lifestyle information, such as height, weight, eating and drinking patterns, and amount of exercise. They can also enter health-related questions and concerns. For example, if a user enters their height as 170 cm, weight as 80 kg, daily alcohol consumption, a diet lacking in vegetables, 5,000 steps per day, and gym visits twice a week, the device sends this data to the server. The transmitted data is communicated using a secure protocol.

[1742] Data storage and preprocessing

[1743] The server saves the received data in a database. After saving, it checks for blanks and outliers, and performs completion and filtering as necessary. If an outlier is detected, the server prompts the user to re-enter the data.

[1744] Data analysis and health risk assessment

[1745] The server analyzes the collected data, including the following specific analysis items:

[1746] Calculate BMI: BMI = weight (kg) / (height (m) height (m)). For example, 80kg / (1.7m 1.7m) = 27.7.

[1747] Analysis of dietary patterns: Assessment of calorie surpluses and nutritional deficiencies. For example, alcohol intake and vegetable intake are compared with baseline values.

[1748] Physical activity assessment: Analyze the number of steps taken and frequency of gym visits and compare them with the recommended standard (e.g., 10,000 steps per day).

[1749] This allows the server to assess the user's health risks and predict future illnesses. For example, if the BMI is 27.7, it is determined that the user is at risk of obesity, diabetes, and high blood pressure.

[1750] Emotion recognition by emotion engine

[1751] The server is equipped with an emotion engine that estimates the user's emotional state based on the user's input data and behavioral data. For example, when a user inputs detailed records of their diet and exercise, the server infers the user's emotional state from the frequency and content of these records. The server can also extract the user's emotions from the content of questions and inquiries.

[1752] Comprehensive health assessment and preventative care

[1753] The server integrates health data and emotional data to perform a comprehensive health risk assessment and generate specific advice on preventative measures and lifestyle improvements. For example, based on the analysis of the emotional engine, it generates specific advice such as, "You've been feeling stressed recently, so try incorporating relaxation techniques."

[1754] Meal menu suggestions

[1755] The server then proposes an optimal meal plan based on the individual's health risk and emotional data. For example, if the individual is at high risk, it will propose a low-calorie, high-nutrition menu, and if the individual is at low risk, it will propose a balanced, regular menu.

[1756] Examples of specific prompts are:

[1757] The user's height is 170cm, weight is 80kg, eating habits are high in fat, and exercise habits are low. The user also says, "I've been feeling a lot of stress lately..." Based on this data, assess health risks and suggest an appropriate meal plan.

[1758] This will enable comprehensive health management for users and provide individually optimized preventative measures and meal menus.

[1759] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1760] Step 1:

[1761] A user logs in to the application using a device such as a smartphone or computer.

[1762] Input: User's height, weight, eating and drinking patterns, amount of exercise, health questions and concerns.

[1763] How it works: The user enters this data into the application's input form.

[1764] Output: The entered data is recorded on the terminal.

[1765] Step 2:

[1766] The terminal transmits the input data to the server.

[1767] Input: User data entered in step 1.

[1768] How it works: The device sends data to the server using a secure protocol.

[1769] Output: The data is passed to the server.

[1770] Step 3:

[1771] The server stores the received data in a database.

[1772] Input: User data received in step 2.

[1773] Operation: The server saves data to the database and checks whether the saved contents are normal.

[1774] Output: The data is stored in a database, and outlier detection and imputation are performed as needed.

[1775] Step 4:

[1776] The server analyzes the stored data and assesses health risks.

[1777] Input: User data stored in the database.

[1778] How it works: The server calculates BMI, analyzes eating patterns (assessing calorie surpluses and nutritional deficiencies), and assesses physical activity.

[1779] Output: Health risk assessment results. For example, if your BMI is 27.7, you are at high risk for obesity, diabetes, and high blood pressure.

[1780] Step 5:

[1781] The server uses an emotion engine to analyze the user's emotional state.

[1782] Input: Consultation details and behavioral data stored in the database.

[1783] How it works: The server uses an emotion engine to extract emotions from text data, for example identifying stress states from input such as "I've been feeling stressed lately."

[1784] Output: The result of the emotion analysis is obtained. For example, the emotional state is analyzed as "high stress."

[1785] Step 6:

[1786] The server integrates the health risk assessment results and emotion analysis results to perform a comprehensive health risk assessment.

[1787] Input: Health risk assessment results from step 4 and sentiment analysis results from step 5.

[1788] How it works: The server combines these results to perform a comprehensive risk assessment.

[1789] Output: A comprehensive health risk assessment result is obtained. For example, if a person has a high risk of obesity and high stress, the overall risk will be assessed as very high.

[1790] Step 7:

[1791] Based on the overall evaluation results, the server generates specific advice on preventive measures and lifestyle improvements.

[1792] Input: Overall health risk assessment results obtained in Step 6.

[1793] How it works: The server uses the generative AI model to generate specific advice to address the risk, such as "You've been feeling stressed lately, so try some relaxation techniques."

[1794] Output: Advice on preventive measures and lifestyle changes.

[1795] Step 8:

[1796] The server will suggest optimal meal plans for each individual based on health risks and emotional data.

[1797] Input: Health risk assessment results and emotion data analyzed in Step 4 and Step 5.

[1798] Operation: The server generates and proposes a meal menu tailored to the user's health condition. For example, if the user is at high risk, it proposes a low-calorie, high-nutrition menu.

[1799] Output: Personalized optimized meal menu suggestions.

[1800] Step 9:

[1801] The device will notify the user of suggested preventative measures, advice on lifestyle changes, and meal plans.

[1802] Input: Step 7 advice and step 8 meal menu.

[1803] How it works: The device provides information to the user via push notifications, emails, and in-app notifications.

[1804] Output: The user checks the advice and meal menu through the app.

[1805] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1806] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1807] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1808] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1809] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1810] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1811] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1812] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / na...

Claims

1. means for a user to input their height, weight, eating and drinking patterns, and amount of exercise; means for collecting the input data and storing it in a database; an analysis means for assessing health risks using the stored data; A means for predicting a disease that the person may suffer from in the future based on the health risks identified by the analysis means; A means for generating advice on preventive measures and lifestyle improvements for the predicted disease; A system including a means for accepting health-related questions and inquiries from a user and generating corresponding answers.

2. 2. The system according to claim 1, wherein said data collection means includes means for checking for abnormal values ​​in the data entered by the user and, if an abnormality is found, forcing the user to re-enter the data.

3. 2. The system according to claim 1, wherein the analysis means includes means for calculating the user's BMI, analyzing eating and drinking patterns, and checking the amount of exercise.

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