System

Abstract

A system is provided.SOLUTION: A system comprising: input means for inputting age and medical examination data of a user; transmission means for transmitting data inputted by the input means to a server; prediction means for analyzing data received via the transmission means to predict a disease risk; countermeasure generation means for generating a countermeasure based on the risk obtained by the prediction means; and presentation means for presenting result data generated by the countermeasure generation means to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, the early detection and prevention of lifestyle-related and chronic diseases is extremely important. However, there is a lack of systems that predict future disease risks based on individual health checkup results and provide specific improvement measures. As a result, many people find it difficult to accurately understand their health risks and take appropriate preventive measures. [Means for solving the problem]

[0005] This invention provides a system that allows a user to input their age and health checkup data, predicts disease risk based on the data, and provides specific improvement measures. The system includes an input means for the user to input their age and health checkup data, a transmission means for transmitting the input data to a server, a prediction means for analyzing the data and predicting disease risk, a measure generation means for generating improvement measures based on the risk, and a presentation means for presenting the improvement measures to the user. The prediction means uses a statistical model or machine learning model, and the measure generation means includes specific dietary improvement measures, exercise suggestions, and recommendations for doctor visits. This allows users to easily understand their health risks and take specific improvement measures.

[0006] "Input means" refers to a device or interface that allows a user to input their age and health checkup data into the system.

[0007] The "transmission means" is a function or device for transmitting data input by the input means to the server.

[0008] The "prediction means" is a function or device for predicting disease risk using statistical models or machine learning models based on data sent to the server.

[0009] The "measure generation means" is a function or device for generating specific improvement measures based on the disease risk obtained by the prediction means.

[0010] The "presentation means" is a device or interface for visually presenting the improvement measures generated by the measure generation means to the user.

[0011] A "statistical model" is a mathematical model that analyzes probabilities and trends based on past data and predicts future risks.

[0012] A "machine learning model" is a computational model that uses large amounts of data to train algorithms to perform tasks such as prediction and classification.

[0013] "Health checkup data" refers to various numerical data and records that represent the user's health condition. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention is a system that allows a user to input their age and health checkup data, predicts disease risk based on the data, and suggests specific measures to improve the risk. Specific embodiments of the present invention will be described below.

[0036] The user uses the terminal interface to input their age and health check data, such as total cholesterol level, blood pressure, weight, etc. This data is entered into the system through the input means of the terminal.

[0037] The terminal receives the input data and sends it to the server. The sending means plays this role, creating an HTTP request to send the data to the server and communicating. The server processes the received data and stores it in a database for analysis.

[0038] The server predicts disease risk based on the submitted data. Statistical and machine learning models are used to predict the user's future disease risk based on their age and health checkup data. For example, if high cholesterol or high blood pressure indicates a risk of cardiovascular disease, the server identifies this risk.

[0039] Next, the server generates specific improvement measures based on the disease risk prediction results. This function is performed by the measure generation means, which generates specific dietary improvements, exercise suggestions, and even measures recommending regular doctor visits. For example, a user with high cholesterol levels would be recommended to "reduce salt intake and engage in moderate exercise."

[0040] The improvement measures and prediction results generated by the server are sent to the user via a presentation device. The device receives this data and visually displays it to the user, allowing the user to easily understand their own health risks and the measures they need to take, and take action to improve their health.

[0041] As a specific example, consider the case where a user is "50 years old" and inputs the following health checkup data: "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," and "weight: 80 kg." The user inputs the data into the terminal, and the transmission means sends this data to the server. The server receives the data, analyzes it using statistical and machine learning models, and predicts that "high cholesterol and high blood pressure result in a high risk of cardiovascular disease." The countermeasure generation means generates improvement countermeasures recommending "a low-salt diet, aerobic exercise, and receiving medical advice," and the presentation means presents these to the user. The user can check the results and countermeasures on the terminal and improve their lifestyle habits based on the recognized risks.

[0042] In this way, the system of the present invention can accurately predict the user's health risks and provide specific remedial measures, thereby supporting the user in taking appropriate health care.

[0043] The processing flow will be explained below.

[0044] Step 1: User enters age and health data

[0045] The user enters their age and health check data (e.g., "50 years old," "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," "weight: 80 kg," etc.) into the device interface.

[0046] The terminal checks the validity of the format and content of the entered data, and if inappropriate data is entered, prompts the user to re-enter it.

[0047] Step 2: The device sends the data to the server

[0048] The device correctly formats the valid data and prepares it to be sent to the server as an HTTP request.

[0049] The device makes an HTTP request and sends the data to the server.

[0050] Step 3: The server receives the data

[0051] The server receives the HTTP request and analyzes the received data (age and health checkup data).

[0052] The server rechecks the validity of the data, and if it contains inappropriate data, it generates an error message and sends it to the terminal.

[0053] Step 4: The server runs the disease risk prediction algorithm

[0054] The server stores the relevant data in a database and invokes a disease risk prediction algorithm.

[0055] The predictive means in the server uses statistical and machine learning models to analyze health risks based on the data.

[0056] Predictive algorithms predict a user's risk of cardiovascular disease and other illnesses based on their specific health check data (e.g., total cholesterol, blood pressure).

[0057] Step 5: The server generates the prediction results and improvement measures

[0058] The server analyzes the prediction results and generates specific improvement measures based on the risks.

[0059] The countermeasure generation means generates countermeasures corresponding to the predicted risk of the user (for example, since the cholesterol level is high, a low-salt diet is recommended, and moderate aerobic exercise is recommended).

[0060] The server generates a detailed report containing the measures taken.

[0061] Step 6: The server sends the report to the device

[0062] Create an HTTP response to send the report (prediction results and specific improvement measures) generated by the server to the terminal.

[0063] The server sends the report to the device via an HTTP response.

[0064] Step 7: The device displays the report to the user

[0065] The terminal analyzes the report received from the server and displays it to the user in a visually easy-to-understand format.

[0066] The user checks the report and understands the predicted health risks and the corresponding remedial measures.

[0067] Example 1

[0068] 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."

[0069] Health management is becoming an increasingly important issue in modern society. In particular, individuals living busy lives need systems that efficiently manage daily health data and provide appropriate health risk predictions and improvement advice based on that data. However, existing systems are difficult to use because they require users to analyze their own health data and find appropriate measures, requiring significant effort and knowledge. This creates problems such as delays in the early detection of health risks and the implementation of improvement actions.

[0070] 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.

[0071] In this invention, the server includes input means for a user to input their age and health checkup data, transmission means for transmitting the data input using the input means to the server, database storage means for saving and formatting the data received via the transmission means, prediction means for analyzing the data saved using the storage means with a statistical model or a machine learning model to predict disease risk, measure generation means for generating improvement measures using a generative AI model based on the risk obtained by the prediction means, and presentation means for presenting the result data generated by the measure generation means to the user. This allows users to easily analyze their own health data and obtain specific and feasible improvement measures.

[0072] "User" refers to an individual who uses the system to manage their own health data and receive disease risk predictions and improvement measures.

[0073] "Input means" refers to the device or software that allows a user to input age and health check data into the system.

[0074] The "transmission means" refers to a communication mechanism or protocol for transmitting data input using the input means to the server.

[0075] "Database Storage Means" means a database and associated software and hardware for storing and formatting data received via Transmission Means.

[0076] "Statistical model" refers to a mathematical model used to predict a user's health risk based on past data.

[0077] A "machine learning model" refers to a type of artificial intelligence that uses algorithms to automatically learn from large amounts of data and make predictions.

[0078] "Prediction tool" refers to a device or algorithm that uses statistical or machine learning models to predict a user's risk of disease.

[0079] A "generative AI model" refers to a model that uses artificial intelligence to generate improvement measures based on a user's health data.

[0080] The "measure generation means" refers to a device or algorithm that generates specific improvement measures based on the risks obtained by the prediction means.

[0081] The "presentation means" refers to a device or interface for presenting the result data generated by the countermeasure generation means to the user.

[0082] The present invention is a system that allows a user to input their age and health checkup data, predicts their risk of illness based on the data, and suggests specific measures to improve the situation. Specific embodiments for carrying out the present invention will be described below.

[0083] User Data Entry

[0084] The user uses their own terminal to input their age and health checkup data. At this time, an input form is provided on the terminal interface, and the user inputs their age and various health checkup data (e.g., total cholesterol level, blood pressure, weight, etc.). After inputting, the user presses the "send" button to send the data to the server.

[0085] Sending data

[0086] The terminal receives the data entered by the user, creates an HTTP request and sends it to the server, where the data is converted into JSON format and sent to the server over the Internet.

[0087] Data storage and analysis

[0088] The server stores the received data in a database. It uses a database engine (such as MySQL (registered trademark) or PostgreSQL) to format the data appropriately before storing it. The server then analyzes the stored data using statistical and machine learning models (using Python libraries such as scikit-learn and TENSORFLOW (registered trademark)) to predict disease risk.

[0089] Disease risk prediction

[0090] The server uses statistical and machine learning models to analyze disease risk based on the data received. For example, if high cholesterol or high blood pressure indicate a risk of cardiovascular disease, the server identifies these risks and predicts the user's risk of disease.

[0091] Generate remedial measures

[0092] Based on the predicted risks, the server uses a generative AI model (such as GPT-4 (registered trademark)) to generate specific remedial measures, including dietary improvements, exercise suggestions, and even recommendations for regular doctor visits. The remedial measures are presented to the user in an actionable and easy-to-understand format.

[0093] Presentation of results

[0094] The server sends the generated improvement measures and disease risk prediction results to the user's device, which then visually displays the received data, for example, in a pop-up window or notification area, making it easy for the user to understand.

[0095] Specific examples

[0096] For example, if a user is "50 years old" and enters the following health checkup data: "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," and "weight: 80 kg," the device converts this data into JSON format and sends it to the server. After receiving the data, the server stores it in a database and analyzes it using statistical and machine learning models. The analysis results predict that "high cholesterol and high blood pressure result in a high risk of cardiovascular disease," and a generative AI model is used to generate improvement measures recommending "a low-salt diet, aerobic exercise, and consultation with a doctor." This is then sent to the device and presented to the user in a pop-up window.

[0097] Prompt Sentence Examples

[0098] Examples of prompts that can be created using a generative AI model include:

[0099] Write a program for a system that predicts future disease risk and suggests specific measures to improve it based on a user's age and health checkup data. Data entered by the user includes age, total cholesterol level, blood pressure, and weight. Use statistical and machine learning models to predict disease risk, and recommend dietary improvements, exercise suggestions, and regular doctor visits as improvement measures.

[0100] In this way, the system of the present invention can accurately predict the user's health risks and provide specific remedial measures, thereby supporting the user in taking appropriate health management in advance.

[0101] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0102] Program processing flow

[0103] Step 1: User enters data

[0104] Users input their age and health checkup data through the terminal interface, including age, total cholesterol level, blood pressure, weight, etc.

[0105] Input: Age, total cholesterol level, blood pressure, weight, etc.

[0106] Output: User health data (JSON format)

[0107] Step 2: The device sends the data to the server

[0108] The terminal receives the data entered by the user, creates an HTTP request and sends it to the server, where the data is converted to JSON format and sent over the Internet.

[0109] Input: User's health data (JSON format)

[0110] Output: HTTP request

[0111] Step 3: The server stores and formats the data

[0112] The server stores the received data in a database and optionally formats the data, for example by using queries to convert the data into the appropriate format.

[0113] Input: User health data received as an HTTP request

[0114] Output: Formatted data stored in the database

[0115] Step 4: The server analyzes using statistical and machine learning models

[0116] The server analyzes the data stored in the database using statistical and machine learning models to predict disease risk, using Python libraries such as scikit-learn and TensorFlow.

[0117] Input: Formatted data stored in a database

[0118] Output: Disease risk prediction results

[0119] Step 5: The server generates improvement measures using the generated AI model

[0120] The server uses a generative AI model (such as GPT-4) based on the disease risk prediction results to generate specific improvement measures.

[0121] Input: Disease risk prediction results

[0122] Output: Generated remediation measures

[0123] Step 6: The server sends the results to the user's device

[0124] The server then sends the generated improvement measures and disease risk prediction results to the user's device. The data is again sent as an HTTP request.

[0125] Input: Generated remedial measures and predicted disease risk results

[0126] Output: HTTP request

[0127] Step 7: The device receives the results and displays them to the user

[0128] The device uses HTML and CSS to visually display the received data and present it to the user, typically using a pop-up window or notification area.

[0129] Input: Result data received as an HTTP request

[0130] Output: The visual representation presented to the user

[0131] (Application example 1)

[0132] 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."

[0133] Conventional health management systems have limited means for users to easily predict disease risk based on their own health checkup data and obtain specific measures to improve their condition. It is also difficult for users to receive effective training and dietary advice in real time at fitness gyms and other physical stores. This makes it difficult for users to comprehensively manage their own health status and take appropriate measures.

[0134] 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.

[0135] In this invention, the server includes input means for a user to input their age and health checkup data, transmission means for transmitting the data input using the input means to the server, prediction means for analyzing the data received via the transmission means to predict disease risk, and measure generation means for generating improvement measures based on the risk obtained by the prediction means. This allows users to manage their own health condition at the fitness gym and receive advice on training and diet in real time.

[0136] "User" refers to an individual who uses the system to manage their own health condition.

[0137] "Age" refers to the number of years calculated from the user's date of birth that the user enters into the system.

[0138] "Health checkup data" refers to various health checkup result data such as total cholesterol level, blood pressure, and weight for measuring the user's health condition.

[0139] "Input means" refers to a means that provides an interface for a user to input age and health data into the system.

[0140] The "transmission means" refers to a communication means for transmitting data input via the input means to the server.

[0141] "Server" refers to a central processing unit that analyzes received data and performs various calculations and predictions.

[0142] "Prediction means" refers to means that includes a statistical model or machine learning model for analyzing data received via the transmission means and predicting disease risk.

[0143] The "measure generation means" refers to a means for generating specific improvement measures based on the risks obtained by the prediction means.

[0144] The "presentation means" refers to a means for visually presenting the result data generated by the countermeasure generation means to the user.

[0145] "Feedback means" refers to a means by which users can manage their own health status at a fitness gym and receive training and dietary advice in real time.

[0146] A "statistical model" refers to a mathematical model that uses various statistical analyses based on past data to predict future disease risk.

[0147] "Machine learning model" refers to a model that incorporates artificial intelligence techniques to learn from data and use that knowledge to predict disease risk.

[0148] MODE FOR CARRYING OUT THE INVENTION

[0149] In this application example, a system is provided that allows a user to manage their own health condition at a fitness gym and receive training and dietary advice in real time. Specific embodiments for implementing this system are described below.

[0150] A user inputs his / her health checkup data using a smartphone or smart glasses. This interface is realized using an input means, and data such as age, total cholesterol level, blood pressure, weight, etc. For example, consider that a user inputs the following data using a smartphone:

[0151] Age: 50

[0152] Total cholesterol level: 240 mg / dL

[0153] Blood pressure: 150 / 90 mmHg

[0154] Weight: 80 kg

[0155] The input data is sent to the server via a sending means, which typically communicates using an HTTP request.

[0156] The server analyzes the received data and predicts disease risk using predictive methods, such as statistical and machine learning models, to determine risk based on the user's health status. For example, if high cholesterol or high blood pressure indicates a risk of cardiovascular disease, the server identifies this risk.

[0157] The countermeasure generator then generates specific remedial measures based on the risk, including specific dietary improvements, exercise suggestions, and recommendations to see a doctor. For example, a user with high cholesterol might be recommended to "consume a low-salt diet and engage in 30 minutes of aerobic exercise."

[0158] The result data generated by the countermeasure generation means is presented to the user using the presentation means. The user can check the results on their smartphone or smart glasses and make training plans at the fitness gym or meal plans. This allows the user to understand their own health condition in real time and take necessary countermeasures promptly.

[0159] As an example, the following prompt sentence can be used:

[0160] Example prompt sentence:

[0161] "A 50-year-old man has entered his health checkup data (total cholesterol level: 240 mg / dL, blood pressure: 150 / 90 mmHg, weight: 80 kg). Based on this data, please predict his disease risk and suggest specific measures to improve his condition."

[0162] The hardware used includes smartphones and smart glasses for users to input data. Specifically, iPhone® and Google® Glass® are examples. On the server side, machine learning libraries such as Scikit-learn are used for data analysis and prediction, and Django or Flask can be used as web frameworks. MySQL or PostgreSQL are also suitable as databases.

[0163] In this way, the system of the present invention helps users effectively manage their health at the fitness gym and receive appropriate training and dietary advice in real time.

[0164] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0165] Step 1:

[0166] The user inputs their age and health checkup data into the interface using a smartphone or smart glasses. The input data includes age, total cholesterol level, blood pressure, weight, etc. The input data is temporarily stored in the terminal's memory via the input means.

[0167] Step 2:

[0168] The terminal sends the input data to the server. An HTTP request is used as the transmission method. The data is converted to JSON format and sent to the server. Specifically, the following JSON data is sent.

[0169] json

[0170] {

[0171] "age": 50,

[0172] "total_cholesterol": 240,

[0173] "blood_pressure": "150 / 90",

[0174] "weight": 80

[0175] }

[0176] Step 3:

[0177] The server analyzes the received data. The server analyzes the received JSON data and extracts various health checkup data. This data is stored in an internal database and transferred to the prediction method. MySQL or PostgreSQL is used as the database.

[0178] Step 4:

[0179] The server uses a predictive method to predict disease risk. Machine learning models such as Scikit-learn are used for prediction. Input data is fed into the model, which generates a risk prediction value, such as "cardiovascular disease risk." The model then evaluates risk by referencing past data.

[0180] Step 5:

[0181] The server generates specific improvement measures using a countermeasure generation means. Based on the predicted risk, it generates dietary improvement measures, exercise suggestions, and recommendations for doctor consultations according to statistical models and pre-programmed rules. For example, in the case of a high risk, specific advice such as "eat a low-salt diet and engage in 30 minutes of aerobic exercise" is generated.

[0182] Step 6:

[0183] The server sends the generated remediation measures to the user's device via the presentation means. The predicted risks and remediation measures are sent as presentation data in JSON format. For example, the following data is sent:

[0184] json

[0185] {

[0186] "risk_prediction": "high risk",

[0187] "improvement_measures": {

[0188] "diet": "Eat a low-salt diet",

[0189] "exercise": "30 minutes of cardio"

[0190] }

[0191] }

[0192] Step 7:

[0193] The device uses a presentation tool to display the predicted results and improvement measures to the user. The results are presented visually using a smartphone or smart glasses. The user can check the results and adjust their fitness training or meal plan based on the results.

[0194] Through these processing steps, users can understand their own health status in real time and quickly obtain and implement necessary improvement measures.

[0195] 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.

[0196] The present invention is a system that allows a user to input their age and health checkup data, predicts disease risk based on that data, and suggests specific measures to improve the condition, and also recognizes the user's emotions to customize the measures. Specific embodiments of the present invention will be described below.

[0197] The user uses the terminal interface to input their age and health checkup data. The health checkup data includes, for example, total cholesterol level, blood pressure, and weight. This data is entered into the system through the terminal's input means. The terminal is also equipped with emotion recognition sensors such as a camera and microphone, which collect emotion data from the user's facial expressions and voice.

[0198] The device receives the input data and sends it to the server. The sending means takes care of this, and the data is sent as an HTTP request. The server processes the received data and stores it in a database for analysis. Emotion data is also sent and stored in the same way.

[0199] The server predicts disease risk based on the submitted data. Statistical and machine learning models are used as the prediction method. This allows the server to analyze future disease risk based on the user's age, health checkup data, and emotional data. For example, if high cholesterol levels or high blood pressure indicate a risk of cardiovascular disease, the server will identify this risk. The server also evaluates the user's stress level and emotional state based on the emotional data.

[0200] The server generates specific improvement measures based on the disease risk prediction results. This function is performed by the measure generation means. The generated measures are customized based on the user's emotional state. For example, a user with a high stress level will be recommended relaxation techniques and appropriate exercise to reduce stress.

[0201] The generated improvement measures and prediction results are sent to the user via a presentation device. The device receives this data and visually displays it to the user, allowing the user to easily understand their own health risks and the measures they need to take, and take action to improve their condition.

[0202] As a specific example, consider the case where a user is "50 years old" and enters the following health checkup data: "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," and "weight: 80 kg." The user enters the data into the device, and the transmission means sends this data and emotion data to the server. The server receives the data, analyzes it using statistical and machine learning models, and predicts that "high cholesterol and high blood pressure result in a high risk of cardiovascular disease." The emotion engine also evaluates that the stress level is high. The countermeasure generation means generates improvement countermeasures that recommend "eating a low-salt diet, engaging in aerobic exercise, and receiving medical advice" as well as "practicing meditation and deep breathing to reduce stress," and the presentation means presents these to the user. The user can check the results and countermeasures on the device and improve their lifestyle habits based on the recognized risks.

[0203] In this way, the system of the present invention can accurately predict a user's health risks and emotional state and provide specific, customized remedies to help the user take appropriate health and stress management measures.

[0204] The processing flow will be explained below.

[0205] Step 1: The user enters their age, medical data, and emotional data.

[0206] The user enters their age and health check data (e.g., "50 years old," "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," "weight: 80 kg") into the terminal interface.

[0207] The user's facial expressions and voice are captured by emotion recognition sensors (camera and microphone), and emotion data is collected.

[0208] The device checks the validity of the format and content of the input data and emotion data, and if inappropriate data is entered, prompts the user to re-enter it.

[0209] Step 2: The device sends the data to the server

[0210] The device correctly formats the valid data (age, health check data, emotion data) and prepares to send the data to the server as an HTTP request.

[0211] The device makes an HTTP request and sends the data to the server.

[0212] Step 3: The server receives the data

[0213] The server receives the HTTP request and analyzes the received data (age, health check data, emotion data).

[0214] The server rechecks the validity of the data, and if it contains inappropriate data, it generates an error message and sends it to the terminal.

[0215] Step 4: The server runs the disease risk prediction algorithm

[0216] The server stores the relevant data in a database and invokes a disease risk prediction algorithm.

[0217] The prediction method within the server uses statistical and machine learning models to analyze health risks based on age and health checkup data.

[0218] Predictive algorithms use data such as high cholesterol and high blood pressure to predict risk of cardiovascular disease and other illnesses.

[0219] The server uses an emotion engine to assess the user's stress level and emotional state from the emotion data.

[0220] Step 5: The server generates the prediction results and improvement measures

[0221] The server analyzes the prediction results and generates specific improvement measures based on the risks.

[0222] The countermeasure generation means generates countermeasures (e.g., low-salt diet, moderate exercise, meditation or deep breathing to reduce stress) that correspond to the user's predicted risk and emotional state.

[0223] Step 6: The server sends the report to the device

[0224] Create an HTTP response to send the report (prediction results and specific improvement measures) generated by the server to the terminal.

[0225] The server sends the report to the device via an HTTP response.

[0226] Step 7: The device displays the report to the user

[0227] The terminal analyzes the report received from the server and displays it to the user in a visually easy-to-understand format.

[0228] Users can review the report, understand predicted health risks and corresponding remedial measures, and receive advice tailored to their emotional state.

[0229] As a specific example, consider a case where a user is "50 years old" and enters the following health checkup data: "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," and "weight: 80 kg." Based on facial expressions and voice, the user is evaluated as being in a "high stress state." The user enters the data into the device, and the emotion recognition sensor collects emotional data. The transmission means transmits this to the server. The server receives and analyzes the data, predicts that the user is at high risk for cardiovascular disease due to high cholesterol and high blood pressure, and further evaluates the user as being in a "high stress state." The countermeasure generation means generates improvement measures, recommending "a low-salt diet, aerobic exercise, and consultation with a doctor," as well as "practicing meditation and deep breathing to reduce stress," and the presentation means presents these to the user. The user can check the results and countermeasures on the device and improve their lifestyle based on the recognized risks and emotional state.

[0230] Example 2

[0231] 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."

[0232] Conventional health management systems predict disease risk based solely on a user's health checkup data and suggest general improvement measures. However, because they do not take into account the user's emotional state, they have the problem of being unable to provide specific improvement measures for stress management or individual psychological conditions. This can result in inefficient measures that users can implement, potentially reducing the effectiveness of health improvement.

[0233] 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.

[0234] In this invention, the server includes input means for the user to input their age and health checkup data, transmission means for transmitting the data input using the input means and emotional data to the server, prediction means for analyzing the data received via the transmission means to predict disease risk, measure generation means for generating improvement measures based on the risk obtained by the prediction means, and presentation means for presenting the result data generated by the measure generation means to the user. This makes it possible to present customized improvement measures that take into account the user's emotional state as well as their health checkup data.

[0235] "Input means" refers to an interface for users to input their age and health check data into the system.

[0236] The "transmission means" refers to a function for transmitting data and emotion data input using the input means to the server.

[0237] "Emotional data" refers to data that indicates the emotional state of a user collected from facial expressions and voice.

[0238] The "prediction means" refers to a function for predicting disease risk by analyzing data received via the transmission means.

[0239] A "statistical model" refers to an algorithm for predicting disease risk through statistical analysis of data.

[0240] A "machine learning model" refers to an algorithm that learns from large datasets to predict disease risk.

[0241] The "measure generation means" refers to a function for generating improvement measures based on the risks obtained by the prediction means.

[0242] The "presentation means" refers to an interface for presenting the result data generated by the countermeasure generation means to the user.

[0243] "Improvement measures" refer to specific suggestions for improving diet, exercising, and recommending a doctor's visit to reduce disease risk.

[0244] "Customization" refers to the process of individually tailoring remediation measures based on the user's emotional state and individual data.

[0245] The present invention is a system that allows a user to input their age and health checkup data, predicts disease risk based on that data, and suggests specific measures to improve the condition. Furthermore, it can recognize the user's emotions and customize the measures. Specific embodiments of the present invention are described below.

[0246] User data entry and collection

[0247] The user uses the device's interface to input their age and health checkup data, including total cholesterol, blood pressure, and weight. The device is also equipped with emotion recognition sensors, such as a camera and microphone, which collect emotion data from the user's facial expressions and voice.

[0248] Sending data

[0249] The device receives the input data such as age, health checkup data, and emotion data, and sends it to the server as an HTTP request, where it is stored in a database for analysis.

[0250] Data analysis and disease risk prediction

[0251] The server predicts disease risk based on the submitted data. Statistical and machine learning models are used for the prediction. This allows the server to analyze future disease risk based on the user's age, health checkup data, and emotional data. For example, if high cholesterol levels or high blood pressure indicate a risk of cardiovascular disease, the server will identify this risk. The server also evaluates the user's stress level and emotional state based on emotional data.

[0252] Generate remedial measures

[0253] The server generates specific improvement measures based on the disease risk prediction results. The measure generator performs this function, and the generated measures are customized based on the user's emotional state. For example, a user with a high stress level will be recommended relaxation techniques and appropriate exercise to reduce stress.

[0254] Presenting to the user

[0255] The generated improvement measures and prediction results are sent to the user through a presentation device. The device receives this data and visually displays it to the user, allowing the user to understand their own health risks and the measures they should take.

[0256] Specific examples

[0257] For example, consider the case where a 50-year-old user enters the following health checkup data: total cholesterol level: 240 mg / dL, blood pressure: 150 / 90 mmHg, and weight: 80 kg. The user enters the data into the device, and the transmission means sends this data and emotion data to the server. The server analyzes the received data and predicts that "high cholesterol and high blood pressure result in a high risk of cardiovascular disease." The emotion engine also evaluates that the stress level is high. The countermeasure generation means generates improvement measures, recommending "a low-salt diet, aerobic exercise, and consultation with a doctor," as well as "practice meditation and deep breathing to reduce stress," and the presentation means presents these to the user. The user can check the results and countermeasures on the device and take concrete action.

[0258] Prompt Sentence Examples

[0259] "If I'm 50 years old, my total cholesterol level is 240 mg / dL, my blood pressure is 150 / 90 mmHg, and I weigh 80 kg, what is my risk of cardiovascular disease and what specific steps can I take to improve it? Also, if my stress level is high, can you tell me how to relax?"

[0260] In this way, the system of the present invention accurately predicts the user's health risks and emotional state and provides specific, customized remedies, thereby helping the user to take appropriate health and stress management measures.

[0261] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0262] Step 1:

[0263] The user enters their age and health checkup data into the device's interface. The health checkup data includes total cholesterol level, blood pressure, weight, etc. Emotional data is also collected using the device's camera and microphone.

[0264] Input: Age, total cholesterol level, blood pressure, weight, emotional data (facial expression, voice)

[0265] Output: The set of input data

[0266] Specific behavior:

[0267] The user enters "age 50," "total cholesterol level 240 mg / dL," "blood pressure 150 / 90 mmHg," and "weight 80 kg" into an input form on a smartphone or PC, and then speaks into the camera and microphone.

[0268] Step 2:

[0269] The terminal receives the input age, health check data, and emotion data, and sends this data to the server as an HTTP request.

[0270] Input: User-entered age, health check data, and emotional data

[0271] Output: Data packets sent to the server

[0272] Specific behavior:

[0273] The device converts the user input dataset (age "50 years old", total cholesterol level "240 mg / dL", blood pressure "150 / 90 mmHg", weight "80 kg") and emotion data collected from the camera and microphone into JSON format and sends it to the server via an HTTP request.

[0274] Step 3:

[0275] The server analyzes the received data and stores it in a database, using statistical and machine learning models to predict disease risk.

[0276] Input: Age, health check data, and emotional data sent

[0277] Output: Save to database, disease risk prediction results

[0278] Specific behavior:

[0279] The server stores the received data in a "User Data" table. The server runs statistical and machine learning models to predict that "high cholesterol and high blood pressure increase the risk of cardiovascular disease."

[0280] Step 4:

[0281] The server evaluates the user's stress level based on their emotional data, and generates specific measures to improve the situation based on their age and disease risk based on their health checkup data.

[0282] Input: Disease risk prediction results, emotion evaluation results

[0283] Output: Generated remediation measures

[0284] Specific behavior:

[0285] The server considers emotional data that is evaluated as "high stress level" and generates specific improvement measures based on the predicted disease risk, such as "eat a low-salt diet, engage in aerobic exercise, and receive medical advice," and "practice meditation and deep breathing to reduce stress."

[0286] Step 5:

[0287] The server transmits the generated improvement measures and disease risk prediction results to the terminal, which receives this data and visually displays it to the user.

[0288] Input: Improvement measures and prediction results sent from the server

[0289] Output: Visual display to the user

[0290] Specific behavior:

[0291] The server sends improvement measures such as "low-salt diet and aerobic exercise" and "practice meditation and deep breathing" to the device as an HTTP response, and the device displays the results and measures on the screen. The user can check this and take specific action.

[0292] Through these processing steps, the system is able to provide a customized health and stress management strategy to the user.

[0293] (Application example 2)

[0294] 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."

[0295] Conventional health management systems typically predict disease risk based on health data entered by users and then provide general improvement measures based on the results. However, because they do not take the user's emotional state into account, they are unable to provide improvement measures optimized for each user, which can result in reduced effectiveness. In addition, there is no specific reward for users who achieve their health goals, making it difficult to maintain user motivation.

[0296] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: input means for a user to input their age and health checkup data; transmission means for transmitting the data input using the input means to the server; prediction means for analyzing the data received via the transmission means to predict a disease risk; measure generation means for generating an improvement measure based on the risk obtained by the prediction means; presentation means for presenting the result data generated by the measure generation means to the user; emotion recognition means for recognizing the user's emotional state; customization means for customizing the improvement measure based on the emotion data obtained by the emotion recognition means; and reward provision means for providing a reward for maintaining a specific health index. This allows the user to receive an individually optimized improvement measure and further obtain a reward to maintain motivation for achieving their health goal.

[0297] "Input means" refers to a device or interface that allows a user to input their age and health checkup data.

[0298] The "transmission means" refers to a communication function or system for transmitting data input using the input means to a server.

[0299] The "prediction means" is software or algorithms for analyzing data received via the transmission means and predicting disease risk.

[0300] The "measure generation means" is a system or tool for generating health improvement measures based on the risks obtained by the prediction means.

[0301] The "presentation means" is a device or interface for visually or audibly displaying the result data generated by the countermeasure generation means to the user.

[0302] "Emotion recognition means" refers to sensors or software that analyze the user's facial expressions and voice to recognize their emotional state.

[0303] The "customization means" refers to a system or algorithm for individually optimizing improvement measures based on the emotion data obtained by the emotion recognition means.

[0304] A "reward provider" is a mechanism or system for providing rewards to users who maintain certain health indicators.

[0305] The present invention is a system that allows a user to input their age and health checkup data, predicts disease risk based on that data, and suggests specific measures to improve the condition. It also recognizes the user's emotions and customizes the measures. It also includes a function to provide rewards for maintaining specific health indicators. Specific embodiments for implementing the present invention are described below.

[0306] Hardware and software used

[0307] Hardware: Smartphone, server, emotion recognition sensor (camera, microphone, etc.)

[0308] Software: Python, Flask, Scikit-learn (for machine learning models)

[0309] Processing flow

[0310] Data entry and submission

[0311] Users use devices such as smartphones to input their age and health checkup data (e.g., total cholesterol level, blood pressure, weight). In addition, emotion recognition sensors use the device's built-in camera and microphone to collect the user's facial expressions and voice, obtaining emotional data. This data is then sent to the server using an HTTP request.

[0312] Data analysis

[0313] The server analyzes the received data and predicts disease risk using statistical and machine learning models (e.g., Scikit-learn). For example, high total cholesterol and blood pressure predict a high risk of cardiovascular disease. It also assesses the user's stress level based on emotional data.

[0314] Generate improvement measures

[0315] The server generates specific improvement measures based on the disease risk obtained by the prediction means. The measure generation means includes dietary improvement measures, exercise suggestions, and recommendations for doctor visits. The server also customizes improvement measures based on the emotional state data obtained by the emotion recognition means. For example, for a user with a high stress level, the server recommends meditation or deep breathing to reduce stress.

[0316] Results presentation and rewards

[0317] The generated improvement measures and prediction results are presented to the user through the presentation means. The user can check the visually displayed results on their own device. In addition, if the user maintains the health index for a certain period of time (e.g., one month), the reward provision means will provide a reward such as cash back or point redemption.

[0318] Specific examples

[0319] For example, if a user enters the following data:

[0320] Age: 50

[0321] Total cholesterol level: 240 mg / dL

[0322] Blood pressure: 150 / 90 mmHg

[0323] Weight: 80 kg

[0324] Emotional state: High stress level

[0325] The server receives this data through the transmission means and analyzes it using a machine learning model and an emotion recognition model, predicting a "high risk of cardiovascular disease." The emotion data also evaluates the user as having a "high stress level." The countermeasure generation means generates improvement measures, recommending "a low-salt diet, aerobic exercise, and receiving medical advice," as well as "practicing meditation and deep breathing," and the presentation means presents these to the user. Furthermore, if the user implements these measures and maintains their health index for one month, rewards such as point redemptions are provided.

[0326] Prompt Sentence Examples

[0327] "Please enter the user's age and health checkup data (total cholesterol level, blood pressure, weight). Based on that data, we will predict disease risk, evaluate emotional state, and suggest specific improvement measures. For example, if the user is '50 years old, total cholesterol level 240 mg / dL, blood pressure 150 / 90 mmHg, weight 80 kg, emotional state stress level high,' please generate corresponding advice."

[0328] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0329] Step 1:

[0330] The user enters health data such as their age, total cholesterol level, blood pressure, and weight through the smartphone's input screen. The smartphone's built-in camera and microphone are also used to collect the user's facial expressions and voice, and emotional data is obtained (input).

[0331] Step 2:

[0332] The device collects the input health data and emotion data and sends them as an HTTP request to the server using the transmission means. The transmitted content (output) includes the user's age, total cholesterol level, blood pressure, weight, and emotion recognition data.

[0333] Step 3:

[0334] The server receives the HTTP request and stores it in a database (input). The stored data includes age, total cholesterol level, blood pressure, weight, and emotional data.

[0335] Step 4:

[0336] The server analyzes the stored health data using statistical and machine learning models (using Scikit-learn), specifically processing and calculating the following data:

[0337] Age, total cholesterol level, blood pressure, and weight are provided as input data to a machine learning model to predict disease risk (output: disease risk).

[0338] The emotional data is fed into an emotion recognition model to assess the user's stress level (output: emotional state).

[0339] Step 5:

[0340] The server generates specific health improvement measures using the measure generation means based on the disease risk and emotional state obtained by the prediction means. The generated improvement measures (output) include, for example, dietary improvement measures, exercise suggestions, and recommendations for meditation and deep breathing.

[0341] Step 6:

[0342] The server transmits the generated improvement measures and prediction results to the user via a presentation device. The transmitted content (output) includes specific improvement measures, predicted disease risk, and evaluation results of emotional state.

[0343] Step 7:

[0344] The user visually checks (inputs) the proposed improvement measures and prediction results via their smartphone, and takes action to improve their lifestyle based on this.

[0345] Step 8:

[0346] The server monitors whether the user has maintained their health index for a certain period of time (e.g., one month). If the health index is confirmed to be maintained (input), the server uses the reward provision means to provide rewards such as points or cash back (output).

[0347] Prompt Sentence Examples

[0348] "Please enter the user's age and health checkup data (total cholesterol level, blood pressure, weight). Based on that data, we will predict disease risk, evaluate emotional state, and suggest specific improvement measures. For example, if the user is '50 years old, total cholesterol level 240 mg / dL, blood pressure 150 / 90 mmHg, weight 80 kg, emotional state stress level high,' please generate corresponding advice."

[0349] 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.

[0350] 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.

[0351] 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.

[0352] [Second embodiment]

[0353] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0354] 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.

[0355] 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).

[0356] 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.

[0357] 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.

[0358] 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).

[0359] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0360] 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.

[0361] 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.

[0362] 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.

[0363] 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.

[0364] 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."

[0365] The present invention is a system that allows a user to input their age and health checkup data, predicts disease risk based on the data, and suggests specific measures to improve the risk. Specific embodiments of the present invention will be described below.

[0366] The user uses the terminal interface to input their age and health check data, such as total cholesterol level, blood pressure, weight, etc. This data is entered into the system through the input means of the terminal.

[0367] The terminal receives the input data and sends it to the server. The sending means plays this role, creating an HTTP request to send the data to the server and communicating. The server processes the received data and stores it in a database for analysis.

[0368] The server predicts disease risk based on the submitted data. Statistical and machine learning models are used to predict the user's future disease risk based on their age and health checkup data. For example, if high cholesterol or high blood pressure indicates a risk of cardiovascular disease, the server identifies this risk.

[0369] Next, the server generates specific improvement measures based on the disease risk prediction results. This function is performed by the measure generation means, which generates specific dietary improvements, exercise suggestions, and even measures recommending regular doctor visits. For example, a user with high cholesterol levels would be recommended to "reduce salt intake and engage in moderate exercise."

[0370] The improvement measures and prediction results generated by the server are sent to the user via a presentation device. The device receives this data and visually displays it to the user, allowing the user to easily understand their own health risks and the measures they need to take, and take action to improve their health.

[0371] As a specific example, consider the case where a user is "50 years old" and inputs the following health checkup data: "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," and "weight: 80 kg." The user inputs the data into the terminal, and the transmission means sends this data to the server. The server receives the data, analyzes it using statistical and machine learning models, and predicts that "high cholesterol and high blood pressure result in a high risk of cardiovascular disease." The countermeasure generation means generates improvement countermeasures recommending "a low-salt diet, aerobic exercise, and receiving medical advice," and the presentation means presents these to the user. The user can check the results and countermeasures on the terminal and improve their lifestyle habits based on the recognized risks.

[0372] In this way, the system of the present invention can accurately predict the user's health risks and provide specific remedial measures, thereby supporting the user in taking appropriate health care.

[0373] The processing flow will be explained below.

[0374] Step 1: User enters age and health data

[0375] The user enters their age and health check data (e.g., "50 years old," "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," "weight: 80 kg," etc.) into the device interface.

[0376] The terminal checks the validity of the format and content of the entered data, and if inappropriate data is entered, prompts the user to re-enter it.

[0377] Step 2: The device sends the data to the server

[0378] The device correctly formats the valid data and prepares it to be sent to the server as an HTTP request.

[0379] The device makes an HTTP request and sends the data to the server.

[0380] Step 3: The server receives the data

[0381] The server receives the HTTP request and analyzes the received data (age and health checkup data).

[0382] The server rechecks the validity of the data, and if it contains inappropriate data, it generates an error message and sends it to the terminal.

[0383] Step 4: The server runs the disease risk prediction algorithm

[0384] The server stores the relevant data in a database and invokes a disease risk prediction algorithm.

[0385] The predictive means in the server uses statistical and machine learning models to analyze health risks based on the data.

[0386] Predictive algorithms predict a user's risk of cardiovascular disease and other illnesses based on their specific health check data (e.g., total cholesterol, blood pressure).

[0387] Step 5: The server generates the prediction results and improvement measures

[0388] The server analyzes the prediction results and generates specific improvement measures based on the risks.

[0389] The countermeasure generation means generates countermeasures corresponding to the predicted risk of the user (for example, since the cholesterol level is high, a low-salt diet is recommended, and moderate aerobic exercise is recommended).

[0390] The server generates a detailed report containing the measures taken.

[0391] Step 6: The server sends the report to the device

[0392] Create an HTTP response to send the report (prediction results and specific improvement measures) generated by the server to the terminal.

[0393] The server sends the report to the device via an HTTP response.

[0394] Step 7: The device displays the report to the user

[0395] The terminal analyzes the report received from the server and displays it to the user in a visually easy-to-understand format.

[0396] The user checks the report and understands the predicted health risks and the corresponding remedial measures.

[0397] Example 1

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

[0399] Health management is becoming an increasingly important issue in modern society. In particular, individuals living busy lives need systems that efficiently manage daily health data and provide appropriate health risk predictions and improvement advice based on that data. However, existing systems are difficult to use because they require users to analyze their own health data and find appropriate measures, requiring significant effort and knowledge. This creates problems such as delays in the early detection of health risks and the implementation of improvement actions.

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

[0401] In this invention, the server includes input means for a user to input their age and health checkup data, transmission means for transmitting the data input using the input means to the server, database storage means for saving and formatting the data received via the transmission means, prediction means for analyzing the data saved using the storage means with a statistical model or a machine learning model to predict disease risk, measure generation means for generating improvement measures using a generative AI model based on the risk obtained by the prediction means, and presentation means for presenting the result data generated by the measure generation means to the user. This allows users to easily analyze their own health data and obtain specific and feasible improvement measures.

[0402] "User" refers to an individual who uses the system to manage their own health data and receive disease risk predictions and improvement measures.

[0403] "Input means" refers to the device or software that allows a user to input age and health check data into the system.

[0404] The "transmission means" refers to a communication mechanism or protocol for transmitting data input using the input means to the server.

[0405] "Database Storage Means" means a database and associated software and hardware for storing and formatting data received via Transmission Means.

[0406] "Statistical model" refers to a mathematical model used to predict a user's health risk based on past data.

[0407] A "machine learning model" refers to a type of artificial intelligence that uses algorithms to automatically learn from large amounts of data and make predictions.

[0408] "Prediction tool" refers to a device or algorithm that uses statistical or machine learning models to predict a user's risk of disease.

[0409] A "generative AI model" refers to a model that uses artificial intelligence to generate improvement measures based on a user's health data.

[0410] The "measure generation means" refers to a device or algorithm that generates specific improvement measures based on the risks obtained by the prediction means.

[0411] The "presentation means" refers to a device or interface for presenting the result data generated by the countermeasure generation means to the user.

[0412] The present invention is a system that allows a user to input their age and health checkup data, predicts their risk of illness based on the data, and suggests specific measures to improve the situation. Specific embodiments for carrying out the present invention will be described below.

[0413] User Data Entry

[0414] The user uses their own terminal to input their age and health checkup data. At this time, an input form is provided on the terminal interface, and the user inputs their age and various health checkup data (e.g., total cholesterol level, blood pressure, weight, etc.). After inputting, the user presses the "send" button to send the data to the server.

[0415] Sending data

[0416] The terminal receives the data entered by the user, creates an HTTP request and sends it to the server, where the data is converted into JSON format and sent to the server over the Internet.

[0417] Data storage and analysis

[0418] The server stores the received data in a database, using a database engine (such as MySQL or PostgreSQL) to format the data appropriately.The server then analyzes the stored data using statistical and machine learning models (using Python libraries such as scikit-learn and TensorFlow) to predict disease risk.

[0419] Disease risk prediction

[0420] The server uses statistical and machine learning models to analyze disease risk based on the data received. For example, if high cholesterol or high blood pressure indicate a risk of cardiovascular disease, the server identifies these risks and predicts the user's risk of disease.

[0421] Generate remedial measures

[0422] Based on the predicted risks, the server uses a generative AI model (such as GPT-4) to generate specific remedial measures, including dietary improvements, exercise suggestions, and even recommendations for regular doctor visits. The remedial measures are presented to the user in an actionable and easy-to-understand format.

[0423] Presentation of results

[0424] The server sends the generated improvement measures and disease risk prediction results to the user's device, which then visually displays the received data, for example, in a pop-up window or notification area, making it easy for the user to understand.

[0425] Specific examples

[0426] For example, if a user is "50 years old" and enters the following health checkup data: "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," and "weight: 80 kg," the device converts this data into JSON format and sends it to the server. After receiving the data, the server stores it in a database and analyzes it using statistical and machine learning models. The analysis results predict that "high cholesterol and high blood pressure result in a high risk of cardiovascular disease," and a generative AI model is used to generate improvement measures recommending "a low-salt diet, aerobic exercise, and consultation with a doctor." This is then sent to the device and presented to the user in a pop-up window.

[0427] Prompt Sentence Examples

[0428] Examples of prompts that can be created using a generative AI model include:

[0429] Write a program for a system that predicts future disease risk and suggests specific measures to improve it based on a user's age and health checkup data. Data entered by the user includes age, total cholesterol level, blood pressure, and weight. Use statistical and machine learning models to predict disease risk, and recommend dietary improvements, exercise suggestions, and regular doctor visits as improvement measures.

[0430] In this way, the system of the present invention can accurately predict the user's health risks and provide specific remedial measures, thereby supporting the user in taking appropriate health management in advance.

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

[0432] Program processing flow

[0433] Step 1: User enters data

[0434] Users input their age and health checkup data through the terminal interface, including age, total cholesterol level, blood pressure, weight, etc.

[0435] Input: Age, total cholesterol level, blood pressure, weight, etc.

[0436] Output: User health data (JSON format)

[0437] Step 2: The device sends the data to the server

[0438] The terminal receives the data entered by the user, creates an HTTP request and sends it to the server, where the data is converted to JSON format and sent over the Internet.

[0439] Input: User's health data (JSON format)

[0440] Output: HTTP request

[0441] Step 3: The server stores and formats the data

[0442] The server stores the received data in a database and optionally formats the data, for example by using queries to convert the data into the appropriate format.

[0443] Input: User health data received as an HTTP request

[0444] Output: Formatted data stored in the database

[0445] Step 4: The server analyzes using statistical and machine learning models

[0446] The server analyzes the data stored in the database using statistical and machine learning models to predict disease risk, using Python libraries such as scikit-learn and TensorFlow.

[0447] Input: Formatted data stored in a database

[0448] Output: Disease risk prediction results

[0449] Step 5: The server generates improvement measures using the generated AI model

[0450] The server uses a generative AI model (such as GPT-4) based on the disease risk prediction results to generate specific improvement measures.

[0451] Input: Disease risk prediction results

[0452] Output: Generated remediation measures

[0453] Step 6: The server sends the results to the user's device

[0454] The server then sends the generated improvement measures and disease risk prediction results to the user's device. The data is again sent as an HTTP request.

[0455] Input: Generated remedial measures and predicted disease risk results

[0456] Output: HTTP request

[0457] Step 7: The device receives the results and displays them to the user

[0458] The device uses HTML and CSS to visually display the received data and present it to the user, typically using a pop-up window or notification area.

[0459] Input: Result data received as an HTTP request

[0460] Output: The visual representation presented to the user

[0461] (Application example 1)

[0462] 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."

[0463] Conventional health management systems have limited means for users to easily predict disease risk based on their own health checkup data and obtain specific measures to improve their condition. It is also difficult for users to receive effective training and dietary advice in real time at fitness gyms and other physical stores. This makes it difficult for users to comprehensively manage their own health status and take appropriate measures.

[0464] 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.

[0465] In this invention, the server includes input means for a user to input their age and health checkup data, transmission means for transmitting the data input using the input means to the server, prediction means for analyzing the data received via the transmission means to predict disease risk, and measure generation means for generating improvement measures based on the risk obtained by the prediction means. This allows users to manage their own health condition at the fitness gym and receive advice on training and diet in real time.

[0466] "User" refers to an individual who uses the system to manage their own health condition.

[0467] "Age" refers to the number of years calculated from the user's date of birth that the user enters into the system.

[0468] "Health checkup data" refers to various health checkup result data such as total cholesterol level, blood pressure, and weight for measuring the user's health condition.

[0469] "Input means" refers to a means that provides an interface for a user to input age and health data into the system.

[0470] The "transmission means" refers to a communication means for transmitting data input via the input means to the server.

[0471] "Server" refers to a central processing unit that analyzes received data and performs various calculations and predictions.

[0472] "Prediction means" refers to means that includes a statistical model or machine learning model for analyzing data received via the transmission means and predicting disease risk.

[0473] The "measure generation means" refers to a means for generating specific improvement measures based on the risks obtained by the prediction means.

[0474] The "presentation means" refers to a means for visually presenting the result data generated by the countermeasure generation means to the user.

[0475] "Feedback means" refers to a means by which users can manage their own health status at a fitness gym and receive training and dietary advice in real time.

[0476] A "statistical model" refers to a mathematical model that uses various statistical analyses based on past data to predict future disease risk.

[0477] "Machine learning model" refers to a model that incorporates artificial intelligence techniques to learn from data and use that knowledge to predict disease risk.

[0478] MODE FOR CARRYING OUT THE INVENTION

[0479] In this application example, a system is provided that allows a user to manage their own health condition at a fitness gym and receive training and dietary advice in real time. Specific embodiments for implementing this system are described below.

[0480] A user inputs his / her health checkup data using a smartphone or smart glasses. This interface is realized using an input means, and data such as age, total cholesterol level, blood pressure, weight, etc. For example, consider that a user inputs the following data using a smartphone:

[0481] Age: 50

[0482] Total cholesterol level: 240 mg / dL

[0483] Blood pressure: 150 / 90 mmHg

[0484] Weight: 80 kg

[0485] The input data is sent to the server via a sending means, which typically communicates using an HTTP request.

[0486] The server analyzes the received data and predicts disease risk using predictive methods, such as statistical and machine learning models, to determine risk based on the user's health status. For example, if high cholesterol or high blood pressure indicates a risk of cardiovascular disease, the server identifies this risk.

[0487] The countermeasure generator then generates specific remedial measures based on the risk, including specific dietary improvements, exercise suggestions, and recommendations to see a doctor. For example, a user with high cholesterol might be recommended to "consume a low-salt diet and engage in 30 minutes of aerobic exercise."

[0488] The result data generated by the countermeasure generation means is presented to the user using the presentation means. The user can check the results on their smartphone or smart glasses and make training plans at the fitness gym or meal plans. This allows the user to understand their own health condition in real time and take necessary countermeasures promptly.

[0489] As an example, the following prompt sentence can be used:

[0490] Example prompt sentence:

[0491] "A 50-year-old man has entered his health checkup data (total cholesterol level: 240 mg / dL, blood pressure: 150 / 90 mmHg, weight: 80 kg). Based on this data, please predict his disease risk and suggest specific measures to improve his condition."

[0492] The hardware used includes smartphones and smart glasses for users to input data. Specifically, iPhones and Google Glass are examples. On the server side, machine learning libraries such as Scikit-learn are used for data analysis and prediction, and Django or Flask can be used as web frameworks. MySQL or PostgreSQL are suitable databases.

[0493] In this way, the system of the present invention helps users effectively manage their health at the fitness gym and receive appropriate training and dietary advice in real time.

[0494] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0495] Step 1:

[0496] The user inputs their age and health checkup data into the interface using a smartphone or smart glasses. The input data includes age, total cholesterol level, blood pressure, weight, etc. The input data is temporarily stored in the terminal's memory via the input means.

[0497] Step 2:

[0498] The terminal sends the input data to the server. An HTTP request is used as the transmission method. The data is converted to JSON format and sent to the server. Specifically, the following JSON data is sent.

[0499] json

[0500] {

[0501] "age": 50,

[0502] "total_cholesterol": 240,

[0503] "blood_pressure": "150 / 90",

[0504] "weight": 80

[0505] }

[0506] Step 3:

[0507] The server analyzes the received data. The server analyzes the received JSON data and extracts various health checkup data. This data is stored in an internal database and transferred to the prediction method. MySQL or PostgreSQL is used as the database.

[0508] Step 4:

[0509] The server uses a predictive method to predict disease risk. Machine learning models such as Scikit-learn are used for prediction. Input data is fed into the model, which generates a risk prediction value, such as "cardiovascular disease risk." The model then evaluates risk by referencing past data.

[0510] Step 5:

[0511] The server generates specific improvement measures using a countermeasure generation means. Based on the predicted risk, it generates dietary improvement measures, exercise suggestions, and recommendations for doctor consultations according to statistical models and pre-programmed rules. For example, in the case of a high risk, specific advice such as "eat a low-salt diet and engage in 30 minutes of aerobic exercise" is generated.

[0512] Step 6:

[0513] The server sends the generated remediation measures to the user's device via the presentation means. The predicted risks and remediation measures are sent as presentation data in JSON format. For example, the following data is sent:

[0514] json

[0515] {

[0516] "risk_prediction": "high risk",

[0517] "improvement_measures": {

[0518] "diet": "Eat a low-salt diet",

[0519] "exercise": "30 minutes of cardio"

[0520] }

[0521] }

[0522] Step 7:

[0523] The device uses a presentation tool to display the predicted results and improvement measures to the user. The results are presented visually using a smartphone or smart glasses. The user can check the results and adjust their fitness training or meal plan based on the results.

[0524] Through these processing steps, users can understand their own health status in real time and quickly obtain and implement necessary improvement measures.

[0525] 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.

[0526] The present invention is a system that allows a user to input their age and health checkup data, predicts disease risk based on that data, and suggests specific measures to improve the condition, and also recognizes the user's emotions to customize the measures. Specific embodiments of the present invention will be described below.

[0527] The user uses the terminal interface to input their age and health checkup data. The health checkup data includes, for example, total cholesterol level, blood pressure, and weight. This data is entered into the system through the terminal's input means. The terminal is also equipped with emotion recognition sensors such as a camera and microphone, which collect emotion data from the user's facial expressions and voice.

[0528] The device receives the input data and sends it to the server. The sending means takes care of this, and the data is sent as an HTTP request. The server processes the received data and stores it in a database for analysis. Emotion data is also sent and stored in the same way.

[0529] The server predicts disease risk based on the submitted data. Statistical and machine learning models are used as the prediction method. This allows the server to analyze future disease risk based on the user's age, health checkup data, and emotional data. For example, if high cholesterol levels or high blood pressure indicate a risk of cardiovascular disease, the server will identify this risk. The server also evaluates the user's stress level and emotional state based on the emotional data.

[0530] The server generates specific improvement measures based on the disease risk prediction results. This function is performed by the measure generation means. The generated measures are customized based on the user's emotional state. For example, a user with a high stress level will be recommended relaxation techniques and appropriate exercise to reduce stress.

[0531] The generated improvement measures and prediction results are sent to the user via a presentation device. The device receives this data and visually displays it to the user, allowing the user to easily understand their own health risks and the measures they need to take, and take action to improve their condition.

[0532] As a specific example, consider the case where a user is "50 years old" and enters the following health checkup data: "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," and "weight: 80 kg." The user enters the data into the device, and the transmission means sends this data and emotion data to the server. The server receives the data, analyzes it using statistical and machine learning models, and predicts that "high cholesterol and high blood pressure result in a high risk of cardiovascular disease." The emotion engine also evaluates that the stress level is high. The countermeasure generation means generates improvement countermeasures that recommend "eating a low-salt diet, engaging in aerobic exercise, and receiving medical advice" as well as "practicing meditation and deep breathing to reduce stress," and the presentation means presents these to the user. The user can check the results and countermeasures on the device and improve their lifestyle habits based on the recognized risks.

[0533] In this way, the system of the present invention can accurately predict a user's health risks and emotional state and provide specific, customized remedies to help the user take appropriate health and stress management measures.

[0534] The processing flow will be explained below.

[0535] Step 1: The user enters their age, medical data, and emotional data.

[0536] The user enters their age and health check data (e.g., "50 years old," "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," "weight: 80 kg") into the terminal interface.

[0537] The user's facial expressions and voice are captured by emotion recognition sensors (camera and microphone), and emotion data is collected.

[0538] The device checks the validity of the format and content of the input data and emotion data, and if inappropriate data is entered, prompts the user to re-enter it.

[0539] Step 2: The device sends the data to the server

[0540] The device correctly formats the valid data (age, health check data, emotion data) and prepares to send the data to the server as an HTTP request.

[0541] The device makes an HTTP request and sends the data to the server.

[0542] Step 3: The server receives the data

[0543] The server receives the HTTP request and analyzes the received data (age, health check data, emotion data).

[0544] The server rechecks the validity of the data, and if it contains inappropriate data, it generates an error message and sends it to the terminal.

[0545] Step 4: The server runs the disease risk prediction algorithm

[0546] The server stores the relevant data in a database and invokes a disease risk prediction algorithm.

[0547] The prediction method within the server uses statistical and machine learning models to analyze health risks based on age and health checkup data.

[0548] Predictive algorithms use data such as high cholesterol and high blood pressure to predict risk of cardiovascular disease and other illnesses.

[0549] The server uses an emotion engine to assess the user's stress level and emotional state from the emotion data.

[0550] Step 5: The server generates the prediction results and improvement measures

[0551] The server analyzes the prediction results and generates specific improvement measures based on the risks.

[0552] The countermeasure generation means generates countermeasures (e.g., low-salt diet, moderate exercise, meditation or deep breathing to reduce stress) that correspond to the user's predicted risk and emotional state.

[0553] Step 6: The server sends the report to the device

[0554] Create an HTTP response to send the report (prediction results and specific improvement measures) generated by the server to the terminal.

[0555] The server sends the report to the device via an HTTP response.

[0556] Step 7: The device displays the report to the user

[0557] The terminal analyzes the report received from the server and displays it to the user in a visually easy-to-understand format.

[0558] Users can review the report, understand predicted health risks and corresponding remedial measures, and receive advice tailored to their emotional state.

[0559] As a specific example, consider a case where a user is "50 years old" and enters the following health checkup data: "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," and "weight: 80 kg." Based on facial expressions and voice, the user is evaluated as being in a "high stress state." The user enters the data into the device, and the emotion recognition sensor collects emotional data. The transmission means transmits this to the server. The server receives and analyzes the data, predicts that the user is at high risk for cardiovascular disease due to high cholesterol and high blood pressure, and further evaluates the user as being in a "high stress state." The countermeasure generation means generates improvement measures, recommending "a low-salt diet, aerobic exercise, and consultation with a doctor," as well as "practicing meditation and deep breathing to reduce stress," and the presentation means presents these to the user. The user can check the results and countermeasures on the device and improve their lifestyle based on the recognized risks and emotional state.

[0560] Example 2

[0561] 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."

[0562] Conventional health management systems predict disease risk based solely on a user's health checkup data and suggest general improvement measures. However, because they do not take into account the user's emotional state, they have the problem of being unable to provide specific improvement measures for stress management or individual psychological conditions. This can result in inefficient measures that users can implement, potentially reducing the effectiveness of health improvement.

[0563] 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.

[0564] In this invention, the server includes input means for the user to input their age and health checkup data, transmission means for transmitting the data input using the input means and emotional data to the server, prediction means for analyzing the data received via the transmission means to predict disease risk, measure generation means for generating improvement measures based on the risk obtained by the prediction means, and presentation means for presenting the result data generated by the measure generation means to the user. This makes it possible to present customized improvement measures that take into account the user's emotional state as well as their health checkup data.

[0565] "Input means" refers to an interface for users to input their age and health check data into the system.

[0566] The "transmission means" refers to a function for transmitting data and emotion data input using the input means to the server.

[0567] "Emotional data" refers to data that indicates the emotional state of a user collected from facial expressions and voice.

[0568] The "prediction means" refers to a function for predicting disease risk by analyzing data received via the transmission means.

[0569] A "statistical model" refers to an algorithm for predicting disease risk through statistical analysis of data.

[0570] A "machine learning model" refers to an algorithm that learns from large datasets to predict disease risk.

[0571] The "measure generation means" refers to a function for generating improvement measures based on the risks obtained by the prediction means.

[0572] The "presentation means" refers to an interface for presenting the result data generated by the countermeasure generation means to the user.

[0573] "Improvement measures" refer to specific suggestions for improving diet, exercising, and recommending a doctor's visit to reduce disease risk.

[0574] "Customization" refers to the process of individually tailoring remediation measures based on the user's emotional state and individual data.

[0575] The present invention is a system that allows a user to input their age and health checkup data, predicts disease risk based on that data, and suggests specific measures to improve the condition. Furthermore, it can recognize the user's emotions and customize the measures. Specific embodiments of the present invention are described below.

[0576] User data entry and collection

[0577] The user uses the device's interface to input their age and health checkup data, including total cholesterol, blood pressure, and weight. The device is also equipped with emotion recognition sensors, such as a camera and microphone, which collect emotion data from the user's facial expressions and voice.

[0578] Sending data

[0579] The device receives the input data such as age, health checkup data, and emotion data, and sends it to the server as an HTTP request, where it is stored in a database for analysis.

[0580] Data analysis and disease risk prediction

[0581] The server predicts disease risk based on the submitted data. Statistical and machine learning models are used for the prediction. This allows the server to analyze future disease risk based on the user's age, health checkup data, and emotional data. For example, if high cholesterol levels or high blood pressure indicate a risk of cardiovascular disease, the server will identify this risk. The server also evaluates the user's stress level and emotional state based on emotional data.

[0582] Generate remedial measures

[0583] The server generates specific improvement measures based on the disease risk prediction results. The measure generator performs this function, and the generated measures are customized based on the user's emotional state. For example, a user with a high stress level will be recommended relaxation techniques and appropriate exercise to reduce stress.

[0584] Presenting to the user

[0585] The generated improvement measures and prediction results are sent to the user through a presentation device. The device receives this data and visually displays it to the user, allowing the user to understand their own health risks and the measures they should take.

[0586] Specific examples

[0587] For example, consider the case where a 50-year-old user enters the following health checkup data: total cholesterol level: 240 mg / dL, blood pressure: 150 / 90 mmHg, and weight: 80 kg. The user enters the data into the device, and the transmission means sends this data and emotion data to the server. The server analyzes the received data and predicts that "high cholesterol and high blood pressure result in a high risk of cardiovascular disease." The emotion engine also evaluates that the stress level is high. The countermeasure generation means generates improvement measures, recommending "a low-salt diet, aerobic exercise, and consultation with a doctor," as well as "practice meditation and deep breathing to reduce stress," and the presentation means presents these to the user. The user can check the results and countermeasures on the device and take concrete action.

[0588] Prompt Sentence Examples

[0589] "If I'm 50 years old, my total cholesterol level is 240 mg / dL, my blood pressure is 150 / 90 mmHg, and I weigh 80 kg, what is my risk of cardiovascular disease and what specific steps can I take to improve it? Also, if my stress level is high, can you tell me how to relax?"

[0590] In this way, the system of the present invention accurately predicts the user's health risks and emotional state and provides specific, customized remedies, thereby helping the user to take appropriate health and stress management measures.

[0591] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0592] Step 1:

[0593] The user enters their age and health checkup data into the device's interface. The health checkup data includes total cholesterol level, blood pressure, weight, etc. Emotional data is also collected using the device's camera and microphone.

[0594] Input: Age, total cholesterol level, blood pressure, weight, emotional data (facial expression, voice)

[0595] Output: The set of input data

[0596] Specific behavior:

[0597] The user enters "age 50," "total cholesterol level 240 mg / dL," "blood pressure 150 / 90 mmHg," and "weight 80 kg" into an input form on a smartphone or PC, and then speaks into the camera and microphone.

[0598] Step 2:

[0599] The terminal receives the input age, health check data, and emotion data, and sends this data to the server as an HTTP request.

[0600] Input: User-entered age, health check data, and emotional data

[0601] Output: Data packets sent to the server

[0602] Specific behavior:

[0603] The device converts the user input dataset (age "50 years old", total cholesterol level "240 mg / dL", blood pressure "150 / 90 mmHg", weight "80 kg") and emotion data collected from the camera and microphone into JSON format and sends it to the server via an HTTP request.

[0604] Step 3:

[0605] The server analyzes the received data and stores it in a database, using statistical and machine learning models to predict disease risk.

[0606] Input: Age, health check data, and emotional data sent

[0607] Output: Save to database, disease risk prediction results

[0608] Specific behavior:

[0609] The server stores the received data in a "User Data" table. The server runs statistical and machine learning models to predict that "high cholesterol and high blood pressure increase the risk of cardiovascular disease."

[0610] Step 4:

[0611] The server evaluates the user's stress level based on their emotional data, and generates specific measures to improve the situation based on their age and disease risk based on their health checkup data.

[0612] Input: Disease risk prediction results, emotion evaluation results

[0613] Output: Generated remediation measures

[0614] Specific behavior:

[0615] The server considers emotional data that is evaluated as "high stress level" and generates specific improvement measures based on the predicted disease risk, such as "eat a low-salt diet, engage in aerobic exercise, and receive medical advice," and "practice meditation and deep breathing to reduce stress."

[0616] Step 5:

[0617] The server transmits the generated improvement measures and disease risk prediction results to the terminal, which receives this data and visually displays it to the user.

[0618] Input: Improvement measures and prediction results sent from the server

[0619] Output: Visual display to the user

[0620] Specific behavior:

[0621] The server sends improvement measures such as "low-salt diet and aerobic exercise" and "practice meditation and deep breathing" to the device as an HTTP response, and the device displays the results and measures on the screen. The user can check this and take specific action.

[0622] Through these processing steps, the system is able to provide a customized health and stress management strategy to the user.

[0623] (Application example 2)

[0624] 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."

[0625] Conventional health management systems typically predict disease risk based on health data entered by users and then provide general improvement measures based on the results. However, because they do not take the user's emotional state into account, they are unable to provide improvement measures optimized for each user, which can result in reduced effectiveness. In addition, there is no specific reward for users who achieve their health goals, making it difficult to maintain user motivation.

[0626] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: input means for a user to input their age and health checkup data; transmission means for transmitting the data input using the input means to the server; prediction means for analyzing the data received via the transmission means to predict a disease risk; measure generation means for generating an improvement measure based on the risk obtained by the prediction means; presentation means for presenting the result data generated by the measure generation means to the user; emotion recognition means for recognizing the user's emotional state; customization means for customizing the improvement measure based on the emotion data obtained by the emotion recognition means; and reward provision means for providing a reward for maintaining a specific health index. This allows the user to receive an individually optimized improvement measure and further obtain a reward to maintain motivation for achieving their health goal.

[0627] "Input means" refers to a device or interface that allows a user to input their age and health checkup data.

[0628] The "transmission means" refers to a communication function or system for transmitting data input using the input means to a server.

[0629] The "prediction means" is software or algorithms for analyzing data received via the transmission means and predicting disease risk.

[0630] The "measure generation means" is a system or tool for generating health improvement measures based on the risks obtained by the prediction means.

[0631] The "presentation means" is a device or interface for visually or audibly displaying the result data generated by the countermeasure generation means to the user.

[0632] "Emotion recognition means" refers to sensors or software that analyze the user's facial expressions and voice to recognize their emotional state.

[0633] The "customization means" refers to a system or algorithm for individually optimizing improvement measures based on the emotion data obtained by the emotion recognition means.

[0634] A "reward provider" is a mechanism or system for providing rewards to users who maintain certain health indicators.

[0635] The present invention is a system that allows a user to input their age and health checkup data, predicts disease risk based on that data, and suggests specific measures to improve the condition. It also recognizes the user's emotions and customizes the measures. It also includes a function to provide rewards for maintaining specific health indicators. Specific embodiments for implementing the present invention are described below.

[0636] Hardware and software used

[0637] Hardware: Smartphone, server, emotion recognition sensor (camera, microphone, etc.)

[0638] Software: Python, Flask, Scikit-learn (for machine learning models)

[0639] Processing flow

[0640] Data entry and submission

[0641] Users use devices such as smartphones to input their age and health checkup data (e.g., total cholesterol level, blood pressure, weight). In addition, emotion recognition sensors use the device's built-in camera and microphone to collect the user's facial expressions and voice, obtaining emotional data. This data is then sent to the server using an HTTP request.

[0642] Data analysis

[0643] The server analyzes the received data and predicts disease risk using statistical and machine learning models (e.g., Scikit-learn). For example, high total cholesterol and blood pressure predict a high risk of cardiovascular disease. It also assesses the user's stress level based on emotional data.

[0644] Generate improvement measures

[0645] The server generates specific improvement measures based on the disease risk obtained by the prediction means. The measure generation means includes dietary improvement measures, exercise suggestions, and recommendations for doctor visits. The server also customizes improvement measures based on the emotional state data obtained by the emotion recognition means. For example, for a user with a high stress level, the server recommends meditation or deep breathing to reduce stress.

[0646] Results presentation and rewards

[0647] The generated improvement measures and prediction results are presented to the user through the presentation means. The user can check the visually displayed results on their own device. In addition, if the user maintains the health index for a certain period of time (e.g., one month), the reward provision means will provide a reward such as cash back or point redemption.

[0648] Specific examples

[0649] For example, if a user enters the following data:

[0650] Age: 50

[0651] Total cholesterol level: 240 mg / dL

[0652] Blood pressure: 150 / 90 mmHg

[0653] Weight: 80 kg

[0654] Emotional state: High stress level

[0655] The server receives this data through the transmission means and analyzes it using a machine learning model and an emotion recognition model, predicting a "high risk of cardiovascular disease." The emotion data also evaluates the user as having a "high stress level." The countermeasure generation means generates improvement measures, recommending "a low-salt diet, aerobic exercise, and receiving medical advice," as well as "practicing meditation and deep breathing," and the presentation means presents these to the user. Furthermore, if the user implements these measures and maintains their health index for one month, rewards such as point redemptions are provided.

[0656] Prompt Sentence Examples

[0657] "Please enter the user's age and health checkup data (total cholesterol level, blood pressure, weight). Based on that data, we will predict disease risk, evaluate emotional state, and suggest specific improvement measures. For example, if the user is '50 years old, total cholesterol level 240 mg / dL, blood pressure 150 / 90 mmHg, weight 80 kg, emotional state stress level high,' please generate corresponding advice."

[0658] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0659] Step 1:

[0660] The user enters health data such as their age, total cholesterol level, blood pressure, and weight through the smartphone's input screen. The smartphone's built-in camera and microphone are also used to collect the user's facial expressions and voice, and emotional data is obtained (input).

[0661] Step 2:

[0662] The device collects the input health data and emotion data and sends them as an HTTP request to the server using the transmission means. The transmitted content (output) includes the user's age, total cholesterol level, blood pressure, weight, and emotion recognition data.

[0663] Step 3:

[0664] The server receives the HTTP request and stores it in a database (input). The stored data includes age, total cholesterol level, blood pressure, weight, and emotional data.

[0665] Step 4:

[0666] The server analyzes the stored health data using statistical and machine learning models (using Scikit-learn), specifically processing and calculating the following data:

[0667] Age, total cholesterol level, blood pressure, and weight are provided as input data to a machine learning model to predict disease risk (output: disease risk).

[0668] The emotional data is fed into an emotion recognition model to assess the user's stress level (output: emotional state).

[0669] Step 5:

[0670] The server generates specific health improvement measures using the measure generation means based on the disease risk and emotional state obtained by the prediction means. The generated improvement measures (output) include, for example, dietary improvement measures, exercise suggestions, and recommendations for meditation and deep breathing.

[0671] Step 6:

[0672] The server transmits the generated improvement measures and prediction results to the user via a presentation device. The transmitted content (output) includes specific improvement measures, predicted disease risk, and evaluation results of emotional state.

[0673] Step 7:

[0674] The user visually checks (inputs) the proposed improvement measures and prediction results via their smartphone, and takes action to improve their lifestyle based on this.

[0675] Step 8:

[0676] The server monitors whether the user has maintained their health index for a certain period of time (e.g., one month). If the health index is confirmed to be maintained (input), the server uses the reward provision means to provide rewards such as points or cash back (output).

[0677] Prompt Sentence Examples

[0678] "Please enter the user's age and health checkup data (total cholesterol level, blood pressure, weight). Based on that data, we will predict disease risk, evaluate emotional state, and suggest specific improvement measures. For example, if the user is '50 years old, total cholesterol level 240 mg / dL, blood pressure 150 / 90 mmHg, weight 80 kg, emotional state stress level high,' please generate corresponding advice."

[0679] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

[0682] [Third embodiment]

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

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

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

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

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

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

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

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

[0691] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0694] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0695] The present invention is a system that allows a user to input their age and health checkup data, predicts disease risk based on the data, and suggests specific measures to improve the risk. Specific embodiments of the present invention will be described below.

[0696] The user uses the terminal interface to input their age and health check data, such as total cholesterol level, blood pressure, weight, etc. This data is entered into the system through the input means of the terminal.

[0697] The terminal receives the input data and sends it to the server. The sending means plays this role, creating an HTTP request to send the data to the server and communicating. The server processes the received data and stores it in a database for analysis.

[0698] The server predicts disease risk based on the submitted data. Statistical and machine learning models are used to predict the user's future disease risk based on their age and health checkup data. For example, if high cholesterol or high blood pressure indicates a risk of cardiovascular disease, the server identifies this risk.

[0699] Next, the server generates specific improvement measures based on the disease risk prediction results. This function is performed by the measure generation means, which generates specific dietary improvements, exercise suggestions, and even measures recommending regular doctor visits. For example, a user with high cholesterol levels would be recommended to "reduce salt intake and engage in moderate exercise."

[0700] The improvement measures and prediction results generated by the server are sent to the user via a presentation device. The device receives this data and visually displays it to the user, allowing the user to easily understand their own health risks and the measures they need to take, and take action to improve their health.

[0701] As a specific example, consider the case where a user is "50 years old" and inputs the following health checkup data: "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," and "weight: 80 kg." The user inputs the data into the terminal, and the transmission means sends this data to the server. The server receives the data, analyzes it using statistical and machine learning models, and predicts that "high cholesterol and high blood pressure result in a high risk of cardiovascular disease." The countermeasure generation means generates improvement countermeasures recommending "a low-salt diet, aerobic exercise, and receiving medical advice," and the presentation means presents these to the user. The user can check the results and countermeasures on the terminal and improve their lifestyle habits based on the recognized risks.

[0702] In this way, the system of the present invention can accurately predict the user's health risks and provide specific remedial measures, thereby supporting the user in taking appropriate health care.

[0703] The processing flow will be explained below.

[0704] Step 1: User enters age and health data

[0705] The user enters their age and health check data (e.g., "50 years old," "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," "weight: 80 kg," etc.) into the device interface.

[0706] The terminal checks the validity of the format and content of the entered data, and if inappropriate data is entered, prompts the user to re-enter it.

[0707] Step 2: The device sends the data to the server

[0708] The device correctly formats the valid data and prepares it to be sent to the server as an HTTP request.

[0709] The device makes an HTTP request and sends the data to the server.

[0710] Step 3: The server receives the data

[0711] The server receives the HTTP request and analyzes the received data (age and health checkup data).

[0712] The server rechecks the validity of the data, and if it contains inappropriate data, it generates an error message and sends it to the terminal.

[0713] Step 4: The server runs the disease risk prediction algorithm

[0714] The server stores the relevant data in a database and invokes a disease risk prediction algorithm.

[0715] The predictive means in the server uses statistical and machine learning models to analyze health risks based on the data.

[0716] Predictive algorithms predict a user's risk of cardiovascular disease and other illnesses based on their specific health check data (e.g., total cholesterol, blood pressure).

[0717] Step 5: The server generates the prediction results and improvement measures

[0718] The server analyzes the prediction results and generates specific improvement measures based on the risks.

[0719] The countermeasure generation means generates countermeasures corresponding to the predicted risk of the user (for example, since the cholesterol level is high, a low-salt diet is recommended, and moderate aerobic exercise is recommended).

[0720] The server generates a detailed report containing the measures taken.

[0721] Step 6: The server sends the report to the device

[0722] Create an HTTP response to send the report (prediction results and specific improvement measures) generated by the server to the terminal.

[0723] The server sends the report to the device via an HTTP response.

[0724] Step 7: The device displays the report to the user

[0725] The terminal analyzes the report received from the server and displays it to the user in a visually easy-to-understand format.

[0726] The user checks the report and understands the predicted health risks and the corresponding remedial measures.

[0727] Example 1

[0728] 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."

[0729] Health management is becoming an increasingly important issue in modern society. In particular, individuals living busy lives need systems that efficiently manage daily health data and provide appropriate health risk predictions and improvement advice based on that data. However, existing systems are difficult to use because they require users to analyze their own health data and find appropriate measures, requiring significant effort and knowledge. This creates problems such as delays in the early detection of health risks and the implementation of improvement actions.

[0730] 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.

[0731] In this invention, the server includes input means for a user to input their age and health checkup data, transmission means for transmitting the data input using the input means to the server, database storage means for saving and formatting the data received via the transmission means, prediction means for analyzing the data saved using the storage means with a statistical model or a machine learning model to predict disease risk, measure generation means for generating improvement measures using a generative AI model based on the risk obtained by the prediction means, and presentation means for presenting the result data generated by the measure generation means to the user. This allows users to easily analyze their own health data and obtain specific and feasible improvement measures.

[0732] "User" refers to an individual who uses the system to manage their own health data and receive disease risk predictions and improvement measures.

[0733] "Input means" refers to the device or software that allows a user to input age and health check data into the system.

[0734] The "transmission means" refers to a communication mechanism or protocol for transmitting data input using the input means to the server.

[0735] "Database Storage Means" means a database and associated software and hardware for storing and formatting data received via Transmission Means.

[0736] "Statistical model" refers to a mathematical model used to predict a user's health risk based on past data.

[0737] A "machine learning model" refers to a type of artificial intelligence that uses algorithms to automatically learn from large amounts of data and make predictions.

[0738] "Prediction tool" refers to a device or algorithm that uses statistical or machine learning models to predict a user's risk of disease.

[0739] A "generative AI model" refers to a model that uses artificial intelligence to generate improvement measures based on a user's health data.

[0740] The "measure generation means" refers to a device or algorithm that generates specific improvement measures based on the risks obtained by the prediction means.

[0741] The "presentation means" refers to a device or interface for presenting the result data generated by the countermeasure generation means to the user.

[0742] The present invention is a system that allows a user to input their age and health checkup data, predicts their risk of illness based on the data, and suggests specific measures to improve the situation. Specific embodiments for carrying out the present invention will be described below.

[0743] User Data Entry

[0744] The user uses their own terminal to input their age and health checkup data. At this time, an input form is provided on the terminal interface, and the user inputs their age and various health checkup data (e.g., total cholesterol level, blood pressure, weight, etc.). After inputting, the user presses the "send" button to send the data to the server.

[0745] Sending data

[0746] The terminal receives the data entered by the user, creates an HTTP request and sends it to the server, where the data is converted into JSON format and sent to the server over the Internet.

[0747] Data storage and analysis

[0748] The server stores the received data in a database, using a database engine (such as MySQL or PostgreSQL) to format the data appropriately.The server then analyzes the stored data using statistical and machine learning models (using Python libraries such as scikit-learn and TensorFlow) to predict disease risk.

[0749] Disease risk prediction

[0750] The server uses statistical and machine learning models to analyze disease risk based on the data received. For example, if high cholesterol or high blood pressure indicate a risk of cardiovascular disease, the server identifies these risks and predicts the user's risk of disease.

[0751] Generate remedial measures

[0752] Based on the predicted risks, the server uses a generative AI model (such as GPT-4) to generate specific remedial measures, including dietary improvements, exercise suggestions, and even recommendations for regular doctor visits. The remedial measures are presented to the user in an actionable and easy-to-understand format.

[0753] Presentation of results

[0754] The server sends the generated improvement measures and disease risk prediction results to the user's device, which then visually displays the received data, for example, in a pop-up window or notification area, making it easy for the user to understand.

[0755] Specific examples

[0756] For example, if a user is "50 years old" and enters the following health checkup data: "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," and "weight: 80 kg," the device converts this data into JSON format and sends it to the server. After receiving the data, the server stores it in a database and analyzes it using statistical and machine learning models. The analysis results predict that "high cholesterol and high blood pressure result in a high risk of cardiovascular disease," and a generative AI model is used to generate improvement measures recommending "a low-salt diet, aerobic exercise, and consultation with a doctor." This is then sent to the device and presented to the user in a pop-up window.

[0757] Prompt Sentence Examples

[0758] Examples of prompts that can be created using a generative AI model include:

[0759] Write a program for a system that predicts future disease risk and suggests specific measures to improve it based on a user's age and health checkup data. Data entered by the user includes age, total cholesterol level, blood pressure, and weight. Use statistical and machine learning models to predict disease risk, and recommend dietary improvements, exercise suggestions, and regular doctor visits as improvement measures.

[0760] In this way, the system of the present invention can accurately predict the user's health risks and provide specific remedial measures, thereby supporting the user in taking appropriate health management in advance.

[0761] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0762] Program processing flow

[0763] Step 1: User enters data

[0764] Users input their age and health checkup data through the terminal interface, including age, total cholesterol level, blood pressure, weight, etc.

[0765] Input: Age, total cholesterol level, blood pressure, weight, etc.

[0766] Output: User health data (JSON format)

[0767] Step 2: The device sends the data to the server

[0768] The terminal receives the data entered by the user, creates an HTTP request and sends it to the server, where the data is converted to JSON format and sent over the Internet.

[0769] Input: User's health data (JSON format)

[0770] Output: HTTP request

[0771] Step 3: The server stores and formats the data

[0772] The server stores the received data in a database and optionally formats the data, for example by using queries to convert the data into the appropriate format.

[0773] Input: User health data received as an HTTP request

[0774] Output: Formatted data stored in the database

[0775] Step 4: The server analyzes using statistical and machine learning models

[0776] The server analyzes the data stored in the database using statistical and machine learning models to predict disease risk, using Python libraries such as scikit-learn and TensorFlow.

[0777] Input: Formatted data stored in a database

[0778] Output: Disease risk prediction results

[0779] Step 5: The server generates improvement measures using the generated AI model

[0780] The server uses a generative AI model (such as GPT-4) based on the disease risk prediction results to generate specific improvement measures.

[0781] Input: Disease risk prediction results

[0782] Output: Generated remediation measures

[0783] Step 6: The server sends the results to the user's device

[0784] The server then sends the generated improvement measures and disease risk prediction results to the user's device. The data is again sent as an HTTP request.

[0785] Input: Generated remedial measures and predicted disease risk results

[0786] Output: HTTP request

[0787] Step 7: The device receives the results and displays them to the user

[0788] The device uses HTML and CSS to visually display the received data and present it to the user, typically using a pop-up window or notification area.

[0789] Input: Result data received as an HTTP request

[0790] Output: The visual representation presented to the user

[0791] (Application example 1)

[0792] 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."

[0793] Conventional health management systems have limited means for users to easily predict disease risk based on their own health checkup data and obtain specific measures to improve their condition. It is also difficult for users to receive effective training and dietary advice in real time at fitness gyms and other physical stores. This makes it difficult for users to comprehensively manage their own health status and take appropriate measures.

[0794] 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.

[0795] In this invention, the server includes input means for a user to input their age and health checkup data, transmission means for transmitting the data input using the input means to the server, prediction means for analyzing the data received via the transmission means to predict disease risk, and measure generation means for generating improvement measures based on the risk obtained by the prediction means. This allows users to manage their own health condition at the fitness gym and receive advice on training and diet in real time.

[0796] "User" refers to an individual who uses the system to manage their own health condition.

[0797] "Age" refers to the number of years calculated from the user's date of birth that the user enters into the system.

[0798] "Health checkup data" refers to various health checkup result data such as total cholesterol level, blood pressure, and weight for measuring the user's health condition.

[0799] "Input means" refers to a means that provides an interface for a user to input age and health data into the system.

[0800] The "transmission means" refers to a communication means for transmitting data input via the input means to the server.

[0801] "Server" refers to a central processing unit that analyzes received data and performs various calculations and predictions.

[0802] "Prediction means" refers to means that includes a statistical model or machine learning model for analyzing data received via the transmission means and predicting disease risk.

[0803] The "measure generation means" refers to a means for generating specific improvement measures based on the risks obtained by the prediction means.

[0804] The "presentation means" refers to a means for visually presenting the result data generated by the countermeasure generation means to the user.

[0805] "Feedback means" refers to a means by which users can manage their own health status at a fitness gym and receive training and dietary advice in real time.

[0806] A "statistical model" refers to a mathematical model that uses various statistical analyses based on past data to predict future disease risk.

[0807] "Machine learning model" refers to a model that incorporates artificial intelligence techniques to learn from data and use that knowledge to predict disease risk.

[0808] MODE FOR CARRYING OUT THE INVENTION

[0809] In this application example, a system is provided that allows a user to manage their own health condition at a fitness gym and receive training and dietary advice in real time. Specific embodiments for implementing this system are described below.

[0810] A user inputs his / her health checkup data using a smartphone or smart glasses. This interface is realized using an input means, and data such as age, total cholesterol level, blood pressure, weight, etc. For example, consider that a user inputs the following data using a smartphone:

[0811] Age: 50

[0812] Total cholesterol level: 240 mg / dL

[0813] Blood pressure: 150 / 90 mmHg

[0814] Weight: 80 kg

[0815] The input data is sent to the server via a sending means, which typically communicates using an HTTP request.

[0816] The server analyzes the received data and predicts disease risk using predictive methods, such as statistical and machine learning models, to determine risk based on the user's health status. For example, if high cholesterol or high blood pressure indicates a risk of cardiovascular disease, the server identifies this risk.

[0817] The countermeasure generator then generates specific remedial measures based on the risk, including specific dietary improvements, exercise suggestions, and recommendations to see a doctor. For example, a user with high cholesterol might be recommended to "consume a low-salt diet and engage in 30 minutes of aerobic exercise."

[0818] The result data generated by the countermeasure generation means is presented to the user using the presentation means. The user can check the results on their smartphone or smart glasses and make training plans at the fitness gym or meal plans. This allows the user to understand their own health condition in real time and take necessary countermeasures promptly.

[0819] As an example, the following prompt sentence can be used:

[0820] Example prompt sentence:

[0821] "A 50-year-old man has entered his health checkup data (total cholesterol level: 240 mg / dL, blood pressure: 150 / 90 mmHg, weight: 80 kg). Based on this data, please predict his disease risk and suggest specific measures to improve his condition."

[0822] The hardware used includes smartphones and smart glasses for users to input data. Specifically, iPhones and Google Glass are examples. On the server side, machine learning libraries such as Scikit-learn are used for data analysis and prediction, and Django or Flask can be used as web frameworks. MySQL or PostgreSQL are suitable databases.

[0823] In this way, the system of the present invention helps users effectively manage their health at the fitness gym and receive appropriate training and dietary advice in real time.

[0824] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0825] Step 1:

[0826] The user inputs their age and health checkup data into the interface using a smartphone or smart glasses. The input data includes age, total cholesterol level, blood pressure, weight, etc. The input data is temporarily stored in the terminal's memory via the input means.

[0827] Step 2:

[0828] The terminal sends the input data to the server. An HTTP request is used as the transmission method. The data is converted to JSON format and sent to the server. Specifically, the following JSON data is sent.

[0829] json

[0830] {

[0831] "age": 50,

[0832] "total_cholesterol": 240,

[0833] "blood_pressure": "150 / 90",

[0834] "weight": 80

[0835] }

[0836] Step 3:

[0837] The server analyzes the received data. The server analyzes the received JSON data and extracts various health checkup data. This data is stored in an internal database and transferred to the prediction method. MySQL or PostgreSQL is used as the database.

[0838] Step 4:

[0839] The server uses a predictive method to predict disease risk. Machine learning models such as Scikit-learn are used for prediction. Input data is fed into the model, which generates a risk prediction value, such as "cardiovascular disease risk." The model then evaluates risk by referencing past data.

[0840] Step 5:

[0841] The server generates specific improvement measures using a countermeasure generation means. Based on the predicted risk, it generates dietary improvement measures, exercise suggestions, and recommendations for doctor consultations according to statistical models and pre-programmed rules. For example, in the case of a high risk, specific advice such as "eat a low-salt diet and engage in 30 minutes of aerobic exercise" is generated.

[0842] Step 6:

[0843] The server sends the generated remediation measures to the user's device via the presentation means. The predicted risks and remediation measures are sent as presentation data in JSON format. For example, the following data is sent:

[0844] json

[0845] {

[0846] "risk_prediction": "high risk",

[0847] "improvement_measures": {

[0848] "diet": "Eat a low-salt diet",

[0849] "exercise": "30 minutes of cardio"

[0850] }

[0851] }

[0852] Step 7:

[0853] The device uses a presentation tool to display the predicted results and improvement measures to the user. The results are presented visually using a smartphone or smart glasses. The user can check the results and adjust their fitness training or meal plan based on the results.

[0854] Through these processing steps, users can understand their own health status in real time and quickly obtain and implement necessary improvement measures.

[0855] 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.

[0856] The present invention is a system that allows a user to input their age and health checkup data, predicts disease risk based on that data, and suggests specific measures to improve the condition, and also recognizes the user's emotions to customize the measures. Specific embodiments of the present invention will be described below.

[0857] The user uses the terminal interface to input their age and health checkup data. The health checkup data includes, for example, total cholesterol level, blood pressure, and weight. This data is entered into the system through the terminal's input means. The terminal is also equipped with emotion recognition sensors such as a camera and microphone, which collect emotion data from the user's facial expressions and voice.

[0858] The device receives the input data and sends it to the server. The sending means takes care of this, and the data is sent as an HTTP request. The server processes the received data and stores it in a database for analysis. Emotion data is also sent and stored in the same way.

[0859] The server predicts disease risk based on the submitted data. Statistical and machine learning models are used as the prediction method. This allows the server to analyze future disease risk based on the user's age, health checkup data, and emotional data. For example, if high cholesterol levels or high blood pressure indicate a risk of cardiovascular disease, the server will identify this risk. The server also evaluates the user's stress level and emotional state based on the emotional data.

[0860] The server generates specific improvement measures based on the disease risk prediction results. This function is performed by the measure generation means. The generated measures are customized based on the user's emotional state. For example, a user with a high stress level will be recommended relaxation techniques and appropriate exercise to reduce stress.

[0861] The generated improvement measures and prediction results are sent to the user via a presentation device. The device receives this data and visually displays it to the user, allowing the user to easily understand their own health risks and the measures they need to take, and take action to improve their condition.

[0862] As a specific example, consider the case where a user is "50 years old" and enters the following health checkup data: "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," and "weight: 80 kg." The user enters the data into the device, and the transmission means sends this data and emotion data to the server. The server receives the data, analyzes it using statistical and machine learning models, and predicts that "high cholesterol and high blood pressure result in a high risk of cardiovascular disease." The emotion engine also evaluates that the stress level is high. The countermeasure generation means generates improvement countermeasures that recommend "eating a low-salt diet, engaging in aerobic exercise, and receiving medical advice" as well as "practicing meditation and deep breathing to reduce stress," and the presentation means presents these to the user. The user can check the results and countermeasures on the device and improve their lifestyle habits based on the recognized risks.

[0863] In this way, the system of the present invention can accurately predict a user's health risks and emotional state and provide specific, customized remedies to help the user take appropriate health and stress management measures.

[0864] The processing flow will be explained below.

[0865] Step 1: The user enters their age, medical data, and emotional data.

[0866] The user enters their age and health check data (e.g., "50 years old," "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," "weight: 80 kg") into the terminal interface.

[0867] The user's facial expressions and voice are captured by emotion recognition sensors (camera and microphone), and emotion data is collected.

[0868] The device checks the validity of the format and content of the input data and emotion data, and if inappropriate data is entered, prompts the user to re-enter it.

[0869] Step 2: The device sends the data to the server

[0870] The device correctly formats the valid data (age, health check data, emotion data) and prepares to send the data to the server as an HTTP request.

[0871] The device makes an HTTP request and sends the data to the server.

[0872] Step 3: The server receives the data

[0873] The server receives the HTTP request and analyzes the received data (age, health check data, emotion data).

[0874] The server rechecks the validity of the data, and if it contains inappropriate data, it generates an error message and sends it to the terminal.

[0875] Step 4: The server runs the disease risk prediction algorithm

[0876] The server stores the relevant data in a database and invokes a disease risk prediction algorithm.

[0877] The prediction method within the server uses statistical and machine learning models to analyze health risks based on age and health checkup data.

[0878] Predictive algorithms use data such as high cholesterol and high blood pressure to predict risk of cardiovascular disease and other illnesses.

[0879] The server uses an emotion engine to assess the user's stress level and emotional state from the emotion data.

[0880] Step 5: The server generates the prediction results and improvement measures

[0881] The server analyzes the prediction results and generates specific improvement measures based on the risks.

[0882] The countermeasure generation means generates countermeasures (e.g., low-salt diet, moderate exercise, meditation or deep breathing to reduce stress) that correspond to the user's predicted risk and emotional state.

[0883] Step 6: The server sends the report to the device

[0884] Create an HTTP response to send the report (prediction results and specific improvement measures) generated by the server to the terminal.

[0885] The server sends the report to the device via an HTTP response.

[0886] Step 7: The device displays the report to the user

[0887] The terminal analyzes the report received from the server and displays it to the user in a visually easy-to-understand format.

[0888] Users can review the report, understand predicted health risks and corresponding remedial measures, and receive advice tailored to their emotional state.

[0889] As a specific example, consider a case where a user is "50 years old" and enters the following health checkup data: "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," and "weight: 80 kg." Based on facial expressions and voice, the user is evaluated as being in a "high stress state." The user enters the data into the device, and the emotion recognition sensor collects emotional data. The transmission means transmits this to the server. The server receives and analyzes the data, predicts that the user is at high risk for cardiovascular disease due to high cholesterol and high blood pressure, and further evaluates the user as being in a "high stress state." The countermeasure generation means generates improvement measures, recommending "a low-salt diet, aerobic exercise, and consultation with a doctor," as well as "practicing meditation and deep breathing to reduce stress," and the presentation means presents these to the user. The user can check the results and countermeasures on the device and improve their lifestyle based on the recognized risks and emotional state.

[0890] Example 2

[0891] 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."

[0892] Conventional health management systems predict disease risk based solely on a user's health checkup data and suggest general improvement measures. However, because they do not take into account the user's emotional state, they have the problem of being unable to provide specific improvement measures for stress management or individual psychological conditions. This can result in inefficient measures that users can implement, potentially reducing the effectiveness of health improvement.

[0893] 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.

[0894] In this invention, the server includes input means for the user to input their age and health checkup data, transmission means for transmitting the data input using the input means and emotional data to the server, prediction means for analyzing the data received via the transmission means to predict disease risk, measure generation means for generating improvement measures based on the risk obtained by the prediction means, and presentation means for presenting the result data generated by the measure generation means to the user. This makes it possible to present customized improvement measures that take into account the user's emotional state as well as their health checkup data.

[0895] "Input means" refers to an interface for users to input their age and health check data into the system.

[0896] The "transmission means" refers to a function for transmitting data and emotion data input using the input means to the server.

[0897] "Emotional data" refers to data that indicates the emotional state of a user collected from facial expressions and voice.

[0898] The "prediction means" refers to a function for predicting disease risk by analyzing data received via the transmission means.

[0899] A "statistical model" refers to an algorithm for predicting disease risk through statistical analysis of data.

[0900] A "machine learning model" refers to an algorithm that learns from large datasets to predict disease risk.

[0901] The "measure generation means" refers to a function for generating improvement measures based on the risks obtained by the prediction means.

[0902] The "presentation means" refers to an interface for presenting the result data generated by the countermeasure generation means to the user.

[0903] "Improvement measures" refer to specific suggestions for improving diet, exercising, and recommending a doctor's visit to reduce disease risk.

[0904] "Customization" refers to the process of individually tailoring remediation measures based on the user's emotional state and individual data.

[0905] The present invention is a system that allows a user to input their age and health checkup data, predicts disease risk based on that data, and suggests specific measures to improve the condition. Furthermore, it can recognize the user's emotions and customize the measures. Specific embodiments of the present invention are described below.

[0906] User data entry and collection

[0907] The user uses the device's interface to input their age and health checkup data, including total cholesterol, blood pressure, and weight. The device is also equipped with emotion recognition sensors, such as a camera and microphone, which collect emotion data from the user's facial expressions and voice.

[0908] Sending data

[0909] The device receives the input data such as age, health checkup data, and emotion data, and sends it to the server as an HTTP request, where it is stored in a database for analysis.

[0910] Data analysis and disease risk prediction

[0911] The server predicts disease risk based on the submitted data. Statistical and machine learning models are used for the prediction. This allows the server to analyze future disease risk based on the user's age, health checkup data, and emotional data. For example, if high cholesterol levels or high blood pressure indicate a risk of cardiovascular disease, the server will identify this risk. The server also evaluates the user's stress level and emotional state based on emotional data.

[0912] Generate remedial measures

[0913] The server generates specific improvement measures based on the disease risk prediction results. The measure generator performs this function, and the generated measures are customized based on the user's emotional state. For example, a user with a high stress level will be recommended relaxation techniques and appropriate exercise to reduce stress.

[0914] Presenting to the user

[0915] The generated improvement measures and prediction results are sent to the user through a presentation device. The device receives this data and visually displays it to the user, allowing the user to understand their own health risks and the measures they should take.

[0916] Specific examples

[0917] For example, consider the case where a 50-year-old user enters the following health checkup data: total cholesterol level: 240 mg / dL, blood pressure: 150 / 90 mmHg, and weight: 80 kg. The user enters the data into the device, and the transmission means sends this data and emotion data to the server. The server analyzes the received data and predicts that "high cholesterol and high blood pressure result in a high risk of cardiovascular disease." The emotion engine also evaluates that the stress level is high. The countermeasure generation means generates improvement measures, recommending "a low-salt diet, aerobic exercise, and consultation with a doctor," as well as "practice meditation and deep breathing to reduce stress," and the presentation means presents these to the user. The user can check the results and countermeasures on the device and take concrete action.

[0918] Prompt Sentence Examples

[0919] "If I'm 50 years old, my total cholesterol level is 240 mg / dL, my blood pressure is 150 / 90 mmHg, and I weigh 80 kg, what is my risk of cardiovascular disease and what specific steps can I take to improve it? Also, if my stress level is high, can you tell me how to relax?"

[0920] In this way, the system of the present invention accurately predicts the user's health risks and emotional state and provides specific, customized remedies, thereby helping the user to take appropriate health and stress management measures.

[0921] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0922] Step 1:

[0923] The user enters their age and health checkup data into the device's interface. The health checkup data includes total cholesterol level, blood pressure, weight, etc. Emotional data is also collected using the device's camera and microphone.

[0924] Input: Age, total cholesterol level, blood pressure, weight, emotional data (facial expression, voice)

[0925] Output: The set of input data

[0926] Specific behavior:

[0927] The user enters "age 50," "total cholesterol level 240 mg / dL," "blood pressure 150 / 90 mmHg," and "weight 80 kg" into an input form on a smartphone or PC, and then speaks into the camera and microphone.

[0928] Step 2:

[0929] The terminal receives the input age, health check data, and emotion data, and sends this data to the server as an HTTP request.

[0930] Input: User-entered age, health check data, and emotional data

[0931] Output: Data packets sent to the server

[0932] Specific behavior:

[0933] The device converts the user input dataset (age "50 years old", total cholesterol level "240 mg / dL", blood pressure "150 / 90 mmHg", weight "80 kg") and emotion data collected from the camera and microphone into JSON format and sends it to the server via an HTTP request.

[0934] Step 3:

[0935] The server analyzes the received data and stores it in a database, using statistical and machine learning models to predict disease risk.

[0936] Input: Age, health check data, and emotional data sent

[0937] Output: Save to database, disease risk prediction results

[0938] Specific behavior:

[0939] The server stores the received data in a "User Data" table. The server runs statistical and machine learning models to predict that "high cholesterol and high blood pressure increase the risk of cardiovascular disease."

[0940] Step 4:

[0941] The server evaluates the user's stress level based on their emotional data, and generates specific measures to improve the situation based on their age and disease risk based on their health checkup data.

[0942] Input: Disease risk prediction results, emotion evaluation results

[0943] Output: Generated remediation measures

[0944] Specific behavior:

[0945] The server considers emotional data that is evaluated as "high stress level" and generates specific improvement measures based on the predicted disease risk, such as "eat a low-salt diet, engage in aerobic exercise, and receive medical advice," and "practice meditation and deep breathing to reduce stress."

[0946] Step 5:

[0947] The server transmits the generated improvement measures and disease risk prediction results to the terminal, which receives this data and visually displays it to the user.

[0948] Input: Improvement measures and prediction results sent from the server

[0949] Output: Visual display to the user

[0950] Specific behavior:

[0951] The server sends improvement measures such as "low-salt diet and aerobic exercise" and "practice meditation and deep breathing" to the device as an HTTP response, and the device displays the results and measures on the screen. The user can check this and take specific action.

[0952] Through these processing steps, the system is able to provide a customized health and stress management strategy to the user.

[0953] (Application example 2)

[0954] 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."

[0955] Conventional health management systems typically predict disease risk based on health data entered by users and then provide general improvement measures based on the results. However, because they do not take the user's emotional state into account, they are unable to provide improvement measures optimized for each user, which can result in reduced effectiveness. In addition, there is no specific reward for users who achieve their health goals, making it difficult to maintain user motivation.

[0956] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: input means for a user to input their age and health checkup data; transmission means for transmitting the data input using the input means to the server; prediction means for analyzing the data received via the transmission means to predict a disease risk; measure generation means for generating an improvement measure based on the risk obtained by the prediction means; presentation means for presenting the result data generated by the measure generation means to the user; emotion recognition means for recognizing the user's emotional state; customization means for customizing the improvement measure based on the emotion data obtained by the emotion recognition means; and reward provision means for providing a reward for maintaining a specific health index. This allows the user to receive an individually optimized improvement measure and further obtain a reward to maintain motivation for achieving their health goal.

[0957] "Input means" refers to a device or interface that allows a user to input their age and health checkup data.

[0958] The "transmission means" refers to a communication function or system for transmitting data input using the input means to a server.

[0959] The "prediction means" is software or algorithms for analyzing data received via the transmission means and predicting disease risk.

[0960] The "measure generation means" is a system or tool for generating health improvement measures based on the risks obtained by the prediction means.

[0961] The "presentation means" is a device or interface for visually or audibly displaying the result data generated by the countermeasure generation means to the user.

[0962] "Emotion recognition means" refers to sensors or software that analyze the user's facial expressions and voice to recognize their emotional state.

[0963] The "customization means" refers to a system or algorithm for individually optimizing improvement measures based on the emotion data obtained by the emotion recognition means.

[0964] A "reward provider" is a mechanism or system for providing rewards to users who maintain certain health indicators.

[0965] The present invention is a system that allows a user to input their age and health checkup data, predicts disease risk based on that data, and suggests specific measures to improve the condition. It also recognizes the user's emotions and customizes the measures. It also includes a function to provide rewards for maintaining specific health indicators. Specific embodiments for implementing the present invention are described below.

[0966] Hardware and software used

[0967] Hardware: Smartphone, server, emotion recognition sensor (camera, microphone, etc.)

[0968] Software: Python, Flask, Scikit-learn (for machine learning models)

[0969] Processing flow

[0970] Data entry and submission

[0971] Users use devices such as smartphones to input their age and health checkup data (e.g., total cholesterol level, blood pressure, weight). In addition, emotion recognition sensors use the device's built-in camera and microphone to collect the user's facial expressions and voice, obtaining emotional data. This data is then sent to the server using an HTTP request.

[0972] Data analysis

[0973] The server analyzes the received data and predicts disease risk using statistical and machine learning models (e.g., Scikit-learn). For example, high total cholesterol and blood pressure predict a high risk of cardiovascular disease. It also assesses the user's stress level based on emotional data.

[0974] Generate improvement measures

[0975] The server generates specific improvement measures based on the disease risk obtained by the prediction means. The measure generation means includes dietary improvement measures, exercise suggestions, and recommendations for doctor visits. The server also customizes improvement measures based on the emotional state data obtained by the emotion recognition means. For example, for a user with a high stress level, the server recommends meditation or deep breathing to reduce stress.

[0976] Results presentation and rewards

[0977] The generated improvement measures and prediction results are presented to the user through the presentation means. The user can check the visually displayed results on their own device. In addition, if the user maintains the health index for a certain period of time (e.g., one month), the reward provision means will provide a reward such as cash back or point redemption.

[0978] Specific examples

[0979] For example, if a user enters the following data:

[0980] Age: 50

[0981] Total cholesterol level: 240 mg / dL

[0982] Blood pressure: 150 / 90 mmHg

[0983] Weight: 80 kg

[0984] Emotional state: High stress level

[0985] The server receives this data through the transmission means and analyzes it using a machine learning model and an emotion recognition model, predicting a "high risk of cardiovascular disease." The emotion data also evaluates the user as having a "high stress level." The countermeasure generation means generates improvement measures, recommending "a low-salt diet, aerobic exercise, and receiving medical advice," as well as "practicing meditation and deep breathing," and the presentation means presents these to the user. Furthermore, if the user implements these measures and maintains their health index for one month, rewards such as point redemptions are provided.

[0986] Prompt Sentence Examples

[0987] "Please enter the user's age and health checkup data (total cholesterol level, blood pressure, weight). Based on that data, we will predict disease risk, evaluate emotional state, and suggest specific improvement measures. For example, if the user is '50 years old, total cholesterol level 240 mg / dL, blood pressure 150 / 90 mmHg, weight 80 kg, emotional state stress level high,' please generate corresponding advice."

[0988] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0989] Step 1:

[0990] The user enters health data such as their age, total cholesterol level, blood pressure, and weight through the smartphone's input screen. The smartphone's built-in camera and microphone are also used to collect the user's facial expressions and voice, and emotional data is obtained (input).

[0991] Step 2:

[0992] The device collects the input health data and emotion data and sends them as an HTTP request to the server using the transmission means. The transmitted content (output) includes the user's age, total cholesterol level, blood pressure, weight, and emotion recognition data.

[0993] Step 3:

[0994] The server receives the HTTP request and stores it in a database (input). The stored data includes age, total cholesterol level, blood pressure, weight, and emotional data.

[0995] Step 4:

[0996] The server analyzes the stored health data using statistical and machine learning models (using Scikit-learn), specifically processing and calculating the following data:

[0997] Age, total cholesterol level, blood pressure, and weight are provided as input data to a machine learning model to predict disease risk (output: disease risk).

[0998] The emotional data is fed into an emotion recognition model to assess the user's stress level (output: emotional state).

[0999] Step 5:

[1000] The server generates specific health improvement measures using the measure generation means based on the disease risk and emotional state obtained by the prediction means. The generated improvement measures (output) include, for example, dietary improvement measures, exercise suggestions, and recommendations for meditation and deep breathing.

[1001] Step 6:

[1002] The server transmits the generated improvement measures and prediction results to the user via a presentation device. The transmitted content (output) includes specific improvement measures, predicted disease risk, and evaluation results of emotional state.

[1003] Step 7:

[1004] The user visually checks (inputs) the proposed improvement measures and prediction results via their smartphone, and takes action to improve their lifestyle based on this.

[1005] Step 8:

[1006] The server monitors whether the user has maintained their health index for a certain period of time (e.g., one month). If the health index is confirmed to be maintained (input), the server uses the reward provision means to provide rewards such as points or cash back (output).

[1007] Prompt Sentence Examples

[1008] "Please enter the user's age and health checkup data (total cholesterol level, blood pressure, weight). Based on that data, we will predict disease risk, evaluate emotional state, and suggest specific improvement measures. For example, if the user is '50 years old, total cholesterol level 240 mg / dL, blood pressure 150 / 90 mmHg, weight 80 kg, emotional state stress level high,' please generate corresponding advice."

[1009] 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.

[1010] 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.

[1011] 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.

[1012] [Fourth embodiment]

[1013] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1014] 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.

[1015] 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).

[1016] 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.

[1017] 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.

[1018] 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).

[1019] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1020] 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.

[1021] 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.

[1022] 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.

[1023] 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.

[1024] 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.

[1025] 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."

[1026] The present invention is a system that allows a user to input their age and health checkup data, predicts disease risk based on the data, and suggests specific measures to improve the risk. Specific embodiments of the present invention will be described below.

[1027] The user uses the terminal interface to input their age and health check data, such as total cholesterol level, blood pressure, weight, etc. This data is entered into the system through the input means of the terminal.

[1028] The terminal receives the input data and sends it to the server. The sending means plays this role, creating an HTTP request to send the data to the server and communicating. The server processes the received data and stores it in a database for analysis.

[1029] The server predicts disease risk based on the submitted data. Statistical and machine learning models are used to predict the user's future disease risk based on their age and health checkup data. For example, if high cholesterol or high blood pressure indicates a risk of cardiovascular disease, the server identifies this risk.

[1030] Next, the server generates specific improvement measures based on the disease risk prediction results. This function is performed by the measure generation means, which generates specific dietary improvements, exercise suggestions, and even measures recommending regular doctor visits. For example, a user with high cholesterol levels would be recommended to "reduce salt intake and engage in moderate exercise."

[1031] The improvement measures and prediction results generated by the server are sent to the user via a presentation device. The device receives this data and visually displays it to the user, allowing the user to easily understand their own health risks and the measures they need to take, and take action to improve their health.

[1032] As a specific example, consider the case where a user is "50 years old" and inputs the following health checkup data: "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," and "weight: 80 kg." The user inputs the data into the terminal, and the transmission means sends this data to the server. The server receives the data, analyzes it using statistical and machine learning models, and predicts that "high cholesterol and high blood pressure result in a high risk of cardiovascular disease." The countermeasure generation means generates improvement countermeasures recommending "a low-salt diet, aerobic exercise, and receiving medical advice," and the presentation means presents these to the user. The user can check the results and countermeasures on the terminal and improve their lifestyle habits based on the recognized risks.

[1033] In this way, the system of the present invention can accurately predict the user's health risks and provide specific remedial measures, thereby supporting the user in taking appropriate health care.

[1034] The processing flow will be explained below.

[1035] Step 1: User enters age and health data

[1036] The user enters their age and health check data (e.g., "50 years old," "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," "weight: 80 kg," etc.) into the device interface.

[1037] The terminal checks the validity of the format and content of the entered data, and if inappropriate data is entered, prompts the user to re-enter it.

[1038] Step 2: The device sends the data to the server

[1039] The device correctly formats the valid data and prepares it to be sent to the server as an HTTP request.

[1040] The device makes an HTTP request and sends the data to the server.

[1041] Step 3: The server receives the data

[1042] The server receives the HTTP request and analyzes the received data (age and health checkup data).

[1043] The server rechecks the validity of the data, and if it contains inappropriate data, it generates an error message and sends it to the terminal.

[1044] Step 4: The server runs the disease risk prediction algorithm

[1045] The server stores the relevant data in a database and invokes a disease risk prediction algorithm.

[1046] The predictive means in the server uses statistical and machine learning models to analyze health risks based on the data.

[1047] Predictive algorithms predict a user's risk of cardiovascular disease and other illnesses based on their specific health check data (e.g., total cholesterol, blood pressure).

[1048] Step 5: The server generates the prediction results and improvement measures

[1049] The server analyzes the prediction results and generates specific improvement measures based on the risks.

[1050] The countermeasure generation means generates countermeasures corresponding to the predicted risk of the user (for example, since the cholesterol level is high, a low-salt diet is recommended, and moderate aerobic exercise is recommended).

[1051] The server generates a detailed report containing the measures taken.

[1052] Step 6: The server sends the report to the device

[1053] Create an HTTP response to send the report (prediction results and specific improvement measures) generated by the server to the terminal.

[1054] The server sends the report to the device via an HTTP response.

[1055] Step 7: The device displays the report to the user

[1056] The terminal analyzes the report received from the server and displays it to the user in a visually easy-to-understand format.

[1057] The user checks the report and understands the predicted health risks and the corresponding remedial measures.

[1058] Example 1

[1059] 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."

[1060] Health management is becoming an increasingly important issue in modern society. In particular, individuals living busy lives need systems that efficiently manage daily health data and provide appropriate health risk predictions and improvement advice based on that data. However, existing systems are difficult to use because they require users to analyze their own health data and find appropriate measures, requiring significant effort and knowledge. This creates problems such as delays in the early detection of health risks and the implementation of improvement actions.

[1061] 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.

[1062] In this invention, the server includes input means for a user to input their age and health checkup data, transmission means for transmitting the data input using the input means to the server, database storage means for saving and formatting the data received via the transmission means, prediction means for analyzing the data saved using the storage means with a statistical model or a machine learning model to predict disease risk, measure generation means for generating improvement measures using a generative AI model based on the risk obtained by the prediction means, and presentation means for presenting the result data generated by the measure generation means to the user. This allows users to easily analyze their own health data and obtain specific and feasible improvement measures.

[1063] "User" refers to an individual who uses the system to manage their own health data and receive disease risk predictions and improvement measures.

[1064] "Input means" refers to the device or software that allows a user to input age and health check data into the system.

[1065] The "transmission means" refers to a communication mechanism or protocol for transmitting data input using the input means to the server.

[1066] "Database Storage Means" means a database and associated software and hardware for storing and formatting data received via Transmission Means.

[1067] "Statistical model" refers to a mathematical model used to predict a user's health risk based on past data.

[1068] A "machine learning model" refers to a type of artificial intelligence that uses algorithms to automatically learn from large amounts of data and make predictions.

[1069] "Prediction tool" refers to a device or algorithm that uses statistical or machine learning models to predict a user's risk of disease.

[1070] A "generative AI model" refers to a model that uses artificial intelligence to generate improvement measures based on a user's health data.

[1071] The "measure generation means" refers to a device or algorithm that generates specific improvement measures based on the risks obtained by the prediction means.

[1072] The "presentation means" refers to a device or interface for presenting the result data generated by the countermeasure generation means to the user.

[1073] The present invention is a system that allows a user to input their age and health checkup data, predicts their risk of illness based on the data, and suggests specific measures to improve the situation. Specific embodiments for carrying out the present invention will be described below.

[1074] User Data Entry

[1075] The user uses their own terminal to input their age and health checkup data. At this time, an input form is provided on the terminal interface, and the user inputs their age and various health checkup data (e.g., total cholesterol level, blood pressure, weight, etc.). After inputting, the user presses the "send" button to send the data to the server.

[1076] Sending data

[1077] The terminal receives the data entered by the user, creates an HTTP request and sends it to the server, where the data is converted into JSON format and sent to the server over the Internet.

[1078] Data storage and analysis

[1079] The server stores the received data in a database, using a database engine (such as MySQL or PostgreSQL) to format the data appropriately.The server then analyzes the stored data using statistical and machine learning models (using Python libraries such as scikit-learn and TensorFlow) to predict disease risk.

[1080] Disease risk prediction

[1081] The server uses statistical and machine learning models to analyze disease risk based on the data received. For example, if high cholesterol or high blood pressure indicate a risk of cardiovascular disease, the server identifies these risks and predicts the user's risk of disease.

[1082] Generate remedial measures

[1083] Based on the predicted risks, the server uses a generative AI model (such as GPT-4) to generate specific remedial measures, including dietary improvements, exercise suggestions, and even recommendations for regular doctor visits. The remedial measures are presented to the user in an actionable and easy-to-understand format.

[1084] Presentation of results

[1085] The server sends the generated improvement measures and disease risk prediction results to the user's device, which then visually displays the received data, for example, in a pop-up window or notification area, making it easy for the user to understand.

[1086] Specific examples

[1087] For example, if a user is "50 years old" and enters the following health checkup data: "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," and "weight: 80 kg," the device converts this data into JSON format and sends it to the server. After receiving the data, the server stores it in a database and analyzes it using statistical and machine learning models. The analysis results predict that "high cholesterol and high blood pressure result in a high risk of cardiovascular disease," and a generative AI model is used to generate improvement measures recommending "a low-salt diet, aerobic exercise, and consultation with a doctor." This is then sent to the device and presented to the user in a pop-up window.

[1088] Prompt Sentence Examples

[1089] Examples of prompts that can be created using a generative AI model include:

[1090] Write a program for a system that predicts future disease risk and suggests specific measures to improve it based on a user's age and health checkup data. Data entered by the user includes age, total cholesterol level, blood pressure, and weight. Use statistical and machine learning models to predict disease risk, and recommend dietary improvements, exercise suggestions, and regular doctor visits as improvement measures.

[1091] In this way, the system of the present invention can accurately predict the user's health risks and provide specific remedial measures, thereby supporting the user in taking appropriate health management in advance.

[1092] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1093] Program processing flow

[1094] Step 1: User enters data

[1095] Users input their age and health checkup data through the terminal interface, including age, total cholesterol level, blood pressure, weight, etc.

[1096] Input: Age, total cholesterol level, blood pressure, weight, etc.

[1097] Output: User health data (JSON format)

[1098] Step 2: The device sends the data to the server

[1099] The terminal receives the data entered by the user, creates an HTTP request and sends it to the server, where the data is converted to JSON format and sent over the Internet.

[1100] Input: User's health data (JSON format)

[1101] Output: HTTP request

[1102] Step 3: The server stores and formats the data

[1103] The server stores the received data in a database and optionally formats the data, for example by using queries to convert the data into the appropriate format.

[1104] Input: User health data received as an HTTP request

[1105] Output: Formatted data stored in the database

[1106] Step 4: The server analyzes using statistical and machine learning models

[1107] The server analyzes the data stored in the database using statistical and machine learning models to predict disease risk, using Python libraries such as scikit-learn and TensorFlow.

[1108] Input: Formatted data stored in a database

[1109] Output: Disease risk prediction results

[1110] Step 5: The server generates improvement measures using the generated AI model

[1111] The server uses a generative AI model (such as GPT-4) based on the disease risk prediction results to generate specific improvement measures.

[1112] Input: Disease risk prediction results

[1113] Output: Generated remediation measures

[1114] Step 6: The server sends the results to the user's device

[1115] The server then sends the generated improvement measures and disease risk prediction results to the user's device. The data is again sent as an HTTP request.

[1116] Input: Generated remedial measures and predicted disease risk results

[1117] Output: HTTP request

[1118] Step 7: The device receives the results and displays them to the user

[1119] The device uses HTML and CSS to visually display the received data and present it to the user, typically using a pop-up window or notification area.

[1120] Input: Result data received as an HTTP request

[1121] Output: The visual representation presented to the user

[1122] (Application example 1)

[1123] 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."

[1124] Conventional health management systems have limited means for users to easily predict disease risk based on their own health checkup data and obtain specific measures to improve their condition. It is also difficult for users to receive effective training and dietary advice in real time at fitness gyms and other physical stores. This makes it difficult for users to comprehensively manage their own health status and take appropriate measures.

[1125] 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.

[1126] In this invention, the server includes input means for a user to input their age and health checkup data, transmission means for transmitting the data input using the input means to the server, prediction means for analyzing the data received via the transmission means to predict disease risk, and measure generation means for generating improvement measures based on the risk obtained by the prediction means. This allows users to manage their own health condition at the fitness gym and receive advice on training and diet in real time.

[1127] "User" refers to an individual who uses the system to manage their own health condition.

[1128] "Age" refers to the number of years calculated from the user's date of birth that the user enters into the system.

[1129] "Health checkup data" refers to various health checkup result data such as total cholesterol level, blood pressure, and weight for measuring the user's health condition.

[1130] "Input means" refers to a means that provides an interface for a user to input age and health data into the system.

[1131] The "transmission means" refers to a communication means for transmitting data input via the input means to the server.

[1132] "Server" refers to a central processing unit that analyzes received data and performs various calculations and predictions.

[1133] "Prediction means" refers to means that includes a statistical model or machine learning model for analyzing data received via the transmission means and predicting disease risk.

[1134] The "measure generation means" refers to a means for generating specific improvement measures based on the risks obtained by the prediction means.

[1135] The "presentation means" refers to a means for visually presenting the result data generated by the countermeasure generation means to the user.

[1136] "Feedback means" refers to a means by which users can manage their own health status at a fitness gym and receive training and dietary advice in real time.

[1137] A "statistical model" refers to a mathematical model that uses various statistical analyses based on past data to predict future disease risk.

[1138] "Machine learning model" refers to a model that incorporates artificial intelligence techniques to learn from data and use that knowledge to predict disease risk.

[1139] MODE FOR CARRYING OUT THE INVENTION

[1140] In this application example, a system is provided that allows a user to manage their own health condition at a fitness gym and receive training and dietary advice in real time. Specific embodiments for implementing this system are described below.

[1141] A user inputs his / her health checkup data using a smartphone or smart glasses. This interface is realized using an input means, and data such as age, total cholesterol level, blood pressure, weight, etc. For example, consider that a user inputs the following data using a smartphone:

[1142] Age: 50

[1143] Total cholesterol level: 240 mg / dL

[1144] Blood pressure: 150 / 90 mmHg

[1145] Weight: 80 kg

[1146] The input data is sent to the server via a sending means, which typically communicates using an HTTP request.

[1147] The server analyzes the received data and predicts disease risk using predictive methods, such as statistical and machine learning models, to determine risk based on the user's health status. For example, if high cholesterol or high blood pressure indicates a risk of cardiovascular disease, the server identifies this risk.

[1148] The countermeasure generator then generates specific remedial measures based on the risk, including specific dietary improvements, exercise suggestions, and recommendations to see a doctor. For example, a user with high cholesterol might be recommended to "consume a low-salt diet and engage in 30 minutes of aerobic exercise."

[1149] The result data generated by the countermeasure generation means is presented to the user using the presentation means. The user can check the results on their smartphone or smart glasses and make training plans at the fitness gym or meal plans. This allows the user to understand their own health condition in real time and take necessary countermeasures promptly.

[1150] As an example, the following prompt sentence can be used:

[1151] Example prompt sentence:

[1152] "A 50-year-old man has entered his health checkup data (total cholesterol level: 240 mg / dL, blood pressure: 150 / 90 mmHg, weight: 80 kg). Based on this data, please predict his disease risk and suggest specific measures to improve his condition."

[1153] The hardware used includes smartphones and smart glasses for users to input data. Specifically, iPhones and Google Glass are examples. On the server side, machine learning libraries such as Scikit-learn are used for data analysis and prediction, and Django or Flask can be used as web frameworks. MySQL or PostgreSQL are suitable databases.

[1154] In this way, the system of the present invention helps users effectively manage their health at the fitness gym and receive appropriate training and dietary advice in real time.

[1155] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1156] Step 1:

[1157] The user inputs their age and health checkup data into the interface using a smartphone or smart glasses. The input data includes age, total cholesterol level, blood pressure, weight, etc. The input data is temporarily stored in the terminal's memory via the input means.

[1158] Step 2:

[1159] The terminal sends the input data to the server. An HTTP request is used as the transmission method. The data is converted to JSON format and sent to the server. Specifically, the following JSON data is sent.

[1160] json

[1161] {

[1162] "age": 50,

[1163] "total_cholesterol": 240,

[1164] "blood_pressure": "150 / 90",

[1165] "weight": 80

[1166] }

[1167] Step 3:

[1168] The server analyzes the received data. The server analyzes the received JSON data and extracts various health checkup data. This data is stored in an internal database and transferred to the prediction method. MySQL or PostgreSQL is used as the database.

[1169] Step 4:

[1170] The server uses a predictive method to predict disease risk. Machine learning models such as Scikit-learn are used for prediction. Input data is fed into the model, which generates a risk prediction value, such as "cardiovascular disease risk." The model then evaluates risk by referencing past data.

[1171] Step 5:

[1172] The server generates specific improvement measures using a countermeasure generation means. Based on the predicted risk, it generates dietary improvement measures, exercise suggestions, and recommendations for doctor consultations according to statistical models and pre-programmed rules. For example, in the case of a high risk, specific advice such as "eat a low-salt diet and engage in 30 minutes of aerobic exercise" is generated.

[1173] Step 6:

[1174] The server sends the generated remediation measures to the user's device via the presentation means. The predicted risks and remediation measures are sent as presentation data in JSON format. For example, the following data is sent:

[1175] json

[1176] {

[1177] "risk_prediction": "high risk",

[1178] "improvement_measures": {

[1179] "diet": "Eat a low-salt diet",

[1180] "exercise": "30 minutes of cardio"

[1181] }

[1182] }

[1183] Step 7:

[1184] The device uses a presentation tool to display the predicted results and improvement measures to the user. The results are presented visually using a smartphone or smart glasses. The user can check the results and adjust their fitness training or meal plan based on the results.

[1185] Through these processing steps, users can understand their own health status in real time and quickly obtain and implement necessary improvement measures.

[1186] 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.

[1187] The present invention is a system that allows a user to input their age and health checkup data, predicts disease risk based on that data, and suggests specific measures to improve the condition, and also recognizes the user's emotions to customize the measures. Specific embodiments of the present invention will be described below.

[1188] The user uses the terminal interface to input their age and health checkup data. The health checkup data includes, for example, total cholesterol level, blood pressure, and weight. This data is entered into the system through the terminal's input means. The terminal is also equipped with emotion recognition sensors such as a camera and microphone, which collect emotion data from the user's facial expressions and voice.

[1189] The device receives the input data and sends it to the server. The sending means takes care of this, and the data is sent as an HTTP request. The server processes the received data and stores it in a database for analysis. Emotion data is also sent and stored in the same way.

[1190] The server predicts disease risk based on the submitted data. Statistical and machine learning models are used as the prediction method. This allows the server to analyze future disease risk based on the user's age, health checkup data, and emotional data. For example, if high cholesterol levels or high blood pressure indicate a risk of cardiovascular disease, the server will identify this risk. The server also evaluates the user's stress level and emotional state based on the emotional data.

[1191] The server generates specific improvement measures based on the disease risk prediction results. This function is performed by the measure generation means. The generated measures are customized based on the user's emotional state. For example, a user with a high stress level will be recommended relaxation techniques and appropriate exercise to reduce stress.

[1192] The generated improvement measures and prediction results are sent to the user via a presentation device. The device receives this data and visually displays it to the user, allowing the user to easily understand their own health risks and the measures they need to take, and take action to improve their condition.

[1193] As a specific example, consider the case where a user is "50 years old" and enters the following health checkup data: "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," and "weight: 80 kg." The user enters the data into the device, and the transmission means sends this data and emotion data to the server. The server receives the data, analyzes it using statistical and machine learning models, and predicts that "high cholesterol and high blood pressure result in a high risk of cardiovascular disease." The emotion engine also evaluates that the stress level is high. The countermeasure generation means generates improvement countermeasures that recommend "eating a low-salt diet, engaging in aerobic exercise, and receiving medical advice" as well as "practicing meditation and deep breathing to reduce stress," and the presentation means presents these to the user. The user can check the results and countermeasures on the device and improve their lifestyle habits based on the recognized risks.

[1194] In this way, the system of the present invention can accurately predict a user's health risks and emotional state and provide specific, customized remedies to help the user take appropriate health and stress management measures.

[1195] The processing flow will be explained below.

[1196] Step 1: The user enters their age, medical data, and emotional data.

[1197] The user enters their age and health check data (e.g., "50 years old," "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," "weight: 80 kg") into the terminal interface.

[1198] The user's facial expressions and voice are captured by emotion recognition sensors (camera and microphone), and emotion data is collected.

[1199] The device checks the validity of the format and content of the input data and emotion data, and if inappropriate data is entered, prompts the user to re-enter it.

[1200] Step 2: The device sends the data to the server

[1201] The device correctly formats the valid data (age, health check data, emotion data) and prepares to send the data to the server as an HTTP request.

[1202] The device makes an HTTP request and sends the data to the server.

[1203] Step 3: The server receives the data

[1204] The server receives the HTTP request and analyzes the received data (age, health check data, emotion data).

[1205] The server rechecks the validity of the data, and if it contains inappropriate data, it generates an error message and sends it to the terminal.

[1206] Step 4: The server runs the disease risk prediction algorithm

[1207] The server stores the relevant data in a database and invokes a disease risk prediction algorithm.

[1208] The prediction method within the server uses statistical and machine learning models to analyze health risks based on age and health checkup data.

[1209] Predictive algorithms use data such as high cholesterol and high blood pressure to predict risk of cardiovascular disease and other illnesses.

[1210] The server uses an emotion engine to assess the user's stress level and emotional state from the emotion data.

[1211] Step 5: The server generates the prediction results and improvement measures

[1212] The server analyzes the prediction results and generates specific improvement measures based on the risks.

[1213] The countermeasure generation means generates countermeasures (e.g., low-salt diet, moderate exercise, meditation or deep breathing to reduce stress) that correspond to the user's predicted risk and emotional state.

[1214] Step 6: The server sends the report to the device

[1215] Create an HTTP response to send the report (prediction results and specific improvement measures) generated by the server to the terminal.

[1216] The server sends the report to the device via an HTTP response.

[1217] Step 7: The device displays the report to the user

[1218] The terminal analyzes the report received from the server and displays it to the user in a visually easy-to-understand format.

[1219] Users can review the report, understand predicted health risks and corresponding remedial measures, and receive advice tailored to their emotional state.

[1220] As a specific example, consider a case where a user is "50 years old" and enters the following health checkup data: "total cholesterol level: 240 mg / dL," "blood pressure: 150 / 90 mmHg," and "weight: 80 kg." Based on facial expressions and voice, the user is evaluated as being in a "high stress state." The user enters the data into the device, and the emotion recognition sensor collects emotional data. The transmission means transmits this to the server. The server receives and analyzes the data, predicts that the user is at high risk for cardiovascular disease due to high cholesterol and high blood pressure, and further evaluates the user as being in a "high stress state." The countermeasure generation means generates improvement measures, recommending "a low-salt diet, aerobic exercise, and consultation with a doctor," as well as "practicing meditation and deep breathing to reduce stress," and the presentation means presents these to the user. The user can check the results and countermeasures on the device and improve their lifestyle based on the recognized risks and emotional state.

[1221] Example 2

[1222] 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."

[1223] Conventional health management systems predict disease risk based solely on a user's health checkup data and suggest general improvement measures. However, because they do not take into account the user's emotional state, they have the problem of being unable to provide specific improvement measures for stress management or individual psychological conditions. This can result in inefficient measures that users can implement, potentially reducing the effectiveness of health improvement.

[1224] 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.

[1225] In this invention, the server includes input means for the user to input their age and health checkup data, transmission means for transmitting the data input using the input means and emotional data to the server, prediction means for analyzing the data received via the transmission means to predict disease risk, measure generation means for generating improvement measures based on the risk obtained by the prediction means, and presentation means for presenting the result data generated by the measure generation means to the user. This makes it possible to present customized improvement measures that take into account the user's emotional state as well as their health checkup data.

[1226] "Input means" refers to an interface for users to input their age and health check data into the system.

[1227] The "transmission means" refers to a function for transmitting data and emotion data input using the input means to the server.

[1228] "Emotional data" refers to data that indicates the emotional state of a user collected from facial expressions and voice.

[1229] The "prediction means" refers to a function for predicting disease risk by analyzing data received via the transmission means.

[1230] A "statistical model" refers to an algorithm for predicting disease risk through statistical analysis of data.

[1231] A "machine learning model" refers to an algorithm that learns from large datasets to predict disease risk.

[1232] The "measure generation means" refers to a function for generating improvement measures based on the risks obtained by the prediction means.

[1233] The "presentation means" refers to an interface for presenting the result data generated by the countermeasure generation means to the user.

[1234] "Improvement measures" refer to specific suggestions for improving diet, exercising, and recommending a doctor's visit to reduce disease risk.

[1235] "Customization" refers to the process of individually tailoring remediation measures based on the user's emotional state and individual data.

[1236] The present invention is a system that allows a user to input their age and health checkup data, predicts disease risk based on that data, and suggests specific measures to improve the condition. Furthermore, it can recognize the user's emotions and customize the measures. Specific embodiments of the present invention are described below.

[1237] User data entry and collection

[1238] The user uses the device's interface to input their age and health checkup data, including total cholesterol, blood pressure, and weight. The device is also equipped with emotion recognition sensors, such as a camera and microphone, which collect emotion data from the user's facial expressions and voice.

[1239] Sending data

[1240] The device receives the input data such as age, health checkup data, and emotion data, and sends it to the server as an HTTP request, where it is stored in a database for analysis.

[1241] Data analysis and disease risk prediction

[1242] The server predicts disease risk based on the submitted data. Statistical and machine learning models are used for the prediction. This allows the server to analyze future disease risk based on the user's age, health checkup data, and emotional data. For example, if high cholesterol levels or high blood pressure indicate a risk of cardiovascular disease, the server will identify this risk. The server also evaluates the user's stress level and emotional state based on emotional data.

[1243] Generate remedial measures

[1244] The server generates specific improvement measures based on the disease risk prediction results. The measure generator performs this function, and the generated measures are customized based on the user's emotional state. For example, a user with a high stress level will be recommended relaxation techniques and appropriate exercise to reduce stress.

[1245] Presenting to the user

[1246] The generated improvement measures and prediction results are sent to the user through a presentation device. The device receives this data and visually displays it to the user, allowing the user to understand their own health risks and the measures they should take.

[1247] Specific examples

[1248] For example, consider the case where a 50-year-old user enters the following health checkup data: total cholesterol level: 240 mg / dL, blood pressure: 150 / 90 mmHg, and weight: 80 kg. The user enters the data into the device, and the transmission means sends this data and emotion data to the server. The server analyzes the received data and predicts that "high cholesterol and high blood pressure result in a high risk of cardiovascular disease." The emotion engine also evaluates that the stress level is high. The countermeasure generation means generates improvement measures, recommending "a low-salt diet, aerobic exercise, and consultation with a doctor," as well as "practice meditation and deep breathing to reduce stress," and the presentation means presents these to the user. The user can check the results and countermeasures on the device and take concrete action.

[1249] Prompt Sentence Examples

[1250] "If I'm 50 years old, my total cholesterol level is 240 mg / dL, my blood pressure is 150 / 90 mmHg, and I weigh 80 kg, what is my risk of cardiovascular disease and what specific steps can I take to improve it? Also, if my stress level is high, can you tell me how to relax?"

[1251] In this way, the system of the present invention accurately predicts the user's health risks and emotional state and provides specific, customized remedies, thereby helping the user to take appropriate health and stress management measures.

[1252] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1253] Step 1:

[1254] The user enters their age and health checkup data into the device's interface. The health checkup data includes total cholesterol level, blood pressure, weight, etc. Emotional data is also collected using the device's camera and microphone.

[1255] Input: Age, total cholesterol level, blood pressure, weight, emotional data (facial expression, voice)

[1256] Output: The set of input data

[1257] Specific behavior:

[1258] The user enters "age 50," "total cholesterol level 240 mg / dL," "blood pressure 150 / 90 mmHg," and "weight 80 kg" into an input form on a smartphone or PC, and then speaks into the camera and microphone.

[1259] Step 2:

[1260] The terminal receives the input age, health check data, and emotion data, and sends this data to the server as an HTTP request.

[1261] Input: User-entered age, health check data, and emotional data

[1262] Output: Data packets sent to the server

[1263] Specific behavior:

[1264] The device converts the user input dataset (age "50 years old", total cholesterol level "240 mg / dL", blood pressure "150 / 90 mmHg", weight "80 kg") and emotion data collected from the camera and microphone into JSON format and sends it to the server via an HTTP request.

[1265] Step 3:

[1266] The server analyzes the received data and stores it in a database, using statistical and machine learning models to predict disease risk.

[1267] Input: Age, health check data, and emotional data sent

[1268] Output: Save to database, disease risk prediction results

[1269] Specific behavior:

[1270] The server stores the received data in a "User Data" table. The server runs statistical and machine learning models to predict that "high cholesterol and high blood pressure increase the risk of cardiovascular disease."

[1271] Step 4:

[1272] The server evaluates the user's stress level based on their emotional data, and generates specific measures to improve the situation based on their age and disease risk based on their health checkup data.

[1273] Input: Disease risk prediction results, emotion evaluation results

[1274] Output: Generated remediation measures

[1275] Specific behavior:

[1276] The server considers emotional data that is evaluated as "high stress level" and generates specific improvement measures based on the predicted disease risk, such as "eat a low-salt diet, engage in aerobic exercise, and receive medical advice," and "practice meditation and deep breathing to reduce stress."

[1277] Step 5:

[1278] The server transmits the generated improvement measures and disease risk prediction results to the terminal, which receives this data and visually displays it to the user.

[1279] Input: Improvement measures and prediction results sent from the server

[1280] Output: Visual display to the user

[1281] Specific behavior:

[1282] The server sends improvement measures such as "low-salt diet and aerobic exercise" and "practice meditation and deep breathing" to the device as an HTTP response, and the device displays the results and measures on the screen. The user can check this and take specific action.

[1283] Through these processing steps, the system is able to provide a customized health and stress management strategy to the user.

[1284] (Application example 2)

[1285] 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."

[1286] Conventional health management systems typically predict disease risk based on health data entered by users and then provide general improvement measures based on the results. However, because they do not take the user's emotional state into account, they are unable to provide improvement measures optimized for each user, which can result in reduced effectiveness. In addition, there is no specific reward for users who achieve their health goals, making it difficult to maintain user motivation.

[1287] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: input means for a user to input their age and health checkup data; transmission means for transmitting the data input using the input means to the server; prediction means for analyzing the data received via the transmission means to predict a disease risk; measure generation means for generating an improvement measure based on the risk obtained by the prediction means; presentation means for presenting the result data generated by the measure generation means to the user; emotion recognition means for recognizing the user's emotional state; customization means for customizing the improvement measure based on the emotion data obtained by the emotion recognition means; and reward provision means for providing a reward for maintaining a specific health index. This allows the user to receive an individually optimized improvement measure and further obtain a reward to maintain motivation for achieving their health goal.

[1288] "Input means" refers to a device or interface that allows a user to input their age and health checkup data.

[1289] The "transmission means" refers to a communication function or system for transmitting data input using the input means to a server.

[1290] The "prediction means" is software or algorithms for analyzing data received via the transmission means and predicting disease risk.

[1291] The "measure generation means" is a system or tool for generating health improvement measures based on the risks obtained by the prediction means.

[1292] The "presentation means" is a device or interface for visually or audibly displaying the result data generated by the countermeasure generation means to the user.

[1293] "Emotion recognition means" refers to sensors or software that analyze the user's facial expressions and voice to recognize their emotional state.

[1294] The "customization means" refers to a system or algorithm for individually optimizing improvement measures based on the emotion data obtained by the emotion recognition means.

[1295] A "reward provider" is a mechanism or system for providing rewards to users who maintain certain health indicators.

[1296] The present invention is a system that allows a user to input their age and health checkup data, predicts disease risk based on that data, and suggests specific measures to improve the condition. It also recognizes the user's emotions and customizes the measures. It also includes a function to provide rewards for maintaining specific health indicators. Specific embodiments for implementing the present invention are described below.

[1297] Hardware and software used

[1298] Hardware: Smartphone, server, emotion recognition sensor (camera, microphone, etc.)

[1299] Software: Python, Flask, Scikit-learn (for machine learning models)

[1300] Processing flow

[1301] Data entry and submission

[1302] Users use devices such as smartphones to input their age and health checkup data (e.g., total cholesterol level, blood pressure, weight). In addition, emotion recognition sensors use the device's built-in camera and microphone to collect the user's facial expressions and voice, obtaining emotional data. This data is then sent to the server using an HTTP request.

[1303] Data analysis

[1304] The server analyzes the received data and predicts disease risk using statistical and machine learning models (e.g., Scikit-learn). For example, high total cholesterol and blood pressure predict a high risk of cardiovascular disease. It also assesses the user's stress level based on emotional data.

[1305] Generate improvement measures

[1306] The server generates specific improvement measures based on the disease risk obtained by the prediction means. The measure generation means includes dietary improvement measures, exercise suggestions, and recommendations for doctor visits. The server also customizes improvement measures based on the emotional state data obtained by the emotion recognition means. For example, for a user with a high stress level, the server recommends meditation or deep breathing to reduce stress.

[1307] Results presentation and rewards

[1308] The generated improvement measures and prediction results are presented to the user through the presentation means. The user can check the visually displayed results on their own device. In addition, if the user maintains the health index for a certain period of time (e.g., one month), the reward provision means will provide a reward such as cash back or point redemption.

[1309] Specific examples

[1310] For example, if a user enters the following data:

[1311] Age: 50

[1312] Total cholesterol level: 240 mg / dL

[1313] Blood pressure: 150 / 90 mmHg

[1314] Weight: 80 kg

[1315] Emotional state: High stress level

[1316] The server receives this data through the transmission means and analyzes it using a machine learning model and an emotion recognition model, predicting a "high risk of cardiovascular disease." The emotion data also evaluates the user as having a "high stress level." The countermeasure generation means generates improvement measures, recommending "a low-salt diet, aerobic exercise, and receiving medical advice," as well as "practicing meditation and deep breathing," and the presentation means presents these to the user. Furthermore, if the user implements these measures and maintains their health index for one month, rewards such as point redemptions are provided.

[1317] Prompt Sentence Examples

[1318] "Please enter the user's age and health checkup data (total cholesterol level, blood pressure, weight). Based on that data, we will predict disease risk, evaluate emotional state, and suggest specific improvement measures. For example, if the user is '50 years old, total cholesterol level 240 mg / dL, blood pressure 150 / 90 mmHg, weight 80 kg, emotional state stress level high,' please generate corresponding advice."

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

[1320] Step 1:

[1321] The user enters health data such as their age, total cholesterol level, blood pressure, and weight through the smartphone's input screen. The smartphone's built-in camera and microphone are also used to collect the user's facial expressions and voice, and emotional data is obtained (input).

[1322] Step 2:

[1323] The device collects the input health data and emotion data and sends them as an HTTP request to the server using the transmission means. The transmitted content (output) includes the user's age, total cholesterol level, blood pressure, weight, and emotion recognition data.

[1324] Step 3:

[1325] The server receives the HTTP request and stores it in a database (input). The stored data includes age, total cholesterol level, blood pressure, weight, and emotional data.

[1326] Step 4:

[1327] The server analyzes the stored health data using statistical and machine learning models (using Scikit-learn), specifically processing and calculating the following data:

[1328] Age, total cholesterol level, blood pressure, and weight are provided as input data to a machine learning model to predict disease risk (output: disease risk).

[1329] The emotional data is fed into an emotion recognition model to assess the user's stress level (output: emotional state).

[1330] Step 5:

[1331] The server generates specific health improvement measures using the measure generation means based on the disease risk and emotional state obtained by the prediction means. The generated improvement measures (output) include, for example, dietary improvement measures, exercise suggestions, and recommendations for meditation and deep breathing.

[1332] Step 6:

[1333] The server transmits the generated improvement measures and prediction results to the user via a presentation device. The transmitted content (output) includes specific improvement measures, predicted disease risk, and evaluation results of emotional state.

[1334] Step 7:

[1335] The user visually checks (inputs) the proposed improvement measures and prediction results via their smartphone, and takes action to improve their lifestyle based on this.

[1336] Step 8:

[1337] The server monitors whether the user has maintained their health index for a certain period of time (e.g., one month). If the health index is confirmed to be maintained (input), the server uses the reward provision means to provide rewards such as points or cash back (output).

[1338] Prompt Sentence Examples

[1339] "Please enter the user's age and health checkup data (total cholesterol level, blood pressure, weight). Based on that data, we will predict disease risk, evaluate emotional state, and suggest specific improvement measures. For example, if the user is '50 years old, total cholesterol level 240 mg / dL, blood pressure 150 / 90 mmHg, weight 80 kg, emotional state stress level high,' please generate corresponding advice."

[1340] 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.

[1341] 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.

[1342] 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.

[1343] 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.

[1344] FIG. 9 illustrates 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 behaviors 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.

[1345] 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.

[1346] 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).

[1347] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1348] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1349] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1350] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1351] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1352] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1353] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1354] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1355] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1356] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1357] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1358] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1359] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1360] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1361] The following is further disclosed regarding the above embodiment.

[1362] (Claim 1)

[1363] an input means for a user to input his / her age and health checkup data;

[1364] a transmitting means for transmitting data inputted by said input means to a server;

[1365] a prediction means for analyzing the data received via the transmission means and predicting a disease risk;

[1366] a countermeasure generation means for generating an improvement countermeasure based on the risk obtained by the prediction means;

[1367] a presentation means for presenting the result data generated by the countermeasure generation means to a user;

[1368] A system including:

[1369] (Claim 2)

[1370] 2. The system of claim 1, wherein the predicting means predicts disease risk using a statistical model or a machine learning model.

[1371] (Claim 3)

[1372] 2. The system of claim 1, wherein the countermeasure generating means generates countermeasures including specific dietary improvements, exercise suggestions, and recommendations for medical consultation.

[1373] "Example 1"

[1374] (Claim 1)

[1375] an input means for a user to input his / her age and health checkup data;

[1376] a transmitting means for transmitting data inputted by said input means to a server;

[1377] a database storage means for storing and formatting the data received via said transmission means;

[1378] a prediction means for analyzing the data stored by the storage means using a statistical model or a machine learning model to predict a disease risk;

[1379] a countermeasure generation means for generating an improvement countermeasure using a generation AI model based on the risk obtained by the prediction means;

[1380] a presentation means for presenting the result data generated by the countermeasure generation means to a user;

[1381] A system including:

[1382] (Claim 2)

[1383] 2. The system of claim 1, wherein the predicting means predicts disease risk using a statistical model or a machine learning model.

[1384] (Claim 3)

[1385] 2. The system of claim 1, wherein the countermeasure generating means generates countermeasures including specific dietary improvements, exercise suggestions, and recommendations for medical consultation.

[1386] "Application Example 1"

[1387] (Claim 1)

[1388] an input means for a user to input his / her age and health checkup data;

[1389] a transmitting means for transmitting data inputted by said input means to a server;

[1390] a prediction means for analyzing the data received via the transmission means and predicting a disease risk;

[1391] a countermeasure generation means for generating an improvement countermeasure based on the risk obtained by the prediction means;

[1392] a presentation means for presenting the result data generated by the countermeasure generation means to a user;

[1393] A feedback mechanism for users to manage their health status at the fitness gym and receive training and dietary advice.

[1394] A system including:

[1395] (Claim 2)

[1396] 2. The system of claim 1, wherein the predicting means predicts disease risk using a statistical model or a machine learning model.

[1397] (Claim 3)

[1398] 2. The system of claim 1, wherein the countermeasure generating means generates countermeasures including specific dietary improvements, exercise suggestions, and recommendations for medical consultation.

[1399] "Example 2: Combining Emotion Engines"

[1400] (Claim 1)

[1401] an input means for a user to input his / her age and health checkup data;

[1402] a transmitting means for transmitting the data and emotion data inputted using said input means to a server;

[1403] a prediction means for analyzing the data received via the transmission means and predicting a disease risk;

[1404] a countermeasure generation means for generating an improvement countermeasure based on the risk obtained by the prediction means;

[1405] a presentation means for presenting the result data generated by the countermeasure generation means to a user;

[1406] A system including:

[1407] (Claim 2)

[1408] 2. The system of claim 1, wherein the predicting means predicts disease risk using a statistical model or a machine learning model.

[1409] (Claim 3)

[1410] 2. The system of claim 1, wherein the countermeasure generation means generates countermeasures including specific dietary improvements, exercise suggestions, and recommendations for medical consultation, and further generates customized countermeasures based on the user's emotional state.

[1411] "Application example 2 when combining emotion engines"

[1412] (Claim 1)

[1413] an input means for a user to input his / her age and health checkup data;

[1414] a transmitting means for transmitting data inputted by said input means to a server;

[1415] a prediction means for analyzing the data received via the transmission means and predicting a disease risk;

[1416] a countermeasure generation means for generating an improvement countermeasure based on the risk obtained by the prediction means;

[1417] a presentation means for presenting the result data generated by the countermeasure generation means to a user;

[1418] emotion recognition means for recognizing an emotional state of a user;

[1419] customization means for customizing improvement measures based on the emotion data obtained by the emotion recognition means;

[1420] a reward mechanism that provides rewards for maintaining specific health indicators;

[1421] A system including:

[1422] (Claim 2)

[1423] 2. The system of claim 1, wherein the predicting means predicts disease risk using a statistical model or a machine learning model.

[1424] (Claim 3)

[1425] 2. The system of claim 1, wherein the countermeasure generating means generates countermeasures including specific dietary improvements, exercise suggestions, and recommendations for medical consultation. [Explanation of symbols]

[1426] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. an input means for a user to input his / her age and health checkup data; a transmitting means for transmitting data inputted by said input means to a server; a prediction means for analyzing the data received via the transmission means and predicting a disease risk; a countermeasure generation means for generating an improvement countermeasure based on the risk obtained by the prediction means; a presentation means for presenting the result data generated by the countermeasure generation means to a user; A system including:

2. The system of claim 1 , wherein the predicting means predicts disease risk using a statistical model or a machine learning model.

3. 2. The system of claim 1, wherein the countermeasure generating means generates countermeasures including specific dietary improvements, exercise suggestions, and recommendations for medical consultation.

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