System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving and analyzing a health check result uploaded from a user; means for setting a target value of a health index on the basis of an analysis result; means for collecting and integrating daily health data from a health care terminal of the user; means for analyzing a meal image uploaded from the user and generating nutrient data; means for generating a recommendation related to health management on the basis of the generated data and notifying the user; and means for receiving a health check result of the next year and evaluating a degree of improvement by comparison with previous data.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] Currently, annual health checkups are common, but there is a lack of services that provide comprehensive health care by monitoring improvements based on the results and progress on a daily basis. This leads to users relying solely on self-management, making effective health management difficult. In addition, collecting and analyzing daily health and dietary data is time-consuming, making it difficult to maintain consistent health management. [Means for solving the problem]

[0005] This invention provides a system that can set target values ​​for health indices based on health checkup results by receiving and analyzing health checkup results uploaded by users. It also provides a system that consistently manages daily health conditions by collecting and integrating daily health data from the user's healthcare device and analyzing meal images uploaded by the user to generate nutrient data. It also provides a system that generates health management recommendations based on the generated data and notifies the user. It also provides a system that receives the next year's health checkup results, compares them with the previous data, and evaluates the degree of improvement, allowing users to continuously improve and confirm their health.

[0006] "Health checkup results" are data on health indicators obtained based on a medical checkup that a user undergoes once a year.

[0007] The "analyzing means" refers to a device or software that has the function of allowing the system to receive and analyze the health checkup results provided by the user.

[0008] "Health indexes" are important values ​​for assessing the user's health condition, such as blood pressure, blood sugar level, cholesterol level, and body fat percentage.

[0009] A "means for setting target values" is a device or software that has the function of using a generative AI model to determine appropriate target values ​​based on analyzed health indicators in order to improve the user's health.

[0010] A "healthcare device" is an electronic device, such as a smartwatch or fitness tracker, that is used to monitor a user's health on a daily basis.

[0011] "Health data" refers to data such as the number of steps, heart rate, and body temperature collected from the user's healthcare terminal.

[0012] An "integration means" is a device or software that has the function of centrally managing data obtained from multiple sources and storing it on an analysis platform.

[0013] "Meal images" are photographs of meals that a user regularly consumes, and are input data for generating nutrient data.

[0014] The "means for generating nutrient data" refers to a device or software that has the function of analyzing a food image and calculating the calorie and content of various nutrients.

[0015] "Means for generating and notifying recommendations" refers to a device or software that has the function of analyzing a user's health and nutritional data, creating specific recommendations for improving health, and notifying the user of the recommendations.

[0016] "Means for comparison and evaluation of improvement" refers to a device or software that has the function of comparing the results of the health checkup from the previous year with the results of the health checkup from the following year and calculating the degree of improvement for various KPIs. [Brief explanation of the drawings]

[0017] [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 illustrating 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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The system of this invention realizes comprehensive health management by integrating daily health data and dietary data based on the results of a user's health checkup. The system of this invention is premised on the use of specific hardware and software configurations, and an embodiment thereof is shown below as an example.

[0039] System Overview

[0040] Importing and analyzing health checkup results

[0041] Users upload their annual health checkup results to the system using a device such as a smartphone. The server that receives the upload checks the file format and loads the contents into a generative AI model. The AI ​​model analyzes the health checkup results and extracts key health indicators (e.g., blood pressure, blood sugar, cholesterol, body fat percentage, etc.).

[0042] Setting health indicator targets

[0043] The server sets various KPIs (Key Performance Indicators) based on the analysis results. These target values ​​are important for improving the user's health. For example, they include target values ​​for reducing body fat percentage and target values ​​for returning blood pressure to the normal range. These target values ​​are notified to the user so that they can be checked.

[0044] Daily data collection and integration

[0045] Users use healthcare devices such as smartwatches and fitness trackers on a daily basis to collect data such as the number of steps taken, heart rate, and body temperature. This data is periodically synchronized with a server, which then integrates and manages the data in real time.

[0046] Dietary data collection and analysis

[0047] Users take photos of their meals using their smartphones and upload them to the system. These meal images are then analyzed by the server, and various nutrients and calories are automatically calculated. The analysis results are stored in the user's database and can be used to help with daily dietary management.

[0048] Comprehensive data analysis and recommendation generation

[0049] The server integrates daily health and dietary data and evaluates the user's overall health status using a generative AI model. Based on the results of this evaluation, specific health management recommendations are generated for the user, including the amount of exercise required and recommended dietary content.

[0050] Progress management and feedback

[0051] The user's progress towards their KPIs is monitored regularly and the server provides this information as feedback to the user in the form of reports, allowing the user to stay aware of their health status and identify areas for improvement.

[0052] Evaluation based on the results of the following year's health check

[0053] When the next year's health checkup results are uploaded, the server loads the new data into the AI ​​model and compares it with the previous year's data. This evaluates the degree of improvement in various KPIs and provides feedback to the user. Specific results include, for example, "body fat percentage reduced by 10%" or "blood pressure returned to normal range."

[0054] Specific examples

[0055] 1. Reading and analyzing health checkup results

[0056] The user scans the results of the health check and uploads them to the system.

[0057] The server receives the result file and analyzes it using an AI model.

[0058] Extract indicators such as blood pressure, blood sugar level, and body fat percentage.

[0059] 2. Setting health indicator targets

[0060] The server sets a target value based on the analysis results.

[0061] Examples: Lowering blood pressure to 130 / 80 mmHg, reducing body fat percentage to 25%.

[0062] 3. Daily data collection and integration

[0063] Users use smartwatches to collect daily health data.

[0064] The server synchronizes and manages this data in real time.

[0065] 4. Dietary data collection and analysis

[0066] Users take photos of their meals and upload them to the system.

[0067] The server analyzes the image and calculates calories and various nutrients.

[0068] 5. Comprehensive data analysis and recommendation generation

[0069] The server integrates healthcare and dietary data to conduct a health assessment.

[0070] A generative AI model generates recommendations and notifies the user.

[0071] 6. Progress Management and Feedback

[0072] The server monitors progress against KPIs and generates weekly or monthly reports.

[0073] User receives and acknowledges feedback.

[0074] 7. Evaluation based on the results of the following year's health check

[0075] New health checkup results are fed into the AI ​​model and compared with the previous year.

[0076] Evaluate the degree of improvement and notify users of specific results.

[0077] In this way, the system of the present invention provides comprehensive care for the user's health condition and achieves continuous improvement in health.

[0078] The processing flow will be explained below.

[0079] Step 1: User uploads health check results

[0080] Users take a photo of their annual health checkup results with their smartphone and upload them to the system via a dedicated application.

[0081] The user taps the upload button, selects a file from local storage, and sends it.

[0082] Step 2: The server receives the health check results

[0083] The server receives and stores the health check result file sent by the user.

[0084] The server checks the file format and passes the data to the appropriate analysis module.

[0085] Step 3: The server loads the data into the generative AI model

[0086] The server loads the received health check results into a generative AI model.

[0087] An AI model analyzes the data and extracts key health indicators (blood pressure, blood sugar levels, body fat percentage, etc.).

[0088] Step 4: The server sets the target value for the health index.

[0089] The server sets target values ​​(KPIs) for health indicators based on the data analyzed by the AI ​​model.

[0090] For example, the target body fat percentage is set to 25% and the target blood pressure is set to 130 / 80 mmHg.

[0091] Step 5: The server notifies the user of the target value

[0092] The server notifies the user's terminal of the set target value.

[0093] The user receives a notification and confirms the target value.

[0094] Step 6: Users collect data using healthcare devices

[0095] Users use smartwatches and fitness trackers to collect daily data such as steps taken, heart rate, and body temperature.

[0096] This data is stored on the device.

[0097] Step 7: The device synchronizes the collected data with the server

[0098] The user's device periodically synchronizes the collected health data with the server.

[0099] The server stores the received data in real time and integrates it into a database.

[0100] Step 8: User uploads food photo

[0101] Users take photos of their daily meals with their smartphones and upload them to a dedicated application.

[0102] The user taps the upload button to submit the photo.

[0103] Step 9: The server analyzes the food image

[0104] The server receives the uploaded food images and passes the data to the image analysis module.

[0105] The image analysis module analyzes the contents of the meal and calculates various nutrients and calories.

[0106] Step 10: Server consolidates data

[0107] The server consolidates the daily health data and dietary data and stores them in a database.

[0108] Based on the integrated data, a generative AI model performs a comprehensive health assessment.

[0109] Step 11: Server generates recommendations

[0110] The server generates specific health management recommendations for the user based on the analysis results of the AI ​​model.

[0111] For example, it includes specific advice such as "Take a few more steps today" or "Choose foods that are lower in fat."

[0112] Step 12: The server notifies the user of the recommendation

[0113] The server notifies the user's terminal of the generated recommendations.

[0114] The user receives a notification, confirms the recommendation, and acts on it.

[0115] Step 13: Server monitors user progress

[0116] The server routinely monitors the user's progress against the set KPIs and records the progress data.

[0117] The server periodically generates progress reports to provide feedback to the user.

[0118] Step 14: User uploads new health check results

[0119] The following year, the user uploads new medical results back into the system.

[0120] The diagnostic results are photographed with a smartphone and sent via a dedicated application.

[0121] Step 15: Server parses new data

[0122] The server receives new health checkup results and loads them into the generative AI model.

[0123] The AI ​​model compares the previous year's data with the new data and calculates the degree of improvement for various KPIs.

[0124] Step 16: The server notifies the evaluation result

[0125] The server evaluates the degree of improvement and notifies the user of specific results, such as "body fat percentage reduced by 10%" or "blood pressure returned to normal range."

[0126] Users can view results and understand their progress in improving their health.

[0127] Example 1

[0128] 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."

[0129] Health management is an important issue in modern society, but many people are unable to effectively manage their daily health and dietary data. Furthermore, there is a lack of an integrated system for properly comparing and evaluating annual health checkup results and promoting sustainable health improvement. Currently, data collection, analysis, and integration are fragmented, preventing users from achieving comprehensive health management.

[0130] 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.

[0131] In this invention, the server includes means for receiving and analyzing electronic files uploaded by users, means for setting reference values ​​based on the analysis results, means for collecting and integrating daily information from users' electronic devices, means for analyzing images uploaded by users and generating numerical data, means for generating management suggestions based on the generated data and notifying the users, and means for receiving electronic files for the next year and comparing and evaluating them with previous data, thereby enabling users to comprehensively understand their own health status and make continuous improvements to their health.

[0132] "Electronic files" are digital documents or data uploaded by users, such as health checkup results.

[0133] The "reference value" is a target value of a health index that is set based on the analysis results, and indicates the standard of health status that the user should achieve.

[0134] "Electronic devices" are devices that collect users' daily health data, such as smartwatches and fitness trackers.

[0135] "Daily information" refers to daily health-related data such as the number of steps taken, heart rate, and body temperature that users collect through their electronic devices.

[0136] "Images" are visual data such as photos of meals taken by a user using a smartphone or the like.

[0137] "Numerical data" refers to quantitative data such as calories and nutrients obtained through image analysis.

[0138] "Management suggestions" are specific advice and recommendations for health management generated by the server, including, for example, the amount of exercise and recommended dietary content.

[0139] "Evaluation" refers to the act of comparing the results of the next year's health check with the data from the previous year and analyzing the progress and degree of improvement of the user's health indicators.

[0140] This system integrates the results of a user's health checkup, daily health data, and dietary data to realize comprehensive health management. The system mainly involves a server, a terminal, and a user.

[0141] 1. Reading and analyzing health checkup results

[0142] Users scan their health checkup results using a smartphone camera app and upload the data to the system. The server receives the electronic file, verifies the file format, and then uses a generative AI model to analyze the file's contents and extract key health indicators (e.g., blood pressure, blood glucose level, cholesterol level, body fat percentage).

[0143] 2. Setting health indicator targets

[0144] The server sets various benchmarks based on the analysis results, including specific goals such as lowering blood pressure to 130 / 80 mmHg or reducing body fat to 25%. The set benchmarks are then notified to the user.

[0145] 3. Daily data collection and integration

[0146] Users use electronic devices such as smartwatches and fitness trackers on a daily basis to collect daily information such as the number of steps taken, heart rate, body temperature, etc. This data is periodically synchronized via the device to a server, which then integrates and manages this data in real time.

[0147] 4. Dietary data collection and analysis

[0148] Users take photos of their meals with their smartphones and upload them to the system. The server receives the images and uses a generative AI model to analyze the meal's contents and calculate calories and nutrients. This generates numerical data that is stored in the user's database.

[0149] 5. Comprehensive data analysis and recommendation generation

[0150] The server integrates the user's daily health and dietary data and evaluates their overall health status using a generative AI model. Based on the results of this evaluation, management suggestions are generated. For example, the amount of exercise required and recommended dietary content may be recommended. These suggestions are then notified to the user.

[0151] 6. Progress Management and Feedback

[0152] The server periodically monitors the user's progress against the baseline and generates weekly or monthly reports that the user can use to assess their health status and understand any improvements that may be needed.

[0153] 7. Evaluation based on the results of the following year's health check

[0154] When the user uploads the following year's health checkup results to the system, the server loads the new data into the generative AI model and compares it with the previous year's data. This evaluates the degree of improvement against various benchmarks and notifies the user of specific results (e.g., a 10% decrease in body fat percentage, or blood pressure returning to normal range).

[0155] Specific examples

[0156] 1. Reading and analyzing health checkup results

[0157] Users scan their health check results using a dedicated app and upload them to the system.

[0158] The server receives the result file and analyzes it using the generative AI model.

[0159] Extract key health indicators such as blood pressure, blood sugar levels, and body fat percentage.

[0160] 2. Daily data collection and integration

[0161] The user's smartwatch collects daily steps and heart rate data and sends them to a server.

[0162] The server aggregates this data in real time.

[0163] 3. Dietary data collection and analysis

[0164] Users take photos of their meals and upload them to the system.

[0165] The server analyzes the images and calculates nutrients and calories.

[0166] Prompt Sentence Examples

[0167] "Based on the results of last year's health check, please set your health goals for this year."

[0168] "Please analyze your recent dietary and daily health data and tell us your overall health status."

[0169] By using the above prompt sentences, this system can provide appropriate health management suggestions to the user.

[0170] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0171] Step 1:

[0172] The user scans the health checkup results. As input, the user takes a photo of the health checkup results using the camera app on their smartphone. The output is a scanned image file. The user uploads the scanned image file to the system using a dedicated application.

[0173] Step 2:

[0174] The server receives an electronic file. The input is a health checkup result file uploaded by a user. When the server receives this file, it first verifies the file format. If it is confirmed to be in the correct format, the server proceeds to the next analysis step. The output is a verified health checkup result file.

[0175] Step 3:

[0176] Analysis of health checkup results using an AI model. The input is a health checkup result file received by the server. The server loads this file into a generative AI model. The AI ​​model analyzes the data and extracts key health indicators such as blood pressure, blood sugar level, cholesterol level, and body fat percentage. The output is the analyzed health indicator data.

[0177] Step 4:

[0178] The server sets the target value for the health index. The input is the health index data analyzed by the AI ​​model. The server sets the reference value based on this data (e.g., lower blood pressure to 130 / 80 mmHg, reduce body fat percentage to 25%). The output is the set reference value. The reference value is notified to the user.

[0179] Step 5:

[0180] The user collects daily health data. The input is daily health data (e.g., number of steps, heart rate, body temperature, etc.) collected from electronic devices such as smart watches and fitness trackers used by the user. The output is this health data. This data is periodically synchronized to a server via the device.

[0181] Step 6:

[0182] The server integrates daily health data. The input is daily health data synchronized from the user's electronic devices. The server receives, integrates, and manages this data in real time. The output is the integrated health data.

[0183] Step 7:

[0184] The user collects meal data. The input is a photo of the meal taken by the user with a smartphone. The user uploads this to the system. The output is the uploaded meal image.

[0185] Step 8:

[0186] The server analyzes the food images. The input is the food images uploaded by the user. The server analyzes the images using a generative AI model and automatically calculates various nutrients and calories. The output is the analyzed numerical data (calories and nutrients). This data is stored in the user's database.

[0187] Step 9:

[0188] The server performs comprehensive data analysis and generates recommendations. The input is the integrated health data and analyzed dietary data. The server uses a generative AI model to comprehensively analyze this data and evaluate the user's health status. The output is the comprehensive evaluation result and specific health management suggestions based on it (e.g., the amount of exercise required, recommended dietary content). These suggestions are notified to the user.

[0189] Step 10:

[0190] The server monitors progress and provides feedback. The input is the user's baseline and daily health data. The server monitors these data periodically and generates progress reports. The output is a progress report that is sent to the user on a weekly or monthly basis.

[0191] Step 11:

[0192] The user uploads new health checkup results. The input is the health checkup results for the next year. The user uploads this to the system again. The output is a new health checkup results file.

[0193] Step 12:

[0194] The server compares and evaluates new health checkup results. The input is the next year's health checkup results and the previous year's data. The server loads the new data into a generative AI model and compares it with the previous year's data. The output is the degree of improvement against various benchmark values ​​and specific results (e.g., body fat percentage reduced by 10%, blood pressure returned to normal range). The evaluation results are notified to the user.

[0195] (Application example 1)

[0196] 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."

[0197] In modern society, many people are required to collect various data for health management and improve their behavior based on the results. However, existing health management systems have difficulty integrating individual data to perform comprehensive health management, and have limitations in recommending specific behaviors and supporting their implementation. In particular, with regard to dietary management, there are few systems that provide specific recommendations that are easy to apply to daily life. Furthermore, there are insufficient means for users to actually obtain recommended meals. There is a need to build a system that can solve these issues and enable users to continuously improve their health.

[0198] 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.

[0199] In this invention, the server includes means for receiving and analyzing health checkup results uploaded by the user, means for setting target values ​​for health indices based on the analysis results, means for collecting and integrating daily health data from the user's healthcare terminal, means for analyzing meal images uploaded by the user and generating nutrient data, means for generating health management recommendations based on the generated data and notifying the user, means for providing the recommended meals in cooperation with a food delivery service, and means for receiving next year's health checkup results and comparing them with the previous data to evaluate the degree of improvement. This makes it possible to integrate health checkup results, daily health data, and dietary data to perform comprehensive health management, provide specific dietary recommendations, and consistently provide support for implementing them.

[0200] "User" refers to an individual who uses the system.

[0201] "Uploading" refers to the act of a user sending their own digital data to a server.

[0202] "Health checkup results" refers to data obtained as a result of a health check conducted at a medical institution.

[0203] "Analysis" refers to the act of analyzing acquired data and extracting information.

[0204] "Health index" refers to specific values ​​that indicate the user's health condition, such as blood pressure, blood sugar level, and body fat percentage.

[0205] "Target value" refers to a specific indicator value set for improving health.

[0206] "Healthcare devices" refer to devices that collect users' health data, such as smartwatches and fitness trackers.

[0207] "Daily health data" refers to data related to the user's physical activity and physiological condition in their daily lives.

[0208] "Integration" refers to the act of centrally managing multiple data in a single system.

[0209] "Meal image" refers to photographic data of a user's own meal.

[0210] "Analyzing" refers to the act of extracting necessary information (such as nutrients and calories) from image data.

[0211] "Nutrient data" refers to data that indicates the nutritional components and calorie content of a meal.

[0212] "Recommendations" refer to specific advice or suggestions that contribute to improving the user's health.

[0213] "Food delivery service" refers to a delivery service that provides recommended meals to users.

[0214] "Evaluation" refers to the act of measuring the degree of improvement in the user's health based on the acquired data.

[0215] A "generative AI model" refers to an algorithm that uses machine learning technology to analyze data and generate appropriate recommendations.

[0216] This invention relates to a system for comprehensive health management that integrates a user's health checkup results, daily health data, and dietary data. This system includes a smartphone application, a cloud server, a generative AI model, and collaboration with a food delivery service. Detailed embodiments of this system and the program processing that realizes it are described below.

[0217] System configuration

[0218] Hardware

[0219] 1. Smartphones: Devices for collecting and displaying user data

[0220] 2. Healthcare devices: devices that collect health data, such as smartwatches and fitness trackers

[0221] 3. Cloud server: A server for data analysis and management

[0222] software

[0223] 1. Image analysis tools: Uses OpenCV and TensorFlow

[0224] 2. Generative AI model: Uses GPT-3 and Stable Diffusion

[0225] Data collection and processing

[0226] 1. Importing health checkup results

[0227] Users upload their health checkup results using a smartphone app. The server receives the results and analyzes them using an AI model. This analysis extracts key health indicators such as blood pressure, blood sugar levels, and body fat percentage.

[0228] 2. Collecting daily health data

[0229] Daily health data (number of steps, heart rate, body temperature, etc.) from the user's healthcare device is periodically synchronized with a cloud server, which then integrates and manages this data in real time.

[0230] 3. Dietary data collection and analysis

[0231] Users take photos of their meals with their smartphones and upload them to the app, which uses image analysis tools to analyze these photos and calculate the nutritional and calorie content of each ingredient.

[0232] 4. Comprehensive data analysis and recommendation generation

[0233] The server uses a generative AI model to analyze the collected health and dietary data and generate optimal health management recommendations for the user, including the amount of exercise required and recommended dietary content.

[0234] 5. Collaboration with food delivery services

[0235] The recommended meals are linked to food delivery services, allowing users to easily order them within the app, making it easier to maintain healthy lifestyles.

[0236] Specific examples

[0237] 1. Examples of prompts for analyzing health checkup data

[0238] "Analyze health checkup results and extract health indicators. Sample data: Blood pressure: 120 / 80 mmHg, Blood glucose: 90 mg / dL, Cholesterol: 180 mg / dL."

[0239] 2. Food recommendation prompt examples

[0240] "Please suggest an appropriate meal based on the user's daily health status. Health data sample: Steps: 10,000 steps / day, Heart rate: 70 bpm, Food data sample: Mushroom risotto, Calories: 400 kcal, Protein: 15 g, Carbohydrates: 55 g, Fat: 10 g."

[0241] The system of the present invention allows users to perform comprehensive health management by integrating health checkup results, daily health data, and dietary data, and easily take specific actions to improve their health. In particular, by linking with food delivery services, users can easily obtain recommended meals, enabling sustainable health improvement.

[0242] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0243] Step 1:

[0244] A user uploads their health checkup results using a smartphone app. The input is a health checkup result file, and the output is the health checkup data sent to the cloud server. The server checks the format of the received data and prepares it for passing to the AI ​​model.

[0245] Step 2:

[0246] The server receives the health checkup results and analyzes the data using an AI model. The input is the health checkup data, and the output is key health indicators (blood pressure, blood sugar level, body fat percentage, etc.). The AI ​​model extracts these indicators and stores them in a database.

[0247] Step 3:

[0248] The user's healthcare device (smartwatch or fitness tracker) collects daily health data and synchronizes it to a cloud server. The input is daily health data (number of steps, heart rate, body temperature, etc.), and the output is the integrated data stored on the server. The server manages this data in real time.

[0249] Step 4:

[0250] Users take photos of their meals with their smartphones and upload them to the app. The input is the photo file of the meal, and the output is image data sent to a cloud server. The server receives this image data and prepares it for analysis.

[0251] Step 5:

[0252] The server uses image analysis tools (OpenCV and TensorFlow) to analyze photos of meals and calculate nutrients and calories. The input is image data, and the output is nutrient data and calorie data. The server stores this data in a database.

[0253] Step 6:

[0254] The server uses a generative AI model to integrate health checkup data, daily health data, and nutritional data to perform a comprehensive health assessment. All of this data is input, and the output is a health assessment and recommendations. The generative AI model generates recommendations that suggest the optimal amount of exercise and diet for the user and notifies the user.

[0255] Step 7:

[0256] The server connects with food delivery services to provide recommended meals. The input is recommendation data, and the output is a food delivery order. Users can easily order meals from within the app.

[0257] Step 8:

[0258] The server receives the next year's health checkup results and compares them with the previous data to evaluate the degree of improvement. The input is the new health checkup data, and the output is the evaluation result of the degree of improvement. The server then runs this data through the AI ​​model again and provides feedback to the user.

[0259] 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.

[0260] The system of this invention not only realizes comprehensive health management by integrating daily health data and dietary data based on the user's health checkup results, but also evaluates the user's mental health state by collecting and analyzing the user's emotional data and provides appropriate feedback. Specific embodiments of the invention are described below.

[0261] System Overview

[0262] Importing and analyzing health checkup results

[0263] Users upload their annual health checkup results to the system using a device such as a smartphone. The server that receives the upload checks the file format and loads the contents into a generative AI model. The AI ​​model analyzes the health checkup results and extracts key health indicators (e.g., blood pressure, blood sugar, cholesterol, body fat percentage, etc.).

[0264] Setting health indicator targets

[0265] The server sets various KPIs (Key Performance Indicators) based on the analysis results. These target values ​​are important for improving the user's health. For example, they include target values ​​for reducing body fat percentage and target values ​​for returning blood pressure to the normal range. These target values ​​are notified to the user so that they can be checked.

[0266] Daily data collection and integration

[0267] Users use healthcare devices such as smartwatches and fitness trackers on a daily basis to collect data such as the number of steps taken, heart rate, and body temperature. This data is periodically synchronized with a server, which then integrates and manages the data in real time.

[0268] Dietary data collection and analysis

[0269] Users take photos of their meals using their smartphones and upload them to the system. These meal images are then analyzed by the server, and various nutrients and calories are automatically calculated. The analysis results are stored in the user's database and can be used to help with daily dietary management.

[0270] Emotion data collection and analysis

[0271] The device collects emotional data that users input on a daily basis, such as periodic self-evaluations of their emotions and facial recognition using a smartphone camera. This data is sent to a server and analyzed by an emotion engine. The emotion engine evaluates the user's emotional state and identifies their daily stress level and mental health.

[0272] Comprehensive data analysis and recommendation generation

[0273] The server integrates daily health, dietary, and emotional data to evaluate the user's overall health using a generative AI model. Based on the results of this evaluation, specific health management recommendations are generated for the user, including the amount of exercise required, recommended dietary habits, and mental health advice.

[0274] Progress management and feedback

[0275] The user's progress towards their KPIs is regularly monitored, and the server provides this information as feedback to the user in the form of a report. This allows users to constantly understand their own health status and identify areas for improvement. Feedback based on emotional data is also provided periodically, allowing users to check their mental health.

[0276] Evaluation based on the results of the following year's health check

[0277] When the next year's health checkup results are uploaded, the server loads the new data into the AI ​​model and compares it with the previous year's data. This evaluates the degree of improvement in various KPIs and provides feedback to the user. Specific results include, for example, "body fat percentage reduced by 10%" or "blood pressure returned to normal range."

[0278] Specific examples

[0279] 1. Reading and analyzing health checkup results

[0280] The user scans the results of the health check and uploads them to the system.

[0281] The server receives the result file and analyzes it using an AI model.

[0282] Extract indicators such as blood pressure, blood sugar level, and body fat percentage.

[0283] 2. Setting health indicator targets

[0284] The server sets a target value based on the analysis results.

[0285] Examples: Lowering blood pressure to 130 / 80 mmHg, reducing body fat percentage to 25%.

[0286] 3. Daily data collection and integration

[0287] Users use smartwatches to collect daily health data.

[0288] The server synchronizes and manages this data in real time.

[0289] 4. Dietary data collection and analysis

[0290] Users take photos of their meals and upload them to the system.

[0291] The server analyzes the image and calculates calories and various nutrients.

[0292] 5. Emotional Data Collection and Analysis

[0293] Users enter their own self-evaluation of emotions on a daily basis, and facial expression recognition data is collected using the smartphone camera.

[0294] The server receives the emotion data and analyzes it using the emotion engine.

[0295] 6. Comprehensive data analysis and recommendation generation

[0296] The server integrates healthcare data, dietary data, and emotional data to provide a comprehensive assessment of health status.

[0297] A generative AI model generates recommendations and notifies the user.

[0298] Specific examples include advice such as "Take a few more steps today," "Choose lower-fat foods," and "Try meditation to relax."

[0299] 7. Progress Management and Feedback

[0300] The server monitors progress against KPIs and generates weekly or monthly reports.

[0301] User receives and acknowledges feedback.

[0302] This also includes feedback based on emotional data.

[0303] 8. Evaluation based on the results of the following year's health check

[0304] New health checkup results are fed into the AI ​​model and compared with the previous year.

[0305] Evaluate the degree of improvement and notify users of specific results.

[0306] In this way, the system of the present invention provides comprehensive care for the user's health condition, realizing continuous improvement in health and management of mental health.

[0307] The processing flow will be explained below.

[0308] Step 1: User uploads health check results

[0309] Users take a photo of their annual health checkup results with their smartphone and upload them to the system via a dedicated application.

[0310] The user taps the upload button, selects a file from local storage, and sends it.

[0311] Step 2: The server receives the health check results

[0312] The server receives and stores the health check result file sent by the user.

[0313] The server checks the file format and passes the data to the appropriate analysis module.

[0314] Step 3: The server loads the data into the generative AI model

[0315] The server loads the received health check results into a generative AI model.

[0316] An AI model analyzes the data and extracts key health indicators (blood pressure, blood sugar levels, body fat percentage, etc.).

[0317] Step 4: The server sets the target value for the health index.

[0318] The server sets target values ​​(KPIs) for health indicators based on the data analyzed by the AI ​​model.

[0319] For example, the target body fat percentage is set to 25% and the target blood pressure is set to 130 / 80 mmHg.

[0320] Step 5: The server notifies the user of the target value

[0321] The server notifies the user's terminal of the set target value.

[0322] The user receives a notification and confirms the target value.

[0323] Step 6: Users collect data using healthcare devices

[0324] Users use smartwatches and fitness trackers to collect daily data such as steps taken, heart rate, and body temperature.

[0325] This data is stored on the device.

[0326] Step 7: The device synchronizes the collected data with the server

[0327] The user's device periodically synchronizes the collected health data with the server.

[0328] The server stores the received data in real time and integrates it into a database.

[0329] Step 8: User uploads food photo

[0330] Users take photos of their daily meals with their smartphones and upload them to a dedicated application.

[0331] The user taps the upload button to submit the photo.

[0332] Step 9: The server analyzes the food image

[0333] The server receives the uploaded food images and passes the data to the image analysis module.

[0334] The image analysis module analyzes the contents of the meal and calculates various nutrients and calories.

[0335] Step 10: User enters emotion data

[0336] Users input their daily emotional data through a dedicated application.

[0337] Emotion entry includes text entry, facial expression recognition, and multiple choice emotion checklists.

[0338] Step 11: The device sends the emotion data to the server

[0339] The user's device transmits the collected emotion data to the server.

[0340] The server passes the received emotion data to the emotion engine for appropriate analysis.

[0341] Step 12: Server consolidates data

[0342] The server integrates daily health data, dietary data, and emotional data and stores them in a database.

[0343] Based on the integrated data, a generative AI model performs a comprehensive health assessment.

[0344] Step 13: Server generates recommendations

[0345] The server generates specific health management recommendations for the user based on the analysis results of the AI ​​model.

[0346] For example, it includes specific advice such as "Take a few more steps today," "Choose leaner foods," or "Try meditation to relax."

[0347] Step 14: The server notifies the user of the recommendation

[0348] The server notifies the user's terminal of the generated recommendations.

[0349] The user receives a notification, confirms the recommendation, and acts on it.

[0350] Step 15: Server monitors user progress

[0351] The server routinely monitors the user's progress against the set KPIs and records the progress data.

[0352] The server periodically generates progress reports to provide feedback to the user.

[0353] Step 16: User uploads new health check results

[0354] The following year, the user uploads new medical results back into the system.

[0355] The diagnostic results are photographed with a smartphone and sent via a dedicated application.

[0356] Step 17: Server parses new data

[0357] The server receives new health checkup results and loads them into the generative AI model.

[0358] The AI ​​model compares the previous year's data with the new data and calculates the degree of improvement for various KPIs.

[0359] Step 18: The server notifies the evaluation result

[0360] The server evaluates the degree of improvement and notifies the user of specific results, such as "body fat percentage reduced by 10%" or "blood pressure returned to normal range."

[0361] Users can view results and understand their progress in improving their health.

[0362] Example 2

[0363] 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."

[0364] While conventional health management systems could collect and analyze a user's health checkup results, daily health data, and dietary data individually, it was difficult to integrate them for comprehensive health management. Furthermore, they lacked a means to consider mental health status, making it impossible to evaluate a user's overall health status. As a result, users could only obtain partial health information, making it difficult to effectively improve their health.

[0365] 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.

[0366] In this invention, the server includes means for receiving and analyzing health checkup results uploaded by a user, means for setting target values ​​for health indices based on the analysis results, means for collecting and integrating daily health data from the user's healthcare terminal, means for analyzing meal images uploaded by the user and generating nutrient data, means for receiving and analyzing preset emotion data, means for integrating the generated data, generating recommendations for comprehensive health management, and notifying the user, and means for receiving next year's health checkup results and comparing them with the previous data to evaluate the degree of improvement. This makes it possible to comprehensively evaluate the user's physical and mental health status and provide specific improvements and recommendations.

[0367] "Health checkup results" refers to numerical data on health indicators such as blood pressure, blood sugar level, cholesterol level, and body fat percentage obtained through a health checkup conducted by a user once a year at a medical institution or other facility.

[0368] "Analysis means" refers to tools and algorithms used to process uploaded data on the server and extract and analyze specific information.

[0369] "Health index" refers to specific numerical data that indicates the user's health condition, such as blood pressure, blood sugar level, cholesterol level, and body fat percentage.

[0370] The "target value" is set based on the analysis results and refers to a specific numerical value that the user should achieve in order to improve their health.

[0371] A "healthcare device" is an electronic device, such as a smartwatch or fitness tracker, that is used to collect a user's daily health data.

[0372] "Daily health data" refers to health-related data collected daily by a healthcare terminal, such as the user's number of steps, heart rate, and body temperature.

[0373] A "meal image" is a photograph of a meal that a user takes using a smartphone or other device and uploads to the system.

[0374] "Nutrient data" refers to information about various nutrients contained in food, such as carbohydrates, proteins, and lipids, obtained by the server analyzing the food image.

[0375] "Emotional data" refers to data about a user's emotional state and stress level, collected through a user's self-assessment or facial expression recognition using a smartphone camera.

[0376] "Recommendations" refer to specific health management advice and suggestions provided to users based on data collected and analyzed by the server.

[0377] A "generative AI model" is an artificial intelligence model that analyzes collected health checkup results, daily health data, dietary data, and emotional data to generate comprehensive evaluations and recommendations.

[0378] "Improvement" refers to a numerical value or evaluation that indicates how much the user's health index has improved compared to the previous year.

[0379] The system of the present invention provides comprehensive health management based on the user's health checkup results, daily health data, dietary data, and emotional data.

[0380] First, users upload their annual health checkup results to the system using a device such as a smartphone. The device then sends the uploaded health checkup result file to the server. The server checks the format of the received file to ensure it is in the correct format. The server then analyzes the health checkup results using a generative AI model to extract key health indicators (blood pressure, blood sugar level, cholesterol level, body fat percentage, etc.). These extracted health indicators are then recorded in a database. Based on the analysis results, the server then sets individual KPIs (Key Performance Indicators) to aim for health improvement. The set target values ​​are notified to the user and displayed on the device.

[0381] Next, users use healthcare devices such as smartwatches and fitness trackers on a daily basis to collect data such as the number of steps taken, heart rate, and body temperature. The devices periodically synchronize this data with a server, which then integrates and manages the data in real time and stores it in a database.

[0382] Users can also take photos of their meals with their smartphones and upload them to the system. The device then sends the meal images to a server, which then uses a generative AI model to analyze the images and calculate various nutrients and calories. The analysis results are stored in a database and used to help users manage their diet.

[0383] Furthermore, users can collect emotional data by entering their own emotional assessments or by using their smartphone camera. The device then transmits this emotional data to a server, which then uses an emotion engine to analyze the data and evaluate the user's emotional state, stress level, and mental health.

[0384] The server integrates daily health data, dietary data, and emotional data, and uses a generative AI model to assess the user's overall health. Based on the results of this assessment, the server generates specific health management recommendations (e.g., the amount of exercise required, recommended dietary content, mental health advice, etc.) and notifies the user.

[0385] The server periodically monitors the user's progress against KPIs and generates a report on a weekly or monthly basis. The report is then sent to the user and displayed on their device. The user can review the report and identify any necessary improvements. Feedback based on emotional data is also provided on a regular basis.

[0386] When the next year's health checkup results are uploaded, the server loads the new data into the generative AI model and compares it with the previous year's data. This evaluates the degree of improvement in various KPIs and notifies the user of specific results (e.g., a 10% decrease in body fat, or blood pressure returning to normal range).

[0387] Prompt Sentence Examples

[0388] "Create a program that generates a comprehensive health assessment and appropriate feedback based on the user's health checkup results, daily health data, dietary data, and emotional data."

[0389] The system is able to comprehensively assess a user's physical and mental health and provide specific improvements and recommendations.

[0390] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0391] Step 1:

[0392] The user uploads the health check result file (e.g. PDF, JPEG, etc.) to the system using a device such as a smartphone.

[0393] Input: Health check result file

[0394] Output: The uploaded file is saved to the device.

[0395] Step 2:

[0396] The terminal sends the uploaded file to the server.

[0397] Input: Health check result file (saved on the device)

[0398] Output: The health check result file is transferred to the server.

[0399] Step 3:

[0400] The server checks the format of the received file to ensure it is in the correct format, and if it is not, it will display an error message to the user.

[0401] Input: Health check result file

[0402] Output: File format check result (success / failure)

[0403] Step 4:

[0404] The server uses a generative AI model to analyze health checkup results and extract key health indicators (blood pressure, blood sugar levels, cholesterol levels, body fat percentage, etc.).

[0405] Input: Health check result file

[0406] Output: Extracted key health indicators

[0407] Step 5:

[0408] The server records the extracted health indicators in a database and sets KPIs for each user based on the analysis results.

[0409] Input: Key health indicators

[0410] Output: Setting KPIs and recording them in a database

[0411] Step 6:

[0412] The server notifies the user of the set KPIs and displays them to the user.

[0413] Input: The configured KPI

[0414] Output: Notification and display on the user's terminal

[0415] Step 7:

[0416] Users use healthcare devices such as smartwatches and fitness trackers on a daily basis to collect data such as the number of steps taken, heart rate, and body temperature.

[0417] Input: Smartwatch or fitness tracker data

[0418] Output: Collection of daily health data

[0419] Step 8:

[0420] The device periodically synchronizes the collected daily health data with the server.

[0421] Input: Daily health data (saved on the device)

[0422] Output: Synchronization of daily health data to the server

[0423] Step 9:

[0424] The server integrates and manages the received daily health data in real time and stores it in a database.

[0425] Input: Synced daily health data

[0426] Output: Consolidated daily health data stored in a database

[0427] Step 10:

[0428] Users take photos of their meals with their smartphones and upload them to the system.

[0429] Input: Food photo

[0430] Output: Uploaded photos are saved on the device

[0431] Step 11:

[0432] The device sends the uploaded photos to the server.

[0433] Input: Food photo (saved on device)

[0434] Output: Meal photos are sent to the server

[0435] Step 12:

[0436] The server uses a generative AI model to analyze the food images and calculate various nutrients and calories.

[0437] Input: Food photo

[0438] Output: Analyzed nutrient data and calories

[0439] Step 13:

[0440] The server stores the analysis results in a database to help users manage their diet.

[0441] Input: Parsed nutrient data and calories

[0442] Output: Save nutrient data to database

[0443] Step 14:

[0444] Users can collect emotional data by entering their own emotional assessments on a daily basis or by using their smartphone camera.

[0445] Input: Emotion self-assessment data or facial expression recognition data

[0446] Output: The input emotion data is saved on the device.

[0447] Step 15:

[0448] The terminal transmits this emotion data to the server.

[0449] Input: Emotion data (saved on the device)

[0450] Output: Emotion data is sent to the server

[0451] Step 16:

[0452] The server uses an emotion engine to analyze the emotion data and evaluate the user's emotional state and stress level.

[0453] Input: Emotion data

[0454] Output: Analyzed emotional state and stress level

[0455] Step 17:

[0456] The server integrates daily health data, dietary data, and emotional data, and uses a generative AI model to assess overall health status.

[0457] Input: Daily health data, dietary data, emotional data

[0458] Output: Overall health status assessment results

[0459] Step 18:

[0460] Based on the evaluation results, the server generates specific health management recommendations and notifies the user.

[0461] Input: Overall health status assessment results

[0462] Output: Generated health care recommendations and notification to the user

[0463] Step 19:

[0464] The server periodically monitors the user's progress against the KPIs and generates reports that are then sent to the user.

[0465] Input: KPI progress data

[0466] Output: Generated scheduled reports and notifications to users

[0467] Step 20:

[0468] When the next year's health checkup results are uploaded, the user's device will send the new checkup results to the server.

[0469] Input: New medical examination results

[0470] Output: New health check results are sent to the server.

[0471] Step 21:

[0472] The server loads the new health check results into a generative AI model and compares them with the previous year's data.

[0473] Input: New health checkup results, previous year's health checkup data

[0474] Output: KPI improvement evaluation results

[0475] Step 22:

[0476] The server notifies the user of specific results, such as "body fat percentage reduced by 10%" or "blood pressure returned to normal range."

[0477] Input: KPI improvement evaluation results

[0478] Output: Notification of results to the user

[0479] (Application example 2)

[0480] 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."

[0481] In modern society, health management has become increasingly difficult due to the diversification of people's lifestyles and dietary habits. In particular, conventional health management systems only collect and analyze physical data, and do not adequately evaluate and manage mental health status. Therefore, there is a demand for multifaceted, comprehensive health management. In response to this, the present invention aims to promote health improvement by comprehensively evaluating a user's physical and mental health status and providing personalized health management.

[0482] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0483] In this invention, the server includes a means for receiving and analyzing health checkup results uploaded by a user, a means for setting target values ​​for health indices based on the analysis results, and a means for collecting and integrating daily health data from the user's healthcare device, thereby enabling a comprehensive evaluation of the user's physical and mental health status and the generation of specific health management recommendations for the user.

[0484] "Health checkup results" are the results of tests that a user undergoes periodically at a medical institution, and are data including major health indicators such as blood pressure, blood sugar level, cholesterol level, and body fat percentage.

[0485] "Health index" is specific numerical data for evaluating the health condition of a user, and includes, for example, blood pressure, blood sugar level, cholesterol level, and body fat percentage.

[0486] "Daily health data" refers to data measured by users in their daily lives, including the number of steps taken, heart rate, body temperature, etc. collected by healthcare devices such as smartwatches and fitness trackers.

[0487] "Meal images" are photographs of meals taken by the user, and are data that are uploaded to the system and used to analyze nutrient data.

[0488] "Nutrient data" is information on various nutrients and calories analyzed from a meal image, and is data that is useful for the user's dietary management.

[0489] "Emotion data" is data collected to assess a user's mental health, and includes data from self-assessment of emotions and facial expression recognition.

[0490] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning, and is an algorithm that comprehensively evaluates a user's health condition based on large amounts of data and generates optimal recommendations.

[0491] The system for implementing this invention integrates a user's health checkup results, daily health data, dietary data, and emotional data, evaluates their health status comprehensively using a generative AI model, and provides appropriate feedback to the user.

[0492] composition

[0493] 1. Reading and analyzing health checkup results

[0494] Users upload their annual health checkup results to the system using a device such as a smartphone. The server receives the upload, checks the file format, and loads the contents into a generative AI model. The AI ​​model analyzes the health checkup results and extracts key health indicators.

[0495] 2. Setting health indicator targets

[0496] The server sets various KPIs (Key Performance Indicators) based on the analysis results. These target values ​​aim to improve the user's health, and include blood pressure, body fat percentage, etc. These target values ​​are notified to the user.

[0497] 3. Daily data collection and integration

[0498] Users use smartwatches and fitness trackers on a daily basis to collect data such as the number of steps taken, heart rate, and body temperature. This data is periodically synchronized with a server and integrated and managed in real time.

[0499] 4. Dietary data collection and analysis

[0500] Users take photos of their meals using their smartphones and upload them to the system. The server analyzes the meal images and automatically calculates various nutrients and calories. The analysis results are stored in the user's database.

[0501] 5. Emotional Data Collection and Analysis

[0502] Users enter their own emotional self-assessment on a daily basis and also collect facial expression recognition data using their smartphone camera. This data is sent to a server and analyzed by the emotion engine. The emotion engine evaluates the user's emotional state and grasps their daily stress level and mental health.

[0503] 6. Comprehensive data analysis and recommendation generation

[0504] The server integrates daily health data, dietary data, and emotional data, and uses a generative AI model to comprehensively evaluate the user's health. Based on the results of this evaluation, specific health management recommendations are generated for the user, such as the amount of exercise required, recommended dietary content, and advice on mental health.

[0505] 7. Progress Management and Feedback

[0506] The user's progress towards their KPIs is regularly monitored, and the server provides this information as feedback to the user in the form of a report. This allows users to constantly understand their own health status and identify areas for improvement. Feedback based on emotional data is also provided periodically, allowing users to check their mental health.

[0507] 8. Evaluation based on the results of the following year's health check

[0508] When the next year's health checkup results are uploaded, the server loads them into the AI ​​model and compares them with the previous year's data. This evaluates the degree of improvement in various KPIs and notifies the user of specific results. Examples include "body fat percentage reduced by 10%" and "blood pressure returned to normal range."

[0509] Hardware and software used

[0510] Hardware:

[0511] Smartphone

[0512] Smartwatch

[0513] Fitness tracker

[0514] software:

[0515] AI model for health data analysis

[0516] Sentiment analysis engine

[0517] Nutrient analysis model

[0518] Specific examples

[0519] The user scans their health checkup results and uploads them to the system. The server receives the result file and analyzes it using an AI model to extract indicators such as blood pressure, blood sugar level, and body fat percentage. Based on the analysis results, the server sets target values ​​for health indicators and notifies the user. The user uses the smartwatch daily and synchronizes the acquired data (number of steps, heart rate, etc.) with the server. Next, they take photos of their meals and upload them to the system, which analyzes the nutritional data. The user also enters a self-evaluation of their emotions and collects facial recognition data using the smartphone camera. The server integrates this data, evaluates their overall health, and notifies the user of specific health management recommendations. Examples include advice such as "Take more steps today," "Choose lower-fat meals," and "Try meditation to relax."

[0520] Prompt Sentence Examples

[0521] Health checkup results: Blood pressure 140 / 90, blood sugar 6.5, cholesterol 200, body fat 30%

[0522] Daily data: steps 8000, heart rate 75, body temperature 36.5℃

[0523] Dietary information: Breakfast: 500 calories, 50g carbohydrates, 30g fat

[0524] Emotion data: Facial expression High stress

[0525] This allows a comprehensive assessment of the user's physical and mental health status and provides the user with specific health management recommendations.

[0526] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0527] Step 1:

[0528] A user scans their health checkup results with their smartphone and uploads them to the system. The input is an image file of the health checkup results. The server receives this file and checks the file format. If the format is correct, the contents of the result file are loaded into a generative AI model to extract health indicators such as blood pressure, blood sugar level, and body fat percentage. The output is the extracted health indicator data.

[0529] Step 2:

[0530] The server sets various KPIs (Key Performance Indicators) based on the extracted health indicator data. The extracted health indicator data is the input. The server sets appropriate target values ​​for each health indicator and notifies the user. The output is the set KPI target values.

[0531] Step 3:

[0532] Users use smartwatches and fitness trackers on a daily basis to collect daily health data such as steps taken, heart rate, and body temperature. The input is the data collected from the health device. The device periodically synchronizes this data with the server. The server receives this data and integrates and manages it in real time. The output is the integrated daily health data.

[0533] Step 4:

[0534] Users take photos of their meals using their smartphones and upload them to the system. The input is the meal image. The server receives the image, analyzes it, and calculates various nutrients and calories. The output is the analyzed nutrient data.

[0535] Step 5:

[0536] Users input their self-evaluation of emotions on a daily basis and also collect facial expression recognition data using their smartphone camera. The input is the self-evaluation data and facial expression recognition images. The server receives this data and analyzes it using an emotion engine. The output is the analyzed emotional data.

[0537] Step 6:

[0538] The server integrates daily health data, nutritional data, and emotional data, and evaluates the overall health status using a generative AI model. The input is the integrated health data. The generative AI model generates specific health management recommendations based on this data and notifies the user. The output is the generated health management recommendations.

[0539] Step 7:

[0540] The server periodically monitors the user's progress against KPIs. The input is the integrated health data and the set KPI targets. The server generates a progress report based on this and provides feedback to the user. The output is the progress report.

[0541] Step 8:

[0542] The following year, the user uploads new health checkup results. The input is the new health checkup result file. The server receives this and compares it with the data from the previous year. The server evaluates it using a generative AI model, assesses the degree of improvement against various KPIs, and notifies the user. The output is the improvement evaluation result.

[0543] 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.

[0544] 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.

[0545] 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.

[0546] [Second embodiment]

[0547] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0548] 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.

[0549] 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).

[0550] 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.

[0551] 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.

[0552] 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).

[0553] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0554] 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.

[0555] 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.

[0556] 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.

[0557] 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.

[0558] 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."

[0559] The system of this invention realizes comprehensive health management by integrating daily health data and dietary data based on the results of a user's health checkup. The system of this invention is premised on the use of specific hardware and software configurations, and an embodiment thereof is shown below as an example.

[0560] System Overview

[0561] Importing and analyzing health checkup results

[0562] Users upload their annual health checkup results to the system using a device such as a smartphone. The server that receives the upload checks the file format and loads the contents into a generative AI model. The AI ​​model analyzes the health checkup results and extracts key health indicators (e.g., blood pressure, blood sugar, cholesterol, body fat percentage, etc.).

[0563] Setting health indicator targets

[0564] The server sets various KPIs (Key Performance Indicators) based on the analysis results. These target values ​​are important for improving the user's health. For example, they include target values ​​for reducing body fat percentage and target values ​​for returning blood pressure to the normal range. These target values ​​are notified to the user so that they can be checked.

[0565] Daily data collection and integration

[0566] Users use healthcare devices such as smartwatches and fitness trackers on a daily basis to collect data such as the number of steps taken, heart rate, and body temperature. This data is periodically synchronized with a server, which then integrates and manages the data in real time.

[0567] Dietary data collection and analysis

[0568] Users take photos of their meals using their smartphones and upload them to the system. These meal images are then analyzed by the server, and various nutrients and calories are automatically calculated. The analysis results are stored in the user's database and can be used to help with daily dietary management.

[0569] Comprehensive data analysis and recommendation generation

[0570] The server integrates daily health and dietary data and evaluates the user's overall health status using a generative AI model. Based on the results of this evaluation, specific health management recommendations are generated for the user, including the amount of exercise required and recommended dietary content.

[0571] Progress management and feedback

[0572] The user's progress towards their KPIs is monitored regularly and the server provides this information as feedback to the user in the form of reports, allowing the user to stay aware of their health status and identify areas for improvement.

[0573] Evaluation based on the results of the following year's health check

[0574] When the next year's health checkup results are uploaded, the server loads the new data into the AI ​​model and compares it with the previous year's data. This evaluates the degree of improvement in various KPIs and provides feedback to the user. Specific results include, for example, "body fat percentage reduced by 10%" or "blood pressure returned to normal range."

[0575] Specific examples

[0576] 1. Reading and analyzing health checkup results

[0577] The user scans the results of the health check and uploads them to the system.

[0578] The server receives the result file and analyzes it using an AI model.

[0579] Extract indicators such as blood pressure, blood sugar level, and body fat percentage.

[0580] 2. Setting health indicator targets

[0581] The server sets a target value based on the analysis results.

[0582] Examples: Lowering blood pressure to 130 / 80 mmHg, reducing body fat percentage to 25%.

[0583] 3. Daily data collection and integration

[0584] Users use smartwatches to collect daily health data.

[0585] The server synchronizes and manages this data in real time.

[0586] 4. Dietary data collection and analysis

[0587] Users take photos of their meals and upload them to the system.

[0588] The server analyzes the image and calculates calories and various nutrients.

[0589] 5. Comprehensive data analysis and recommendation generation

[0590] The server integrates healthcare and dietary data to conduct a health assessment.

[0591] A generative AI model generates recommendations and notifies the user.

[0592] 6. Progress Management and Feedback

[0593] The server monitors progress against KPIs and generates weekly or monthly reports.

[0594] User receives and acknowledges feedback.

[0595] 7. Evaluation based on the results of the following year's health check

[0596] New health checkup results are fed into the AI ​​model and compared with the previous year.

[0597] Evaluate the degree of improvement and notify users of specific results.

[0598] In this way, the system of the present invention provides comprehensive care for the user's health condition and achieves continuous improvement in health.

[0599] The processing flow will be explained below.

[0600] Step 1: User uploads health check results

[0601] Users take a photo of their annual health checkup results with their smartphone and upload them to the system via a dedicated application.

[0602] The user taps the upload button, selects a file from local storage, and sends it.

[0603] Step 2: The server receives the health check results

[0604] The server receives and stores the health check result file sent by the user.

[0605] The server checks the file format and passes the data to the appropriate analysis module.

[0606] Step 3: The server loads the data into the generative AI model

[0607] The server loads the received health check results into a generative AI model.

[0608] An AI model analyzes the data and extracts key health indicators (blood pressure, blood sugar levels, body fat percentage, etc.).

[0609] Step 4: The server sets the target value for the health index.

[0610] The server sets target values ​​(KPIs) for health indicators based on the data analyzed by the AI ​​model.

[0611] For example, the target body fat percentage is set to 25% and the target blood pressure is set to 130 / 80 mmHg.

[0612] Step 5: The server notifies the user of the target value

[0613] The server notifies the user's terminal of the set target value.

[0614] The user receives a notification and confirms the target value.

[0615] Step 6: Users collect data using healthcare devices

[0616] Users use smartwatches and fitness trackers to collect daily data such as steps taken, heart rate, and body temperature.

[0617] This data is stored on the device.

[0618] Step 7: The device synchronizes the collected data with the server

[0619] The user's device periodically synchronizes the collected health data with the server.

[0620] The server stores the received data in real time and integrates it into a database.

[0621] Step 8: User uploads food photo

[0622] Users take photos of their daily meals with their smartphones and upload them to a dedicated application.

[0623] The user taps the upload button to submit the photo.

[0624] Step 9: The server analyzes the food image

[0625] The server receives the uploaded food images and passes the data to the image analysis module.

[0626] The image analysis module analyzes the contents of the meal and calculates various nutrients and calories.

[0627] Step 10: Server consolidates data

[0628] The server consolidates the daily health data and dietary data and stores them in a database.

[0629] Based on the integrated data, a generative AI model performs a comprehensive health assessment.

[0630] Step 11: Server generates recommendations

[0631] The server generates specific health management recommendations for the user based on the analysis results of the AI ​​model.

[0632] For example, it includes specific advice such as "Take a few more steps today" or "Choose foods that are lower in fat."

[0633] Step 12: The server notifies the user of the recommendation

[0634] The server notifies the user's terminal of the generated recommendations.

[0635] The user receives a notification, confirms the recommendation, and acts on it.

[0636] Step 13: Server monitors user progress

[0637] The server routinely monitors the user's progress against the set KPIs and records the progress data.

[0638] The server periodically generates progress reports to provide feedback to the user.

[0639] Step 14: User uploads new health check results

[0640] The following year, the user uploads new medical results back into the system.

[0641] The diagnostic results are photographed with a smartphone and sent via a dedicated application.

[0642] Step 15: Server parses new data

[0643] The server receives new health checkup results and loads them into the generative AI model.

[0644] The AI ​​model compares the previous year's data with the new data and calculates the degree of improvement for various KPIs.

[0645] Step 16: The server notifies the evaluation result

[0646] The server evaluates the degree of improvement and notifies the user of specific results, such as "body fat percentage reduced by 10%" or "blood pressure returned to normal range."

[0647] Users can view results and understand their progress in improving their health.

[0648] Example 1

[0649] 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."

[0650] Health management is an important issue in modern society, but many people are unable to effectively manage their daily health and dietary data. Furthermore, there is a lack of an integrated system for properly comparing and evaluating annual health checkup results and promoting sustainable health improvement. Currently, data collection, analysis, and integration are fragmented, preventing users from achieving comprehensive health management.

[0651] 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.

[0652] In this invention, the server includes means for receiving and analyzing electronic files uploaded by users, means for setting reference values ​​based on the analysis results, means for collecting and integrating daily information from users' electronic devices, means for analyzing images uploaded by users and generating numerical data, means for generating management suggestions based on the generated data and notifying the users, and means for receiving electronic files for the next year and comparing and evaluating them with previous data, thereby enabling users to comprehensively understand their own health status and make continuous improvements to their health.

[0653] "Electronic files" are digital documents or data uploaded by users, such as health checkup results.

[0654] The "reference value" is a target value of a health index that is set based on the analysis results, and indicates the standard of health status that the user should achieve.

[0655] "Electronic devices" are devices that collect users' daily health data, such as smartwatches and fitness trackers.

[0656] "Daily information" refers to daily health-related data such as the number of steps taken, heart rate, and body temperature that users collect through their electronic devices.

[0657] "Images" are visual data such as photos of meals taken by a user using a smartphone or the like.

[0658] "Numerical data" refers to quantitative data such as calories and nutrients obtained through image analysis.

[0659] "Management suggestions" are specific advice and recommendations for health management generated by the server, including, for example, the amount of exercise and recommended dietary content.

[0660] "Evaluation" refers to the act of comparing the results of the next year's health check with the data from the previous year and analyzing the progress and degree of improvement of the user's health indicators.

[0661] This system integrates the results of a user's health checkup, daily health data, and dietary data to realize comprehensive health management. The system mainly involves a server, a terminal, and a user.

[0662] 1. Reading and analyzing health checkup results

[0663] Users scan their health checkup results using a smartphone camera app and upload the data to the system. The server receives the electronic file, verifies the file format, and then uses a generative AI model to analyze the file's contents and extract key health indicators (e.g., blood pressure, blood glucose level, cholesterol level, body fat percentage).

[0664] 2. Setting health indicator targets

[0665] The server sets various benchmarks based on the analysis results, including specific goals such as lowering blood pressure to 130 / 80 mmHg or reducing body fat to 25%. The set benchmarks are then notified to the user.

[0666] 3. Daily data collection and integration

[0667] Users use electronic devices such as smartwatches and fitness trackers on a daily basis to collect daily information such as the number of steps taken, heart rate, body temperature, etc. This data is periodically synchronized via the device to a server, which then integrates and manages this data in real time.

[0668] 4. Dietary data collection and analysis

[0669] Users take photos of their meals with their smartphones and upload them to the system. The server receives the images and uses a generative AI model to analyze the meal's contents and calculate calories and nutrients. This generates numerical data that is stored in the user's database.

[0670] 5. Comprehensive data analysis and recommendation generation

[0671] The server integrates the user's daily health and dietary data and evaluates their overall health status using a generative AI model. Based on the results of this evaluation, management suggestions are generated. For example, the amount of exercise required and recommended dietary content may be recommended. These suggestions are then notified to the user.

[0672] 6. Progress Management and Feedback

[0673] The server periodically monitors the user's progress against the baseline and generates weekly or monthly reports that the user can use to assess their health status and understand any improvements that may be needed.

[0674] 7. Evaluation based on the results of the following year's health check

[0675] When the user uploads the following year's health checkup results to the system, the server loads the new data into the generative AI model and compares it with the previous year's data. This evaluates the degree of improvement against various benchmarks and notifies the user of specific results (e.g., a 10% decrease in body fat percentage, or blood pressure returning to normal range).

[0676] Specific examples

[0677] 1. Reading and analyzing health checkup results

[0678] Users scan their health check results using a dedicated app and upload them to the system.

[0679] The server receives the result file and analyzes it using the generative AI model.

[0680] Extract key health indicators such as blood pressure, blood sugar levels, and body fat percentage.

[0681] 2. Daily data collection and integration

[0682] The user's smartwatch collects daily steps and heart rate data and sends them to a server.

[0683] The server aggregates this data in real time.

[0684] 3. Dietary data collection and analysis

[0685] Users take photos of their meals and upload them to the system.

[0686] The server analyzes the images and calculates nutrients and calories.

[0687] Prompt Sentence Examples

[0688] "Based on the results of last year's health check, please set your health goals for this year."

[0689] "Please analyze your recent dietary and daily health data and tell us your overall health status."

[0690] By using the above prompt sentences, this system can provide appropriate health management suggestions to the user.

[0691] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0692] Step 1:

[0693] The user scans the health checkup results. As input, the user takes a photo of the health checkup results using the camera app on their smartphone. The output is a scanned image file. The user uploads the scanned image file to the system using a dedicated application.

[0694] Step 2:

[0695] The server receives an electronic file. The input is a health checkup result file uploaded by a user. When the server receives this file, it first verifies the file format. If it is confirmed to be in the correct format, the server proceeds to the next analysis step. The output is a verified health checkup result file.

[0696] Step 3:

[0697] Analysis of health checkup results using an AI model. The input is a health checkup result file received by the server. The server loads this file into a generative AI model. The AI ​​model analyzes the data and extracts key health indicators such as blood pressure, blood sugar level, cholesterol level, and body fat percentage. The output is the analyzed health indicator data.

[0698] Step 4:

[0699] The server sets the target value for the health index. The input is the health index data analyzed by the AI ​​model. The server sets the reference value based on this data (e.g., lower blood pressure to 130 / 80 mmHg, reduce body fat percentage to 25%). The output is the set reference value. The reference value is notified to the user.

[0700] Step 5:

[0701] The user collects daily health data. The input is daily health data (e.g., number of steps, heart rate, body temperature, etc.) collected from electronic devices such as smart watches and fitness trackers used by the user. The output is this health data. This data is periodically synchronized to a server via the device.

[0702] Step 6:

[0703] The server integrates daily health data. The input is daily health data synchronized from the user's electronic devices. The server receives, integrates, and manages this data in real time. The output is the integrated health data.

[0704] Step 7:

[0705] The user collects meal data. The input is a photo of the meal taken by the user with a smartphone. The user uploads this to the system. The output is the uploaded meal image.

[0706] Step 8:

[0707] The server analyzes the food images. The input is the food images uploaded by the user. The server analyzes the images using a generative AI model and automatically calculates various nutrients and calories. The output is the analyzed numerical data (calories and nutrients). This data is stored in the user's database.

[0708] Step 9:

[0709] The server performs comprehensive data analysis and generates recommendations. The input is the integrated health data and analyzed dietary data. The server uses a generative AI model to comprehensively analyze this data and evaluate the user's health status. The output is the comprehensive evaluation result and specific health management suggestions based on it (e.g., the amount of exercise required, recommended dietary content). These suggestions are notified to the user.

[0710] Step 10:

[0711] The server monitors progress and provides feedback. The input is the user's baseline and daily health data. The server monitors these data periodically and generates progress reports. The output is a progress report that is sent to the user on a weekly or monthly basis.

[0712] Step 11:

[0713] The user uploads new health checkup results. The input is the health checkup results for the next year. The user uploads this to the system again. The output is a new health checkup results file.

[0714] Step 12:

[0715] The server compares and evaluates new health checkup results. The input is the next year's health checkup results and the previous year's data. The server loads the new data into a generative AI model and compares it with the previous year's data. The output is the degree of improvement against various benchmark values ​​and specific results (e.g., body fat percentage reduced by 10%, blood pressure returned to normal range). The evaluation results are notified to the user.

[0716] (Application example 1)

[0717] 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."

[0718] In modern society, many people are required to collect various data for health management and improve their behavior based on the results. However, existing health management systems have difficulty integrating individual data to perform comprehensive health management, and have limitations in recommending specific behaviors and supporting their implementation. In particular, with regard to dietary management, there are few systems that provide specific recommendations that are easy to apply to daily life. Furthermore, there are insufficient means for users to actually obtain recommended meals. There is a need to build a system that can solve these issues and enable users to continuously improve their health.

[0719] 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.

[0720] In this invention, the server includes means for receiving and analyzing health checkup results uploaded by the user, means for setting target values ​​for health indices based on the analysis results, means for collecting and integrating daily health data from the user's healthcare terminal, means for analyzing meal images uploaded by the user and generating nutrient data, means for generating health management recommendations based on the generated data and notifying the user, means for providing the recommended meals in cooperation with a food delivery service, and means for receiving next year's health checkup results and comparing them with the previous data to evaluate the degree of improvement. This makes it possible to integrate health checkup results, daily health data, and dietary data to perform comprehensive health management, provide specific dietary recommendations, and consistently provide support for implementing them.

[0721] "User" refers to an individual who uses the system.

[0722] "Uploading" refers to the act of a user sending their own digital data to a server.

[0723] "Health checkup results" refers to data obtained as a result of a health check conducted at a medical institution.

[0724] "Analysis" refers to the act of analyzing acquired data and extracting information.

[0725] "Health index" refers to specific values ​​that indicate the user's health condition, such as blood pressure, blood sugar level, and body fat percentage.

[0726] "Target value" refers to a specific indicator value set for improving health.

[0727] "Healthcare devices" refer to devices that collect users' health data, such as smartwatches and fitness trackers.

[0728] "Daily health data" refers to data related to the user's physical activity and physiological condition in their daily lives.

[0729] "Integration" refers to the act of centrally managing multiple data in a single system.

[0730] "Meal image" refers to photographic data of a user's own meal.

[0731] "Analyzing" refers to the act of extracting necessary information (such as nutrients and calories) from image data.

[0732] "Nutrient data" refers to data that indicates the nutritional components and calorie content of a meal.

[0733] "Recommendations" refer to specific advice or suggestions that contribute to improving the user's health.

[0734] "Food delivery service" refers to a delivery service that provides recommended meals to users.

[0735] "Evaluation" refers to the act of measuring the degree of improvement in the user's health based on the acquired data.

[0736] A "generative AI model" refers to an algorithm that uses machine learning technology to analyze data and generate appropriate recommendations.

[0737] This invention relates to a system for comprehensive health management that integrates a user's health checkup results, daily health data, and dietary data. This system includes a smartphone application, a cloud server, a generative AI model, and collaboration with a food delivery service. Detailed embodiments of this system and the program processing that realizes it are described below.

[0738] System configuration

[0739] Hardware

[0740] 1. Smartphones: Devices for collecting and displaying user data

[0741] 2. Healthcare devices: devices that collect health data, such as smartwatches and fitness trackers

[0742] 3. Cloud server: A server for data analysis and management

[0743] software

[0744] 1. Image analysis tools: Uses OpenCV and TensorFlow

[0745] 2. Generative AI model: Uses GPT-3 and Stable Diffusion

[0746] Data collection and processing

[0747] 1. Importing health checkup results

[0748] Users upload their health checkup results using a smartphone app. The server receives the results and analyzes them using an AI model. This analysis extracts key health indicators such as blood pressure, blood sugar levels, and body fat percentage.

[0749] 2. Collecting daily health data

[0750] Daily health data (number of steps, heart rate, body temperature, etc.) from the user's healthcare device is periodically synchronized with a cloud server, which then integrates and manages this data in real time.

[0751] 3. Dietary data collection and analysis

[0752] Users take photos of their meals with their smartphones and upload them to the app, which uses image analysis tools to analyze these photos and calculate the nutritional and calorie content of each ingredient.

[0753] 4. Comprehensive data analysis and recommendation generation

[0754] The server uses a generative AI model to analyze the collected health and dietary data and generate optimal health management recommendations for the user, including the amount of exercise required and recommended dietary content.

[0755] 5. Collaboration with food delivery services

[0756] The recommended meals are linked to food delivery services, allowing users to easily order them within the app, making it easier to maintain healthy lifestyles.

[0757] Specific examples

[0758] 1. Examples of prompts for analyzing health checkup data

[0759] "Analyze health checkup results and extract health indicators. Sample data: Blood pressure: 120 / 80 mmHg, Blood glucose: 90 mg / dL, Cholesterol: 180 mg / dL."

[0760] 2. Food recommendation prompt examples

[0761] "Please suggest an appropriate meal based on the user's daily health status. Health data sample: Steps: 10,000 steps / day, Heart rate: 70 bpm, Food data sample: Mushroom risotto, Calories: 400 kcal, Protein: 15 g, Carbohydrates: 55 g, Fat: 10 g."

[0762] The system of the present invention allows users to perform comprehensive health management by integrating health checkup results, daily health data, and dietary data, and easily take specific actions to improve their health. In particular, by linking with food delivery services, users can easily obtain recommended meals, enabling sustainable health improvement.

[0763] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0764] Step 1:

[0765] A user uploads their health checkup results using a smartphone app. The input is a health checkup result file, and the output is the health checkup data sent to the cloud server. The server checks the format of the received data and prepares it for passing to the AI ​​model.

[0766] Step 2:

[0767] The server receives the health checkup results and analyzes the data using an AI model. The input is the health checkup data, and the output is key health indicators (blood pressure, blood sugar level, body fat percentage, etc.). The AI ​​model extracts these indicators and stores them in a database.

[0768] Step 3:

[0769] The user's healthcare device (smartwatch or fitness tracker) collects daily health data and synchronizes it to a cloud server. The input is daily health data (number of steps, heart rate, body temperature, etc.), and the output is the integrated data stored on the server. The server manages this data in real time.

[0770] Step 4:

[0771] Users take photos of their meals with their smartphones and upload them to the app. The input is the photo file of the meal, and the output is image data sent to a cloud server. The server receives this image data and prepares it for analysis.

[0772] Step 5:

[0773] The server uses image analysis tools (OpenCV and TensorFlow) to analyze photos of meals and calculate nutrients and calories. The input is image data, and the output is nutrient data and calorie data. The server stores this data in a database.

[0774] Step 6:

[0775] The server uses a generative AI model to integrate health checkup data, daily health data, and nutritional data to perform a comprehensive health assessment. All of this data is input, and the output is a health assessment and recommendations. The generative AI model generates recommendations that suggest the optimal amount of exercise and diet for the user and notifies the user.

[0776] Step 7:

[0777] The server connects with food delivery services to provide recommended meals. The input is recommendation data, and the output is a food delivery order. Users can easily order meals from within the app.

[0778] Step 8:

[0779] The server receives the next year's health checkup results and compares them with the previous data to evaluate the degree of improvement. The input is the new health checkup data, and the output is the evaluation result of the degree of improvement. The server then runs this data through the AI ​​model again and provides feedback to the user.

[0780] 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.

[0781] The system of this invention not only realizes comprehensive health management by integrating daily health data and dietary data based on the user's health checkup results, but also evaluates the user's mental health state by collecting and analyzing the user's emotional data and provides appropriate feedback. Specific embodiments of the invention are described below.

[0782] System Overview

[0783] Importing and analyzing health checkup results

[0784] Users upload their annual health checkup results to the system using a device such as a smartphone. The server that receives the upload checks the file format and loads the contents into a generative AI model. The AI ​​model analyzes the health checkup results and extracts key health indicators (e.g., blood pressure, blood sugar, cholesterol, body fat percentage, etc.).

[0785] Setting health indicator targets

[0786] The server sets various KPIs (Key Performance Indicators) based on the analysis results. These target values ​​are important for improving the user's health. For example, they include target values ​​for reducing body fat percentage and target values ​​for returning blood pressure to the normal range. These target values ​​are notified to the user so that they can be checked.

[0787] Daily data collection and integration

[0788] Users use healthcare devices such as smartwatches and fitness trackers on a daily basis to collect data such as the number of steps taken, heart rate, and body temperature. This data is periodically synchronized with a server, which then integrates and manages the data in real time.

[0789] Dietary data collection and analysis

[0790] Users take photos of their meals using their smartphones and upload them to the system. These meal images are then analyzed by the server, and various nutrients and calories are automatically calculated. The analysis results are stored in the user's database and can be used to help with daily dietary management.

[0791] Emotion data collection and analysis

[0792] The device collects emotional data that users input on a daily basis, such as periodic self-evaluations of their emotions and facial recognition using a smartphone camera. This data is sent to a server and analyzed by an emotion engine. The emotion engine evaluates the user's emotional state and identifies their daily stress level and mental health.

[0793] Comprehensive data analysis and recommendation generation

[0794] The server integrates daily health, dietary, and emotional data to evaluate the user's overall health using a generative AI model. Based on the results of this evaluation, specific health management recommendations are generated for the user, including the amount of exercise required, recommended dietary habits, and mental health advice.

[0795] Progress management and feedback

[0796] The user's progress towards their KPIs is regularly monitored, and the server provides this information as feedback to the user in the form of a report. This allows users to constantly understand their own health status and identify areas for improvement. Feedback based on emotional data is also provided periodically, allowing users to check their mental health.

[0797] Evaluation based on the results of the following year's health check

[0798] When the next year's health checkup results are uploaded, the server loads the new data into the AI ​​model and compares it with the previous year's data. This evaluates the degree of improvement in various KPIs and provides feedback to the user. Specific results include, for example, "body fat percentage reduced by 10%" or "blood pressure returned to normal range."

[0799] Specific examples

[0800] 1. Reading and analyzing health checkup results

[0801] The user scans the results of the health check and uploads them to the system.

[0802] The server receives the result file and analyzes it using an AI model.

[0803] Extract indicators such as blood pressure, blood sugar level, and body fat percentage.

[0804] 2. Setting health indicator targets

[0805] The server sets a target value based on the analysis results.

[0806] Examples: Lowering blood pressure to 130 / 80 mmHg, reducing body fat percentage to 25%.

[0807] 3. Daily data collection and integration

[0808] Users use smartwatches to collect daily health data.

[0809] The server synchronizes and manages this data in real time.

[0810] 4. Dietary data collection and analysis

[0811] Users take photos of their meals and upload them to the system.

[0812] The server analyzes the image and calculates calories and various nutrients.

[0813] 5. Emotional Data Collection and Analysis

[0814] Users enter their own self-evaluation of emotions on a daily basis, and facial expression recognition data is collected using the smartphone camera.

[0815] The server receives the emotion data and analyzes it using the emotion engine.

[0816] 6. Comprehensive data analysis and recommendation generation

[0817] The server integrates healthcare data, dietary data, and emotional data to provide a comprehensive assessment of health status.

[0818] A generative AI model generates recommendations and notifies the user.

[0819] Specific examples include advice such as "Take a few more steps today," "Choose lower-fat foods," and "Try meditation to relax."

[0820] 7. Progress Management and Feedback

[0821] The server monitors progress against KPIs and generates weekly or monthly reports.

[0822] User receives and acknowledges feedback.

[0823] This also includes feedback based on emotional data.

[0824] 8. Evaluation based on the results of the following year's health check

[0825] New health checkup results are fed into the AI ​​model and compared with the previous year.

[0826] Evaluate the degree of improvement and notify users of specific results.

[0827] In this way, the system of the present invention provides comprehensive care for the user's health condition, realizing continuous improvement in health and management of mental health.

[0828] The processing flow will be explained below.

[0829] Step 1: User uploads health check results

[0830] Users take a photo of their annual health checkup results with their smartphone and upload them to the system via a dedicated application.

[0831] The user taps the upload button, selects a file from local storage, and sends it.

[0832] Step 2: The server receives the health check results

[0833] The server receives and stores the health check result file sent by the user.

[0834] The server checks the file format and passes the data to the appropriate analysis module.

[0835] Step 3: The server loads the data into the generative AI model

[0836] The server loads the received health check results into a generative AI model.

[0837] An AI model analyzes the data and extracts key health indicators (blood pressure, blood sugar levels, body fat percentage, etc.).

[0838] Step 4: The server sets the target value for the health index.

[0839] The server sets target values ​​(KPIs) for health indicators based on the data analyzed by the AI ​​model.

[0840] For example, the target body fat percentage is set to 25% and the target blood pressure is set to 130 / 80 mmHg.

[0841] Step 5: The server notifies the user of the target value

[0842] The server notifies the user's terminal of the set target value.

[0843] The user receives a notification and confirms the target value.

[0844] Step 6: Users collect data using healthcare devices

[0845] Users use smartwatches and fitness trackers to collect daily data such as steps taken, heart rate, and body temperature.

[0846] This data is stored on the device.

[0847] Step 7: The device synchronizes the collected data with the server

[0848] The user's device periodically synchronizes the collected health data with the server.

[0849] The server stores the received data in real time and integrates it into a database.

[0850] Step 8: User uploads food photo

[0851] Users take photos of their daily meals with their smartphones and upload them to a dedicated application.

[0852] The user taps the upload button to submit the photo.

[0853] Step 9: The server analyzes the food image

[0854] The server receives the uploaded food images and passes the data to the image analysis module.

[0855] The image analysis module analyzes the contents of the meal and calculates various nutrients and calories.

[0856] Step 10: User enters emotion data

[0857] Users input their daily emotional data through a dedicated application.

[0858] Emotion entry includes text entry, facial expression recognition, and multiple choice emotion checklists.

[0859] Step 11: The device sends the emotion data to the server

[0860] The user's device transmits the collected emotion data to the server.

[0861] The server passes the received emotion data to the emotion engine for appropriate analysis.

[0862] Step 12: Server consolidates data

[0863] The server integrates daily health data, dietary data, and emotional data and stores them in a database.

[0864] Based on the integrated data, a generative AI model performs a comprehensive health assessment.

[0865] Step 13: Server generates recommendations

[0866] The server generates specific health management recommendations for the user based on the analysis results of the AI ​​model.

[0867] For example, it includes specific advice such as "Take a few more steps today," "Choose leaner foods," or "Try meditation to relax."

[0868] Step 14: The server notifies the user of the recommendation

[0869] The server notifies the user's terminal of the generated recommendations.

[0870] The user receives a notification, confirms the recommendation, and acts on it.

[0871] Step 15: Server monitors user progress

[0872] The server routinely monitors the user's progress against the set KPIs and records the progress data.

[0873] The server periodically generates progress reports to provide feedback to the user.

[0874] Step 16: User uploads new health check results

[0875] The following year, the user uploads new medical results back into the system.

[0876] The diagnostic results are photographed with a smartphone and sent via a dedicated application.

[0877] Step 17: Server parses new data

[0878] The server receives new health checkup results and loads them into the generative AI model.

[0879] The AI ​​model compares the previous year's data with the new data and calculates the degree of improvement for various KPIs.

[0880] Step 18: The server notifies the evaluation result

[0881] The server evaluates the degree of improvement and notifies the user of specific results, such as "body fat percentage reduced by 10%" or "blood pressure returned to normal range."

[0882] Users can view results and understand their progress in improving their health.

[0883] Example 2

[0884] 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."

[0885] While conventional health management systems could collect and analyze a user's health checkup results, daily health data, and dietary data individually, it was difficult to integrate them for comprehensive health management. Furthermore, they lacked a means to consider mental health status, making it impossible to evaluate a user's overall health status. As a result, users could only obtain partial health information, making it difficult to effectively improve their health.

[0886] 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.

[0887] In this invention, the server includes means for receiving and analyzing health checkup results uploaded by a user, means for setting target values ​​for health indices based on the analysis results, means for collecting and integrating daily health data from the user's healthcare terminal, means for analyzing meal images uploaded by the user and generating nutrient data, means for receiving and analyzing preset emotion data, means for integrating the generated data, generating recommendations for comprehensive health management, and notifying the user, and means for receiving next year's health checkup results and comparing them with the previous data to evaluate the degree of improvement. This makes it possible to comprehensively evaluate the user's physical and mental health status and provide specific improvements and recommendations.

[0888] "Health checkup results" refers to numerical data on health indicators such as blood pressure, blood sugar level, cholesterol level, and body fat percentage obtained through a health checkup conducted by a user once a year at a medical institution or other facility.

[0889] "Analysis means" refers to tools and algorithms used to process uploaded data on the server and extract and analyze specific information.

[0890] "Health index" refers to specific numerical data that indicates the user's health condition, such as blood pressure, blood sugar level, cholesterol level, and body fat percentage.

[0891] The "target value" is set based on the analysis results and refers to a specific numerical value that the user should achieve in order to improve their health.

[0892] A "healthcare device" is an electronic device, such as a smartwatch or fitness tracker, that is used to collect a user's daily health data.

[0893] "Daily health data" refers to health-related data collected daily by a healthcare terminal, such as the user's number of steps, heart rate, and body temperature.

[0894] A "meal image" is a photograph of a meal that a user takes using a smartphone or other device and uploads to the system.

[0895] "Nutrient data" refers to information about various nutrients contained in food, such as carbohydrates, proteins, and lipids, obtained by the server analyzing the food image.

[0896] "Emotional data" refers to data about a user's emotional state and stress level, collected through a user's self-assessment or facial expression recognition using a smartphone camera.

[0897] "Recommendations" refer to specific health management advice and suggestions provided to users based on data collected and analyzed by the server.

[0898] A "generative AI model" is an artificial intelligence model that analyzes collected health checkup results, daily health data, dietary data, and emotional data to generate comprehensive evaluations and recommendations.

[0899] "Improvement" refers to a numerical value or evaluation that indicates how much the user's health index has improved compared to the previous year.

[0900] The system of the present invention provides comprehensive health management based on the user's health checkup results, daily health data, dietary data, and emotional data.

[0901] First, users upload their annual health checkup results to the system using a device such as a smartphone. The device then sends the uploaded health checkup result file to the server. The server checks the format of the received file to ensure it is in the correct format. The server then analyzes the health checkup results using a generative AI model to extract key health indicators (blood pressure, blood sugar level, cholesterol level, body fat percentage, etc.). These extracted health indicators are then recorded in a database. Based on the analysis results, the server then sets individual KPIs (Key Performance Indicators) to aim for health improvement. The set target values ​​are notified to the user and displayed on the device.

[0902] Next, users use healthcare devices such as smartwatches and fitness trackers on a daily basis to collect data such as the number of steps taken, heart rate, and body temperature. The devices periodically synchronize this data with a server, which then integrates and manages the data in real time and stores it in a database.

[0903] Users can also take photos of their meals with their smartphones and upload them to the system. The device then sends the meal images to a server, which then uses a generative AI model to analyze the images and calculate various nutrients and calories. The analysis results are stored in a database and used to help users manage their diet.

[0904] Furthermore, users can collect emotional data by entering their own emotional assessments or by using their smartphone camera. The device then transmits this emotional data to a server, which then uses an emotion engine to analyze the data and evaluate the user's emotional state, stress level, and mental health.

[0905] The server integrates daily health data, dietary data, and emotional data, and uses a generative AI model to assess the user's overall health. Based on the results of this assessment, the server generates specific health management recommendations (e.g., the amount of exercise required, recommended dietary content, mental health advice, etc.) and notifies the user.

[0906] The server periodically monitors the user's progress against KPIs and generates a report on a weekly or monthly basis. The report is then sent to the user and displayed on their device. The user can review the report and identify any necessary improvements. Feedback based on emotional data is also provided on a regular basis.

[0907] When the next year's health checkup results are uploaded, the server loads the new data into the generative AI model and compares it with the previous year's data. This evaluates the degree of improvement in various KPIs and notifies the user of specific results (e.g., a 10% decrease in body fat, or blood pressure returning to normal range).

[0908] Prompt Sentence Examples

[0909] "Create a program that generates a comprehensive health assessment and appropriate feedback based on the user's health checkup results, daily health data, dietary data, and emotional data."

[0910] The system is able to comprehensively assess a user's physical and mental health and provide specific improvements and recommendations.

[0911] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0912] Step 1:

[0913] The user uploads the health check result file (e.g. PDF, JPEG, etc.) to the system using a device such as a smartphone.

[0914] Input: Health check result file

[0915] Output: The uploaded file is saved to the device.

[0916] Step 2:

[0917] The terminal sends the uploaded file to the server.

[0918] Input: Health check result file (saved on the device)

[0919] Output: The health check result file is transferred to the server.

[0920] Step 3:

[0921] The server checks the format of the received file to ensure it is in the correct format, and if it is not, it will display an error message to the user.

[0922] Input: Health check result file

[0923] Output: File format check result (success / failure)

[0924] Step 4:

[0925] The server uses a generative AI model to analyze health checkup results and extract key health indicators (blood pressure, blood sugar levels, cholesterol levels, body fat percentage, etc.).

[0926] Input: Health check result file

[0927] Output: Extracted key health indicators

[0928] Step 5:

[0929] The server records the extracted health indicators in a database and sets KPIs for each user based on the analysis results.

[0930] Input: Key health indicators

[0931] Output: Setting KPIs and recording them in a database

[0932] Step 6:

[0933] The server notifies the user of the set KPIs and displays them to the user.

[0934] Input: The configured KPI

[0935] Output: Notification and display on the user's terminal

[0936] Step 7:

[0937] Users use healthcare devices such as smartwatches and fitness trackers on a daily basis to collect data such as the number of steps taken, heart rate, and body temperature.

[0938] Input: Smartwatch or fitness tracker data

[0939] Output: Collection of daily health data

[0940] Step 8:

[0941] The device periodically synchronizes the collected daily health data with the server.

[0942] Input: Daily health data (saved on the device)

[0943] Output: Synchronization of daily health data to the server

[0944] Step 9:

[0945] The server integrates and manages the received daily health data in real time and stores it in a database.

[0946] Input: Synced daily health data

[0947] Output: Consolidated daily health data stored in a database

[0948] Step 10:

[0949] Users take photos of their meals with their smartphones and upload them to the system.

[0950] Input: Food photo

[0951] Output: Uploaded photos are saved on the device

[0952] Step 11:

[0953] The device sends the uploaded photos to the server.

[0954] Input: Food photo (saved on device)

[0955] Output: Meal photos are sent to the server

[0956] Step 12:

[0957] The server uses a generative AI model to analyze the food images and calculate various nutrients and calories.

[0958] Input: Food photo

[0959] Output: Analyzed nutrient data and calories

[0960] Step 13:

[0961] The server stores the analysis results in a database to help users manage their diet.

[0962] Input: Parsed nutrient data and calories

[0963] Output: Save nutrient data to database

[0964] Step 14:

[0965] Users can collect emotional data by entering their own emotional assessments on a daily basis or by using their smartphone camera.

[0966] Input: Emotion self-assessment data or facial expression recognition data

[0967] Output: The input emotion data is saved on the device.

[0968] Step 15:

[0969] The terminal transmits this emotion data to the server.

[0970] Input: Emotion data (saved on the device)

[0971] Output: Emotion data is sent to the server

[0972] Step 16:

[0973] The server uses an emotion engine to analyze the emotion data and evaluate the user's emotional state and stress level.

[0974] Input: Emotion data

[0975] Output: Analyzed emotional state and stress level

[0976] Step 17:

[0977] The server integrates daily health data, dietary data, and emotional data, and uses a generative AI model to assess overall health status.

[0978] Input: Daily health data, dietary data, emotional data

[0979] Output: Overall health status assessment results

[0980] Step 18:

[0981] Based on the evaluation results, the server generates specific health management recommendations and notifies the user.

[0982] Input: Overall health status assessment results

[0983] Output: Generated health care recommendations and notification to the user

[0984] Step 19:

[0985] The server periodically monitors the user's progress against the KPIs and generates reports that are then sent to the user.

[0986] Input: KPI progress data

[0987] Output: Generated scheduled reports and notifications to users

[0988] Step 20:

[0989] When the next year's health checkup results are uploaded, the user's device will send the new checkup results to the server.

[0990] Input: New medical examination results

[0991] Output: New health check results are sent to the server.

[0992] Step 21:

[0993] The server loads the new health check results into a generative AI model and compares them with the previous year's data.

[0994] Input: New health checkup results, previous year's health checkup data

[0995] Output: KPI improvement evaluation results

[0996] Step 22:

[0997] The server notifies the user of specific results, such as "body fat percentage reduced by 10%" or "blood pressure returned to normal range."

[0998] Input: KPI improvement evaluation results

[0999] Output: Notification of results to the user

[1000] (Application example 2)

[1001] 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."

[1002] In modern society, health management has become increasingly difficult due to the diversification of people's lifestyles and dietary habits. In particular, conventional health management systems only collect and analyze physical data, and do not adequately evaluate and manage mental health status. Therefore, there is a demand for multifaceted, comprehensive health management. In response to this, the present invention aims to promote health improvement by comprehensively evaluating a user's physical and mental health status and providing personalized health management.

[1003] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1004] In this invention, the server includes a means for receiving and analyzing health checkup results uploaded by a user, a means for setting target values ​​for health indices based on the analysis results, and a means for collecting and integrating daily health data from the user's healthcare device, thereby enabling a comprehensive evaluation of the user's physical and mental health status and the generation of specific health management recommendations for the user.

[1005] "Health checkup results" are the results of tests that a user undergoes periodically at a medical institution, and are data including major health indicators such as blood pressure, blood sugar level, cholesterol level, and body fat percentage.

[1006] "Health index" is specific numerical data for evaluating the health condition of a user, and includes, for example, blood pressure, blood sugar level, cholesterol level, and body fat percentage.

[1007] "Daily health data" refers to data measured by users in their daily lives, including the number of steps taken, heart rate, body temperature, etc. collected by healthcare devices such as smartwatches and fitness trackers.

[1008] "Meal images" are photographs of meals taken by the user, and are data that are uploaded to the system and used to analyze nutrient data.

[1009] "Nutrient data" is information on various nutrients and calories analyzed from a meal image, and is data that is useful for the user's dietary management.

[1010] "Emotion data" is data collected to assess a user's mental health, and includes data from self-assessment of emotions and facial expression recognition.

[1011] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning, and is an algorithm that comprehensively evaluates a user's health condition based on large amounts of data and generates optimal recommendations.

[1012] The system for implementing this invention integrates a user's health checkup results, daily health data, dietary data, and emotional data, evaluates their health status comprehensively using a generative AI model, and provides appropriate feedback to the user.

[1013] composition

[1014] 1. Reading and analyzing health checkup results

[1015] Users upload their annual health checkup results to the system using a device such as a smartphone. The server receives the upload, checks the file format, and loads the contents into a generative AI model. The AI ​​model analyzes the health checkup results and extracts key health indicators.

[1016] 2. Setting health indicator targets

[1017] The server sets various KPIs (Key Performance Indicators) based on the analysis results. These target values ​​aim to improve the user's health, and include blood pressure, body fat percentage, etc. These target values ​​are notified to the user.

[1018] 3. Daily data collection and integration

[1019] Users use smartwatches and fitness trackers on a daily basis to collect data such as the number of steps taken, heart rate, and body temperature. This data is periodically synchronized with a server and integrated and managed in real time.

[1020] 4. Dietary data collection and analysis

[1021] Users take photos of their meals using their smartphones and upload them to the system. The server analyzes the meal images and automatically calculates various nutrients and calories. The analysis results are stored in the user's database.

[1022] 5. Emotional Data Collection and Analysis

[1023] Users enter their own emotional self-assessment on a daily basis and also collect facial expression recognition data using their smartphone camera. This data is sent to a server and analyzed by the emotion engine. The emotion engine evaluates the user's emotional state and grasps their daily stress level and mental health.

[1024] 6. Comprehensive data analysis and recommendation generation

[1025] The server integrates daily health data, dietary data, and emotional data, and uses a generative AI model to comprehensively evaluate the user's health. Based on the results of this evaluation, specific health management recommendations are generated for the user, such as the amount of exercise required, recommended dietary content, and advice on mental health.

[1026] 7. Progress Management and Feedback

[1027] The user's progress towards their KPIs is regularly monitored, and the server provides this information as feedback to the user in the form of a report. This allows users to constantly understand their own health status and identify areas for improvement. Feedback based on emotional data is also provided periodically, allowing users to check their mental health.

[1028] 8. Evaluation based on the results of the following year's health check

[1029] When the next year's health checkup results are uploaded, the server loads them into the AI ​​model and compares them with the previous year's data. This evaluates the degree of improvement in various KPIs and notifies the user of specific results. Examples include "body fat percentage reduced by 10%" and "blood pressure returned to normal range."

[1030] Hardware and software used

[1031] Hardware:

[1032] Smartphone

[1033] Smartwatch

[1034] Fitness tracker

[1035] software:

[1036] AI model for health data analysis

[1037] Sentiment analysis engine

[1038] Nutrient analysis model

[1039] Specific examples

[1040] The user scans their health checkup results and uploads them to the system. The server receives the result file and analyzes it using an AI model to extract indicators such as blood pressure, blood sugar level, and body fat percentage. Based on the analysis results, the server sets target values ​​for health indicators and notifies the user. The user uses the smartwatch daily and synchronizes the acquired data (number of steps, heart rate, etc.) with the server. Next, they take photos of their meals and upload them to the system, which analyzes the nutritional data. The user also enters a self-evaluation of their emotions and collects facial recognition data using the smartphone camera. The server integrates this data, evaluates their overall health, and notifies the user of specific health management recommendations. Examples include advice such as "Take more steps today," "Choose lower-fat meals," and "Try meditation to relax."

[1041] Prompt Sentence Examples

[1042] Health checkup results: Blood pressure 140 / 90, blood sugar 6.5, cholesterol 200, body fat 30%

[1043] Daily data: steps 8000, heart rate 75, body temperature 36.5℃

[1044] Dietary information: Breakfast: 500 calories, 50g carbohydrates, 30g fat

[1045] Emotion data: Facial expression High stress

[1046] This allows a comprehensive assessment of the user's physical and mental health status and provides the user with specific health management recommendations.

[1047] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1048] Step 1:

[1049] A user scans their health checkup results with their smartphone and uploads them to the system. The input is an image file of the health checkup results. The server receives this file and checks the file format. If the format is correct, the contents of the result file are loaded into a generative AI model to extract health indicators such as blood pressure, blood sugar level, and body fat percentage. The output is the extracted health indicator data.

[1050] Step 2:

[1051] The server sets various KPIs (Key Performance Indicators) based on the extracted health indicator data. The extracted health indicator data is the input. The server sets appropriate target values ​​for each health indicator and notifies the user. The output is the set KPI target values.

[1052] Step 3:

[1053] Users use smartwatches and fitness trackers on a daily basis to collect daily health data such as steps taken, heart rate, and body temperature. The input is the data collected from the health device. The device periodically synchronizes this data with the server. The server receives this data and integrates and manages it in real time. The output is the integrated daily health data.

[1054] Step 4:

[1055] Users take photos of their meals using their smartphones and upload them to the system. The input is the meal image. The server receives the image, analyzes it, and calculates various nutrients and calories. The output is the analyzed nutrient data.

[1056] Step 5:

[1057] Users input their self-evaluation of emotions on a daily basis and also collect facial expression recognition data using their smartphone camera. The input is the self-evaluation data and facial expression recognition images. The server receives this data and analyzes it using an emotion engine. The output is the analyzed emotional data.

[1058] Step 6:

[1059] The server integrates daily health data, nutritional data, and emotional data, and evaluates the overall health status using a generative AI model. The input is the integrated health data. The generative AI model generates specific health management recommendations based on this data and notifies the user. The output is the generated health management recommendations.

[1060] Step 7:

[1061] The server periodically monitors the user's progress against KPIs. The input is the integrated health data and the set KPI targets. The server generates a progress report based on this and provides feedback to the user. The output is the progress report.

[1062] Step 8:

[1063] The following year, the user uploads new health checkup results. The input is the new health checkup result file. The server receives this and compares it with the data from the previous year. The server evaluates it using a generative AI model, assesses the degree of improvement against various KPIs, and notifies the user. The output is the improvement evaluation result.

[1064] 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.

[1065] 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.

[1066] 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.

[1067] [Third embodiment]

[1068] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1069] 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.

[1070] 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).

[1071] 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.

[1072] 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.

[1073] 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).

[1074] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1075] 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.

[1076] 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.

[1077] 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.

[1078] 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.

[1079] 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."

[1080] The system of this invention realizes comprehensive health management by integrating daily health data and dietary data based on the results of a user's health checkup. The system of this invention is premised on the use of specific hardware and software configurations, and an embodiment thereof is shown below as an example.

[1081] System Overview

[1082] Importing and analyzing health checkup results

[1083] Users upload their annual health checkup results to the system using a device such as a smartphone. The server that receives the upload checks the file format and loads the contents into a generative AI model. The AI ​​model analyzes the health checkup results and extracts key health indicators (e.g., blood pressure, blood sugar, cholesterol, body fat percentage, etc.).

[1084] Setting health indicator targets

[1085] The server sets various KPIs (Key Performance Indicators) based on the analysis results. These target values ​​are important for improving the user's health. For example, they include target values ​​for reducing body fat percentage and target values ​​for returning blood pressure to the normal range. These target values ​​are notified to the user so that they can be checked.

[1086] Daily data collection and integration

[1087] Users use healthcare devices such as smartwatches and fitness trackers on a daily basis to collect data such as the number of steps taken, heart rate, and body temperature. This data is periodically synchronized with a server, which then integrates and manages the data in real time.

[1088] Dietary data collection and analysis

[1089] Users take photos of their meals using their smartphones and upload them to the system. These meal images are then analyzed by the server, and various nutrients and calories are automatically calculated. The analysis results are stored in the user's database and can be used to help with daily dietary management.

[1090] Comprehensive data analysis and recommendation generation

[1091] The server integrates daily health and dietary data and evaluates the user's overall health status using a generative AI model. Based on the results of this evaluation, specific health management recommendations are generated for the user, including the amount of exercise required and recommended dietary content.

[1092] Progress management and feedback

[1093] The user's progress towards their KPIs is monitored regularly and the server provides this information as feedback to the user in the form of reports, allowing the user to stay aware of their health status and identify areas for improvement.

[1094] Evaluation based on the results of the following year's health check

[1095] When the next year's health checkup results are uploaded, the server loads the new data into the AI ​​model and compares it with the previous year's data. This evaluates the degree of improvement in various KPIs and provides feedback to the user. Specific results include, for example, "body fat percentage reduced by 10%" or "blood pressure returned to normal range."

[1096] Specific examples

[1097] 1. Reading and analyzing health checkup results

[1098] The user scans the results of the health check and uploads them to the system.

[1099] The server receives the result file and analyzes it using an AI model.

[1100] Extract indicators such as blood pressure, blood sugar level, and body fat percentage.

[1101] 2. Setting health indicator targets

[1102] The server sets a target value based on the analysis results.

[1103] Examples: Lowering blood pressure to 130 / 80 mmHg, reducing body fat percentage to 25%.

[1104] 3. Daily data collection and integration

[1105] Users use smartwatches to collect daily health data.

[1106] The server synchronizes and manages this data in real time.

[1107] 4. Dietary data collection and analysis

[1108] Users take photos of their meals and upload them to the system.

[1109] The server analyzes the image and calculates calories and various nutrients.

[1110] 5. Comprehensive data analysis and recommendation generation

[1111] The server integrates healthcare and dietary data to conduct a health assessment.

[1112] A generative AI model generates recommendations and notifies the user.

[1113] 6. Progress Management and Feedback

[1114] The server monitors progress against KPIs and generates weekly or monthly reports.

[1115] User receives and acknowledges feedback.

[1116] 7. Evaluation based on the results of the following year's health check

[1117] New health checkup results are fed into the AI ​​model and compared with the previous year.

[1118] Evaluate the degree of improvement and notify users of specific results.

[1119] In this way, the system of the present invention provides comprehensive care for the user's health condition and achieves continuous improvement in health.

[1120] The processing flow will be explained below.

[1121] Step 1: User uploads health check results

[1122] Users take a photo of their annual health checkup results with their smartphone and upload them to the system via a dedicated application.

[1123] The user taps the upload button, selects a file from local storage, and sends it.

[1124] Step 2: The server receives the health check results

[1125] The server receives and stores the health check result file sent by the user.

[1126] The server checks the file format and passes the data to the appropriate analysis module.

[1127] Step 3: The server loads the data into the generative AI model

[1128] The server loads the received health check results into a generative AI model.

[1129] An AI model analyzes the data and extracts key health indicators (blood pressure, blood sugar levels, body fat percentage, etc.).

[1130] Step 4: The server sets the target value for the health index.

[1131] The server sets target values ​​(KPIs) for health indicators based on the data analyzed by the AI ​​model.

[1132] For example, the target body fat percentage is set to 25% and the target blood pressure is set to 130 / 80 mmHg.

[1133] Step 5: The server notifies the user of the target value

[1134] The server notifies the user's terminal of the set target value.

[1135] The user receives a notification and confirms the target value.

[1136] Step 6: Users collect data using healthcare devices

[1137] Users use smartwatches and fitness trackers to collect daily data such as steps taken, heart rate, and body temperature.

[1138] This data is stored on the device.

[1139] Step 7: The device synchronizes the collected data with the server

[1140] The user's device periodically synchronizes the collected health data with the server.

[1141] The server stores the received data in real time and integrates it into a database.

[1142] Step 8: User uploads food photo

[1143] Users take photos of their daily meals with their smartphones and upload them to a dedicated application.

[1144] The user taps the upload button to submit the photo.

[1145] Step 9: The server analyzes the food image

[1146] The server receives the uploaded food images and passes the data to the image analysis module.

[1147] The image analysis module analyzes the contents of the meal and calculates various nutrients and calories.

[1148] Step 10: Server consolidates data

[1149] The server consolidates the daily health data and dietary data and stores them in a database.

[1150] Based on the integrated data, a generative AI model performs a comprehensive health assessment.

[1151] Step 11: Server generates recommendations

[1152] The server generates specific health management recommendations for the user based on the analysis results of the AI ​​model.

[1153] For example, it includes specific advice such as "Take a few more steps today" or "Choose foods that are lower in fat."

[1154] Step 12: The server notifies the user of the recommendation

[1155] The server notifies the user's terminal of the generated recommendations.

[1156] The user receives a notification, confirms the recommendation, and acts on it.

[1157] Step 13: Server monitors user progress

[1158] The server routinely monitors the user's progress against the set KPIs and records the progress data.

[1159] The server periodically generates progress reports to provide feedback to the user.

[1160] Step 14: User uploads new health check results

[1161] The following year, the user uploads new medical results back into the system.

[1162] The diagnostic results are photographed with a smartphone and sent via a dedicated application.

[1163] Step 15: Server parses new data

[1164] The server receives new health checkup results and loads them into the generative AI model.

[1165] The AI ​​model compares the previous year's data with the new data and calculates the degree of improvement for various KPIs.

[1166] Step 16: The server notifies the evaluation result

[1167] The server evaluates the degree of improvement and notifies the user of specific results, such as "body fat percentage reduced by 10%" or "blood pressure returned to normal range."

[1168] Users can view results and understand their progress in improving their health.

[1169] Example 1

[1170] 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."

[1171] Health management is an important issue in modern society, but many people are unable to effectively manage their daily health and dietary data. Furthermore, there is a lack of an integrated system for properly comparing and evaluating annual health checkup results and promoting sustainable health improvement. Currently, data collection, analysis, and integration are fragmented, preventing users from achieving comprehensive health management.

[1172] 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.

[1173] In this invention, the server includes means for receiving and analyzing electronic files uploaded by users, means for setting reference values ​​based on the analysis results, means for collecting and integrating daily information from users' electronic devices, means for analyzing images uploaded by users and generating numerical data, means for generating management suggestions based on the generated data and notifying the users, and means for receiving electronic files for the next year and comparing and evaluating them with previous data, thereby enabling users to comprehensively understand their own health status and make continuous improvements to their health.

[1174] "Electronic files" are digital documents or data uploaded by users, such as health checkup results.

[1175] The "reference value" is a target value of a health index that is set based on the analysis results, and indicates the standard of health status that the user should achieve.

[1176] "Electronic devices" are devices that collect users' daily health data, such as smartwatches and fitness trackers.

[1177] "Daily information" refers to daily health-related data such as the number of steps taken, heart rate, and body temperature that users collect through their electronic devices.

[1178] "Images" are visual data such as photos of meals taken by a user using a smartphone or the like.

[1179] "Numerical data" refers to quantitative data such as calories and nutrients obtained through image analysis.

[1180] "Management suggestions" are specific advice and recommendations for health management generated by the server, including, for example, the amount of exercise and recommended dietary content.

[1181] "Evaluation" refers to the act of comparing the results of the next year's health check with the data from the previous year and analyzing the progress and degree of improvement of the user's health indicators.

[1182] This system integrates the results of a user's health checkup, daily health data, and dietary data to realize comprehensive health management. The system mainly involves a server, a terminal, and a user.

[1183] 1. Reading and analyzing health checkup results

[1184] Users scan their health checkup results using a smartphone camera app and upload the data to the system. The server receives the electronic file, verifies the file format, and then uses a generative AI model to analyze the file's contents and extract key health indicators (e.g., blood pressure, blood glucose level, cholesterol level, body fat percentage).

[1185] 2. Setting health indicator targets

[1186] The server sets various benchmarks based on the analysis results, including specific goals such as lowering blood pressure to 130 / 80 mmHg or reducing body fat to 25%. The set benchmarks are then notified to the user.

[1187] 3. Daily data collection and integration

[1188] Users use electronic devices such as smartwatches and fitness trackers on a daily basis to collect daily information such as the number of steps taken, heart rate, body temperature, etc. This data is periodically synchronized via the device to a server, which then integrates and manages this data in real time.

[1189] 4. Dietary data collection and analysis

[1190] Users take photos of their meals with their smartphones and upload them to the system. The server receives the images and uses a generative AI model to analyze the meal's contents and calculate calories and nutrients. This generates numerical data that is stored in the user's database.

[1191] 5. Comprehensive data analysis and recommendation generation

[1192] The server integrates the user's daily health and dietary data and evaluates their overall health status using a generative AI model. Based on the results of this evaluation, management suggestions are generated. For example, the amount of exercise required and recommended dietary content may be recommended. These suggestions are then notified to the user.

[1193] 6. Progress Management and Feedback

[1194] The server periodically monitors the user's progress against the baseline and generates weekly or monthly reports that the user can use to assess their health status and understand any improvements that may be needed.

[1195] 7. Evaluation based on the results of the following year's health check

[1196] When the user uploads the following year's health checkup results to the system, the server loads the new data into the generative AI model and compares it with the previous year's data. This evaluates the degree of improvement against various benchmarks and notifies the user of specific results (e.g., a 10% decrease in body fat percentage, or blood pressure returning to normal range).

[1197] Specific examples

[1198] 1. Reading and analyzing health checkup results

[1199] Users scan their health check results using a dedicated app and upload them to the system.

[1200] The server receives the result file and analyzes it using the generative AI model.

[1201] Extract key health indicators such as blood pressure, blood sugar levels, and body fat percentage.

[1202] 2. Daily data collection and integration

[1203] The user's smartwatch collects daily steps and heart rate data and sends them to a server.

[1204] The server aggregates this data in real time.

[1205] 3. Dietary data collection and analysis

[1206] Users take photos of their meals and upload them to the system.

[1207] The server analyzes the images and calculates nutrients and calories.

[1208] Prompt Sentence Examples

[1209] "Based on the results of last year's health check, please set your health goals for this year."

[1210] "Please analyze your recent dietary and daily health data and tell us your overall health status."

[1211] By using the above prompt sentences, this system can provide appropriate health management suggestions to the user.

[1212] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1213] Step 1:

[1214] The user scans the health checkup results. As input, the user takes a photo of the health checkup results using the camera app on their smartphone. The output is a scanned image file. The user uploads the scanned image file to the system using a dedicated application.

[1215] Step 2:

[1216] The server receives an electronic file. The input is a health checkup result file uploaded by a user. When the server receives this file, it first verifies the file format. If it is confirmed to be in the correct format, the server proceeds to the next analysis step. The output is a verified health checkup result file.

[1217] Step 3:

[1218] Analysis of health checkup results using an AI model. The input is a health checkup result file received by the server. The server loads this file into a generative AI model. The AI ​​model analyzes the data and extracts key health indicators such as blood pressure, blood sugar level, cholesterol level, and body fat percentage. The output is the analyzed health indicator data.

[1219] Step 4:

[1220] The server sets the target value for the health index. The input is the health index data analyzed by the AI ​​model. The server sets the reference value based on this data (e.g., lower blood pressure to 130 / 80 mmHg, reduce body fat percentage to 25%). The output is the set reference value. The reference value is notified to the user.

[1221] Step 5:

[1222] The user collects daily health data. The input is daily health data (e.g., number of steps, heart rate, body temperature, etc.) collected from electronic devices such as smart watches and fitness trackers used by the user. The output is this health data. This data is periodically synchronized to a server via the device.

[1223] Step 6:

[1224] The server integrates daily health data. The input is daily health data synchronized from the user's electronic devices. The server receives, integrates, and manages this data in real time. The output is the integrated health data.

[1225] Step 7:

[1226] The user collects meal data. The input is a photo of the meal taken by the user with a smartphone. The user uploads this to the system. The output is the uploaded meal image.

[1227] Step 8:

[1228] The server analyzes the food images. The input is the food images uploaded by the user. The server analyzes the images using a generative AI model and automatically calculates various nutrients and calories. The output is the analyzed numerical data (calories and nutrients). This data is stored in the user's database.

[1229] Step 9:

[1230] The server performs comprehensive data analysis and generates recommendations. The input is the integrated health data and analyzed dietary data. The server uses a generative AI model to comprehensively analyze this data and evaluate the user's health status. The output is the comprehensive evaluation result and specific health management suggestions based on it (e.g., the amount of exercise required, recommended dietary content). These suggestions are notified to the user.

[1231] Step 10:

[1232] The server monitors progress and provides feedback. The input is the user's baseline and daily health data. The server monitors these data periodically and generates progress reports. The output is a progress report that is sent to the user on a weekly or monthly basis.

[1233] Step 11:

[1234] The user uploads new health checkup results. The input is the health checkup results for the next year. The user uploads this to the system again. The output is a new health checkup results file.

[1235] Step 12:

[1236] The server compares and evaluates new health checkup results. The input is the next year's health checkup results and the previous year's data. The server loads the new data into a generative AI model and compares it with the previous year's data. The output is the degree of improvement against various benchmark values ​​and specific results (e.g., body fat percentage reduced by 10%, blood pressure returned to normal range). The evaluation results are notified to the user.

[1237] (Application example 1)

[1238] 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."

[1239] In modern society, many people are required to collect various data for health management and improve their behavior based on the results. However, existing health management systems have difficulty integrating individual data to perform comprehensive health management, and have limitations in recommending specific behaviors and supporting their implementation. In particular, with regard to dietary management, there are few systems that provide specific recommendations that are easy to apply to daily life. Furthermore, there are insufficient means for users to actually obtain recommended meals. There is a need to build a system that can solve these issues and enable users to continuously improve their health.

[1240] 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.

[1241] In this invention, the server includes means for receiving and analyzing health checkup results uploaded by the user, means for setting target values ​​for health indices based on the analysis results, means for collecting and integrating daily health data from the user's healthcare terminal, means for analyzing meal images uploaded by the user and generating nutrient data, means for generating health management recommendations based on the generated data and notifying the user, means for providing the recommended meals in cooperation with a food delivery service, and means for receiving next year's health checkup results and comparing them with the previous data to evaluate the degree of improvement. This makes it possible to integrate health checkup results, daily health data, and dietary data to perform comprehensive health management, provide specific dietary recommendations, and consistently provide support for implementing them.

[1242] "User" refers to an individual who uses the system.

[1243] "Uploading" refers to the act of a user sending their own digital data to a server.

[1244] "Health checkup results" refers to data obtained as a result of a health check conducted at a medical institution.

[1245] "Analysis" refers to the act of analyzing acquired data and extracting information.

[1246] "Health index" refers to specific values ​​that indicate the user's health condition, such as blood pressure, blood sugar level, and body fat percentage.

[1247] "Target value" refers to a specific indicator value set for improving health.

[1248] "Healthcare devices" refer to devices that collect users' health data, such as smartwatches and fitness trackers.

[1249] "Daily health data" refers to data related to the user's physical activity and physiological condition in their daily lives.

[1250] "Integration" refers to the act of centrally managing multiple data in a single system.

[1251] "Meal image" refers to photographic data of a user's own meal.

[1252] "Analyzing" refers to the act of extracting necessary information (such as nutrients and calories) from image data.

[1253] "Nutrient data" refers to data that indicates the nutritional components and calorie content of a meal.

[1254] "Recommendations" refer to specific advice or suggestions that contribute to improving the user's health.

[1255] "Food delivery service" refers to a delivery service that provides recommended meals to users.

[1256] "Evaluation" refers to the act of measuring the degree of improvement in the user's health based on the acquired data.

[1257] A "generative AI model" refers to an algorithm that uses machine learning technology to analyze data and generate appropriate recommendations.

[1258] This invention relates to a system for comprehensive health management that integrates a user's health checkup results, daily health data, and dietary data. This system includes a smartphone application, a cloud server, a generative AI model, and collaboration with a food delivery service. Detailed embodiments of this system and the program processing that realizes it are described below.

[1259] System configuration

[1260] Hardware

[1261] 1. Smartphones: Devices for collecting and displaying user data

[1262] 2. Healthcare devices: devices that collect health data, such as smartwatches and fitness trackers

[1263] 3. Cloud server: A server for data analysis and management

[1264] software

[1265] 1. Image analysis tools: Uses OpenCV and TensorFlow

[1266] 2. Generative AI model: Uses GPT-3 and Stable Diffusion

[1267] Data collection and processing

[1268] 1. Importing health checkup results

[1269] Users upload their health checkup results using a smartphone app. The server receives the results and analyzes them using an AI model. This analysis extracts key health indicators such as blood pressure, blood sugar levels, and body fat percentage.

[1270] 2. Collecting daily health data

[1271] Daily health data (number of steps, heart rate, body temperature, etc.) from the user's healthcare device is periodically synchronized with a cloud server, which then integrates and manages this data in real time.

[1272] 3. Dietary data collection and analysis

[1273] Users take photos of their meals with their smartphones and upload them to the app, which uses image analysis tools to analyze these photos and calculate the nutritional and calorie content of each ingredient.

[1274] 4. Comprehensive data analysis and recommendation generation

[1275] The server uses a generative AI model to analyze the collected health and dietary data and generate optimal health management recommendations for the user, including the amount of exercise required and recommended dietary content.

[1276] 5. Collaboration with food delivery services

[1277] The recommended meals are linked to food delivery services, allowing users to easily order them within the app, making it easier to maintain healthy lifestyles.

[1278] Specific examples

[1279] 1. Examples of prompts for analyzing health checkup data

[1280] "Analyze health checkup results and extract health indicators. Sample data: Blood pressure: 120 / 80 mmHg, Blood glucose: 90 mg / dL, Cholesterol: 180 mg / dL."

[1281] 2. Food recommendation prompt examples

[1282] "Please suggest an appropriate meal based on the user's daily health status. Health data sample: Steps: 10,000 steps / day, Heart rate: 70 bpm, Food data sample: Mushroom risotto, Calories: 400 kcal, Protein: 15 g, Carbohydrates: 55 g, Fat: 10 g."

[1283] The system of the present invention allows users to perform comprehensive health management by integrating health checkup results, daily health data, and dietary data, and easily take specific actions to improve their health. In particular, by linking with food delivery services, users can easily obtain recommended meals, enabling sustainable health improvement.

[1284] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1285] Step 1:

[1286] A user uploads their health checkup results using a smartphone app. The input is a health checkup result file, and the output is the health checkup data sent to the cloud server. The server checks the format of the received data and prepares it for passing to the AI ​​model.

[1287] Step 2:

[1288] The server receives the health checkup results and analyzes the data using an AI model. The input is the health checkup data, and the output is key health indicators (blood pressure, blood sugar level, body fat percentage, etc.). The AI ​​model extracts these indicators and stores them in a database.

[1289] Step 3:

[1290] The user's healthcare device (smartwatch or fitness tracker) collects daily health data and synchronizes it to a cloud server. The input is daily health data (number of steps, heart rate, body temperature, etc.), and the output is the integrated data stored on the server. The server manages this data in real time.

[1291] Step 4:

[1292] Users take photos of their meals with their smartphones and upload them to the app. The input is the photo file of the meal, and the output is image data sent to a cloud server. The server receives this image data and prepares it for analysis.

[1293] Step 5:

[1294] The server uses image analysis tools (OpenCV and TensorFlow) to analyze photos of meals and calculate nutrients and calories. The input is image data, and the output is nutrient data and calorie data. The server stores this data in a database.

[1295] Step 6:

[1296] The server uses a generative AI model to integrate health checkup data, daily health data, and nutritional data to perform a comprehensive health assessment. All of this data is input, and the output is a health assessment and recommendations. The generative AI model generates recommendations that suggest the optimal amount of exercise and diet for the user and notifies the user.

[1297] Step 7:

[1298] The server connects with food delivery services to provide recommended meals. The input is recommendation data, and the output is a food delivery order. Users can easily order meals from within the app.

[1299] Step 8:

[1300] The server receives the next year's health checkup results and compares them with the previous data to evaluate the degree of improvement. The input is the new health checkup data, and the output is the evaluation result of the degree of improvement. The server then runs this data through the AI ​​model again and provides feedback to the user.

[1301] 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.

[1302] The system of this invention not only realizes comprehensive health management by integrating daily health data and dietary data based on the user's health checkup results, but also evaluates the user's mental health state by collecting and analyzing the user's emotional data and provides appropriate feedback. Specific embodiments of the invention are described below.

[1303] System Overview

[1304] Importing and analyzing health checkup results

[1305] Users upload their annual health checkup results to the system using a device such as a smartphone. The server that receives the upload checks the file format and loads the contents into a generative AI model. The AI ​​model analyzes the health checkup results and extracts key health indicators (e.g., blood pressure, blood sugar, cholesterol, body fat percentage, etc.).

[1306] Setting health indicator targets

[1307] The server sets various KPIs (Key Performance Indicators) based on the analysis results. These target values ​​are important for improving the user's health. For example, they include target values ​​for reducing body fat percentage and target values ​​for returning blood pressure to the normal range. These target values ​​are notified to the user so that they can be checked.

[1308] Daily data collection and integration

[1309] Users use healthcare devices such as smartwatches and fitness trackers on a daily basis to collect data such as the number of steps taken, heart rate, and body temperature. This data is periodically synchronized with a server, which then integrates and manages the data in real time.

[1310] Dietary data collection and analysis

[1311] Users take photos of their meals using their smartphones and upload them to the system. These meal images are then analyzed by the server, and various nutrients and calories are automatically calculated. The analysis results are stored in the user's database and can be used to help with daily dietary management.

[1312] Emotion data collection and analysis

[1313] The device collects emotional data that users input on a daily basis, such as periodic self-evaluations of their emotions and facial recognition using a smartphone camera. This data is sent to a server and analyzed by an emotion engine. The emotion engine evaluates the user's emotional state and identifies their daily stress level and mental health.

[1314] Comprehensive data analysis and recommendation generation

[1315] The server integrates daily health, dietary, and emotional data to evaluate the user's overall health using a generative AI model. Based on the results of this evaluation, specific health management recommendations are generated for the user, including the amount of exercise required, recommended dietary habits, and mental health advice.

[1316] Progress management and feedback

[1317] The user's progress towards their KPIs is regularly monitored, and the server provides this information as feedback to the user in the form of a report. This allows users to constantly understand their own health status and identify areas for improvement. Feedback based on emotional data is also provided periodically, allowing users to check their mental health.

[1318] Evaluation based on the results of the following year's health check

[1319] When the next year's health checkup results are uploaded, the server loads the new data into the AI ​​model and compares it with the previous year's data. This evaluates the degree of improvement in various KPIs and provides feedback to the user. Specific results include, for example, "body fat percentage reduced by 10%" or "blood pressure returned to normal range."

[1320] Specific examples

[1321] 1. Reading and analyzing health checkup results

[1322] The user scans the results of the health check and uploads them to the system.

[1323] The server receives the result file and analyzes it using an AI model.

[1324] Extract indicators such as blood pressure, blood sugar level, and body fat percentage.

[1325] 2. Setting health indicator targets

[1326] The server sets a target value based on the analysis results.

[1327] Examples: Lowering blood pressure to 130 / 80 mmHg, reducing body fat percentage to 25%.

[1328] 3. Daily data collection and integration

[1329] Users use smartwatches to collect daily health data.

[1330] The server synchronizes and manages this data in real time.

[1331] 4. Dietary data collection and analysis

[1332] Users take photos of their meals and upload them to the system.

[1333] The server analyzes the image and calculates calories and various nutrients.

[1334] 5. Emotional Data Collection and Analysis

[1335] Users enter their own self-evaluation of emotions on a daily basis, and facial expression recognition data is collected using the smartphone camera.

[1336] The server receives the emotion data and analyzes it using the emotion engine.

[1337] 6. Comprehensive data analysis and recommendation generation

[1338] The server integrates healthcare data, dietary data, and emotional data to provide a comprehensive assessment of health status.

[1339] A generative AI model generates recommendations and notifies the user.

[1340] Specific examples include advice such as "Take a few more steps today," "Choose lower-fat foods," and "Try meditation to relax."

[1341] 7. Progress Management and Feedback

[1342] The server monitors progress against KPIs and generates weekly or monthly reports.

[1343] User receives and acknowledges feedback.

[1344] This also includes feedback based on emotional data.

[1345] 8. Evaluation based on the results of the following year's health check

[1346] New health checkup results are fed into the AI ​​model and compared with the previous year.

[1347] Evaluate the degree of improvement and notify users of specific results.

[1348] In this way, the system of the present invention provides comprehensive care for the user's health condition, realizing continuous improvement in health and management of mental health.

[1349] The processing flow will be explained below.

[1350] Step 1: User uploads health check results

[1351] Users take a photo of their annual health checkup results with their smartphone and upload them to the system via a dedicated application.

[1352] The user taps the upload button, selects a file from local storage, and sends it.

[1353] Step 2: The server receives the health check results

[1354] The server receives and stores the health check result file sent by the user.

[1355] The server checks the file format and passes the data to the appropriate analysis module.

[1356] Step 3: The server loads the data into the generative AI model

[1357] The server loads the received health check results into a generative AI model.

[1358] An AI model analyzes the data and extracts key health indicators (blood pressure, blood sugar levels, body fat percentage, etc.).

[1359] Step 4: The server sets the target value for the health index.

[1360] The server sets target values ​​(KPIs) for health indicators based on the data analyzed by the AI ​​model.

[1361] For example, the target body fat percentage is set to 25% and the target blood pressure is set to 130 / 80 mmHg.

[1362] Step 5: The server notifies the user of the target value

[1363] The server notifies the user's terminal of the set target value.

[1364] The user receives a notification and confirms the target value.

[1365] Step 6: Users collect data using healthcare devices

[1366] Users use smartwatches and fitness trackers to collect daily data such as steps taken, heart rate, and body temperature.

[1367] This data is stored on the device.

[1368] Step 7: The device synchronizes the collected data with the server

[1369] The user's device periodically synchronizes the collected health data with the server.

[1370] The server stores the received data in real time and integrates it into a database.

[1371] Step 8: User uploads food photo

[1372] Users take photos of their daily meals with their smartphones and upload them to a dedicated application.

[1373] The user taps the upload button to submit the photo.

[1374] Step 9: The server analyzes the food image

[1375] The server receives the uploaded food images and passes the data to the image analysis module.

[1376] The image analysis module analyzes the contents of the meal and calculates various nutrients and calories.

[1377] Step 10: User enters emotion data

[1378] Users input their daily emotional data through a dedicated application.

[1379] Emotion entry includes text entry, facial expression recognition, and multiple choice emotion checklists.

[1380] Step 11: The device sends the emotion data to the server

[1381] The user's device transmits the collected emotion data to the server.

[1382] The server passes the received emotion data to the emotion engine for appropriate analysis.

[1383] Step 12: Server consolidates data

[1384] The server integrates daily health data, dietary data, and emotional data and stores them in a database.

[1385] Based on the integrated data, a generative AI model performs a comprehensive health assessment.

[1386] Step 13: Server generates recommendations

[1387] The server generates specific health management recommendations for the user based on the analysis results of the AI ​​model.

[1388] For example, it includes specific advice such as "Take a few more steps today," "Choose leaner foods," or "Try meditation to relax."

[1389] Step 14: The server notifies the user of the recommendation

[1390] The server notifies the user's terminal of the generated recommendations.

[1391] The user receives a notification, confirms the recommendation, and acts on it.

[1392] Step 15: Server monitors user progress

[1393] The server routinely monitors the user's progress against the set KPIs and records the progress data.

[1394] The server periodically generates progress reports to provide feedback to the user.

[1395] Step 16: User uploads new health check results

[1396] The following year, the user uploads new medical results back into the system.

[1397] The diagnostic results are photographed with a smartphone and sent via a dedicated application.

[1398] Step 17: Server parses new data

[1399] The server receives new health checkup results and loads them into the generative AI model.

[1400] The AI ​​model compares the previous year's data with the new data and calculates the degree of improvement for various KPIs.

[1401] Step 18: The server notifies the evaluation result

[1402] The server evaluates the degree of improvement and notifies the user of specific results, such as "body fat percentage reduced by 10%" or "blood pressure returned to normal range."

[1403] Users can view results and understand their progress in improving their health.

[1404] Example 2

[1405] 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."

[1406] While conventional health management systems could collect and analyze a user's health checkup results, daily health data, and dietary data individually, it was difficult to integrate them for comprehensive health management. Furthermore, they lacked a means to consider mental health status, making it impossible to evaluate a user's overall health status. As a result, users could only obtain partial health information, making it difficult to effectively improve their health.

[1407] 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.

[1408] In this invention, the server includes means for receiving and analyzing health checkup results uploaded by a user, means for setting target values ​​for health indices based on the analysis results, means for collecting and integrating daily health data from the user's healthcare terminal, means for analyzing meal images uploaded by the user and generating nutrient data, means for receiving and analyzing preset emotion data, means for integrating the generated data, generating recommendations for comprehensive health management, and notifying the user, and means for receiving next year's health checkup results and comparing them with the previous data to evaluate the degree of improvement. This makes it possible to comprehensively evaluate the user's physical and mental health status and provide specific improvements and recommendations.

[1409] "Health checkup results" refers to numerical data on health indicators such as blood pressure, blood sugar level, cholesterol level, and body fat percentage obtained through a health checkup conducted by a user once a year at a medical institution or other facility.

[1410] "Analysis means" refers to tools and algorithms used to process uploaded data on the server and extract and analyze specific information.

[1411] "Health index" refers to specific numerical data that indicates the user's health condition, such as blood pressure, blood sugar level, cholesterol level, and body fat percentage.

[1412] The "target value" is set based on the analysis results and refers to a specific numerical value that the user should achieve in order to improve their health.

[1413] A "healthcare device" is an electronic device, such as a smartwatch or fitness tracker, that is used to collect a user's daily health data.

[1414] "Daily health data" refers to health-related data collected daily by a healthcare terminal, such as the user's number of steps, heart rate, and body temperature.

[1415] A "meal image" is a photograph of a meal that a user takes using a smartphone or other device and uploads to the system.

[1416] "Nutrient data" refers to information about various nutrients contained in food, such as carbohydrates, proteins, and lipids, obtained by the server analyzing the food image.

[1417] "Emotional data" refers to data about a user's emotional state and stress level, collected through a user's self-assessment or facial expression recognition using a smartphone camera.

[1418] "Recommendations" refer to specific health management advice and suggestions provided to users based on data collected and analyzed by the server.

[1419] A "generative AI model" is an artificial intelligence model that analyzes collected health checkup results, daily health data, dietary data, and emotional data to generate comprehensive evaluations and recommendations.

[1420] "Improvement" refers to a numerical value or evaluation that indicates how much the user's health index has improved compared to the previous year.

[1421] The system of the present invention provides comprehensive health management based on the user's health checkup results, daily health data, dietary data, and emotional data.

[1422] First, users upload their annual health checkup results to the system using a device such as a smartphone. The device then sends the uploaded health checkup result file to the server. The server checks the format of the received file to ensure it is in the correct format. The server then analyzes the health checkup results using a generative AI model to extract key health indicators (blood pressure, blood sugar level, cholesterol level, body fat percentage, etc.). These extracted health indicators are then recorded in a database. Based on the analysis results, the server then sets individual KPIs (Key Performance Indicators) to aim for health improvement. The set target values ​​are notified to the user and displayed on the device.

[1423] Next, users use healthcare devices such as smartwatches and fitness trackers on a daily basis to collect data such as the number of steps taken, heart rate, and body temperature. The devices periodically synchronize this data with a server, which then integrates and manages the data in real time and stores it in a database.

[1424] Users can also take photos of their meals with their smartphones and upload them to the system. The device then sends the meal images to a server, which then uses a generative AI model to analyze the images and calculate various nutrients and calories. The analysis results are stored in a database and used to help users manage their diet.

[1425] Furthermore, users can collect emotional data by entering their own emotional assessments or by using their smartphone camera. The device then transmits this emotional data to a server, which then uses an emotion engine to analyze the data and evaluate the user's emotional state, stress level, and mental health.

[1426] The server integrates daily health data, dietary data, and emotional data, and uses a generative AI model to assess the user's overall health. Based on the results of this assessment, the server generates specific health management recommendations (e.g., the amount of exercise required, recommended dietary content, mental health advice, etc.) and notifies the user.

[1427] The server periodically monitors the user's progress against KPIs and generates a report on a weekly or monthly basis. The report is then sent to the user and displayed on their device. The user can review the report and identify any necessary improvements. Feedback based on emotional data is also provided on a regular basis.

[1428] When the next year's health checkup results are uploaded, the server loads the new data into the generative AI model and compares it with the previous year's data. This evaluates the degree of improvement in various KPIs and notifies the user of specific results (e.g., a 10% decrease in body fat, or blood pressure returning to normal range).

[1429] Prompt Sentence Examples

[1430] "Create a program that generates a comprehensive health assessment and appropriate feedback based on the user's health checkup results, daily health data, dietary data, and emotional data."

[1431] The system is able to comprehensively assess a user's physical and mental health and provide specific improvements and recommendations.

[1432] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1433] Step 1:

[1434] The user uploads the health check result file (e.g. PDF, JPEG, etc.) to the system using a device such as a smartphone.

[1435] Input: Health check result file

[1436] Output: The uploaded file is saved to the device.

[1437] Step 2:

[1438] The terminal sends the uploaded file to the server.

[1439] Input: Health check result file (saved on the device)

[1440] Output: The health check result file is transferred to the server.

[1441] Step 3:

[1442] The server checks the format of the received file to ensure it is in the correct format, and if it is not, it will display an error message to the user.

[1443] Input: Health check result file

[1444] Output: File format check result (success / failure)

[1445] Step 4:

[1446] The server uses a generative AI model to analyze health checkup results and extract key health indicators (blood pressure, blood sugar levels, cholesterol levels, body fat percentage, etc.).

[1447] Input: Health check result file

[1448] Output: Extracted key health indicators

[1449] Step 5:

[1450] The server records the extracted health indicators in a database and sets KPIs for each user based on the analysis results.

[1451] Input: Key health indicators

[1452] Output: Setting KPIs and recording them in a database

[1453] Step 6:

[1454] The server notifies the user of the set KPIs and displays them to the user.

[1455] Input: The configured KPI

[1456] Output: Notification and display on the user's terminal

[1457] Step 7:

[1458] Users use healthcare devices such as smartwatches and fitness trackers on a daily basis to collect data such as the number of steps taken, heart rate, and body temperature.

[1459] Input: Smartwatch or fitness tracker data

[1460] Output: Collection of daily health data

[1461] Step 8:

[1462] The device periodically synchronizes the collected daily health data with the server.

[1463] Input: Daily health data (saved on the device)

[1464] Output: Synchronization of daily health data to the server

[1465] Step 9:

[1466] The server integrates and manages the received daily health data in real time and stores it in a database.

[1467] Input: Synced daily health data

[1468] Output: Consolidated daily health data stored in a database

[1469] Step 10:

[1470] Users take photos of their meals with their smartphones and upload them to the system.

[1471] Input: Food photo

[1472] Output: Uploaded photos are saved on the device

[1473] Step 11:

[1474] The device sends the uploaded photos to the server.

[1475] Input: Food photo (saved on device)

[1476] Output: Meal photos are sent to the server

[1477] Step 12:

[1478] The server uses a generative AI model to analyze the food images and calculate various nutrients and calories.

[1479] Input: Food photo

[1480] Output: Analyzed nutrient data and calories

[1481] Step 13:

[1482] The server stores the analysis results in a database to help users manage their diet.

[1483] Input: Parsed nutrient data and calories

[1484] Output: Save nutrient data to database

[1485] Step 14:

[1486] Users can collect emotional data by entering their own emotional assessments on a daily basis or by using their smartphone camera.

[1487] Input: Emotion self-assessment data or facial expression recognition data

[1488] Output: The input emotion data is saved on the device.

[1489] Step 15:

[1490] The terminal transmits this emotion data to the server.

[1491] Input: Emotion data (saved on the device)

[1492] Output: Emotion data is sent to the server

[1493] Step 16:

[1494] The server uses an emotion engine to analyze the emotion data and evaluate the user's emotional state and stress level.

[1495] Input: Emotion data

[1496] Output: Analyzed emotional state and stress level

[1497] Step 17:

[1498] The server integrates daily health data, dietary data, and emotional data, and uses a generative AI model to assess overall health status.

[1499] Input: Daily health data, dietary data, emotional data

[1500] Output: Overall health status assessment results

[1501] Step 18:

[1502] Based on the evaluation results, the server generates specific health management recommendations and notifies the user.

[1503] Input: Overall health status assessment results

[1504] Output: Generated health care recommendations and notification to the user

[1505] Step 19:

[1506] The server periodically monitors the user's progress against the KPIs and generates reports that are then sent to the user.

[1507] Input: KPI progress data

[1508] Output: Generated scheduled reports and notifications to users

[1509] Step 20:

[1510] When the next year's health checkup results are uploaded, the user's device will send the new checkup results to the server.

[1511] Input: New medical examination results

[1512] Output: New health check results are sent to the server.

[1513] Step 21:

[1514] The server loads the new health check results into a generative AI model and compares them with the previous year's data.

[1515] Input: New health checkup results, previous year's health checkup data

[1516] Output: KPI improvement evaluation results

[1517] Step 22:

[1518] The server notifies the user of specific results, such as "body fat percentage reduced by 10%" or "blood pressure returned to normal range."

[1519] Input: KPI improvement evaluation results

[1520] Output: Notification of results to the user

[1521] (Application example 2)

[1522] 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."

[1523] In modern society, health management has become increasingly difficult due to the diversification of people's lifestyles and dietary habits. In particular, conventional health management systems only collect and analyze physical data, and do not adequately evaluate and manage mental health status. Therefore, there is a demand for multifaceted, comprehensive health management. In response to this, the present invention aims to promote health improvement by comprehensively evaluating a user's physical and mental health status and providing personalized health management.

[1524] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1525] In this invention, the server includes a means for receiving and analyzing health checkup results uploaded by a user, a means for setting target values ​​for health indices based on the analysis results, and a means for collecting and integrating daily health data from the user's healthcare device, thereby enabling a comprehensive evaluation of the user's physical and mental health status and the generation of specific health management recommendations for the user.

[1526] "Health checkup results" are the results of tests that a user undergoes periodically at a medical institution, and are data including major health indicators such as blood pressure, blood sugar level, cholesterol level, and body fat percentage.

[1527] "Health index" is specific numerical data for evaluating the health condition of a user, and includes, for example, blood pressure, blood sugar level, cholesterol level, and body fat percentage.

[1528] "Daily health data" refers to data measured by users in their daily lives, including the number of steps taken, heart rate, body temperature, etc. collected by healthcare devices such as smartwatches and fitness trackers.

[1529] "Meal images" are photographs of meals taken by the user, and are data that are uploaded to the system and used to analyze nutrient data.

[1530] "Nutrient data" is information on various nutrients and calories analyzed from a meal image, and is data that is useful for the user's dietary management.

[1531] "Emotion data" is data collected to assess a user's mental health, and includes data from self-assessment of emotions and facial expression recognition.

[1532] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning, and is an algorithm that comprehensively evaluates a user's health condition based on large amounts of data and generates optimal recommendations.

[1533] The system for implementing this invention integrates a user's health checkup results, daily health data, dietary data, and emotional data, evaluates their health status comprehensively using a generative AI model, and provides appropriate feedback to the user.

[1534] composition

[1535] 1. Reading and analyzing health checkup results

[1536] Users upload their annual health checkup results to the system using a device such as a smartphone. The server receives the upload, checks the file format, and loads the contents into a generative AI model. The AI ​​model analyzes the health checkup results and extracts key health indicators.

[1537] 2. Setting health indicator targets

[1538] The server sets various KPIs (Key Performance Indicators) based on the analysis results. These target values ​​aim to improve the user's health, and include blood pressure, body fat percentage, etc. These target values ​​are notified to the user.

[1539] 3. Daily data collection and integration

[1540] Users use smartwatches and fitness trackers on a daily basis to collect data such as the number of steps taken, heart rate, and body temperature. This data is periodically synchronized with a server and integrated and managed in real time.

[1541] 4. Dietary data collection and analysis

[1542] Users take photos of their meals using their smartphones and upload them to the system. The server analyzes the meal images and automatically calculates various nutrients and calories. The analysis results are stored in the user's database.

[1543] 5. Emotional Data Collection and Analysis

[1544] Users enter their own emotional self-assessment on a daily basis and also collect facial expression recognition data using their smartphone camera. This data is sent to a server and analyzed by the emotion engine. The emotion engine evaluates the user's emotional state and grasps their daily stress level and mental health.

[1545] 6. Comprehensive data analysis and recommendation generation

[1546] The server integrates daily health data, dietary data, and emotional data, and uses a generative AI model to comprehensively evaluate the user's health. Based on the results of this evaluation, specific health management recommendations are generated for the user, such as the amount of exercise required, recommended dietary content, and advice on mental health.

[1547] 7. Progress Management and Feedback

[1548] The user's progress towards their KPIs is regularly monitored, and the server provides this information as feedback to the user in the form of a report. This allows users to constantly understand their own health status and identify areas for improvement. Feedback based on emotional data is also provided periodically, allowing users to check their mental health.

[1549] 8. Evaluation based on the results of the following year's health check

[1550] When the next year's health checkup results are uploaded, the server loads them into the AI ​​model and compares them with the previous year's data. This evaluates the degree of improvement in various KPIs and notifies the user of specific results. Examples include "body fat percentage reduced by 10%" and "blood pressure returned to normal range."

[1551] Hardware and software used

[1552] Hardware:

[1553] Smartphone

[1554] Smartwatch

[1555] Fitness tracker

[1556] software:

[1557] AI model for health data analysis

[1558] Sentiment analysis engine

[1559] Nutrient analysis model

[1560] Specific examples

[1561] The user scans their health checkup results and uploads them to the system. The server receives the result file and analyzes it using an AI model to extract indicators such as blood pressure, blood sugar level, and body fat percentage. Based on the analysis results, the server sets target values ​​for health indicators and notifies the user. The user uses the smartwatch daily and synchronizes the acquired data (number of steps, heart rate, etc.) with the server. Next, they take photos of their meals and upload them to the system, which analyzes the nutritional data. The user also enters a self-evaluation of their emotions and collects facial recognition data using the smartphone camera. The server integrates this data, evaluates their overall health, and notifies the user of specific health management recommendations. Examples include advice such as "Take more steps today," "Choose lower-fat meals," and "Try meditation to relax."

[1562] Prompt Sentence Examples

[1563] Health checkup results: Blood pressure 140 / 90, blood sugar 6.5, cholesterol 200, body fat 30%

[1564] Daily data: steps 8000, heart rate 75, body temperature 36.5℃

[1565] Dietary information: Breakfast: 500 calories, 50g carbohydrates, 30g fat

[1566] Emotion data: Facial expression High stress

[1567] This allows a comprehensive assessment of the user's physical and mental health status and provides the user with specific health management recommendations.

[1568] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1569] Step 1:

[1570] A user scans their health checkup results with their smartphone and uploads them to the system. The input is an image file of the health checkup results. The server receives this file and checks the file format. If the format is correct, the contents of the result file are loaded into a generative AI model to extract health indicators such as blood pressure, blood sugar level, and body fat percentage. The output is the extracted health indicator data.

[1571] Step 2:

[1572] The server sets various KPIs (Key Performance Indicators) based on the extracted health indicator data. The extracted health indicator data is the input. The server sets appropriate target values ​​for each health indicator and notifies the user. The output is the set KPI target values.

[1573] Step 3:

[1574] Users use smartwatches and fitness trackers on a daily basis to collect daily health data such as steps taken, heart rate, and body temperature. The input is the data collected from the health device. The device periodically synchronizes this data with the server. The server receives this data and integrates and manages it in real time. The output is the integrated daily health data.

[1575] Step 4:

[1576] Users take photos of their meals using their smartphones and upload them to the system. The input is the meal image. The server receives the image, analyzes it, and calculates various nutrients and calories. The output is the analyzed nutrient data.

[1577] Step 5:

[1578] Users input their self-evaluation of emotions on a daily basis and also collect facial expression recognition data using their smartphone camera. The input is the self-evaluation data and facial expression recognition images. The server receives this data and analyzes it using an emotion engine. The output is the analyzed emotional data.

[1579] Step 6:

[1580] The server integrates daily health data, nutritional data, and emotional data, and evaluates the overall health status using a generative AI model. The input is the integrated health data. The generative AI model generates specific health management recommendations based on this data and notifies the user. The output is the generated health management recommendations.

[1581] Step 7:

[1582] The server periodically monitors the user's progress against KPIs. The input is the integrated health data and the set KPI targets. The server generates a progress report based on this and provides feedback to the user. The output is the progress report.

[1583] Step 8:

[1584] The following year, the user uploads new health checkup results. The input is the new health checkup result file. The server receives this and compares it with the data from the previous year. The server evaluates it using a generative AI model, assesses the degree of improvement against various KPIs, and notifies the user. The output is the improvement evaluation result.

[1585] 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.

[1586] 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.

[1587] 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.

[1588] [Fourth embodiment]

[1589] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1590] 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.

[1591] 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).

[1592] 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.

[1593] 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.

[1594] 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).

[1595] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1596] 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.

[1597] 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.

[1598] 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.

[1599] 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.

[1600] 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.

[1601] 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."

[1602] The system of this invention realizes comprehensive health management by integrating daily health data and dietary data based on the results of a user's health checkup. The system of this invention is premised on the use of specific hardware and software configurations, and an embodiment thereof is shown below as an example.

[1603] System Overview

[1604] Importing and analyzing health checkup results

[1605] Users upload their annual health checkup results to the system using a device such as a smartphone. The server that receives the upload checks the file format and loads the contents into a generative AI model. The AI ​​model analyzes the health checkup results and extracts key health indicators (e.g., blood pressure, blood sugar, cholesterol, body fat percentage, etc.).

[1606] Setting health indicator targets

[1607] The server sets various KPIs (Key Performance Indicators) based on the analysis results. These target values ​​are important for improving the user's health. For example, they include target values ​​for reducing body fat percentage and target values ​​for returning blood pressure to the normal range. These target values ​​are notified to the user so that they can be checked.

[1608] Daily data collection and integration

[1609] Users use healthcare devices such as smartwatches and fitness trackers on a daily basis to collect data such as the number of steps taken, heart rate, and body temperature. This data is periodically synchronized with a server, which then integrates and manages the data in real time.

[1610] Dietary data collection and analysis

[1611] Users take photos of their meals using their smartphones and upload them to the system. These meal images are then analyzed by the server, and various nutrients and calories are automatically calculated. The analysis results are stored in the user's database and can be used to help with daily dietary management.

[1612] Comprehensive data analysis and recommendation generation

[1613] The server integrates daily health and dietary data and evaluates the user's overall health status using a generative AI model. Based on the results of this evaluation, specific health management recommendations are generated for the user, including the amount of exercise required and recommended dietary content.

[1614] Progress management and feedback

[1615] The user's progress towards their KPIs is monitored regularly and the server provides this information as feedback to the user in the form of reports, allowing the user to stay aware of their health status and identify areas for improvement.

[1616] Evaluation based on the results of the following year's health check

[1617] When the next year's health checkup results are uploaded, the server loads the new data into the AI ​​model and compares it with the previous year's data. This evaluates the degree of improvement in various KPIs and provides feedback to the user. Specific results include, for example, "body fat percentage reduced by 10%" or "blood pressure returned to normal range."

[1618] Specific examples

[1619] 1. Reading and analyzing health checkup results

[1620] The user scans the results of the health check and uploads them to the system.

[1621] The server receives the result file and analyzes it using an AI model.

[1622] Extract indicators such as blood pressure, blood sugar level, and body fat percentage.

[1623] 2. Setting health indicator targets

[1624] The server sets a target value based on the analysis results.

[1625] Examples: Lowering blood pressure to 130 / 80 mmHg, reducing body fat percentage to 25%.

[1626] 3. Daily data collection and integration

[1627] Users use smartwatches to collect daily health data.

[1628] The server synchronizes and manages this data in real time.

[1629] 4. Dietary data collection and analysis

[1630] Users take photos of their meals and upload them to the system.

[1631] The server analyzes the image and calculates calories and various nutrients.

[1632] 5. Comprehensive data analysis and recommendation generation

[1633] The server integrates healthcare and dietary data to conduct a health assessment.

[1634] A generative AI model generates recommendations and notifies the user.

[1635] 6. Progress Management and Feedback

[1636] The server monitors progress against KPIs and generates weekly or monthly reports.

[1637] User receives and acknowledges feedback.

[1638] 7. Evaluation based on the results of the following year's health check

[1639] New health checkup results are fed into the AI ​​model and compared with the previous year.

[1640] Evaluate the degree of improvement and notify users of specific results.

[1641] In this way, the system of the present invention provides comprehensive care for the user's health condition and achieves continuous improvement in health.

[1642] The processing flow will be explained below.

[1643] Step 1: User uploads health check results

[1644] Users take a photo of their annual health checkup results with their smartphone and upload them to the system via a dedicated application.

[1645] The user taps the upload button, selects a file from local storage, and sends it.

[1646] Step 2: The server receives the health check results

[1647] The server receives and stores the health check result file sent by the user.

[1648] The server checks the file format and passes the data to the appropriate analysis module.

[1649] Step 3: The server loads the data into the generative AI model

[1650] The server loads the received health check results into a generative AI model.

[1651] An AI model analyzes the data and extracts key health indicators (blood pressure, blood sugar levels, body fat percentage, etc.).

[1652] Step 4: The server sets the target value for the health index.

[1653] The server sets target values ​​(KPIs) for health indicators based on the data analyzed by the AI ​​model.

[1654] For example, the target body fat percentage is set to 25% and the target blood pressure is set to 130 / 80 mmHg.

[1655] Step 5: The server notifies the user of the target value

[1656] The server notifies the user's terminal of the set target value.

[1657] The user receives a notification and confirms the target value.

[1658] Step 6: Users collect data using healthcare devices

[1659] Users use smartwatches and fitness trackers to collect daily data such as steps taken, heart rate, and body temperature.

[1660] This data is stored on the device.

[1661] Step 7: The device synchronizes the collected data with the server

[1662] The user's device periodically synchronizes the collected health data with the server.

[1663] The server stores the received data in real time and integrates it into a database.

[1664] Step 8: User uploads food photo

[1665] Users take photos of their daily meals with their smartphones and upload them to a dedicated application.

[1666] The user taps the upload button to submit the photo.

[1667] Step 9: The server analyzes the food image

[1668] The server receives the uploaded food images and passes the data to the image analysis module.

[1669] The image analysis module analyzes the contents of the meal and calculates various nutrients and calories.

[1670] Step 10: Server consolidates data

[1671] The server consolidates the daily health data and dietary data and stores them in a database.

[1672] Based on the integrated data, a generative AI model performs a comprehensive health assessment.

[1673] Step 11: Server generates recommendations

[1674] The server generates specific health management recommendations for the user based on the analysis results of the AI ​​model.

[1675] For example, it includes specific advice such as "Take a few more steps today" or "Choose foods that are lower in fat."

[1676] Step 12: The server notifies the user of the recommendation

[1677] The server notifies the user's terminal of the generated recommendations.

[1678] The user receives a notification, confirms the recommendation, and acts on it.

[1679] Step 13: Server monitors user progress

[1680] The server routinely monitors the user's progress against the set KPIs and records the progress data.

[1681] The server periodically generates progress reports to provide feedback to the user.

[1682] Step 14: User uploads new health check results

[1683] The following year, the user uploads new medical results back into the system.

[1684] The diagnostic results are photographed with a smartphone and sent via a dedicated application.

[1685] Step 15: Server parses new data

[1686] The server receives new health checkup results and loads them into the generative AI model.

[1687] The AI ​​model compares the previous year's data with the new data and calculates the degree of improvement for various KPIs.

[1688] Step 16: The server notifies the evaluation result

[1689] The server evaluates the degree of improvement and notifies the user of specific results, such as "body fat percentage reduced by 10%" or "blood pressure returned to normal range."

[1690] Users can view results and understand their progress in improving their health.

[1691] Example 1

[1692] 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."

[1693] Health management is an important issue in modern society, but many people are unable to effectively manage their daily health and dietary data. Furthermore, there is a lack of an integrated system for properly comparing and evaluating annual health checkup results and promoting sustainable health improvement. Currently, data collection, analysis, and integration are fragmented, preventing users from achieving comprehensive health management.

[1694] 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.

[1695] In this invention, the server includes means for receiving and analyzing electronic files uploaded by users, means for setting reference values ​​based on the analysis results, means for collecting and integrating daily information from users' electronic devices, means for analyzing images uploaded by users and generating numerical data, means for generating management suggestions based on the generated data and notifying the users, and means for receiving electronic files for the next year and comparing and evaluating them with previous data, thereby enabling users to comprehensively understand their own health status and make continuous improvements to their health.

[1696] "Electronic files" are digital documents or data uploaded by users, such as health checkup results.

[1697] The "reference value" is a target value of a health index that is set based on the analysis results, and indicates the standard of health status that the user should achieve.

[1698] "Electronic devices" are devices that collect users' daily health data, such as smartwatches and fitness trackers.

[1699] "Daily information" refers to daily health-related data such as the number of steps taken, heart rate, and body temperature that users collect through their electronic devices.

[1700] "Images" are visual data such as photos of meals taken by a user using a smartphone or the like.

[1701] "Numerical data" refers to quantitative data such as calories and nutrients obtained through image analysis.

[1702] "Management suggestions" are specific advice and recommendations for health management generated by the server, including, for example, the amount of exercise and recommended dietary content.

[1703] "Evaluation" refers to the act of comparing the results of the next year's health check with the data from the previous year and analyzing the progress and degree of improvement of the user's health indicators.

[1704] This system integrates the results of a user's health checkup, daily health data, and dietary data to realize comprehensive health management. The system mainly involves a server, a terminal, and a user.

[1705] 1. Reading and analyzing health checkup results

[1706] Users scan their health checkup results using a smartphone camera app and upload the data to the system. The server receives the electronic file, verifies the file format, and then uses a generative AI model to analyze the file's contents and extract key health indicators (e.g., blood pressure, blood glucose level, cholesterol level, body fat percentage).

[1707] 2. Setting health indicator targets

[1708] The server sets various benchmarks based on the analysis results, including specific goals such as lowering blood pressure to 130 / 80 mmHg or reducing body fat to 25%. The set benchmarks are then notified to the user.

[1709] 3. Daily data collection and integration

[1710] Users use electronic devices such as smartwatches and fitness trackers on a daily basis to collect daily information such as the number of steps taken, heart rate, body temperature, etc. This data is periodically synchronized via the device to a server, which then integrates and manages this data in real time.

[1711] 4. Dietary data collection and analysis

[1712] Users take photos of their meals with their smartphones and upload them to the system. The server receives the images and uses a generative AI model to analyze the meal's contents and calculate calories and nutrients. This generates numerical data that is stored in the user's database.

[1713] 5. Comprehensive data analysis and recommendation generation

[1714] The server integrates the user's daily health and dietary data and evaluates their overall health status using a generative AI model. Based on the results of this evaluation, management suggestions are generated. For example, the amount of exercise required and recommended dietary content may be recommended. These suggestions are then notified to the user.

[1715] 6. Progress Management and Feedback

[1716] The server periodically monitors the user's progress against the baseline and generates weekly or monthly reports that the user can use to assess their health status and understand any improvements that may be needed.

[1717] 7. Evaluation based on the results of the following year's health check

[1718] When the user uploads the following year's health checkup results to the system, the server loads the new data into the generative AI model and compares it with the previous year's data. This evaluates the degree of improvement against various benchmarks and notifies the user of specific results (e.g., a 10% decrease in body fat percentage, or blood pressure returning to normal range).

[1719] Specific examples

[1720] 1. Reading and analyzing health checkup results

[1721] Users scan their health check results using a dedicated app and upload them to the system.

[1722] The server receives the result file and analyzes it using the generative AI model.

[1723] Extract key health indicators such as blood pressure, blood sugar levels, and body fat percentage.

[1724] 2. Daily data collection and integration

[1725] The user's smartwatch collects daily steps and heart rate data and sends them to a server.

[1726] The server aggregates this data in real time.

[1727] 3. Dietary data collection and analysis

[1728] Users take photos of their meals and upload them to the system.

[1729] The server analyzes the images and calculates nutrients and calories.

[1730] Prompt Sentence Examples

[1731] "Based on the results of last year's health check, please set your health goals for this year."

[1732] "Please analyze your recent dietary and daily health data and tell us your overall health status."

[1733] By using the above prompt sentences, this system can provide appropriate health management suggestions to the user.

[1734] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1735] Step 1:

[1736] The user scans the health checkup results. As input, the user takes a photo of the health checkup results using the camera app on their smartphone. The output is a scanned image file. The user uploads the scanned image file to the system using a dedicated application.

[1737] Step 2:

[1738] The server receives an electronic file. The input is a health checkup result file uploaded by a user. When the server receives this file, it first verifies the file format. If it is confirmed to be in the correct format, the server proceeds to the next analysis step. The output is a verified health checkup result file.

[1739] Step 3:

[1740] Analysis of health checkup results using an AI model. The input is a health checkup result file received by the server. The server loads this file into a generative AI model. The AI ​​model analyzes the data and extracts key health indicators such as blood pressure, blood sugar level, cholesterol level, and body fat percentage. The output is the analyzed health indicator data.

[1741] Step 4:

[1742] The server sets the target value for the health index. The input is the health index data analyzed by the AI ​​model. The server sets the reference value based on this data (e.g., lower blood pressure to 130 / 80 mmHg, reduce body fat percentage to 25%). The output is the set reference value. The reference value is notified to the user.

[1743] Step 5:

[1744] The user collects daily health data. The input is daily health data (e.g., number of steps, heart rate, body temperature, etc.) collected from electronic devices such as smart watches and fitness trackers used by the user. The output is this health data. This data is periodically synchronized to a server via the device.

[1745] Step 6:

[1746] The server integrates daily health data. The input is daily health data synchronized from the user's electronic devices. The server receives, integrates, and manages this data in real time. The output is the integrated health data.

[1747] Step 7:

[1748] The user collects meal data. The input is a photo of the meal taken by the user with a smartphone. The user uploads this to the system. The output is the uploaded meal image.

[1749] Step 8:

[1750] The server analyzes the food images. The input is the food images uploaded by the user. The server analyzes the images using a generative AI model and automatically calculates various nutrients and calories. The output is the analyzed numerical data (calories and nutrients). This data is stored in the user's database.

[1751] Step 9:

[1752] The server performs comprehensive data analysis and generates recommendations. The input is the integrated health data and analyzed dietary data. The server uses a generative AI model to comprehensively analyze this data and evaluate the user's health status. The output is the comprehensive evaluation result and specific health management suggestions based on it (e.g., the amount of exercise required, recommended dietary content). These suggestions are notified to the user.

[1753] Step 10:

[1754] The server monitors progress and provides feedback. The input is the user's baseline and daily health data. The server monitors these data periodically and generates progress reports. The output is a progress report that is sent to the user on a weekly or monthly basis.

[1755] Step 11:

[1756] The user uploads new health checkup results. The input is the health checkup results for the next year. The user uploads this to the system again. The output is a new health checkup results file.

[1757] Step 12:

[1758] The server compares and evaluates new health checkup results. The input is the next year's health checkup results and the previous year's data. The server loads the new data into a generative AI model and compares it with the previous year's data. The output is the degree of improvement against various benchmark values ​​and specific results (e.g., body fat percentage reduced by 10%, blood pressure returned to normal range). The evaluation results are notified to the user.

[1759] (Application example 1)

[1760] 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."

[1761] In modern society, many people are required to collect various data for health management and improve their behavior based on the results. However, existing health management systems have difficulty integrating individual data to perform comprehensive health management, and have limitations in recommending specific behaviors and supporting their implementation. In particular, with regard to dietary management, there are few systems that provide specific recommendations that are easy to apply to daily life. Furthermore, there are insufficient means for users to actually obtain recommended meals. There is a need to build a system that can solve these issues and enable users to continuously improve their health.

[1762] 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.

[1763] In this invention, the server includes means for receiving and analyzing health checkup results uploaded by the user, means for setting target values ​​for health indices based on the analysis results, means for collecting and integrating daily health data from the user's healthcare terminal, means for analyzing meal images uploaded by the user and generating nutrient data, means for generating health management recommendations based on the generated data and notifying the user, means for providing the recommended meals in cooperation with a food delivery service, and means for receiving next year's health checkup results and comparing them with the previous data to evaluate the degree of improvement. This makes it possible to integrate health checkup results, daily health data, and dietary data to perform comprehensive health management, provide specific dietary recommendations, and consistently provide support for implementing them.

[1764] "User" refers to an individual who uses the system.

[1765] "Uploading" refers to the act of a user sending their own digital data to a server.

[1766] "Health checkup results" refers to data obtained as a result of a health check conducted at a medical institution.

[1767] "Analysis" refers to the act of analyzing acquired data and extracting information.

[1768] "Health index" refers to specific values ​​that indicate the user's health condition, such as blood pressure, blood sugar level, and body fat percentage.

[1769] "Target value" refers to a specific indicator value set for improving health.

[1770] "Healthcare devices" refer to devices that collect users' health data, such as smartwatches and fitness trackers.

[1771] "Daily health data" refers to data related to the user's physical activity and physiological condition in their daily lives.

[1772] "Integration" refers to the act of centrally managing multiple data in a single system.

[1773] "Meal image" refers to photographic data of a user's own meal.

[1774] "Analyzing" refers to the act of extracting necessary information (such as nutrients and calories) from image data.

[1775] "Nutrient data" refers to data that indicates the nutritional components and calorie content of a meal.

[1776] "Recommendations" refer to specific advice or suggestions that contribute to improving the user's health.

[1777] "Food delivery service" refers to a delivery service that provides recommended meals to users.

[1778] "Evaluation" refers to the act of measuring the degree of improvement in the user's health based on the acquired data.

[1779] A "generative AI model" refers to an algorithm that uses machine learning technology to analyze data and generate appropriate recommendations.

[1780] This invention relates to a system for comprehensive health management that integrates a user's health checkup results, daily health data, and dietary data. This system includes a smartphone application, a cloud server, a generative AI model, and collaboration with a food delivery service. Detailed embodiments of this system and the program processing that realizes it are described below.

[1781] System configuration

[1782] Hardware

[1783] 1. Smartphones: Devices for collecting and displaying user data

[1784] 2. Healthcare devices: devices that collect health data, such as smartwatches and fitness trackers

[1785] 3. Cloud server: A server for data analysis and management

[1786] software

[1787] 1. Image analysis tools: Uses OpenCV and TensorFlow

[1788] 2. Generative AI model: Uses GPT-3 and Stable Diffusion

[1789] Data collection and processing

[1790] 1. Importing health checkup results

[1791] Users upload their health checkup results using a smartphone app. The server receives the results and analyzes them using an AI model. This analysis extracts key health indicators such as blood pressure, blood sugar levels, and body fat percentage.

[1792] 2. Collecting daily health data

[1793] Daily health data (number of steps, heart rate, body temperature, etc.) from the user's healthcare device is periodically synchronized with a cloud server, which then integrates and manages this data in real time.

[1794] 3. Dietary data collection and analysis

[1795] Users take photos of their meals with their smartphones and upload them to the app, which uses image analysis tools to analyze these photos and calculate the nutritional and calorie content of each ingredient.

[1796] 4. Comprehensive data analysis and recommendation generation

[1797] The server uses a generative AI model to analyze the collected health and dietary data and generate optimal health management recommendations for the user, including the amount of exercise required and recommended dietary content.

[1798] 5. Collaboration with food delivery services

[1799] The recommended meals are linked to food delivery services, allowing users to easily order them within the app, making it easier to maintain healthy lifestyles.

[1800] Specific examples

[1801] 1. Examples of prompts for analyzing health checkup data

[1802] "Analyze health checkup results and extract health indicators. Sample data: Blood pressure: 120 / 80 mmHg, Blood glucose: 90 mg / dL, Cholesterol: 180 mg / dL."

[1803] 2. Food recommendation prompt examples

[1804] "Please suggest an appropriate meal based on the user's daily health status. Health data sample: Steps: 10,000 steps / day, Heart rate: 70 bpm, Food data sample: Mushroom risotto, Calories: 400 kcal, Protein: 15 g, Carbohydrates: 55 g, Fat: 10 g."

[1805] The system of the present invention allows users to perform comprehensive health management by integrating health checkup results, daily health data, and dietary data, and easily take specific actions to improve their health. In particular, by linking with food delivery services, users can easily obtain recommended meals, enabling sustainable health improvement.

[1806] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1807] Step 1:

[1808] A user uploads their health checkup results using a smartphone app. The input is a health checkup result file, and the output is the health checkup data sent to the cloud server. The server checks the format of the received data and prepares it for passing to the AI ​​model.

[1809] Step 2:

[1810] The server receives the health checkup results and analyzes the data using an AI model. The input is the health checkup data, and the output is key health indicators (blood pressure, blood sugar level, body fat percentage, etc.). The AI ​​model extracts these indicators and stores them in a database.

[1811] Step 3:

[1812] The user's healthcare device (smartwatch or fitness tracker) collects daily health data and synchronizes it to a cloud server. The input is daily health data (number of steps, heart rate, body temperature, etc.), and the output is the integrated data stored on the server. The server manages this data in real time.

[1813] Step 4:

[1814] Users take photos of their meals with their smartphones and upload them to the app. The input is the photo file of the meal, and the output is image data sent to a cloud server. The server receives this image data and prepares it for analysis.

[1815] Step 5:

[1816] The server uses image analysis tools (OpenCV and TensorFlow) to analyze photos of meals and calculate nutrients and calories. The input is image data, and the output is nutrient data and calorie data. The server stores this data in a database.

[1817] Step 6:

[1818] The server uses a generative AI model to integrate health checkup data, daily health data, and nutritional data to perform a comprehensive health assessment. All of this data is input, and the output is a health assessment and recommendations. The generative AI model generates recommendations that suggest the optimal amount of exercise and diet for the user and notifies the user.

[1819] Step 7:

[1820] The server connects with food delivery services to provide recommended meals. The input is recommendation data, and the output is a food delivery order. Users can easily order meals from within the app.

[1821] Step 8:

[1822] The server receives the next year's health checkup results and compares them with the previous data to evaluate the degree of improvement. The input is the new health checkup data, and the output is the evaluation result of the degree of improvement. The server then runs this data through the AI ​​model again and provides feedback to the user.

[1823] 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.

[1824] The system of this invention not only realizes comprehensive health management by integrating daily health data and dietary data based on the user's health checkup results, but also evaluates the user's mental health state by collecting and analyzing the user's emotional data and provides appropriate feedback. Specific embodiments of the invention are described below.

[1825] System Overview

[1826] Importing and analyzing health checkup results

[1827] Users upload their annual health checkup results to the system using a device such as a smartphone. The server that receives the upload checks the file format and loads the contents into a generative AI model. The AI ​​model analyzes the health checkup results and extracts key health indicators (e.g., blood pressure, blood sugar, cholesterol, body fat percentage, etc.).

[1828] Setting health indicator targets

[1829] The server sets various KPIs (Key Performance Indicators) based on the analysis results. These target values ​​are important for improving the user's health. For example, they include target values ​​for reducing body fat percentage and target values ​​for returning blood pressure to the normal range. These target values ​​are notified to the user so that they can be checked.

[1830] Daily data collection and integration

[1831] Users use healthcare devices such as smartwatches and fitness trackers on a daily basis to collect data such as the number of steps taken, heart rate, and body temperature. This data is periodically synchronized with a server, which then integrates and manages the data in real time.

[1832] Dietary data collection and analysis

[1833] Users take photos of their meals using their smartphones and upload them to the system. These meal images are then analyzed by the server, and various nutrients and calories are automatically calculated. The analysis results are stored in the user's database and can be used to help with daily dietary management.

[1834] Emotion data collection and analysis

[1835] The device collects emotional data that users input on a daily basis, such as periodic self-evaluations of their emotions and facial recognition using a smartphone camera. This data is sent to a server and analyzed by an emotion engine. The emotion engine evaluates the user's emotional state and identifies their daily stress level and mental health.

[1836] Comprehensive data analysis and recommendation generation

[1837] The server integrates daily health, dietary, and emotional data to evaluate the user's overall health using a generative AI model. Based on the results of this evaluation, specific health management recommendations are generated for the user, including the amount of exercise required, recommended dietary habits, and mental health advice.

[1838] Progress management and feedback

[1839] The user's progress towards their KPIs is regularly monitored, and the server provides this information as feedback to the user in the form of a report. This allows users to constantly understand their own health status and identify areas for improvement. Feedback based on emotional data is also provided periodically, allowing users to check their mental health.

[1840] Evaluation based on the results of the following year's health check

[1841] When the next year's health checkup results are uploaded, the server loads the new data into the AI ​​model and compares it with the previous year's data. This evaluates the degree of improvement in various KPIs and provides feedback to the user. Specific results include, for example, "body fat percentage reduced by 10%" or "blood pressure returned to normal range."

[1842] Specific examples

[1843] 1. Reading and analyzing health checkup results

[1844] The user scans the results of the health check and uploads them to the system.

[1845] The server receives the result file and analyzes it using an AI model.

[1846] Extract indicators such as blood pressure, blood sugar level, and body fat percentage.

[1847] 2. Setting health indicator targets

[1848] The server sets a target value based on the analysis results.

[1849] Examples: Lowering blood pressure to 130 / 80 mmHg, reducing body fat percentage to 25%.

[1850] 3. Daily data collection and integration

[1851] Users use smartwatches to collect daily health data.

[1852] The server synchronizes and manages this data in real time.

[1853] 4. Dietary data collection and analysis

[1854] Users take photos of their meals and upload them to the system.

[1855] The server analyzes the image and calculates calories and various nutrients.

[1856] 5. Emotional Data Collection and Analysis

[1857] Users enter their own self-evaluation of emotions on a daily basis, and facial expression recognition data is collected using the smartphone camera.

[1858] The server receives the emotion data and analyzes it using the emotion engine.

[1859] 6. Comprehensive data analysis and recommendation generation

[1860] The server integrates healthcare data, dietary data, and emotional data to provide a comprehensive assessment of health status.

[1861] A generative AI model generates recommendations and notifies the user.

[1862] Specific examples include advice such as "Take a few more steps today," "Choose lower-fat foods," and "Try meditation to relax."

[1863] 7. Progress Management and Feedback

[1864] The server monitors progress against KPIs and generates weekly or monthly reports.

[1865] User receives and acknowledges feedback.

[1866] This also includes feedback based on emotional data.

[1867] 8. Evaluation based on the results of the following year's health check

[1868] New health checkup results are fed into the AI ​​model and compared with the previous year.

[1869] Evaluate the degree of improvement and notify users of specific results.

[1870] In this way, the system of the present invention provides comprehensive care for the user's health condition, realizing continuous improvement in health and management of mental health.

[1871] The processing flow will be explained below.

[1872] Step 1: User uploads health check results

[1873] Users take a photo of their annual health checkup results with their smartphone and upload them to the system via a dedicated application.

[1874] The user taps the upload button, selects a file from local storage, and sends it.

[1875] Step 2: The server receives the health check results

[1876] The server receives and stores the health check result file sent by the user.

[1877] The server checks the file format and passes the data to the appropriate analysis module.

[1878] Step 3: The server loads the data into the generative AI model

[1879] The server loads the received health check results into a generative AI model.

[1880] An AI model analyzes the data and extracts key health indicators (blood pressure, blood sugar levels, body fat percentage, etc.).

[1881] Step 4: The server sets the target value for the health index.

[1882] The server sets target values ​​(KPIs) for health indicators based on the data analyzed by the AI ​​model.

[1883] For example, the target body fat percentage is set to 25% and the target blood pressure is set to 130 / 80 mmHg.

[1884] Step 5: The server notifies the user of the target value

[1885] The server notifies the user's terminal of the set target value.

[1886] The user receives a notification and confirms the target value.

[1887] Step 6: Users collect data using healthcare devices

[1888] Users use smartwatches and fitness trackers to collect daily data such as steps taken, heart rate, and body temperature.

[1889] This data is stored on the device.

[1890] Step 7: The device synchronizes the collected data with the server

[1891] The user's device periodically synchronizes the collected health data with the server.

[1892] The server stores the received data in real time and integrates it into a database.

[1893] Step 8: User uploads food photo

[1894] Users take photos of their daily meals with their smartphones and upload them to a dedicated application.

[1895] The user taps the upload button to submit the photo.

[1896] Step 9: The server analyzes the food image

[1897] The server receives the uploaded food images and passes the data to the image analysis module.

[1898] The image analysis module analyzes the contents of the meal and calculates various nutrients and calories.

[1899] Step 10: User enters emotion data

[1900] Users input their daily emotional data through a dedicated application.

[1901] Emotion entry includes text entry, facial expression recognition, and multiple choice emotion checklists.

[1902] Step 11: The device sends the emotion data to the server

[1903] The user's device transmits the collected emotion data to the server.

[1904] The server passes the received emotion data to the emotion engine for appropriate analysis.

[1905] Step 12: Server consolidates data

[1906] The server integrates daily health data, dietary data, and emotional data and stores them in a database.

[1907] Based on the integrated data, a generative AI model performs a comprehensive health assessment.

[1908] Step 13: Server generates recommendations

[1909] The server generates specific health management recommendations for the user based on the analysis results of the AI ​​model.

[1910] For example, it includes specific advice such as "Take a few more steps today," "Choose leaner foods," or "Try meditation to relax."

[1911] Step 14: The server notifies the user of the recommendation

[1912] The server notifies the user's terminal of the generated recommendations.

[1913] The user receives a notification, confirms the recommendation, and acts on it.

[1914] Step 15: Server monitors user progress

[1915] The server routinely monitors the user's progress against the set KPIs and records the progress data.

[1916] The server periodically generates progress reports to provide feedback to the user.

[1917] Step 16: User uploads new health check results

[1918] The following year, the user uploads new medical results back into the system.

[1919] The diagnostic results are photographed with a smartphone and sent via a dedicated application.

[1920] Step 17: Server parses new data

[1921] The server receives new health checkup results and loads them into the generative AI model.

[1922] The AI ​​model compares the previous year's data with the new data and calculates the degree of improvement for various KPIs.

[1923] Step 18: The server notifies the evaluation result

[1924] The server evaluates the degree of improvement and notifies the user of specific results, such as "body fat percentage reduced by 10%" or "blood pressure returned to normal range."

[1925] Users can view results and understand their progress in improving their health.

[1926] Example 2

[1927] 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."

[1928] While conventional health management systems could collect and analyze a user's health checkup results, daily health data, and dietary data individually, it was difficult to integrate them for comprehensive health management. Furthermore, they lacked a means to consider mental health status, making it impossible to evaluate a user's overall health status. As a result, users could only obtain partial health information, making it difficult to effectively improve their health.

[1929] 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.

[1930] In this invention, the server includes means for receiving and analyzing health checkup results uploaded by a user, means for setting target values ​​for health indices based on the analysis results, means for collecting and integrating daily health data from the user's healthcare terminal, means for analyzing meal images uploaded by the user and generating nutrient data, means for receiving and analyzing preset emotion data, means for integrating the generated data, generating recommendations for comprehensive health management, and notifying the user, and means for receiving next year's health checkup results and comparing them with the previous data to evaluate the degree of improvement. This makes it possible to comprehensively evaluate the user's physical and mental health status and provide specific improvements and recommendations.

[1931] "Health checkup results" refers to numerical data on health indicators such as blood pressure, blood sugar level, cholesterol level, and body fat percentage obtained through a health checkup conducted by a user once a year at a medical institution or other facility.

[1932] "Analysis means" refers to tools and algorithms used to process uploaded data on the server and extract and analyze specific information.

[1933] "Health index" refers to specific numerical data that indicates the user's health condition, such as blood pressure, blood sugar level, cholesterol level, and body fat percentage.

[1934] The "target value" is set based on the analysis results and refers to a specific numerical value that the user should achieve in order to improve their health.

[1935] A "healthcare device" is an electronic device, such as a smartwatch or fitness tracker, that is used to collect a user's daily health data.

[1936] "Daily health data" refers to health-related data collected daily by a healthcare terminal, such as the user's number of steps, heart rate, and body temperature.

[1937] A "meal image" is a photograph of a meal that a user takes using a smartphone or other device and uploads to the system.

[1938] "Nutrient data" refers to information about various nutrients contained in food, such as carbohydrates, proteins, and lipids, obtained by the server analyzing the food image.

[1939] "Emotional data" refers to data about a user's emotional state and stress level, collected through a user's self-assessment or facial expression recognition using a smartphone camera.

[1940] "Recommendations" refer to specific health management advice and suggestions provided to users based on data collected and analyzed by the server.

[1941] A "generative AI model" is an artificial intelligence model that analyzes collected health checkup results, daily health data, dietary data, and emotional data to generate comprehensive evaluations and recommendations.

[1942] "Improvement" refers to a numerical value or evaluation that indicates how much the user's health index has improved compared to the previous year.

[1943] The system of the present invention provides comprehensive health management based on the user's health checkup results, daily health data, dietary data, and emotional data.

[1944] First, users upload their annual health checkup results to the system using a device such as a smartphone. The device then sends the uploaded health checkup result file to the server. The server checks the format of the received file to ensure it is in the correct format. The server then analyzes the health checkup results using a generative AI model to extract key health indicators (blood pressure, blood sugar level, cholesterol level, body fat percentage, etc.). These extracted health indicators are then recorded in a database. Based on the analysis results, the server then sets individual KPIs (Key Performance Indicators) to aim for health improvement. The set target values ​​are notified to the user and displayed on the device.

[1945] Next, users use healthcare devices such as smartwatches and fitness trackers on a daily basis to collect data such as the number of steps taken, heart rate, and body temperature. The devices periodically synchronize this data with a server, which then integrates and manages the data in real time and stores it in a database.

[1946] Users can also take photos of their meals with their smartphones and upload them to the system. The device then sends the meal images to a server, which then uses a generative AI model to analyze the images and calculate various nutrients and calories. The analysis results are stored in a database and used to help users manage their diet.

[1947] Furthermore, users can collect emotional data by entering their own emotional assessments or by using their smartphone camera. The device then transmits this emotional data to a server, which then uses an emotion engine to analyze the data and evaluate the user's emotional state, stress level, and mental health.

[1948] The server integrates daily health data, dietary data, and emotional data, and uses a generative AI model to assess the user's overall health. Based on the results of this assessment, the server generates specific health management recommendations (e.g., the amount of exercise required, recommended dietary content, mental health advice, etc.) and notifies the user.

[1949] The server periodically monitors the user's progress against KPIs and generates a report on a weekly or monthly basis. The report is then sent to the user and displayed on their device. The user can review the report and identify any necessary improvements. Feedback based on emotional data is also provided on a regular basis.

[1950] When the next year's health checkup results are uploaded, the server loads the new data into the generative AI model and compares it with the previous year's data. This evaluates the degree of improvement in various KPIs and notifies the user of specific results (e.g., a 10% decrease in body fat, or blood pressure returning to normal range).

[1951] Prompt Sentence Examples

[1952] "Create a program that generates a comprehensive health assessment and appropriate feedback based on the user's health checkup results, daily health data, dietary data, and emotional data."

[1953] The system is able to comprehensively assess a user's physical and mental health and provide specific improvements and recommendations.

[1954] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1955] Step 1:

[1956] The user uploads the health check result file (e.g. PDF, JPEG, etc.) to the system using a device such as a smartphone.

[1957] Input: Health check result file

[1958] Output: The uploaded file is saved to the device.

[1959] Step 2:

[1960] The terminal sends the uploaded file to the server.

[1961] Input: Health check result file (saved on the device)

[1962] Output: The health check result file is transferred to the server.

[1963] Step 3:

[1964] The server checks the format of the received file to ensure it is in the correct format, and if it is not, it will display an error message to the user.

[1965] Input: Health check result file

[1966] Output: File format check result (success / failure)

[1967] Step 4:

[1968] The server uses a generative AI model to analyze health checkup results and extract key health indicators (blood pressure, blood sugar levels, cholesterol levels, body fat percentage, etc.).

[1969] Input: Health check result file

[1970] Output: Extracted key health indicators

[1971] Step 5:

[1972] The server records the extracted health indicators in a database and sets KPIs for each user based on the analysis results.

[1973] Input: Key health indicators

[1974] Output: Setting KPIs and recording them in a database

[1975] Step 6:

[1976] The server notifies the user of the set KPIs and displays them to the user.

[1977] Input: The configured KPI

[1978] Output: Notification and display on the user's terminal

[1979] Step 7:

[1980] Users use healthcare devices such as smartwatches and fitness trackers on a daily basis to collect data such as the number of steps taken, heart rate, and body temperature.

[1981] Input: Smartwatch or fitness tracker data

[1982] Output: Collection of daily health data

[1983] Step 8:

[1984] The device periodically synchronizes the collected daily health data with the server.

[1985] Input: Daily health data (saved on the device)

[1986] Output: Synchronization of daily health data to the server

[1987] Step 9:

[1988] The server integrates and manages the received daily health data in real time and stores it in a database.

[1989] Input: Synced daily health data

[1990] Output: Consolidated daily health data stored in a database

[1991] Step 10:

[1992] Users take photos of their meals with their smartphones and upload them to the system.

[1993] Input: Food photo

[1994] Output: Uploaded photos are saved on the device

[1995] Step 11:

[1996] The device sends the uploaded photos to the server.

[1997] Input: Food photo (saved on device)

[1998] Output: Meal photos are sent to the server

[1999] Step 12:

[2000] The server uses a generative AI model to analyze the food images and calculate various nutrients and calories.

[2001] Input: Food photo

[2002] Output: Analyzed nutrient data and calories

[2003] Step 13:

[2004] The server stores the analysis results in a database to help users manage their diet.

[2005] Input: Parsed nutrient data and calories

[2006] Output: Save nutrient data to database

[2007] Step 14:

[2008] Users can collect emotional data by entering their own emotional assessments on a daily basis or by using their smartphone camera.

[2009] Input: Emotion self-assessment data or facial expression recognition data

[2010] Output: The input emotion data is saved on the device.

[2011] Step 15:

[2012] The terminal transmits this emotion data to the server.

[2013] Input: Emotion data (saved on the device)

[2014] Output: Emotion data is sent to the server

[2015] Step 16:

[2016] The server uses an emotion engine to analyze the emotion data and evaluate the user's emotional state and stress level.

[2017] Input: Emotion data

[2018] Output: Analyzed emotional state and stress level

[2019] Step 17:

[2020] The server integrates daily health data, dietary data, and emotional data, and uses a generative AI model to assess overall health status.

[2021] Input: Daily health data, dietary data, emotional data

[2022] Output: Overall health status assessment results

[2023] Step 18:

[2024] Based on the evaluation results, the server generates specific health management recommendations and notifies the user.

[2025] Input: Overall health status assessment results

[2026] Output: Generated health care recommendations and notification to the user

[2027] Step 19:

[2028] The server periodically monitors the user's progress against the KPIs and generates reports that are then sent to the user.

[2029] Input: KPI progress data

[2030] Output: Generated scheduled reports and notifications to users

[2031] Step 20:

[2032] When the next year's health checkup results are uploaded, the user's device will send the new checkup results to the server.

[2033] Input: New medical examination results

[2034] Output: New health check results are sent to the server.

[2035] Step 21:

[2036] The server loads the new health check results into a generative AI model and compares them with the previous year's data.

[2037] Input: New health checkup results, previous year's health checkup data

[2038] Output: KPI improvement evaluation results

[2039] Step 22:

[2040] The server notifies the user of specific results, such as "body fat percentage reduced by 10%" or "blood pressure returned to normal range."

[2041] Input: KPI improvement evaluation results

[2042] Output: Notification of results to the user

[2043] (Application example 2)

[2044] 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."

[2045] In modern society, health management has become increasingly difficult due to the diversification of people's lifestyles and dietary habits. In particular, conventional health management systems only collect and analyze physical data, and do not adequately evaluate and manage mental health status. Therefore, there is a demand for multifaceted, comprehensive health management. In response to this, the present invention aims to promote health improvement by comprehensively evaluating a user's physical and mental health status and providing personalized health management.

[2046] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2047] In this invention, the server includes a means for receiving and analyzing health checkup results uploaded by a user, a means for setting target values ​​for health indices based on the analysis results, and a means for collecting and integrating daily health data from the user's healthcare device, thereby enabling a comprehensive evaluation of the user's physical and mental health status and the generation of specific health management recommendations for the user.

[2048] "Health checkup results" are the results of tests that a user undergoes periodically at a medical institution, and are data including major health indicators such as blood pressure, blood sugar level, cholesterol level, and body fat percentage.

[2049] "Health index" is specific numerical data for evaluating the health condition of a user, and includes, for example, blood pressure, blood sugar level, cholesterol level, and body fat percentage.

[2050] "Daily health data" refers to data measured by users in their daily lives, including the number of steps taken, heart rate, body temperature, etc. collected by healthcare devices such as smartwatches and fitness trackers.

[2051] "Meal images" are photographs of meals taken by the user, and are data that are uploaded to the system and used to analyze nutrient data.

[2052] "Nutrient data" is information on various nutrients and calories analyzed from a meal image, and is data that is useful for the user's dietary management.

[2053] "Emotion data" is data collected to assess a user's mental health, and includes data from self-assessment of emotions and facial expression recognition.

[2054] A "generative AI model" is an artificial intelligence model that uses machine learning and deep learning, and is an algorithm that comprehensively evaluates a user's health condition based on large amounts of data and generates optimal recommendations.

[2055] The system for implementing this invention integrates a user's health checkup results, daily health data, dietary data, and emotional data, evaluates their health status comprehensively using a generative AI model, and provides appropriate feedback to the user.

[2056] composition

[2057] 1. Reading and analyzing health checkup results

[2058] Users upload their annual health checkup results to the system using a device such as a smartphone. The server receives the upload, checks the file format, and loads the contents into a generative AI model. The AI ​​model analyzes the health checkup results and extracts key health indicators.

[2059] 2. Setting health indicator targets

[2060] The server sets various KPIs (Key Performance Indicators) based on the analysis results. These target values ​​aim to improve the user's health, and include blood pressure, body fat percentage, etc. These target values ​​are notified to the user.

[2061] 3. Daily data collection and integration

[2062] Users use smartwatches and fitness trackers on a daily basis to collect data such as the number of steps taken, heart rate, and body temperature. This data is periodically synchronized with a server and integrated and managed in real time.

[2063] 4. Dietary data collection and analysis

[2064] Users take photos of their meals using their smartphones and upload them to the system. The server analyzes the meal images and automatically calculates various nutrients and calories. The analysis results are stored in the user's database.

[2065] 5. Emotional Data Collection and Analysis

[2066] Users enter their own emotional self-assessment on a daily basis and also collect facial expression recognition data using their smartphone camera. This data is sent to a server and analyzed by the emotion engine. The emotion engine evaluates the user's emotional state and grasps their daily stress level and mental health.

[2067] 6. Comprehensive data analysis and recommendation generation

[2068] The server integrates daily health data, dietary data, and emotional data, and uses a generative AI model to comprehensively evaluate the user's health. Based on the results of this evaluation, specific health management recommendations are generated for the user, such as the amount of exercise required, recommended dietary content, and advice on mental health.

[2069] 7. Progress Management and Feedback

[2070] The user's progress towards their KPIs is regularly monitored, and the server provides this information as feedback to the user in the form of a report. This allows users to constantly understand their own health status and identify areas for improvement. Feedback based on emotional data is also provided periodically, allowing users to check their mental health.

[2071] 8. Evaluation based on the results of the following year's health check

[2072] When the next year's health checkup results are uploaded, the server loads them into the AI ​​model and compares them with the previous year's data. This evaluates the degree of improvement in various KPIs and notifies the user of specific results. Examples include "body fat percentage reduced by 10%" and "blood pressure returned to normal range."

[2073] Hardware and software used

[2074] Hardware:

[2075] Smartphone

[2076] Smartwatch

[2077] Fitness tracker

[2078] software:

[2079] AI model for health data analysis

[2080] Sentiment analysis engine

[2081] Nutrient analysis model

[2082] Specific examples

[2083] The user scans their health checkup results and uploads them to the system. The server receives the result file and analyzes it using an AI model to extract indicators such as blood pressure, blood sugar level, and body fat percentage. Based on the analysis results, the server sets target values ​​for health indicators and notifies the user. The user uses the smartwatch daily and synchronizes the acquired data (number of steps, heart rate, etc.) with the server. Next, they take photos of their meals and upload them to the system, which analyzes the nutritional data. The user also enters a self-evaluation of their emotions and collects facial recognition data using the smartphone camera. The server integrates this data, evaluates their overall health, and notifies the user of specific health management recommendations. Examples include advice such as "Take more steps today," "Choose lower-fat meals," and "Try meditation to relax."

[2084] Prompt Sentence Examples

[2085] Health checkup results: Blood pressure 140 / 90, blood sugar 6.5, cholesterol 200, body fat 30%

[2086] Daily data: steps 8000, heart rate 75, body temperature 36.5℃

[2087] Dietary information: Breakfast: 500 calories, 50g carbohydrates, 30g fat

[2088] Emotion data: Facial expression High stress

[2089] This allows a comprehensive assessment of the user's physical and mental health status and provides the user with specific health management recommendations.

[2090] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2091] Step 1:

[2092] A user scans their health checkup results with their smartphone and uploads them to the system. The input is an image file of the health checkup results. The server receives this file and checks the file format. If the format is correct, the contents of the result file are loaded into a generative AI model to extract health indicators such as blood pressure, blood sugar level, and body fat percentage. The output is the extracted health indicator data.

[2093] Step 2:

[2094] The server sets various KPIs (Key Performance Indicators) based on the extracted health indicator data. The extracted health indicator data is the input. The server sets appropriate target values ​​for each health indicator and notifies the user. The output is the set KPI target values.

[2095] Step 3:

[2096] Users use smartwatches and fitness trackers on a daily basis to collect daily health data such as steps taken, heart rate, and body temperature. The input is the data collected from the health device. The device periodically synchronizes this data with the server. The server receives this data and integrates and manages it in real time. The output is the integrated daily health data.

[2097] Step 4:

[2098] Users take photos of their meals using their smartphones and upload them to the system. The input is the meal image. The server receives the image, analyzes it, and calculates various nutrients and calories. The output is the analyzed nutrient data.

[2099] Step 5: ...

Claims

1. A means for receiving and analyzing health checkup results uploaded by users; A means for setting a target value for a health index based on the analysis result; A means for collecting and integrating daily health data from a user's healthcare device; A means for analyzing a meal image uploaded by a user and generating nutrient data; A means for generating health management recommendations based on the generated data and notifying the user of the recommendations; A means to receive the results of the next year's health checkup and compare them with the previous data to evaluate the degree of improvement; A system including:

2. The system of claim 1 , further comprising means for periodically monitoring the progress of a user's health indicators and providing feedback.

3. The system of claim 1, which uses a generative AI model to analyze a user's health checkup results, healthcare data, and dietary data.

Images