System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving medical checkup data; means for analyzing the medical checkup data and extracting necessary health information; means for acquiring lifestyle information; means for generating health advice based on the acquired lifestyle information and the health information; and means for providing the health advice to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Many people undergo health checkups, but often do not check the results in detail, which can lead to overlooking even small changes within the standard range. In such cases, early detection and prevention of serious health problems becomes difficult. Furthermore, there are limited opportunities to receive comprehensive health advice that takes into account individual information such as lifestyle and exercise habits. To solve this problem, a method is needed to effectively utilize the results of health checkups and provide appropriate health advice to each individual. [Means for solving the problem]

[0005] The present invention solves the above problems by providing the following means: A means for receiving health checkup data and a means for analyzing the health checkup data to extract necessary health information are provided. Furthermore, a means for acquiring lifestyle habit information and a means for generating health advice based on the acquired lifestyle habit information and the health information are provided. Finally, the system is equipped with a means for providing the health advice to the user. This allows the user to more effectively utilize the results of the health checkup and obtain appropriate advice that takes into account their own lifestyle habits. Furthermore, by providing a means for receiving data that will become available at a later date, such as blood test results, and updating the health advice, it is possible to always provide advice based on the latest information.

[0006] "Health checkup data" refers to various health-related numerical information obtained during a health checkup, such as blood test results, blood pressure, weight, body fat percentage, and urine test results.

[0007] "Analysis" refers to the data processing and analysis procedures performed to extract health checkup data and identify necessary health information.

[0008] "Lifestyle information" is data related to the user's daily life, such as exercise habits, drinking, smoking, diet, and sleep duration.

[0009] "Acquisition" refers to the act or process of collecting lifestyle information from a user.

[0010] "Generation" refers to the act of creating information or documents for a specific purpose (health advice in this case) based on acquired data.

[0011] "Health advice" is information that provides specific guidance and recommendations for maintaining and improving the user's health based on analyzed health checkup data and lifestyle information.

[0012] "Providing" refers to the act of showing or sending the generated health advice to the user.

[0013] "Receiving" refers to the act of receiving data that will be known at a later date, such as blood test results, again after receiving the initial data.

[0014] "Updating" is the act of revising existing health advice based on newly received data to adapt it to the latest information. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention is a system that analyzes health checkup results and provides individualized health advice to users. This system is implemented based on a series of programs that receive health checkup data, analyze the data, acquire lifestyle information, generate health advice, and provide it.

[0037] (Natural language description of the program's processing)

[0038] Uploading and analyzing health checkup results

[0039] A user uploads a file of their health checkup results to the system from their device. This file contains information such as blood test results, blood pressure, and weight.

[0040] The server receives the uploaded file, converts it into text data using OCR (optical character recognition) technology, and analyzes the data to extract the necessary health information.

[0041] Collection of lifestyle information

[0042] The server generates lifestyle-related questions for the user and sends them to the device, such as "How often do you exercise per week?" and "Do you smoke?"

[0043] Users answer these questions using their devices and send the answers to the server, which then analyzes the received data and formats it into a standard format.

[0044] Generating health advice

[0045] The server combines the health checkup results data and lifestyle information and inputs them into the AI ​​generator, which analyzes this data and generates health advice based on the user's health status and anticipated risks.

[0046] For example, if your blood sugar level is slightly above the normal range, the AI ​​will generate advice such as, "I recommend improving the quality of your diet and doing aerobic exercise several times a week."

[0047] Providing health advice

[0048] The generated health advice is sent from the server to the user's device, where the user can check the advice and use it as a reference for reviewing their own lifestyle habits.

[0049] Re-upload and update of blood test results that will be known at a later date

[0050] The user uploads the blood test results, which are later determined, back into the system.

[0051] The server receives and analyzes this new data, updating existing health advice and providing new advice to the user based on their current situation.

[0052] (Example)

[0053] For example, suppose a user undergoes a health checkup and uploads the results to the system. The results show that their blood sugar level is slightly above the normal range, so they follow the AI's advice to review their diet and start exercising. If they then undergo another health checkup a few weeks later and re-upload the results, they will receive advice based on the new data that reads, "Your blood sugar level is improving. Please continue to maintain your current lifestyle habits."

[0054] In this way, this system supports users' health management by providing health advice tailored to each individual user based on health checkup results and lifestyle data.

[0055] The processing flow will be explained below.

[0056] Step 1:

[0057] The user obtains the results of the health check and uploads the file from the terminal using the system interface.

[0058] Step 2:

[0059] The device sends the uploaded file to a server, which contains health-related information such as blood test results, blood pressure, and weight.

[0060] Step 3:

[0061] The server receives and stores the uploaded file, then uses OCR (Optical Character Recognition) technology to extract text data from the file.

[0062] Step 4:

[0063] The server analyzes the extracted text data and extracts the necessary health information, such as blood sugar levels, cholesterol levels, and blood pressure.

[0064] Step 5:

[0065] The server generates lifestyle questions for the user, such as "How often do you exercise per week?" and "Do you smoke?"

[0066] Step 6:

[0067] The terminal displays the generated questions to the user, who then answers the questions about their lifestyle habits.

[0068] Step 7:

[0069] The user's answers are sent from the device to the server, which receives them and formats them into a standard format.

[0070] Step 8:

[0071] The server integrates the acquired health checkup data and lifestyle information, and inputs this integrated data into the generation AI.

[0072] Step 9:

[0073] The generative AI analyzes the integrated data and generates health advice based on the user's health status and anticipated risks. For example, if blood sugar levels are slightly high, it will generate advice such as "improve the quality of your diet and exercise several times a week."

[0074] Step 10:

[0075] The server compiles the generated health advice into a report for the user, which includes necessary advice and precautions.

[0076] Step 11:

[0077] The server then sends the generated report to the device, where the user can check the report and understand their own health condition and lifestyle improvements.

[0078] Step 12:

[0079] The user uploads the blood test results, which are later known, from the terminal to the system again. The terminal then sends the new result file to the server.

[0080] Step 13:

[0081] The server receives the new data, extracts the text data using OCR technology again, merges the new data with the existing data, and reanalyzes it using the generative AI.

[0082] Step 14:

[0083] Generative AI updates existing health advice based on new data, generating new advice based on the latest information.

[0084] Step 15:

[0085] The server then compiles the new health advice into a report for the user and sends it to the user's device, where the user can view the updated report.

[0086] Example 1

[0087] 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."

[0088] With conventional health management systems, it was difficult to individually manage and analyze users' health checkup data and lifestyle information, making it difficult to efficiently provide appropriate health advice based on that data. In particular, reanalysis of health checkup results and updating of advice based on that data were not performed in real time, which meant that users were unable to manage their health in a timely manner, and it was difficult to expect effective health improvement.

[0089] 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.

[0090] In this invention, the server includes means for receiving health checkup data, means for converting the health checkup data into text data using optical character recognition technology and analyzing it to extract necessary health information, means for interactively acquiring lifestyle habit information, means for integrating the acquired lifestyle habit information and the health information and generating health advice using a generative artificial intelligence model, and means for providing the health advice to the user. This enables the centralized management of a user's health checkup data and lifestyle habit information and the provision of health advice in real time.

[0091] "Health checkup data" refers to data including blood test results, blood pressure, weight, and other biological information obtained during a health checkup.

[0092] "Optical character recognition technology" is a technology that analyzes characters in an image and converts them into digital text.

[0093] "Text data" is digital data expressed as a string of characters.

[0094] "Analysis" is the process of examining data in detail and extracting the necessary information.

[0095] "Health information" is data that indicates health status based on health checkups and lifestyle habits.

[0096] "Lifestyle information" is data relating to the habits of the user in their daily life, including diet, exercise, smoking, and the like.

[0097] "Dialogue" is a method of obtaining information through an exchange of questions and answers.

[0098] "Integration" means bringing together multiple pieces of data into a consistent format.

[0099] A "generative artificial intelligence model" is a model that uses artificial intelligence technology to generate predictions or suggestions based on specific input data.

[0100] "Health advice" means instructions or suggestions provided to improve a user's health.

[0101] "User" means an individual who uses this system.

[0102] A "server" is a computer that processes information and manages data on a network.

[0103] "Terminal" means the device through which a user accesses the system.

[0104] "Real time" means that data processing and information provision are carried out without delay.

[0105] The present invention relates to a system that analyzes health checkup results and provides individualized health advice to users. This system is implemented based on a series of programs that receive health checkup data, analyze the data, acquire lifestyle information, generate health advice, and provide it.

[0106] System configuration and operation

[0107] This system consists of a server and a terminal. Users can access the system using the terminal and input their health checkup results and lifestyle information. The server processes this data and provides appropriate health advice to the user.

[0108] Uploading and analyzing health checkup results

[0109] First, the user uploads a file of their health checkup results to the system from their device. This health checkup result includes information such as blood test results, blood pressure, and weight. The server receives the uploaded file and converts it into text data using OCR (optical character recognition) technology. This OCR technology uses common cloud-based OCR software (e.g., optical character recognition API). The converted text data is then analyzed to extract the necessary health information.

[0110] Collection of lifestyle information

[0111] The server then generates lifestyle-related questions for the user and sends them to the device. For example, questions include, "How often do you exercise per week?" and "Do you smoke?" The user answers these questions using the device and sends the answers to the server. The server then analyzes the received answers and formats them into a standard format.

[0112] Generating health advice

[0113] The server integrates the health checkup result data and lifestyle information and inputs them into a generative AI model. This generative AI model, for example, uses natural language processing (NLP) technology (e.g., a generative language model). The generative AI model analyzes this data and generates health advice based on the user's health status and expected risks.

[0114] Examples of specific prompts that can be used include:

[0115] "Generate personalized health advice based on the user's health checkup results data and lifestyle information. For example, if their blood sugar level exceeds the standard value, include that number and specific advice for improvement."

[0116] Providing health advice

[0117] The generated health advice is sent from the server to the user's device, where the user can check the advice and use it as a reference for reviewing their own lifestyle habits.

[0118] Re-upload and update of blood test results that will be known at a later date

[0119] Furthermore, when the user uploads their blood test results back into the system at a later date, the server receives and analyzes this new data, updating existing health advice and providing new advice to the user based on their current situation.

[0120] Specific examples

[0121] For example, suppose a user undergoes a health checkup and uploads the results data to the system. Because their blood sugar level is slightly above the normal range, they follow the AI's advice to review their diet and start exercising. If they then undergo another health checkup a few weeks later and re-upload the results, they will receive advice based on the new data that reads, "Your blood sugar level is improving. Please continue to maintain your current lifestyle habits."

[0122] In this way, this system supports users' health management by providing health advice tailored to each individual user based on health checkup results and lifestyle data.

[0123] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0124] Step 1:

[0125] The user uses a terminal to upload a health checkup result file to the system. The health checkup result file contains information such as blood test results, blood pressure, and weight. The user clicks the file selection button on the browser, selects the appropriate file, and completes the upload. The input is the health checkup result file, and the output is that the uploaded file is sent to the server.

[0126] Step 2:

[0127] The server receives the uploaded health check result file. The received file is converted into text data using OCR technology. OCR software such as Google Cloud Vision API is used. The input is the health check result file, and the output is text data. The server analyzes this text data and extracts the necessary health information. Specifically, information such as blood test results, blood pressure, and weight is extracted.

[0128] Step 3:

[0129] The server generates lifestyle-related questions for the user and sends them to the device. For example, questions include "How often do you exercise per week?" and "Do you smoke?" The input is a question template generated within the server, and the output is a list of questions sent to the device. This prepares the user to enter their own lifestyle information.

[0130] Step 4:

[0131] The user answers questions about their lifestyle habits using a device. The user enters their lifestyle information through a UI that displays input fields and options, and then presses the send button to send the answers to the server. The input is the answer data about the user's lifestyle habits, and the output is the answer data sent to the server.

[0132] Step 5:

[0133] The server analyzes the received lifestyle information and formats it into a standard format. The input is the lifestyle information submitted by the user, and the output is formatted lifestyle data. The server then integrates this lifestyle data with the health checkup result data. This integration process creates a consistent data set.

[0134] Step 6:

[0135] The server inputs the integrated data into the generative AI model. Specifically, the user's health checkup result data and lifestyle habit information are compiled into a single prompt statement, which is then provided to the generative AI model. An example of the prompt statement is, "Please generate appropriate health advice based on this user's health checkup result and lifestyle habit information." The input is the integrated data and prompt statement, and the output is health advice generated by the generative AI model.

[0136] Step 7:

[0137] The server sends the generated health advice to the user's device. The advice is in text format and may be visualized using graphs or charts. The input is the health advice generated by the generative AI model, and the output is the advice displayed on the user's device. The user can review this advice and use it as a reference for reviewing their lifestyle habits.

[0138] Step 8:

[0139] If new blood test results become available at a later date, the user uploads the data to the system again. The upload procedure is the same as in step 1. The input is the new blood test result file, and the output is the new data sent to the server.

[0140] Step 9:

[0141] The server reanalyzes the newly received data and updates the existing health advice. The new data is integrated with the existing health information and fed back into the generative AI model to generate new advice. This updated advice is provided based on the user's latest health status. The input is the new health check data and the existing dataset, and the output is the updated health advice.

[0142] (Application example 1)

[0143] 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."

[0144] Nowadays, systems that provide personalized health advice based on health checkup data and lifestyle information are widely used. However, autonomous vehicles lack the functionality to set driving modes and suggest rest breaks based on the user's health status. As a result, long driving hours may have a negative impact on the user's health. Therefore, there is a need to add autonomous driving support functions based on the user's health status to current systems to further protect the user's safety and health.

[0145] 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.

[0146] In this invention, the server includes means for receiving health checkup data, means for analyzing the health checkup data and extracting necessary health information, means for acquiring lifestyle information, means for generating health advice based on the acquired lifestyle information and the health information, means for providing the health advice to a user, and means for generating and setting a driving mode and rest suggestions based on the user's health condition in an autonomous vehicle, thereby enabling the provision of driving support and advice according to the user's health condition.

[0147] "Health checkup data" refers to data including the results of diagnoses and tests conducted at medical institutions or the like to evaluate the user's health condition.

[0148] "Lifestyle information" is information relating to the user's daily life, and includes data such as the amount of exercise, diet, smoking status, and alcohol consumption.

[0149] "Health advice" includes specific instructions and recommendations for improving and maintaining a user's health, which are generated based on health checkup data and lifestyle information.

[0150] An "autonomous vehicle" is a vehicle that can perform autonomous driving operations without requiring user input or driver intervention.

[0151] "Driving mode" refers to the driving settings and driving method of an autonomous vehicle, and includes a driving style that corresponds to the user's health condition.

[0152] The "rest suggestion" includes a suggestion to take a rest at a specific time based on the user's health condition.

[0153] "Analysis" refers to the process of analyzing health checkup data and lifestyle information and extracting necessary health information.

[0154] The present invention relates to a system that provides personalized driving support advice based on a user's health condition based on health checkup data and lifestyle information. This system optimizes the user's health and safety through conditional driving modes and rest suggestions, particularly for autonomous vehicles.

[0155] This system is configured as follows:

[0156] 1. Processing and analysis of health examination data

[0157] The user uploads a file (e.g., PDF format) of the health checkup results from their device to the server. This file contains health information such as blood test results, blood pressure, and weight.

[0158] The server uses OCR technology to convert the uploaded file into text data, and then analyzes and extracts the necessary health information. This analysis is performed using an OCR module and a health data analysis module.

[0159] 2. Collection of lifestyle information

[0160] The server generates lifestyle questions for the user and sends them to the device, such as "How often do you exercise per week?" and "Do you smoke?"

[0161] Users answer these questions using their devices and send the answers to the server, which then analyzes the received data and formats it into a standard format.

[0162] 3. Generating health advice

[0163] The server combines the health checkup results data and lifestyle information and inputs them into a generative AI model, which analyzes this data and generates driving support advice based on the user's health condition and anticipated risks.

[0164] For example, if your blood sugar level is slightly above the normal range, the generated advice might include, "We recommend setting your driving mode to Relaxed mode and taking a break every two hours."

[0165] 4. Providing driving support

[0166] The generated driving support advice is sent from the server to the autonomous vehicle's system, which then sets a driving mode based on the user's health condition and suggests breaks at appropriate times.

[0167] For example, users with high blood sugar levels are encouraged to drive in relaxed mode and are instructed to take breaks every two hours.

[0168] Below are some specific examples of prompts for generative AI models:

[0169] Example prompt sentence:

[0170] Analyze the user's health check results and lifestyle information to generate the following driving advice:

[0171] Blood glucose level: 110 mg / dL, blood pressure: 130 / 85 mmHg

[0172] Weekly exercise time: 30 minutes, Smoking: No

[0173] In this way, this system provides driving support advice tailored to each individual user based on health checkup data and lifestyle data, making it possible to provide driving assistance that takes into account the health status of users of autonomous vehicles.

[0174] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0175] Step 1:

[0176] The user uploads the health check result file from the terminal to the server.

[0177] Input: Health check result file (PDF format)

[0178] Output: Uploaded files on the server

[0179] Specific operation: The user uses the upload function of the device to send the health check result file to the server.

[0180] Step 2:

[0181] The server uses OCR technology to convert the uploaded file into text data, and then analyzes and extracts the necessary health information.

[0182] Input: Uploaded health check result file

[0183] Output: Extracted health information (text data)

[0184] Specific operation: The server uses an OCR module (e.g., Tesseract) to extract text from the PDF file, and then uses a health data analysis module to analyze and extract the required information.

[0185] Step 3:

[0186] The server generates questions about the user's lifestyle habits and sends them to the terminal.

[0187] Input: None

[0188] Output: Generated question (displayed on terminal)

[0189] Specific operation: The server uses the lifestyle information collection module to generate questions about the user and instructs the terminal to display them.

[0190] Step 4:

[0191] The user uses the terminal to answer questions about lifestyle habits and transmits the answers to the server.

[0192] Input: User's answer (lifestyle information)

[0193] Output: Lifestyle information sent to the server

[0194] Specific operation: The user answers questions displayed on the terminal and sends the answer data to the server.

[0195] Step 5:

[0196] The server analyzes the lifestyle information it receives and formats it into a standard format.

[0197] Input: User's answer (lifestyle information)

[0198] Output: lifestyle information in a standard format

[0199] Specific operation: The server uses the lifestyle information analysis module to analyze the user's response data and convert it into a standard format.

[0200] Step 6:

[0201] The server integrates health checkup result data and lifestyle information and inputs them into the generative AI model.

[0202] Input: Health checkup result data and lifestyle information in a standard format

[0203] Output: Generated health advice

[0204] How it works: The server integrates both sets of data and generates health advice using a generative AI model (e.g., GPT-3).

[0205] Step 7:

[0206] The server transmits the generated health advice to the autonomous vehicle's system.

[0207] Input: Generated health advice

[0208] Output: Advice sent to the autonomous vehicle system

[0209] Specific operation: The server sends advice data to the autonomous vehicle and instructs it to set the vehicle's driving mode and suggest rest breaks.

[0210] Step 8:

[0211] The self-driving vehicle will set a driving mode based on the user's health status and suggest rest breaks at appropriate times.

[0212] Input: Health advice sent from the server

[0213] Output: Notification of set operation mode and break suggestion

[0214] Specific operation: Based on the advice received from the server, the autonomous vehicle sets the driving mode to relaxation mode and suggests taking a break every two hours.

[0215] In this way, driving support based on the user's health information is realized through each processing step.

[0216] 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.

[0217] The present invention is a system that analyzes health checkup results and provides individualized health advice to users. By combining this system with an emotion engine, it is possible to grasp the user's emotions and provide appropriate health advice based on those emotions. This system includes a series of programs that receive health checkup data, analyze the data, acquire lifestyle information, generate health advice, and provide it. Furthermore, the system incorporates an emotion engine and includes a function for analyzing the user's emotions and adjusting the content of the advice.

[0218] (Natural language description of the program's processing)

[0219] Uploading and analyzing health checkup results

[0220] The user uploads the health check result file, which includes blood test results, blood pressure, weight, etc., to the system from their terminal.

[0221] The server receives the uploaded file and converts it into text data using OCR (optical character recognition) technology, then analyzes the data to extract the necessary health information.

[0222] Collection of lifestyle information

[0223] The server generates lifestyle-related questions for the user and sends them to the device, such as "How often do you exercise per week?" and "Do you smoke?"

[0224] The user answers these questions using a terminal, and the answers are sent from the terminal to the server.

[0225] The server analyzes the received response data and formats it into a standard format.

[0226] Emotion analysis using an emotion engine

[0227] The server transmits text contained in the response data collected from the user to the emotion engine.

[0228] The emotion engine uses natural language processing techniques to analyze the user's emotions (e.g., stress, joy, anxiety, etc.).

[0229] Generating health advice

[0230] The server integrates health checkup results data, lifestyle information, and emotion analysis results from the emotion engine, and inputs this integrated data into the generative AI.

[0231] The generative AI analyzes this data and generates health advice based on the user's health status and anticipated risks. For example, if blood sugar levels are slightly high, it will generate advice such as "improve the quality of your diet and exercise several times a week."

[0232] Providing health advice

[0233] The generated health advice is sent from the server to the user's device, where the user can check the advice and use it as a reference for reviewing their own lifestyle habits.

[0234] Re-upload and update of blood test results that will be known at a later date

[0235] The user uploads the blood test results, which are later known, to the system from their device. The new result file is sent to the server.

[0236] The server receives the new data, extracts the text data using OCR technology, and then integrates the new data with the existing data and reanalyzes it using the generative AI.

[0237] As a result of the reanalysis, the generative AI updates existing health advice and generates new advice based on the latest information.

[0238] (Example)

[0239] For example, suppose a user undergoes a health check and uploads the results to the system. The results show that the user's blood sugar level is slightly high, and the emotion engine analyzes the data to determine that the user is under high stress. The generative AI generates advice such as, "To manage stress, try to do aerobic exercise several times a week and eat a balanced diet. We also recommend incorporating relaxation activities."

[0240] In this way, this system provides health advice tailored to each individual user based on health checkup results, lifestyle habit data, and emotional data, providing comprehensive support for the user's health management.

[0241] The processing flow will be explained below.

[0242] Step 1:

[0243] The user obtains the health check result file and uploads it from the terminal using the system interface.

[0244] Step 2:

[0245] The device sends the selected file to the server, which contains health-related information such as blood test results, blood pressure, and weight.

[0246] Step 3:

[0247] The server receives and saves the uploaded file, and depending on the format of the saved file, converts it into text data using OCR (Optical Character Recognition) technology.

[0248] Step 4:

[0249] The server analyzes the extracted text data to identify and extract the necessary health information, such as blood sugar, cholesterol, and blood pressure.

[0250] Step 5:

[0251] The server generates questions about the user's lifestyle and sends them to the device, such as "How often do you exercise per week?" or "Do you smoke?"

[0252] Step 6:

[0253] The terminal displays the generated questions to the user, who answers the questions and enters the answers using the terminal.

[0254] Step 7:

[0255] The user's answers are sent from the device to the server, which then analyzes the received answer data and formats it into a standard format.

[0256] Step 8:

[0257] The server sends the user's response data to the emotion engine, which uses natural language processing technology to analyze the user's emotions (e.g., stress, joy, anxiety, etc.).

[0258] Step 9:

[0259] The analysis results of the emotion engine are returned to the server, which then integrates the received emotion analysis results with the lifestyle information and health checkup results.

[0260] Step 10:

[0261] The server inputs the integrated data into the generation AI, which then analyzes the data. Based on the user's health and emotional state, the AI ​​generates appropriate health advice. For example, if the user's blood sugar level is slightly high and stress is high, the AI ​​generates advice such as "Improve the quality of your diet and incorporate relaxation activities to manage stress."

[0262] Step 11:

[0263] The server compiles the generated health advice into a report and sends it to the device, which includes the user's current health status and recommended actions.

[0264] Step 12:

[0265] The user can then use the device to check the sent report, and based on the report, review their lifestyle habits and plan specific actions to improve them.

[0266] Step 13:

[0267] At a later date, the user uploads the newly discovered blood test results to the system from the terminal again, and the terminal sends the new result file to the server.

[0268] Step 14:

[0269] The server receives the new data, converts it into text data using OCR technology again, then integrates the new data with the existing data and reanalyzes it using the generative AI.

[0270] Step 15:

[0271] Generative AI updates existing health advice based on new data and generates new advice that reflects the latest information.

[0272] Step 16:

[0273] The server then compiles new health advice in the form of a report and sends it to the device, where the user can check the updated report and use it to further improve their lifestyle habits.

[0274] Example 2

[0275] 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."

[0276] Conventional systems generate health advice based on health checkup data and lifestyle information, but because they do not take the user's emotional state into account, it is difficult to provide appropriate advice tailored to each individual's condition.In addition, the system lacks the functionality to update health advice to reflect blood test results that are discovered at a later date, which means that advice based on the latest health information is not provided.

[0277] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving health checkup data, means for analyzing the health checkup data and extracting necessary health information, means for acquiring lifestyle habit information, means for generating health advice based on the acquired lifestyle habit information and the health information, means for providing the health advice to the user, means for collecting and analyzing emotional data, and means for generating health advice by integrating the emotional data, lifestyle habit information, and health information. This makes it possible to provide individual health advice that takes the user's emotional state into consideration, and to update the advice to reflect the latest health information that becomes available at a later date.

[0278] "Health checkup data" refers to test result data obtained during regular health checkups that users undergo. This includes information such as blood test results, blood pressure, and weight.

[0279] "Analysis" refers to processing and analyzing the received data to extract necessary information. This processing includes converting it to text data and formatting the data.

[0280] "Health information" refers to information about the user's health condition extracted through analysis. Specifically, it includes indicators such as blood sugar level, blood pressure, and weight.

[0281] "Lifestyle information" is data related to the user's daily life, including exercise frequency, dietary habits, smoking status, etc.

[0282] "Health advice" is specific advice for maintaining or improving the user's health, generated based on health checkup data and lifestyle information.

[0283] "Providing" refers to communicating the generated health advice to the user, often via an electronic device.

[0284] "Emotional data" refers to data that indicates the user's emotional state. This data is analyzed from the user's text input and responses.

[0285] "Sentiment analysis" refers to analyzing a user's emotional state using emotional data, using natural language processing techniques.

[0286] "Integration" means combining multiple different data sets to create a single, consistent data set.

[0287] "Generative AI" is a system that uses artificial intelligence to analyze data and automatically generate appropriate health advice.

[0288] "Updating" means bringing existing information and advice up to date with new data.

[0289] The present invention is a system that analyzes health checkup results and provides individual health advice to users, and by combining it with an emotion engine, it grasps the user's emotions and provides appropriate health advice according to those emotions. Specific embodiments of the system are described in detail below.

[0290] This system mainly consists of a server, a terminal, and a user. The server receives and analyzes health checkup data and extracts health information. Specifically, it uses Microsoft Azure's OCR technology to convert the health checkup results into text data, and then analyzes the data using Python analysis libraries (e.g., Pandas, Numpy). The necessary health information is then extracted. The terminal functions as an interface for users to upload health checkup result files.

[0291] The server then begins the process of collecting lifestyle information. Using the Django framework, it generates a questionnaire for the user and sends it to the device. The user answers these questions through the device, and the response data is sent to the server. The server then uses the Python Pandas library to format the received data and standardize it.

[0292] For emotion analysis using the emotion engine, the server sends the user's response data to IBM Watson Natural Language Understanding, which analyzes the user's emotional state (e.g., stress, joy, anxiety). The emotion analysis results are sent back to the server and used to generate health advice.

[0293] The generative AI model, OpenAI GPT-4, integrates health checkup data, lifestyle information, and emotion analysis results to generate personalized health advice. The server then sends the generated health advice to the user's device, where the user can review it. This allows the user to understand their own health status and obtain specific guidelines for improvement.

[0294] Furthermore, if new data, such as blood test results, becomes available at a later date, the user can upload that data again to the system. The server then analyzes the new data again using Microsoft Azure's OCR technology, combines it with the existing data, and has the generative AI perform a re-analysis. This generates health advice based on the latest information and provides it to the user.

[0295] Specific examples

[0296] For example, suppose a user undergoes a health check and uploads the results to the system. If the blood sugar level is 105 mg / dL and the emotion engine analysis reveals a high stress level, the generative AI will generate advice such as, "To manage stress, we recommend aerobic exercise, a balanced diet, and relaxation activities."

[0297] Prompt Sentence Examples

[0298] Generate personalized health advice based on the following health checkup data, lifestyle information, and sentiment analysis results:

[0299] Health checkup result data:

[0300] Blood glucose level: 105 mg / dL

[0301] Blood pressure: 130 / 85 mmHg

[0302] Weight: 70 kg

[0303] Height: 170 cm

[0304] Lifestyle information:

[0305] Exercise frequency: twice a week

[0306] Smoking: No

[0307] Diet: Balanced diet

[0308] Emotion analysis results:

[0309] Stress level: High

[0310] Joy: Medium

[0311] Use this information to provide appropriate health advice to your users.

[0312] This system provides health advice tailored to each individual user based on the user's health checkup results, lifestyle data, and emotional data, providing comprehensive support for the user's health management.

[0313] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0314] Step 1:

[0315] The user uploads the health check result file from the terminal to the system. The user selects the health check result file (e.g. PDF, image) through the terminal's web browser and clicks the upload button. The uploaded file becomes the input and is sent to the server.

[0316] Step 2:

[0317] The server receives the file and converts it to text data using OCR technology. The server calls the Microsoft Azure OCR service to convert the uploaded file to text data. The input is the file received in step 1, and the output is the converted text data.

[0318] Step 3:

[0319] The server analyzes the health checkup data and extracts the necessary health information. The server uses Python analysis libraries (e.g., Pandas, Numpy) to analyze the text data and extract health information such as blood glucose levels, blood pressure, and weight. The input is text data converted by OCR, and the output is the extracted health information.

[0320] Step 4:

[0321] The server generates questions about the user's lifestyle and sends them to the device as a web form. The server uses the Django framework to generate questions about the user's lifestyle and sends them to the device as a web form. The input is a fixed question template, and the output is the generated web form.

[0322] Step 5:

[0323] The user answers the generated questions using the terminal. The user inputs answers to the questions displayed on the terminal's browser. The input is the user's answer data, which is sent from the terminal to the server.

[0324] Step 6:

[0325] The server analyzes the received response data and formats it into a standard format. The server uses the Python Pandas library to format the received data and unify it into a standard format. The input is the user's response data, and the output is formatted lifestyle habit information.

[0326] Step 7:

[0327] The server sends the received response data to the emotion engine. The server then calls the IBM Watson Natural Language Understanding service and sends the user's response data. The input is lifestyle habit information, and the output is the emotion analysis results.

[0328] Step 8:

[0329] The emotion engine analyzes the user's emotions and returns the analysis results to the server. The emotion engine uses natural language processing technology to analyze the user's emotional state (e.g., stress, elation, anxiety) and returns the results to the server. The input is lifestyle information, and the output is the emotion analysis results.

[0330] Step 9:

[0331] The server integrates the health checkup result data, lifestyle information, and emotion analysis results, and inputs them into the generative AI model. After integrating these data, the server inputs them into the OpenAI GPT-4 model. The inputs are the health checkup data, lifestyle information, and emotion analysis results, and the output is the input data for the generative AI model.

[0332] Step 10:

[0333] The generative AI analyzes the input data and generates health advice. The OpenAI GPT-4 model analyzes the input data and generates personalized health advice for the user. The input is the synthesized data, and the output is the generated health advice.

[0334] Step 11:

[0335] The server sends the generated health advice to the terminal. The server sends the generated health advice to the user's terminal, and the user uses the terminal to check the advice. The input is the generated health advice, and the output is the health advice displayed on the user's terminal.

[0336] Step 12:

[0337] The user uploads the blood test results, which will be known at a later date, from their terminal to the system again. The user uploads a new test result file from their terminal to the system, which is then sent to the server. The input is the new test result file, and the output is the file sent to the server.

[0338] Step 13:

[0339] The server receives the new data and extracts the text data using OCR technology again. The server then converts the new test result file into text data using Microsoft Azure's OCR service again. The input is the new test result file, and the output is text data.

[0340] Step 14:

[0341] The server integrates the new data with the existing data and reanalyzes it into the generative AI model. The server integrates the new data with the existing health information and inputs it into the OpenAI GPT-4 model again for analysis. The input is the integrated data, and the output is updated health advice.

[0342] Step 15:

[0343] The server sends the health advice generated as a result of the reanalysis to the user's device. The server then sends the updated health advice to the user's device, and the user uses the device to check the new advice. The input is the updated health advice, and the output is the new health advice displayed on the user's device.

[0344] (Application example 2)

[0345] 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."

[0346] In modern society, personal health management is important, but opportunities to receive appropriate advice based on health checkup results and lifestyle habits are limited. Furthermore, conventional health management systems rarely take into account the user's emotional state, resulting in issues with the accuracy and appropriateness of individual health advice. Furthermore, the advice provided cannot be visually confirmed immediately, making it difficult for users to use. There is a need to solve these issues and provide more effective and personalized health advice.

[0347] 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.

[0348] In this invention, the server includes means for receiving health checkup data, means for analyzing the health checkup data to extract necessary health information, means for acquiring lifestyle habit information, means for analyzing the emotional data, means for integrating the emotional data, the health information, and the lifestyle habit information and generating personalized health advice using a generative AI model, and means including a visualization device for visually presenting the generated health advice. This allows the personalized health advice to be provided taking into account the user's emotional state, and allows for quick visual confirmation.

[0349] "Medical checkup data" refers to medical data such as blood test results, blood pressure, and weight provided as a result of a medical checkup conducted by a user.

[0350] "Lifestyle information" refers to data such as the user's exercise habits, dietary habits, smoking and drinking habits in their daily lives.

[0351] "Emotion data" is data that indicates the user's emotional state, and is emotional information such as stress, joy, and anxiety extracted using natural language processing technology and image recognition technology.

[0352] A "generative artificial intelligence model" is an artificial intelligence system that analyzes a user's health and emotional state based on collected data and generates personalized health advice.

[0353] A "visual device" is a device for visually presenting the generated health advice to a user, and includes visual display devices such as head-mounted displays and smart glasses.

[0354] "Health advice" refers to specific suggestions and advice for improving the user's health, generated by a generative artificial intelligence model based on health checkup data, lifestyle information, and emotional data.

[0355] The present invention relates to a system that integrates health checkup data, lifestyle information, and user emotion data to provide personalized health advice. Specific embodiments for carrying out the present invention will be described below.

[0356] The entire system mainly consists of a server, a terminal, and smart glasses (visual device).

[0357] Receiving and analyzing health checkup data

[0358] The server first receives the health checkup data uploaded by the user via their device. This data includes information such as blood test results, blood pressure, and weight. The server then uses OCR technology to convert this data into text data and extract the necessary health information. Standard OCR software is used for the OCR technology.

[0359] Obtaining lifestyle information

[0360] The server generates lifestyle questions for the user and sends them to the device. Examples of questions include, "How often do you exercise per week?" or "Do you smoke?" The user answers the questions through the device, and the data is sent to the server. The server formats the data into a standard format.

[0361] Emotional Data Analysis

[0362] The smart glasses' built-in camera captures the user's facial expressions and tone of voice in real time, and uses emotion analysis software to analyze the user's emotional data, which includes stress, joy, anxiety, etc. This analysis is performed using an emotion analysis engine called EmotionEngine.

[0363] Generating health advice

[0364] The server integrates health checkup results, lifestyle information, and emotional data, and generates personalized health advice using a generative AI model. The generative AI model analyzes this data and provides advice based on the user's health and emotional state. For example, if blood sugar levels are high, it suggests exercise and dietary recommendations for stress management.

[0365] Example prompt sentence:

[0366] "User health data: {health_data}, Emotion: {emotion}. Provide personalized health advice based on these details."

[0367] Providing health advice

[0368] The generated health advice is sent from the server to the smart glasses, where users can visually confirm the advice in real time through the lenses of the smart glasses, enabling them to immediately take measures based on their health status.

[0369] Specific use cases

[0370] For example, after a user undergoes a health check, the results are uploaded to the system. The server analyzes the results and finds that the user's blood sugar level is slightly high. At the same time, facial expression data captured by the smart glasses reveals that the user is feeling stressed. Based on this information, the generative AI model generates advice such as, "To manage stress, try to do aerobic exercise several times a week and eat a balanced diet. We also recommend incorporating relaxation activities," and displays it on the lenses of the smart glasses.

[0371] In this way, the system of the present invention provides individual health advice based on the results of health checkups, lifestyle information, and emotional data, and provides comprehensive support for the user's health management.

[0372] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0373] Step 1:

[0374] Receiving health checkup data

[0375] The user uploads the results of their health checkup to the system from their device. The health checkup results include information such as blood test results, blood pressure, and weight. This data is sent to the server. The input is the health checkup data, and the output is text data stored on the server.

[0376] Step 2:

[0377] Analysis of health checkup data

[0378] The server uses OCR technology to convert the uploaded health checkup results into text data and extract the necessary health information. This information includes blood glucose levels, cholesterol levels, blood pressure, etc. The input is the health checkup data, and the output is the extracted health information. Specifically, the OCR software processes the scanned data and generates structured data.

[0379] Step 3:

[0380] Obtaining lifestyle information

[0381] The server generates lifestyle-related questions for the user and sends them to the device. For example, questions include, "How often do you exercise per week?" and "Do you smoke?" The user answers these questions via the device. The input is the lifestyle-related questions and the user's answers, and the output is lifestyle information. The server analyzes these answer data and formats them into a standard format.

[0382] Step 4:

[0383] Emotional Data Analysis

[0384] The camera built into the smart glasses captures the user's facial expressions and tone of voice in real time, and uses emotion analysis software to analyze the user's emotional data. Emotional data includes stress, joy, anxiety, etc. The input is the camera footage, and the output is the analyzed emotional data. Specifically, the EmotionEngine analyzes facial expressions and generates quantitative emotional data.

[0385] Step 5:

[0386] Integrating data and generating advice

[0387] The server integrates health checkup result data, lifestyle information, and emotional data. The integrated data is input into a generative AI model, which generates personalized health advice based on the user's health and emotional state. The input is the integrated data, and the output is health advice. Specifically, the AI ​​model analyzes the data and generates personalized advice based on a prompt. An example of the prompt is, "User health data: {health_data}, Emotion: {emotion}. Provide personalized health advice based on these details."

[0388] Step 6:

[0389] Providing health advice

[0390] The generated health advice is sent from the server to the smart glasses. The user can visually confirm the advice in real time through the lenses of the smart glasses. The input is the generated health advice, and the output is the user's visual confirmation. Specifically, the display function of the smart glasses displays the advice.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] [Second embodiment]

[0395] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0396] 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.

[0397] 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).

[0398] 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.

[0399] 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.

[0400] 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).

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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."

[0407] The present invention is a system that analyzes health checkup results and provides individualized health advice to users. This system is implemented based on a series of programs that receive health checkup data, analyze the data, acquire lifestyle information, generate health advice, and provide it.

[0408] (Natural language description of the program's processing)

[0409] Uploading and analyzing health checkup results

[0410] A user uploads a file of their health checkup results to the system from their device. This file contains information such as blood test results, blood pressure, and weight.

[0411] The server receives the uploaded file, converts it into text data using OCR (optical character recognition) technology, and analyzes the data to extract the necessary health information.

[0412] Collection of lifestyle information

[0413] The server generates lifestyle-related questions for the user and sends them to the device, such as "How often do you exercise per week?" and "Do you smoke?"

[0414] Users answer these questions using their devices and send the answers to the server, which then analyzes the received data and formats it into a standard format.

[0415] Generating health advice

[0416] The server combines the health checkup results data and lifestyle information and inputs them into the AI ​​generator, which analyzes this data and generates health advice based on the user's health status and anticipated risks.

[0417] For example, if your blood sugar level is slightly above the normal range, the AI ​​will generate advice such as, "I recommend improving the quality of your diet and doing aerobic exercise several times a week."

[0418] Providing health advice

[0419] The generated health advice is sent from the server to the user's device, where the user can check the advice and use it as a reference for reviewing their own lifestyle habits.

[0420] Re-upload and update of blood test results that will be known at a later date

[0421] The user uploads the blood test results, which are later determined, back into the system.

[0422] The server receives and analyzes this new data, updating existing health advice and providing new advice to the user based on their current situation.

[0423] (Example)

[0424] For example, suppose a user undergoes a health checkup and uploads the results to the system. The results show that their blood sugar level is slightly above the normal range, so they follow the AI's advice to review their diet and start exercising. If they then undergo another health checkup a few weeks later and re-upload the results, they will receive advice based on the new data that reads, "Your blood sugar level is improving. Please continue to maintain your current lifestyle habits."

[0425] In this way, this system supports users' health management by providing health advice tailored to each individual user based on health checkup results and lifestyle data.

[0426] The processing flow will be explained below.

[0427] Step 1:

[0428] The user obtains the results of the health check and uploads the file from the terminal using the system interface.

[0429] Step 2:

[0430] The device sends the uploaded file to a server, which contains health-related information such as blood test results, blood pressure, and weight.

[0431] Step 3:

[0432] The server receives and stores the uploaded file, then uses OCR (Optical Character Recognition) technology to extract text data from the file.

[0433] Step 4:

[0434] The server analyzes the extracted text data and extracts the necessary health information, such as blood sugar levels, cholesterol levels, and blood pressure.

[0435] Step 5:

[0436] The server generates lifestyle questions for the user, such as "How often do you exercise per week?" and "Do you smoke?"

[0437] Step 6:

[0438] The terminal displays the generated questions to the user, who then answers the questions about their lifestyle habits.

[0439] Step 7:

[0440] The user's answers are sent from the device to the server, which receives them and formats them into a standard format.

[0441] Step 8:

[0442] The server integrates the acquired health checkup data and lifestyle information, and inputs this integrated data into the generation AI.

[0443] Step 9:

[0444] The generative AI analyzes the integrated data and generates health advice based on the user's health status and anticipated risks. For example, if blood sugar levels are slightly high, it will generate advice such as "improve the quality of your diet and exercise several times a week."

[0445] Step 10:

[0446] The server compiles the generated health advice into a report for the user, which includes necessary advice and precautions.

[0447] Step 11:

[0448] The server then sends the generated report to the device, where the user can check the report and understand their own health condition and lifestyle improvements.

[0449] Step 12:

[0450] The user uploads the blood test results, which are later known, from the terminal to the system again. The terminal then sends the new result file to the server.

[0451] Step 13:

[0452] The server receives the new data, extracts the text data using OCR technology again, merges the new data with the existing data, and reanalyzes it using the generative AI.

[0453] Step 14:

[0454] Generative AI updates existing health advice based on new data, generating new advice based on the latest information.

[0455] Step 15:

[0456] The server then compiles the new health advice into a report for the user and sends it to the user's device, where the user can view the updated report.

[0457] Example 1

[0458] 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."

[0459] With conventional health management systems, it was difficult to individually manage and analyze users' health checkup data and lifestyle information, making it difficult to efficiently provide appropriate health advice based on that data. In particular, reanalysis of health checkup results and updating of advice based on that data were not performed in real time, which meant that users were unable to manage their health in a timely manner, and it was difficult to expect effective health improvement.

[0460] 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.

[0461] In this invention, the server includes means for receiving health checkup data, means for converting the health checkup data into text data using optical character recognition technology and analyzing it to extract necessary health information, means for interactively acquiring lifestyle habit information, means for integrating the acquired lifestyle habit information and the health information and generating health advice using a generative artificial intelligence model, and means for providing the health advice to the user. This enables the centralized management of a user's health checkup data and lifestyle habit information and the provision of health advice in real time.

[0462] "Health checkup data" refers to data including blood test results, blood pressure, weight, and other biological information obtained during a health checkup.

[0463] "Optical character recognition technology" is a technology that analyzes characters in an image and converts them into digital text.

[0464] "Text data" is digital data expressed as a string of characters.

[0465] "Analysis" is the process of examining data in detail and extracting the necessary information.

[0466] "Health information" is data that indicates health status based on health checkups and lifestyle habits.

[0467] "Lifestyle information" is data relating to the habits of the user in their daily life, including diet, exercise, smoking, and the like.

[0468] "Dialogue" is a method of obtaining information through an exchange of questions and answers.

[0469] "Integration" means bringing together multiple pieces of data into a consistent format.

[0470] A "generative artificial intelligence model" is a model that uses artificial intelligence technology to generate predictions or suggestions based on specific input data.

[0471] "Health advice" means instructions or suggestions provided to improve a user's health.

[0472] "User" means an individual who uses this system.

[0473] A "server" is a computer that processes information and manages data on a network.

[0474] "Terminal" means the device through which a user accesses the system.

[0475] "Real time" means that data processing and information provision are carried out without delay.

[0476] The present invention relates to a system that analyzes health checkup results and provides individualized health advice to users. This system is implemented based on a series of programs that receive health checkup data, analyze the data, acquire lifestyle information, generate health advice, and provide it.

[0477] System configuration and operation

[0478] This system consists of a server and a terminal. Users can access the system using the terminal and input their health checkup results and lifestyle information. The server processes this data and provides appropriate health advice to the user.

[0479] Uploading and analyzing health checkup results

[0480] First, the user uploads a file of their health checkup results to the system from their device. This health checkup result includes information such as blood test results, blood pressure, and weight. The server receives the uploaded file and converts it into text data using OCR (optical character recognition) technology. This OCR technology uses common cloud-based OCR software (e.g., optical character recognition API). The converted text data is then analyzed to extract the necessary health information.

[0481] Collection of lifestyle information

[0482] The server then generates lifestyle-related questions for the user and sends them to the device. For example, questions include, "How often do you exercise per week?" and "Do you smoke?" The user answers these questions using the device and sends the answers to the server. The server then analyzes the received answers and formats them into a standard format.

[0483] Generating health advice

[0484] The server integrates the health checkup result data and lifestyle information and inputs them into a generative AI model. This generative AI model, for example, uses natural language processing (NLP) technology (e.g., a generative language model). The generative AI model analyzes this data and generates health advice based on the user's health status and expected risks.

[0485] Examples of specific prompts that can be used include:

[0486] "Generate personalized health advice based on the user's health checkup results data and lifestyle information. For example, if their blood sugar level exceeds the standard value, include that number and specific advice for improvement."

[0487] Providing health advice

[0488] The generated health advice is sent from the server to the user's device, where the user can check the advice and use it as a reference for reviewing their own lifestyle habits.

[0489] Re-upload and update of blood test results that will be known at a later date

[0490] Furthermore, when the user uploads their blood test results back into the system at a later date, the server receives and analyzes this new data, updating existing health advice and providing new advice to the user based on their current situation.

[0491] Specific examples

[0492] For example, suppose a user undergoes a health checkup and uploads the results data to the system. Because their blood sugar level is slightly above the normal range, they follow the AI's advice to review their diet and start exercising. If they then undergo another health checkup a few weeks later and re-upload the results, they will receive advice based on the new data that reads, "Your blood sugar level is improving. Please continue to maintain your current lifestyle habits."

[0493] In this way, this system supports users' health management by providing health advice tailored to each individual user based on health checkup results and lifestyle data.

[0494] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0495] Step 1:

[0496] The user uses a terminal to upload a health checkup result file to the system. The health checkup result file contains information such as blood test results, blood pressure, and weight. The user clicks the file selection button on the browser, selects the appropriate file, and completes the upload. The input is the health checkup result file, and the output is that the uploaded file is sent to the server.

[0497] Step 2:

[0498] The server receives the uploaded health check result file. The received file is converted into text data using OCR technology. OCR software such as Google Cloud Vision API is used. The input is the health check result file, and the output is text data. The server analyzes this text data and extracts the necessary health information. Specifically, information such as blood test results, blood pressure, and weight is extracted.

[0499] Step 3:

[0500] The server generates lifestyle-related questions for the user and sends them to the device. For example, questions include "How often do you exercise per week?" and "Do you smoke?" The input is a question template generated within the server, and the output is a list of questions sent to the device. This prepares the user to enter their own lifestyle information.

[0501] Step 4:

[0502] The user answers questions about their lifestyle habits using a device. The user enters their lifestyle information through a UI that displays input fields and options, and then presses the send button to send the answers to the server. The input is the answer data about the user's lifestyle habits, and the output is the answer data sent to the server.

[0503] Step 5:

[0504] The server analyzes the received lifestyle information and formats it into a standard format. The input is the lifestyle information submitted by the user, and the output is formatted lifestyle data. The server then integrates this lifestyle data with the health checkup result data. This integration process creates a consistent data set.

[0505] Step 6:

[0506] The server inputs the integrated data into the generative AI model. Specifically, the user's health checkup result data and lifestyle habit information are compiled into a single prompt statement, which is then provided to the generative AI model. An example of the prompt statement is, "Please generate appropriate health advice based on this user's health checkup result and lifestyle habit information." The input is the integrated data and prompt statement, and the output is health advice generated by the generative AI model.

[0507] Step 7:

[0508] The server sends the generated health advice to the user's device. The advice is in text format and may be visualized using graphs or charts. The input is the health advice generated by the generative AI model, and the output is the advice displayed on the user's device. The user can review this advice and use it as a reference for reviewing their lifestyle habits.

[0509] Step 8:

[0510] If new blood test results become available at a later date, the user uploads the data to the system again. The upload procedure is the same as in step 1. The input is the new blood test result file, and the output is the new data sent to the server.

[0511] Step 9:

[0512] The server reanalyzes the newly received data and updates the existing health advice. The new data is integrated with the existing health information and fed back into the generative AI model to generate new advice. This updated advice is provided based on the user's latest health status. The input is the new health check data and the existing dataset, and the output is the updated health advice.

[0513] (Application example 1)

[0514] 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."

[0515] Nowadays, systems that provide personalized health advice based on health checkup data and lifestyle information are widely used. However, autonomous vehicles lack the functionality to set driving modes and suggest rest breaks based on the user's health status. As a result, long driving hours may have a negative impact on the user's health. Therefore, there is a need to add autonomous driving support functions based on the user's health status to current systems to further protect the user's safety and health.

[0516] 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.

[0517] In this invention, the server includes means for receiving health checkup data, means for analyzing the health checkup data and extracting necessary health information, means for acquiring lifestyle information, means for generating health advice based on the acquired lifestyle information and the health information, means for providing the health advice to a user, and means for generating and setting a driving mode and rest suggestions based on the user's health condition in an autonomous vehicle, thereby enabling the provision of driving support and advice according to the user's health condition.

[0518] "Health checkup data" refers to data including the results of diagnoses and tests conducted at medical institutions or the like to evaluate the user's health condition.

[0519] "Lifestyle information" is information relating to the user's daily life, and includes data such as the amount of exercise, diet, smoking status, and alcohol consumption.

[0520] "Health advice" includes specific instructions and recommendations for improving and maintaining a user's health, which are generated based on health checkup data and lifestyle information.

[0521] An "autonomous vehicle" is a vehicle that can perform autonomous driving operations without requiring user input or driver intervention.

[0522] "Driving mode" refers to the driving settings and driving method of an autonomous vehicle, and includes a driving style that corresponds to the user's health condition.

[0523] The "rest suggestion" includes a suggestion to take a rest at a specific time based on the user's health condition.

[0524] "Analysis" refers to the process of analyzing health checkup data and lifestyle information and extracting necessary health information.

[0525] The present invention relates to a system that provides personalized driving support advice based on a user's health condition based on health checkup data and lifestyle information. This system optimizes the user's health and safety through conditional driving modes and rest suggestions, particularly for autonomous vehicles.

[0526] This system is configured as follows:

[0527] 1. Processing and analysis of health examination data

[0528] The user uploads a file (e.g., PDF format) of the health checkup results from their device to the server. This file contains health information such as blood test results, blood pressure, and weight.

[0529] The server uses OCR technology to convert the uploaded file into text data, and then analyzes and extracts the necessary health information. This analysis is performed using an OCR module and a health data analysis module.

[0530] 2. Collection of lifestyle information

[0531] The server generates lifestyle questions for the user and sends them to the device, such as "How often do you exercise per week?" and "Do you smoke?"

[0532] Users answer these questions using their devices and send the answers to the server, which then analyzes the received data and formats it into a standard format.

[0533] 3. Generating health advice

[0534] The server combines the health checkup results data and lifestyle information and inputs them into a generative AI model, which analyzes this data and generates driving support advice based on the user's health condition and anticipated risks.

[0535] For example, if your blood sugar level is slightly above the normal range, the generated advice might include, "We recommend setting your driving mode to Relaxed mode and taking a break every two hours."

[0536] 4. Providing driving support

[0537] The generated driving support advice is sent from the server to the autonomous vehicle's system, which then sets a driving mode based on the user's health condition and suggests breaks at appropriate times.

[0538] For example, users with high blood sugar levels are encouraged to drive in relaxed mode and are instructed to take breaks every two hours.

[0539] Below are some specific examples of prompts for generative AI models:

[0540] Example prompt sentence:

[0541] Analyze the user's health check results and lifestyle information to generate the following driving advice:

[0542] Blood glucose level: 110 mg / dL, blood pressure: 130 / 85 mmHg

[0543] Weekly exercise time: 30 minutes, Smoking: No

[0544] In this way, this system provides driving support advice tailored to each individual user based on health checkup data and lifestyle data, making it possible to provide driving assistance that takes into account the health status of users of autonomous vehicles.

[0545] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0546] Step 1:

[0547] The user uploads the health check result file from the terminal to the server.

[0548] Input: Health check result file (PDF format)

[0549] Output: Uploaded files on the server

[0550] Specific operation: The user uses the upload function of the device to send the health check result file to the server.

[0551] Step 2:

[0552] The server uses OCR technology to convert the uploaded file into text data, and then analyzes and extracts the necessary health information.

[0553] Input: Uploaded health check result file

[0554] Output: Extracted health information (text data)

[0555] Specific operation: The server uses an OCR module (e.g., Tesseract) to extract text from the PDF file, and then uses a health data analysis module to analyze and extract the required information.

[0556] Step 3:

[0557] The server generates questions about the user's lifestyle habits and sends them to the terminal.

[0558] Input: None

[0559] Output: Generated question (displayed on terminal)

[0560] Specific operation: The server uses the lifestyle information collection module to generate questions about the user and instructs the terminal to display them.

[0561] Step 4:

[0562] The user uses the terminal to answer questions about lifestyle habits and transmits the answers to the server.

[0563] Input: User's answer (lifestyle information)

[0564] Output: Lifestyle information sent to the server

[0565] Specific operation: The user answers questions displayed on the terminal and sends the answer data to the server.

[0566] Step 5:

[0567] The server analyzes the lifestyle information it receives and formats it into a standard format.

[0568] Input: User's answer (lifestyle information)

[0569] Output: lifestyle information in a standard format

[0570] Specific operation: The server uses the lifestyle information analysis module to analyze the user's response data and convert it into a standard format.

[0571] Step 6:

[0572] The server integrates health checkup result data and lifestyle information and inputs them into the generative AI model.

[0573] Input: Health checkup result data and lifestyle information in a standard format

[0574] Output: Generated health advice

[0575] How it works: The server integrates both sets of data and generates health advice using a generative AI model (e.g., GPT-3).

[0576] Step 7:

[0577] The server transmits the generated health advice to the autonomous vehicle's system.

[0578] Input: Generated health advice

[0579] Output: Advice sent to the autonomous vehicle system

[0580] Specific operation: The server sends advice data to the autonomous vehicle and instructs it to set the vehicle's driving mode and suggest rest breaks.

[0581] Step 8:

[0582] The self-driving vehicle will set a driving mode based on the user's health status and suggest rest breaks at appropriate times.

[0583] Input: Health advice sent from the server

[0584] Output: Notification of set operation mode and break suggestion

[0585] Specific operation: Based on the advice received from the server, the autonomous vehicle sets the driving mode to relaxation mode and suggests taking a break every two hours.

[0586] In this way, driving support based on the user's health information is realized through each processing step.

[0587] 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.

[0588] The present invention is a system that analyzes health checkup results and provides individualized health advice to users. By combining this system with an emotion engine, it is possible to grasp the user's emotions and provide appropriate health advice based on those emotions. This system includes a series of programs that receive health checkup data, analyze the data, acquire lifestyle information, generate health advice, and provide it. Furthermore, the system incorporates an emotion engine and includes a function for analyzing the user's emotions and adjusting the content of the advice.

[0589] (Natural language description of the program's processing)

[0590] Uploading and analyzing health checkup results

[0591] The user uploads the health check result file, which includes blood test results, blood pressure, weight, etc., to the system from their terminal.

[0592] The server receives the uploaded file and converts it into text data using OCR (optical character recognition) technology, then analyzes the data to extract the necessary health information.

[0593] Collection of lifestyle information

[0594] The server generates lifestyle-related questions for the user and sends them to the device, such as "How often do you exercise per week?" and "Do you smoke?"

[0595] The user answers these questions using a terminal, and the answers are sent from the terminal to the server.

[0596] The server analyzes the received response data and formats it into a standard format.

[0597] Emotion analysis using an emotion engine

[0598] The server transmits text contained in the response data collected from the user to the emotion engine.

[0599] The emotion engine uses natural language processing techniques to analyze the user's emotions (e.g., stress, joy, anxiety, etc.).

[0600] Generating health advice

[0601] The server integrates health checkup results data, lifestyle information, and emotion analysis results from the emotion engine, and inputs this integrated data into the generative AI.

[0602] The generative AI analyzes this data and generates health advice based on the user's health status and anticipated risks. For example, if blood sugar levels are slightly high, it will generate advice such as "improve the quality of your diet and exercise several times a week."

[0603] Providing health advice

[0604] The generated health advice is sent from the server to the user's device, where the user can check the advice and use it as a reference for reviewing their own lifestyle habits.

[0605] Re-upload and update of blood test results that will be known at a later date

[0606] The user uploads the blood test results, which are later known, to the system from their device. The new result file is sent to the server.

[0607] The server receives the new data, extracts the text data using OCR technology, and then integrates the new data with the existing data and reanalyzes it using the generative AI.

[0608] As a result of the reanalysis, the generative AI updates existing health advice and generates new advice based on the latest information.

[0609] (Example)

[0610] For example, suppose a user undergoes a health check and uploads the results to the system. The results show that the user's blood sugar level is slightly high, and the emotion engine analyzes the data to determine that the user is under high stress. The generative AI generates advice such as, "To manage stress, try to do aerobic exercise several times a week and eat a balanced diet. We also recommend incorporating relaxation activities."

[0611] In this way, this system provides health advice tailored to each individual user based on health checkup results, lifestyle habit data, and emotional data, providing comprehensive support for the user's health management.

[0612] The processing flow will be explained below.

[0613] Step 1:

[0614] The user obtains the health check result file and uploads it from the terminal using the system interface.

[0615] Step 2:

[0616] The device sends the selected file to the server, which contains health-related information such as blood test results, blood pressure, and weight.

[0617] Step 3:

[0618] The server receives and saves the uploaded file, and depending on the format of the saved file, converts it into text data using OCR (Optical Character Recognition) technology.

[0619] Step 4:

[0620] The server analyzes the extracted text data to identify and extract the necessary health information, such as blood sugar, cholesterol, and blood pressure.

[0621] Step 5:

[0622] The server generates questions about the user's lifestyle and sends them to the device, such as "How often do you exercise per week?" or "Do you smoke?"

[0623] Step 6:

[0624] The terminal displays the generated questions to the user, who answers the questions and enters the answers using the terminal.

[0625] Step 7:

[0626] The user's answers are sent from the device to the server, which then analyzes the received answer data and formats it into a standard format.

[0627] Step 8:

[0628] The server sends the user's response data to the emotion engine, which uses natural language processing technology to analyze the user's emotions (e.g., stress, joy, anxiety, etc.).

[0629] Step 9:

[0630] The analysis results of the emotion engine are returned to the server, which then integrates the received emotion analysis results with the lifestyle information and health checkup results.

[0631] Step 10:

[0632] The server inputs the integrated data into the generation AI, which then analyzes the data. Based on the user's health and emotional state, the AI ​​generates appropriate health advice. For example, if the user's blood sugar level is slightly high and stress is high, the AI ​​generates advice such as "Improve the quality of your diet and incorporate relaxation activities to manage stress."

[0633] Step 11:

[0634] The server compiles the generated health advice into a report and sends it to the device, which includes the user's current health status and recommended actions.

[0635] Step 12:

[0636] The user can then use the device to check the sent report, and based on the report, review their lifestyle habits and plan specific actions to improve them.

[0637] Step 13:

[0638] At a later date, the user uploads the newly discovered blood test results to the system from the terminal again, and the terminal sends the new result file to the server.

[0639] Step 14:

[0640] The server receives the new data, converts it into text data using OCR technology again, then integrates the new data with the existing data and reanalyzes it using the generative AI.

[0641] Step 15:

[0642] Generative AI updates existing health advice based on new data and generates new advice that reflects the latest information.

[0643] Step 16:

[0644] The server then compiles new health advice in the form of a report and sends it to the device, where the user can check the updated report and use it to further improve their lifestyle habits.

[0645] Example 2

[0646] 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."

[0647] Conventional systems generate health advice based on health checkup data and lifestyle information, but because they do not take the user's emotional state into account, it is difficult to provide appropriate advice tailored to each individual's condition.In addition, the system lacks the functionality to update health advice to reflect blood test results that are discovered at a later date, which means that advice based on the latest health information is not provided.

[0648] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving health checkup data, means for analyzing the health checkup data and extracting necessary health information, means for acquiring lifestyle habit information, means for generating health advice based on the acquired lifestyle habit information and the health information, means for providing the health advice to the user, means for collecting and analyzing emotional data, and means for generating health advice by integrating the emotional data, lifestyle habit information, and health information. This makes it possible to provide individual health advice that takes the user's emotional state into consideration, and to update the advice to reflect the latest health information that becomes available at a later date.

[0649] "Health checkup data" refers to test result data obtained during regular health checkups that users undergo. This includes information such as blood test results, blood pressure, and weight.

[0650] "Analysis" refers to processing and analyzing the received data to extract necessary information. This processing includes converting it to text data and formatting the data.

[0651] "Health information" refers to information about the user's health condition extracted through analysis. Specifically, it includes indicators such as blood sugar level, blood pressure, and weight.

[0652] "Lifestyle information" is data related to the user's daily life, including exercise frequency, dietary habits, smoking status, etc.

[0653] "Health advice" is specific advice for maintaining or improving the user's health, generated based on health checkup data and lifestyle information.

[0654] "Providing" refers to communicating the generated health advice to the user, often via an electronic device.

[0655] "Emotional data" refers to data that indicates the user's emotional state. This data is analyzed from the user's text input and responses.

[0656] "Sentiment analysis" refers to analyzing a user's emotional state using emotional data, using natural language processing techniques.

[0657] "Integration" means combining multiple different data sets to create a single, consistent data set.

[0658] "Generative AI" is a system that uses artificial intelligence to analyze data and automatically generate appropriate health advice.

[0659] "Updating" means bringing existing information and advice up to date with new data.

[0660] The present invention is a system that analyzes health checkup results and provides individual health advice to users, and by combining it with an emotion engine, it grasps the user's emotions and provides appropriate health advice according to those emotions. Specific embodiments of the system are described in detail below.

[0661] This system mainly consists of a server, a terminal, and a user. The server receives and analyzes health checkup data and extracts health information. Specifically, it uses Microsoft Azure's OCR technology to convert the health checkup results into text data, and then analyzes the data using Python analysis libraries (e.g., Pandas, Numpy). The necessary health information is then extracted. The terminal functions as an interface for users to upload health checkup result files.

[0662] The server then begins the process of collecting lifestyle information. Using the Django framework, it generates a questionnaire for the user and sends it to the device. The user answers these questions through the device, and the response data is sent to the server. The server then uses the Python Pandas library to format the received data and standardize it.

[0663] For emotion analysis using the emotion engine, the server sends the user's response data to IBM Watson Natural Language Understanding, which analyzes the user's emotional state (e.g., stress, joy, anxiety). The emotion analysis results are sent back to the server and used to generate health advice.

[0664] The generative AI model, OpenAI GPT-4, integrates health checkup data, lifestyle information, and emotion analysis results to generate personalized health advice. The server then sends the generated health advice to the user's device, where the user can review it. This allows the user to understand their own health status and obtain specific guidelines for improvement.

[0665] Furthermore, if new data, such as blood test results, becomes available at a later date, the user can upload that data again to the system. The server then analyzes the new data again using Microsoft Azure's OCR technology, combines it with the existing data, and has the generative AI perform a re-analysis. This generates health advice based on the latest information and provides it to the user.

[0666] Specific examples

[0667] For example, suppose a user undergoes a health check and uploads the results to the system. If the blood sugar level is 105 mg / dL and the emotion engine analysis reveals a high stress level, the generative AI will generate advice such as, "To manage stress, we recommend aerobic exercise, a balanced diet, and relaxation activities."

[0668] Prompt Sentence Examples

[0669] Generate personalized health advice based on the following health checkup data, lifestyle information, and sentiment analysis results:

[0670] Health checkup result data:

[0671] Blood glucose level: 105 mg / dL

[0672] Blood pressure: 130 / 85 mmHg

[0673] Weight: 70 kg

[0674] Height: 170 cm

[0675] Lifestyle information:

[0676] Exercise frequency: twice a week

[0677] Smoking: No

[0678] Diet: Balanced diet

[0679] Emotion analysis results:

[0680] Stress level: High

[0681] Joy: Medium

[0682] Use this information to provide appropriate health advice to your users.

[0683] This system provides health advice tailored to each individual user based on the user's health checkup results, lifestyle data, and emotional data, providing comprehensive support for the user's health management.

[0684] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0685] Step 1:

[0686] The user uploads the health check result file from the terminal to the system. The user selects the health check result file (e.g. PDF, image) through the terminal's web browser and clicks the upload button. The uploaded file becomes the input and is sent to the server.

[0687] Step 2:

[0688] The server receives the file and converts it to text data using OCR technology. The server calls the Microsoft Azure OCR service to convert the uploaded file to text data. The input is the file received in step 1, and the output is the converted text data.

[0689] Step 3:

[0690] The server analyzes the health checkup data and extracts the necessary health information. The server uses Python analysis libraries (e.g., Pandas, Numpy) to analyze the text data and extract health information such as blood glucose levels, blood pressure, and weight. The input is text data converted by OCR, and the output is the extracted health information.

[0691] Step 4:

[0692] The server generates questions about the user's lifestyle and sends them to the device as a web form. The server uses the Django framework to generate questions about the user's lifestyle and sends them to the device as a web form. The input is a fixed question template, and the output is the generated web form.

[0693] Step 5:

[0694] The user answers the generated questions using the terminal. The user inputs answers to the questions displayed on the terminal's browser. The input is the user's answer data, which is sent from the terminal to the server.

[0695] Step 6:

[0696] The server analyzes the received response data and formats it into a standard format. The server uses the Python Pandas library to format the received data and unify it into a standard format. The input is the user's response data, and the output is formatted lifestyle habit information.

[0697] Step 7:

[0698] The server sends the received response data to the emotion engine. The server then calls the IBM Watson Natural Language Understanding service and sends the user's response data. The input is lifestyle habit information, and the output is the emotion analysis results.

[0699] Step 8:

[0700] The emotion engine analyzes the user's emotions and returns the analysis results to the server. The emotion engine uses natural language processing technology to analyze the user's emotional state (e.g., stress, elation, anxiety) and returns the results to the server. The input is lifestyle information, and the output is the emotion analysis results.

[0701] Step 9:

[0702] The server integrates the health checkup result data, lifestyle information, and emotion analysis results, and inputs them into the generative AI model. After integrating these data, the server inputs them into the OpenAI GPT-4 model. The inputs are the health checkup data, lifestyle information, and emotion analysis results, and the output is the input data for the generative AI model.

[0703] Step 10:

[0704] The generative AI analyzes the input data and generates health advice. The OpenAI GPT-4 model analyzes the input data and generates personalized health advice for the user. The input is the synthesized data, and the output is the generated health advice.

[0705] Step 11:

[0706] The server sends the generated health advice to the terminal. The server sends the generated health advice to the user's terminal, and the user uses the terminal to check the advice. The input is the generated health advice, and the output is the health advice displayed on the user's terminal.

[0707] Step 12:

[0708] The user uploads the blood test results, which will be known at a later date, from their terminal to the system again. The user uploads a new test result file from their terminal to the system, which is then sent to the server. The input is the new test result file, and the output is the file sent to the server.

[0709] Step 13:

[0710] The server receives the new data and extracts the text data using OCR technology again. The server then converts the new test result file into text data using Microsoft Azure's OCR service again. The input is the new test result file, and the output is text data.

[0711] Step 14:

[0712] The server integrates the new data with the existing data and reanalyzes it into the generative AI model. The server integrates the new data with the existing health information and inputs it into the OpenAI GPT-4 model again for analysis. The input is the integrated data, and the output is updated health advice.

[0713] Step 15:

[0714] The server sends the health advice generated as a result of the reanalysis to the user's device. The server then sends the updated health advice to the user's device, and the user uses the device to check the new advice. The input is the updated health advice, and the output is the new health advice displayed on the user's device.

[0715] (Application example 2)

[0716] 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."

[0717] In modern society, personal health management is important, but opportunities to receive appropriate advice based on health checkup results and lifestyle habits are limited. Furthermore, conventional health management systems rarely take into account the user's emotional state, resulting in issues with the accuracy and appropriateness of individual health advice. Furthermore, the advice provided cannot be visually confirmed immediately, making it difficult for users to use. There is a need to solve these issues and provide more effective and personalized health advice.

[0718] 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.

[0719] In this invention, the server includes means for receiving health checkup data, means for analyzing the health checkup data to extract necessary health information, means for acquiring lifestyle habit information, means for analyzing the emotional data, means for integrating the emotional data, the health information, and the lifestyle habit information and generating personalized health advice using a generative AI model, and means including a visualization device for visually presenting the generated health advice. This allows the personalized health advice to be provided taking into account the user's emotional state, and allows for quick visual confirmation.

[0720] "Medical checkup data" refers to medical data such as blood test results, blood pressure, and weight provided as a result of a medical checkup conducted by a user.

[0721] "Lifestyle information" refers to data such as the user's exercise habits, dietary habits, smoking and drinking habits in their daily lives.

[0722] "Emotion data" is data that indicates the user's emotional state, and is emotional information such as stress, joy, and anxiety extracted using natural language processing technology and image recognition technology.

[0723] A "generative artificial intelligence model" is an artificial intelligence system that analyzes a user's health and emotional state based on collected data and generates personalized health advice.

[0724] A "visual device" is a device for visually presenting the generated health advice to a user, and includes visual display devices such as head-mounted displays and smart glasses.

[0725] "Health advice" refers to specific suggestions and advice for improving the user's health, generated by a generative artificial intelligence model based on health checkup data, lifestyle information, and emotional data.

[0726] The present invention relates to a system that integrates health checkup data, lifestyle information, and user emotion data to provide personalized health advice. Specific embodiments for carrying out the present invention will be described below.

[0727] The entire system mainly consists of a server, a terminal, and smart glasses (visual device).

[0728] Receiving and analyzing health checkup data

[0729] The server first receives the health checkup data uploaded by the user via their device. This data includes information such as blood test results, blood pressure, and weight. The server then uses OCR technology to convert this data into text data and extract the necessary health information. Standard OCR software is used for the OCR technology.

[0730] Obtaining lifestyle information

[0731] The server generates lifestyle questions for the user and sends them to the device. Examples of questions include, "How often do you exercise per week?" or "Do you smoke?" The user answers the questions through the device, and the data is sent to the server. The server formats the data into a standard format.

[0732] Emotional Data Analysis

[0733] The smart glasses' built-in camera captures the user's facial expressions and tone of voice in real time, and uses emotion analysis software to analyze the user's emotional data, which includes stress, joy, anxiety, etc. This analysis is performed using an emotion analysis engine called EmotionEngine.

[0734] Generating health advice

[0735] The server integrates health checkup results, lifestyle information, and emotional data, and generates personalized health advice using a generative AI model. The generative AI model analyzes this data and provides advice based on the user's health and emotional state. For example, if blood sugar levels are high, it suggests exercise and dietary recommendations for stress management.

[0736] Example prompt sentence:

[0737] "User health data: {health_data}, Emotion: {emotion}. Provide personalized health advice based on these details."

[0738] Providing health advice

[0739] The generated health advice is sent from the server to the smart glasses, where users can visually confirm the advice in real time through the lenses of the smart glasses, enabling them to immediately take measures based on their health status.

[0740] Specific use cases

[0741] For example, after a user undergoes a health check, the results are uploaded to the system. The server analyzes the results and finds that the user's blood sugar level is slightly high. At the same time, facial expression data captured by the smart glasses reveals that the user is feeling stressed. Based on this information, the generative AI model generates advice such as, "To manage stress, try to do aerobic exercise several times a week and eat a balanced diet. We also recommend incorporating relaxation activities," and displays it on the lenses of the smart glasses.

[0742] In this way, the system of the present invention provides individual health advice based on the results of health checkups, lifestyle information, and emotional data, and provides comprehensive support for the user's health management.

[0743] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0744] Step 1:

[0745] Receiving health checkup data

[0746] The user uploads the results of their health checkup to the system from their device. The health checkup results include information such as blood test results, blood pressure, and weight. This data is sent to the server. The input is the health checkup data, and the output is text data stored on the server.

[0747] Step 2:

[0748] Analysis of health checkup data

[0749] The server uses OCR technology to convert the uploaded health checkup results into text data and extract the necessary health information. This information includes blood glucose levels, cholesterol levels, blood pressure, etc. The input is the health checkup data, and the output is the extracted health information. Specifically, the OCR software processes the scanned data and generates structured data.

[0750] Step 3:

[0751] Obtaining lifestyle information

[0752] The server generates lifestyle-related questions for the user and sends them to the device. For example, questions include, "How often do you exercise per week?" and "Do you smoke?" The user answers these questions via the device. The input is the lifestyle-related questions and the user's answers, and the output is lifestyle information. The server analyzes these answer data and formats them into a standard format.

[0753] Step 4:

[0754] Emotional Data Analysis

[0755] The camera built into the smart glasses captures the user's facial expressions and tone of voice in real time, and uses emotion analysis software to analyze the user's emotional data. Emotional data includes stress, joy, anxiety, etc. The input is the camera footage, and the output is the analyzed emotional data. Specifically, the EmotionEngine analyzes facial expressions and generates quantitative emotional data.

[0756] Step 5:

[0757] Integrating data and generating advice

[0758] The server integrates health checkup result data, lifestyle information, and emotional data. The integrated data is input into a generative AI model, which generates personalized health advice based on the user's health and emotional state. The input is the integrated data, and the output is health advice. Specifically, the AI ​​model analyzes the data and generates personalized advice based on a prompt. An example of the prompt is, "User health data: {health_data}, Emotion: {emotion}. Provide personalized health advice based on these details."

[0759] Step 6:

[0760] Providing health advice

[0761] The generated health advice is sent from the server to the smart glasses. The user can visually confirm the advice in real time through the lenses of the smart glasses. The input is the generated health advice, and the output is the user's visual confirmation. Specifically, the display function of the smart glasses displays the advice.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] [Third embodiment]

[0766] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0767] 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.

[0768] 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).

[0769] 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.

[0770] 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.

[0771] 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).

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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."

[0778] The present invention is a system that analyzes health checkup results and provides individualized health advice to users. This system is implemented based on a series of programs that receive health checkup data, analyze the data, acquire lifestyle information, generate health advice, and provide it.

[0779] (Natural language description of the program's processing)

[0780] Uploading and analyzing health checkup results

[0781] A user uploads a file of their health checkup results to the system from their device. This file contains information such as blood test results, blood pressure, and weight.

[0782] The server receives the uploaded file, converts it into text data using OCR (optical character recognition) technology, and analyzes the data to extract the necessary health information.

[0783] Collection of lifestyle information

[0784] The server generates lifestyle-related questions for the user and sends them to the device, such as "How often do you exercise per week?" and "Do you smoke?"

[0785] Users answer these questions using their devices and send the answers to the server, which then analyzes the received data and formats it into a standard format.

[0786] Generating health advice

[0787] The server combines the health checkup results data and lifestyle information and inputs them into the AI ​​generator, which analyzes this data and generates health advice based on the user's health status and anticipated risks.

[0788] For example, if your blood sugar level is slightly above the normal range, the AI ​​will generate advice such as, "I recommend improving the quality of your diet and doing aerobic exercise several times a week."

[0789] Providing health advice

[0790] The generated health advice is sent from the server to the user's device, where the user can check the advice and use it as a reference for reviewing their own lifestyle habits.

[0791] Re-upload and update of blood test results that will be known at a later date

[0792] The user uploads the blood test results, which are later determined, back into the system.

[0793] The server receives and analyzes this new data, updating existing health advice and providing new advice to the user based on their current situation.

[0794] (Example)

[0795] For example, suppose a user undergoes a health checkup and uploads the results to the system. The results show that their blood sugar level is slightly above the normal range, so they follow the AI's advice to review their diet and start exercising. If they then undergo another health checkup a few weeks later and re-upload the results, they will receive advice based on the new data that reads, "Your blood sugar level is improving. Please continue to maintain your current lifestyle habits."

[0796] In this way, this system supports users' health management by providing health advice tailored to each individual user based on health checkup results and lifestyle data.

[0797] The processing flow will be explained below.

[0798] Step 1:

[0799] The user obtains the results of the health check and uploads the file from the terminal using the system interface.

[0800] Step 2:

[0801] The device sends the uploaded file to a server, which contains health-related information such as blood test results, blood pressure, and weight.

[0802] Step 3:

[0803] The server receives and stores the uploaded file, then uses OCR (Optical Character Recognition) technology to extract text data from the file.

[0804] Step 4:

[0805] The server analyzes the extracted text data and extracts the necessary health information, such as blood sugar levels, cholesterol levels, and blood pressure.

[0806] Step 5:

[0807] The server generates lifestyle questions for the user, such as "How often do you exercise per week?" and "Do you smoke?"

[0808] Step 6:

[0809] The terminal displays the generated questions to the user, who then answers the questions about their lifestyle habits.

[0810] Step 7:

[0811] The user's answers are sent from the device to the server, which receives them and formats them into a standard format.

[0812] Step 8:

[0813] The server integrates the acquired health checkup data and lifestyle information, and inputs this integrated data into the generation AI.

[0814] Step 9:

[0815] The generative AI analyzes the integrated data and generates health advice based on the user's health status and anticipated risks. For example, if blood sugar levels are slightly high, it will generate advice such as "improve the quality of your diet and exercise several times a week."

[0816] Step 10:

[0817] The server compiles the generated health advice into a report for the user, which includes necessary advice and precautions.

[0818] Step 11:

[0819] The server then sends the generated report to the device, where the user can check the report and understand their own health condition and lifestyle improvements.

[0820] Step 12:

[0821] The user uploads the blood test results, which are later known, from the terminal to the system again. The terminal then sends the new result file to the server.

[0822] Step 13:

[0823] The server receives the new data, extracts the text data using OCR technology again, merges the new data with the existing data, and reanalyzes it using the generative AI.

[0824] Step 14:

[0825] Generative AI updates existing health advice based on new data, generating new advice based on the latest information.

[0826] Step 15:

[0827] The server then compiles the new health advice into a report for the user and sends it to the user's device, where the user can view the updated report.

[0828] Example 1

[0829] 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."

[0830] With conventional health management systems, it was difficult to individually manage and analyze users' health checkup data and lifestyle information, making it difficult to efficiently provide appropriate health advice based on that data. In particular, reanalysis of health checkup results and updating of advice based on that data were not performed in real time, which meant that users were unable to manage their health in a timely manner, and it was difficult to expect effective health improvement.

[0831] 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.

[0832] In this invention, the server includes means for receiving health checkup data, means for converting the health checkup data into text data using optical character recognition technology and analyzing it to extract necessary health information, means for interactively acquiring lifestyle habit information, means for integrating the acquired lifestyle habit information and the health information and generating health advice using a generative artificial intelligence model, and means for providing the health advice to the user. This enables the centralized management of a user's health checkup data and lifestyle habit information and the provision of health advice in real time.

[0833] "Health checkup data" refers to data including blood test results, blood pressure, weight, and other biological information obtained during a health checkup.

[0834] "Optical character recognition technology" is a technology that analyzes characters in an image and converts them into digital text.

[0835] "Text data" is digital data expressed as a string of characters.

[0836] "Analysis" is the process of examining data in detail and extracting the necessary information.

[0837] "Health information" is data that indicates health status based on health checkups and lifestyle habits.

[0838] "Lifestyle information" is data relating to the habits of the user in their daily life, including diet, exercise, smoking, and the like.

[0839] "Dialogue" is a method of obtaining information through an exchange of questions and answers.

[0840] "Integration" means bringing together multiple pieces of data into a consistent format.

[0841] A "generative artificial intelligence model" is a model that uses artificial intelligence technology to generate predictions or suggestions based on specific input data.

[0842] "Health advice" means instructions or suggestions provided to improve a user's health.

[0843] "User" means an individual who uses this system.

[0844] A "server" is a computer that processes information and manages data on a network.

[0845] "Terminal" means the device through which a user accesses the system.

[0846] "Real time" means that data processing and information provision are carried out without delay.

[0847] The present invention relates to a system that analyzes health checkup results and provides individualized health advice to users. This system is implemented based on a series of programs that receive health checkup data, analyze the data, acquire lifestyle information, generate health advice, and provide it.

[0848] System configuration and operation

[0849] This system consists of a server and a terminal. Users can access the system using the terminal and input their health checkup results and lifestyle information. The server processes this data and provides appropriate health advice to the user.

[0850] Uploading and analyzing health checkup results

[0851] First, the user uploads a file of their health checkup results to the system from their device. This health checkup result includes information such as blood test results, blood pressure, and weight. The server receives the uploaded file and converts it into text data using OCR (optical character recognition) technology. This OCR technology uses common cloud-based OCR software (e.g., optical character recognition API). The converted text data is then analyzed to extract the necessary health information.

[0852] Collection of lifestyle information

[0853] The server then generates lifestyle-related questions for the user and sends them to the device. For example, questions include, "How often do you exercise per week?" and "Do you smoke?" The user answers these questions using the device and sends the answers to the server. The server then analyzes the received answers and formats them into a standard format.

[0854] Generating health advice

[0855] The server integrates the health checkup result data and lifestyle information and inputs them into a generative AI model. This generative AI model, for example, uses natural language processing (NLP) technology (e.g., a generative language model). The generative AI model analyzes this data and generates health advice based on the user's health status and expected risks.

[0856] Examples of specific prompts that can be used include:

[0857] "Generate personalized health advice based on the user's health checkup results data and lifestyle information. For example, if their blood sugar level exceeds the standard value, include that number and specific advice for improvement."

[0858] Providing health advice

[0859] The generated health advice is sent from the server to the user's device, where the user can check the advice and use it as a reference for reviewing their own lifestyle habits.

[0860] Re-upload and update of blood test results that will be known at a later date

[0861] Furthermore, when the user uploads their blood test results back into the system at a later date, the server receives and analyzes this new data, updating existing health advice and providing new advice to the user based on their current situation.

[0862] Specific examples

[0863] For example, suppose a user undergoes a health checkup and uploads the results data to the system. Because their blood sugar level is slightly above the normal range, they follow the AI's advice to review their diet and start exercising. If they then undergo another health checkup a few weeks later and re-upload the results, they will receive advice based on the new data that reads, "Your blood sugar level is improving. Please continue to maintain your current lifestyle habits."

[0864] In this way, this system supports users' health management by providing health advice tailored to each individual user based on health checkup results and lifestyle data.

[0865] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0866] Step 1:

[0867] The user uses a terminal to upload a health checkup result file to the system. The health checkup result file contains information such as blood test results, blood pressure, and weight. The user clicks the file selection button on the browser, selects the appropriate file, and completes the upload. The input is the health checkup result file, and the output is that the uploaded file is sent to the server.

[0868] Step 2:

[0869] The server receives the uploaded health check result file. The received file is converted into text data using OCR technology. OCR software such as Google Cloud Vision API is used. The input is the health check result file, and the output is text data. The server analyzes this text data and extracts the necessary health information. Specifically, information such as blood test results, blood pressure, and weight is extracted.

[0870] Step 3:

[0871] The server generates lifestyle-related questions for the user and sends them to the device. For example, questions include "How often do you exercise per week?" and "Do you smoke?" The input is a question template generated within the server, and the output is a list of questions sent to the device. This prepares the user to enter their own lifestyle information.

[0872] Step 4:

[0873] The user answers questions about their lifestyle habits using a device. The user enters their lifestyle information through a UI that displays input fields and options, and then presses the send button to send the answers to the server. The input is the answer data about the user's lifestyle habits, and the output is the answer data sent to the server.

[0874] Step 5:

[0875] The server analyzes the received lifestyle information and formats it into a standard format. The input is the lifestyle information submitted by the user, and the output is formatted lifestyle data. The server then integrates this lifestyle data with the health checkup result data. This integration process creates a consistent data set.

[0876] Step 6:

[0877] The server inputs the integrated data into the generative AI model. Specifically, the user's health checkup result data and lifestyle habit information are compiled into a single prompt statement, which is then provided to the generative AI model. An example of the prompt statement is, "Please generate appropriate health advice based on this user's health checkup result and lifestyle habit information." The input is the integrated data and prompt statement, and the output is health advice generated by the generative AI model.

[0878] Step 7:

[0879] The server sends the generated health advice to the user's device. The advice is in text format and may be visualized using graphs or charts. The input is the health advice generated by the generative AI model, and the output is the advice displayed on the user's device. The user can review this advice and use it as a reference for reviewing their lifestyle habits.

[0880] Step 8:

[0881] If new blood test results become available at a later date, the user uploads the data to the system again. The upload procedure is the same as in step 1. The input is the new blood test result file, and the output is the new data sent to the server.

[0882] Step 9:

[0883] The server reanalyzes the newly received data and updates the existing health advice. The new data is integrated with the existing health information and fed back into the generative AI model to generate new advice. This updated advice is provided based on the user's latest health status. The input is the new health check data and the existing dataset, and the output is the updated health advice.

[0884] (Application example 1)

[0885] 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."

[0886] Nowadays, systems that provide personalized health advice based on health checkup data and lifestyle information are widely used. However, autonomous vehicles lack the functionality to set driving modes and suggest rest breaks based on the user's health status. As a result, long driving hours may have a negative impact on the user's health. Therefore, there is a need to add autonomous driving support functions based on the user's health status to current systems to further protect the user's safety and health.

[0887] 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.

[0888] In this invention, the server includes means for receiving health checkup data, means for analyzing the health checkup data and extracting necessary health information, means for acquiring lifestyle information, means for generating health advice based on the acquired lifestyle information and the health information, means for providing the health advice to a user, and means for generating and setting a driving mode and rest suggestions based on the user's health condition in an autonomous vehicle, thereby enabling the provision of driving support and advice according to the user's health condition.

[0889] "Health checkup data" refers to data including the results of diagnoses and tests conducted at medical institutions or the like to evaluate the user's health condition.

[0890] "Lifestyle information" is information relating to the user's daily life, and includes data such as the amount of exercise, diet, smoking status, and alcohol consumption.

[0891] "Health advice" includes specific instructions and recommendations for improving and maintaining a user's health, which are generated based on health checkup data and lifestyle information.

[0892] An "autonomous vehicle" is a vehicle that can perform autonomous driving operations without requiring user input or driver intervention.

[0893] "Driving mode" refers to the driving settings and driving method of an autonomous vehicle, and includes a driving style that corresponds to the user's health condition.

[0894] The "rest suggestion" includes a suggestion to take a rest at a specific time based on the user's health condition.

[0895] "Analysis" refers to the process of analyzing health checkup data and lifestyle information and extracting necessary health information.

[0896] The present invention relates to a system that provides personalized driving support advice based on a user's health condition based on health checkup data and lifestyle information. This system optimizes the user's health and safety through conditional driving modes and rest suggestions, particularly for autonomous vehicles.

[0897] This system is configured as follows:

[0898] 1. Processing and analysis of health examination data

[0899] The user uploads a file (e.g., PDF format) of the health checkup results from their device to the server. This file contains health information such as blood test results, blood pressure, and weight.

[0900] The server uses OCR technology to convert the uploaded file into text data, and then analyzes and extracts the necessary health information. This analysis is performed using an OCR module and a health data analysis module.

[0901] 2. Collection of lifestyle information

[0902] The server generates lifestyle questions for the user and sends them to the device, such as "How often do you exercise per week?" and "Do you smoke?"

[0903] Users answer these questions using their devices and send the answers to the server, which then analyzes the received data and formats it into a standard format.

[0904] 3. Generating health advice

[0905] The server combines the health checkup results data and lifestyle information and inputs them into a generative AI model, which analyzes this data and generates driving support advice based on the user's health condition and anticipated risks.

[0906] For example, if your blood sugar level is slightly above the normal range, the generated advice might include, "We recommend setting your driving mode to Relaxed mode and taking a break every two hours."

[0907] 4. Providing driving support

[0908] The generated driving support advice is sent from the server to the autonomous vehicle's system, which then sets a driving mode based on the user's health condition and suggests breaks at appropriate times.

[0909] For example, users with high blood sugar levels are encouraged to drive in relaxed mode and are instructed to take breaks every two hours.

[0910] Below are some specific examples of prompts for generative AI models:

[0911] Example prompt sentence:

[0912] Analyze the user's health check results and lifestyle information to generate the following driving advice:

[0913] Blood glucose level: 110 mg / dL, blood pressure: 130 / 85 mmHg

[0914] Weekly exercise time: 30 minutes, Smoking: No

[0915] In this way, this system provides driving support advice tailored to each individual user based on health checkup data and lifestyle data, making it possible to provide driving assistance that takes into account the health status of users of autonomous vehicles.

[0916] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0917] Step 1:

[0918] The user uploads the health check result file from the terminal to the server.

[0919] Input: Health check result file (PDF format)

[0920] Output: Uploaded files on the server

[0921] Specific operation: The user uses the upload function of the device to send the health check result file to the server.

[0922] Step 2:

[0923] The server uses OCR technology to convert the uploaded file into text data, and then analyzes and extracts the necessary health information.

[0924] Input: Uploaded health check result file

[0925] Output: Extracted health information (text data)

[0926] Specific operation: The server uses an OCR module (e.g., Tesseract) to extract text from the PDF file, and then uses a health data analysis module to analyze and extract the required information.

[0927] Step 3:

[0928] The server generates questions about the user's lifestyle habits and sends them to the terminal.

[0929] Input: None

[0930] Output: Generated question (displayed on terminal)

[0931] Specific operation: The server uses the lifestyle information collection module to generate questions about the user and instructs the terminal to display them.

[0932] Step 4:

[0933] The user uses the terminal to answer questions about lifestyle habits and transmits the answers to the server.

[0934] Input: User's answer (lifestyle information)

[0935] Output: Lifestyle information sent to the server

[0936] Specific operation: The user answers questions displayed on the terminal and sends the answer data to the server.

[0937] Step 5:

[0938] The server analyzes the lifestyle information it receives and formats it into a standard format.

[0939] Input: User's answer (lifestyle information)

[0940] Output: lifestyle information in a standard format

[0941] Specific operation: The server uses the lifestyle information analysis module to analyze the user's response data and convert it into a standard format.

[0942] Step 6:

[0943] The server integrates health checkup result data and lifestyle information and inputs them into the generative AI model.

[0944] Input: Health checkup result data and lifestyle information in a standard format

[0945] Output: Generated health advice

[0946] How it works: The server integrates both sets of data and generates health advice using a generative AI model (e.g., GPT-3).

[0947] Step 7:

[0948] The server transmits the generated health advice to the autonomous vehicle's system.

[0949] Input: Generated health advice

[0950] Output: Advice sent to the autonomous vehicle system

[0951] Specific operation: The server sends advice data to the autonomous vehicle and instructs it to set the vehicle's driving mode and suggest rest breaks.

[0952] Step 8:

[0953] The self-driving vehicle will set a driving mode based on the user's health status and suggest rest breaks at appropriate times.

[0954] Input: Health advice sent from the server

[0955] Output: Notification of set operation mode and break suggestion

[0956] Specific operation: Based on the advice received from the server, the autonomous vehicle sets the driving mode to relaxation mode and suggests taking a break every two hours.

[0957] In this way, driving support based on the user's health information is realized through each processing step.

[0958] 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.

[0959] The present invention is a system that analyzes health checkup results and provides individualized health advice to users. By combining this system with an emotion engine, it is possible to grasp the user's emotions and provide appropriate health advice based on those emotions. This system includes a series of programs that receive health checkup data, analyze the data, acquire lifestyle information, generate health advice, and provide it. Furthermore, the system incorporates an emotion engine and includes a function for analyzing the user's emotions and adjusting the content of the advice.

[0960] (Natural language description of the program's processing)

[0961] Uploading and analyzing health checkup results

[0962] The user uploads the health check result file, which includes blood test results, blood pressure, weight, etc., to the system from their terminal.

[0963] The server receives the uploaded file and converts it into text data using OCR (optical character recognition) technology, then analyzes the data to extract the necessary health information.

[0964] Collection of lifestyle information

[0965] The server generates lifestyle-related questions for the user and sends them to the device, such as "How often do you exercise per week?" and "Do you smoke?"

[0966] The user answers these questions using a terminal, and the answers are sent from the terminal to the server.

[0967] The server analyzes the received response data and formats it into a standard format.

[0968] Emotion analysis using an emotion engine

[0969] The server transmits text contained in the response data collected from the user to the emotion engine.

[0970] The emotion engine uses natural language processing techniques to analyze the user's emotions (e.g., stress, joy, anxiety, etc.).

[0971] Generating health advice

[0972] The server integrates health checkup results data, lifestyle information, and emotion analysis results from the emotion engine, and inputs this integrated data into the generative AI.

[0973] The generative AI analyzes this data and generates health advice based on the user's health status and anticipated risks. For example, if blood sugar levels are slightly high, it will generate advice such as "improve the quality of your diet and exercise several times a week."

[0974] Providing health advice

[0975] The generated health advice is sent from the server to the user's device, where the user can check the advice and use it as a reference for reviewing their own lifestyle habits.

[0976] Re-upload and update of blood test results that will be known at a later date

[0977] The user uploads the blood test results, which are later known, to the system from their device. The new result file is sent to the server.

[0978] The server receives the new data, extracts the text data using OCR technology, and then integrates the new data with the existing data and reanalyzes it using the generative AI.

[0979] As a result of the reanalysis, the generative AI updates existing health advice and generates new advice based on the latest information.

[0980] (Example)

[0981] For example, suppose a user undergoes a health check and uploads the results to the system. The results show that the user's blood sugar level is slightly high, and the emotion engine analyzes the data to determine that the user is under high stress. The generative AI generates advice such as, "To manage stress, try to do aerobic exercise several times a week and eat a balanced diet. We also recommend incorporating relaxation activities."

[0982] In this way, this system provides health advice tailored to each individual user based on health checkup results, lifestyle habit data, and emotional data, providing comprehensive support for the user's health management.

[0983] The processing flow will be explained below.

[0984] Step 1:

[0985] The user obtains the health check result file and uploads it from the terminal using the system interface.

[0986] Step 2:

[0987] The device sends the selected file to the server, which contains health-related information such as blood test results, blood pressure, and weight.

[0988] Step 3:

[0989] The server receives and saves the uploaded file, and depending on the format of the saved file, converts it into text data using OCR (Optical Character Recognition) technology.

[0990] Step 4:

[0991] The server analyzes the extracted text data to identify and extract the necessary health information, such as blood sugar, cholesterol, and blood pressure.

[0992] Step 5:

[0993] The server generates questions about the user's lifestyle and sends them to the device, such as "How often do you exercise per week?" or "Do you smoke?"

[0994] Step 6:

[0995] The terminal displays the generated questions to the user, who answers the questions and enters the answers using the terminal.

[0996] Step 7:

[0997] The user's answers are sent from the device to the server, which then analyzes the received answer data and formats it into a standard format.

[0998] Step 8:

[0999] The server sends the user's response data to the emotion engine, which uses natural language processing technology to analyze the user's emotions (e.g., stress, joy, anxiety, etc.).

[1000] Step 9:

[1001] The analysis results of the emotion engine are returned to the server, which then integrates the received emotion analysis results with the lifestyle information and health checkup results.

[1002] Step 10:

[1003] The server inputs the integrated data into the generation AI, which then analyzes the data. Based on the user's health and emotional state, the AI ​​generates appropriate health advice. For example, if the user's blood sugar level is slightly high and stress is high, the AI ​​generates advice such as "Improve the quality of your diet and incorporate relaxation activities to manage stress."

[1004] Step 11:

[1005] The server compiles the generated health advice into a report and sends it to the device, which includes the user's current health status and recommended actions.

[1006] Step 12:

[1007] The user can then use the device to check the sent report, and based on the report, review their lifestyle habits and plan specific actions to improve them.

[1008] Step 13:

[1009] At a later date, the user uploads the newly discovered blood test results to the system from the terminal again, and the terminal sends the new result file to the server.

[1010] Step 14:

[1011] The server receives the new data, converts it into text data using OCR technology again, then integrates the new data with the existing data and reanalyzes it using the generative AI.

[1012] Step 15:

[1013] Generative AI updates existing health advice based on new data and generates new advice that reflects the latest information.

[1014] Step 16:

[1015] The server then compiles new health advice in the form of a report and sends it to the device, where the user can check the updated report and use it to further improve their lifestyle habits.

[1016] Example 2

[1017] 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."

[1018] Conventional systems generate health advice based on health checkup data and lifestyle information, but because they do not take the user's emotional state into account, it is difficult to provide appropriate advice tailored to each individual's condition.In addition, the system lacks the functionality to update health advice to reflect blood test results that are discovered at a later date, which means that advice based on the latest health information is not provided.

[1019] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving health checkup data, means for analyzing the health checkup data and extracting necessary health information, means for acquiring lifestyle habit information, means for generating health advice based on the acquired lifestyle habit information and the health information, means for providing the health advice to the user, means for collecting and analyzing emotional data, and means for generating health advice by integrating the emotional data, lifestyle habit information, and health information. This makes it possible to provide individual health advice that takes the user's emotional state into consideration, and to update the advice to reflect the latest health information that becomes available at a later date.

[1020] "Health checkup data" refers to test result data obtained during regular health checkups that users undergo. This includes information such as blood test results, blood pressure, and weight.

[1021] "Analysis" refers to processing and analyzing the received data to extract necessary information. This processing includes converting it to text data and formatting the data.

[1022] "Health information" refers to information about the user's health condition extracted through analysis. Specifically, it includes indicators such as blood sugar level, blood pressure, and weight.

[1023] "Lifestyle information" is data related to the user's daily life, including exercise frequency, dietary habits, smoking status, etc.

[1024] "Health advice" is specific advice for maintaining or improving the user's health, generated based on health checkup data and lifestyle information.

[1025] "Providing" refers to communicating the generated health advice to the user, often via an electronic device.

[1026] "Emotional data" refers to data that indicates the user's emotional state. This data is analyzed from the user's text input and responses.

[1027] "Sentiment analysis" refers to analyzing a user's emotional state using emotional data, using natural language processing techniques.

[1028] "Integration" means combining multiple different data sets to create a single, consistent data set.

[1029] "Generative AI" is a system that uses artificial intelligence to analyze data and automatically generate appropriate health advice.

[1030] "Updating" means bringing existing information and advice up to date with new data.

[1031] The present invention is a system that analyzes health checkup results and provides individual health advice to users, and by combining it with an emotion engine, it grasps the user's emotions and provides appropriate health advice according to those emotions. Specific embodiments of the system are described in detail below.

[1032] This system mainly consists of a server, a terminal, and a user. The server receives and analyzes health checkup data and extracts health information. Specifically, it uses Microsoft Azure's OCR technology to convert the health checkup results into text data, and then analyzes the data using Python analysis libraries (e.g., Pandas, Numpy). The necessary health information is then extracted. The terminal functions as an interface for users to upload health checkup result files.

[1033] The server then begins the process of collecting lifestyle information. Using the Django framework, it generates a questionnaire for the user and sends it to the device. The user answers these questions through the device, and the response data is sent to the server. The server then uses the Python Pandas library to format the received data and standardize it.

[1034] For emotion analysis using the emotion engine, the server sends the user's response data to IBM Watson Natural Language Understanding, which analyzes the user's emotional state (e.g., stress, joy, anxiety). The emotion analysis results are sent back to the server and used to generate health advice.

[1035] The generative AI model, OpenAI GPT-4, integrates health checkup data, lifestyle information, and emotion analysis results to generate personalized health advice. The server then sends the generated health advice to the user's device, where the user can review it. This allows the user to understand their own health status and obtain specific guidelines for improvement.

[1036] Furthermore, if new data, such as blood test results, becomes available at a later date, the user can upload that data again to the system. The server then analyzes the new data again using Microsoft Azure's OCR technology, combines it with the existing data, and has the generative AI perform a re-analysis. This generates health advice based on the latest information and provides it to the user.

[1037] Specific examples

[1038] For example, suppose a user undergoes a health check and uploads the results to the system. If the blood sugar level is 105 mg / dL and the emotion engine analysis reveals a high stress level, the generative AI will generate advice such as, "To manage stress, we recommend aerobic exercise, a balanced diet, and relaxation activities."

[1039] Prompt Sentence Examples

[1040] Generate personalized health advice based on the following health checkup data, lifestyle information, and sentiment analysis results:

[1041] Health checkup result data:

[1042] Blood glucose level: 105 mg / dL

[1043] Blood pressure: 130 / 85 mmHg

[1044] Weight: 70 kg

[1045] Height: 170 cm

[1046] Lifestyle information:

[1047] Exercise frequency: twice a week

[1048] Smoking: No

[1049] Diet: Balanced diet

[1050] Emotion analysis results:

[1051] Stress level: High

[1052] Joy: Medium

[1053] Use this information to provide appropriate health advice to your users.

[1054] This system provides health advice tailored to each individual user based on the user's health checkup results, lifestyle data, and emotional data, providing comprehensive support for the user's health management.

[1055] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1056] Step 1:

[1057] The user uploads the health check result file from the terminal to the system. The user selects the health check result file (e.g. PDF, image) through the terminal's web browser and clicks the upload button. The uploaded file becomes the input and is sent to the server.

[1058] Step 2:

[1059] The server receives the file and converts it to text data using OCR technology. The server calls the Microsoft Azure OCR service to convert the uploaded file to text data. The input is the file received in step 1, and the output is the converted text data.

[1060] Step 3:

[1061] The server analyzes the health checkup data and extracts the necessary health information. The server uses Python analysis libraries (e.g., Pandas, Numpy) to analyze the text data and extract health information such as blood glucose levels, blood pressure, and weight. The input is text data converted by OCR, and the output is the extracted health information.

[1062] Step 4:

[1063] The server generates questions about the user's lifestyle and sends them to the device as a web form. The server uses the Django framework to generate questions about the user's lifestyle and sends them to the device as a web form. The input is a fixed question template, and the output is the generated web form.

[1064] Step 5:

[1065] The user answers the generated questions using the terminal. The user inputs answers to the questions displayed on the terminal's browser. The input is the user's answer data, which is sent from the terminal to the server.

[1066] Step 6:

[1067] The server analyzes the received response data and formats it into a standard format. The server uses the Python Pandas library to format the received data and unify it into a standard format. The input is the user's response data, and the output is formatted lifestyle habit information.

[1068] Step 7:

[1069] The server sends the received response data to the emotion engine. The server then calls the IBM Watson Natural Language Understanding service and sends the user's response data. The input is lifestyle habit information, and the output is the emotion analysis results.

[1070] Step 8:

[1071] The emotion engine analyzes the user's emotions and returns the analysis results to the server. The emotion engine uses natural language processing technology to analyze the user's emotional state (e.g., stress, elation, anxiety) and returns the results to the server. The input is lifestyle information, and the output is the emotion analysis results.

[1072] Step 9:

[1073] The server integrates the health checkup result data, lifestyle information, and emotion analysis results, and inputs them into the generative AI model. After integrating these data, the server inputs them into the OpenAI GPT-4 model. The inputs are the health checkup data, lifestyle information, and emotion analysis results, and the output is the input data for the generative AI model.

[1074] Step 10:

[1075] The generative AI analyzes the input data and generates health advice. The OpenAI GPT-4 model analyzes the input data and generates personalized health advice for the user. The input is the synthesized data, and the output is the generated health advice.

[1076] Step 11:

[1077] The server sends the generated health advice to the terminal. The server sends the generated health advice to the user's terminal, and the user uses the terminal to check the advice. The input is the generated health advice, and the output is the health advice displayed on the user's terminal.

[1078] Step 12:

[1079] The user uploads the blood test results, which will be known at a later date, from their terminal to the system again. The user uploads a new test result file from their terminal to the system, which is then sent to the server. The input is the new test result file, and the output is the file sent to the server.

[1080] Step 13:

[1081] The server receives the new data and extracts the text data using OCR technology again. The server then converts the new test result file into text data using Microsoft Azure's OCR service again. The input is the new test result file, and the output is text data.

[1082] Step 14:

[1083] The server integrates the new data with the existing data and reanalyzes it into the generative AI model. The server integrates the new data with the existing health information and inputs it into the OpenAI GPT-4 model again for analysis. The input is the integrated data, and the output is updated health advice.

[1084] Step 15:

[1085] The server sends the health advice generated as a result of the reanalysis to the user's device. The server then sends the updated health advice to the user's device, and the user uses the device to check the new advice. The input is the updated health advice, and the output is the new health advice displayed on the user's device.

[1086] (Application example 2)

[1087] 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."

[1088] In modern society, personal health management is important, but opportunities to receive appropriate advice based on health checkup results and lifestyle habits are limited. Furthermore, conventional health management systems rarely take into account the user's emotional state, resulting in issues with the accuracy and appropriateness of individual health advice. Furthermore, the advice provided cannot be visually confirmed immediately, making it difficult for users to use. There is a need to solve these issues and provide more effective and personalized health advice.

[1089] 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.

[1090] In this invention, the server includes means for receiving health checkup data, means for analyzing the health checkup data to extract necessary health information, means for acquiring lifestyle habit information, means for analyzing the emotional data, means for integrating the emotional data, the health information, and the lifestyle habit information and generating personalized health advice using a generative AI model, and means including a visualization device for visually presenting the generated health advice. This allows the personalized health advice to be provided taking into account the user's emotional state, and allows for quick visual confirmation.

[1091] "Medical checkup data" refers to medical data such as blood test results, blood pressure, and weight provided as a result of a medical checkup conducted by a user.

[1092] "Lifestyle information" refers to data such as the user's exercise habits, dietary habits, smoking and drinking habits in their daily lives.

[1093] "Emotion data" is data that indicates the user's emotional state, and is emotional information such as stress, joy, and anxiety extracted using natural language processing technology and image recognition technology.

[1094] A "generative artificial intelligence model" is an artificial intelligence system that analyzes a user's health and emotional state based on collected data and generates personalized health advice.

[1095] A "visual device" is a device for visually presenting the generated health advice to a user, and includes visual display devices such as head-mounted displays and smart glasses.

[1096] "Health advice" refers to specific suggestions and advice for improving the user's health, generated by a generative artificial intelligence model based on health checkup data, lifestyle information, and emotional data.

[1097] The present invention relates to a system that integrates health checkup data, lifestyle information, and user emotion data to provide personalized health advice. Specific embodiments for carrying out the present invention will be described below.

[1098] The entire system mainly consists of a server, a terminal, and smart glasses (visual device).

[1099] Receiving and analyzing health checkup data

[1100] The server first receives the health checkup data uploaded by the user via their device. This data includes information such as blood test results, blood pressure, and weight. The server then uses OCR technology to convert this data into text data and extract the necessary health information. Standard OCR software is used for the OCR technology.

[1101] Obtaining lifestyle information

[1102] The server generates lifestyle questions for the user and sends them to the device. Examples of questions include, "How often do you exercise per week?" or "Do you smoke?" The user answers the questions through the device, and the data is sent to the server. The server formats the data into a standard format.

[1103] Emotional Data Analysis

[1104] The smart glasses' built-in camera captures the user's facial expressions and tone of voice in real time, and uses emotion analysis software to analyze the user's emotional data, which includes stress, joy, anxiety, etc. This analysis is performed using an emotion analysis engine called EmotionEngine.

[1105] Generating health advice

[1106] The server integrates health checkup results, lifestyle information, and emotional data, and generates personalized health advice using a generative AI model. The generative AI model analyzes this data and provides advice based on the user's health and emotional state. For example, if blood sugar levels are high, it suggests exercise and dietary recommendations for stress management.

[1107] Example prompt sentence:

[1108] "User health data: {health_data}, Emotion: {emotion}. Provide personalized health advice based on these details."

[1109] Providing health advice

[1110] The generated health advice is sent from the server to the smart glasses, where users can visually confirm the advice in real time through the lenses of the smart glasses, enabling them to immediately take measures based on their health status.

[1111] Specific use cases

[1112] For example, after a user undergoes a health check, the results are uploaded to the system. The server analyzes the results and finds that the user's blood sugar level is slightly high. At the same time, facial expression data captured by the smart glasses reveals that the user is feeling stressed. Based on this information, the generative AI model generates advice such as, "To manage stress, try to do aerobic exercise several times a week and eat a balanced diet. We also recommend incorporating relaxation activities," and displays it on the lenses of the smart glasses.

[1113] In this way, the system of the present invention provides individual health advice based on the results of health checkups, lifestyle information, and emotional data, and provides comprehensive support for the user's health management.

[1114] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1115] Step 1:

[1116] Receiving health checkup data

[1117] The user uploads the results of their health checkup to the system from their device. The health checkup results include information such as blood test results, blood pressure, and weight. This data is sent to the server. The input is the health checkup data, and the output is text data stored on the server.

[1118] Step 2:

[1119] Analysis of health checkup data

[1120] The server uses OCR technology to convert the uploaded health checkup results into text data and extract the necessary health information. This information includes blood glucose levels, cholesterol levels, blood pressure, etc. The input is the health checkup data, and the output is the extracted health information. Specifically, the OCR software processes the scanned data and generates structured data.

[1121] Step 3:

[1122] Obtaining lifestyle information

[1123] The server generates lifestyle-related questions for the user and sends them to the device. For example, questions include, "How often do you exercise per week?" and "Do you smoke?" The user answers these questions via the device. The input is the lifestyle-related questions and the user's answers, and the output is lifestyle information. The server analyzes these answer data and formats them into a standard format.

[1124] Step 4:

[1125] Emotional Data Analysis

[1126] The camera built into the smart glasses captures the user's facial expressions and tone of voice in real time, and uses emotion analysis software to analyze the user's emotional data. Emotional data includes stress, joy, anxiety, etc. The input is the camera footage, and the output is the analyzed emotional data. Specifically, the EmotionEngine analyzes facial expressions and generates quantitative emotional data.

[1127] Step 5:

[1128] Integrating data and generating advice

[1129] The server integrates health checkup result data, lifestyle information, and emotional data. The integrated data is input into a generative AI model, which generates personalized health advice based on the user's health and emotional state. The input is the integrated data, and the output is health advice. Specifically, the AI ​​model analyzes the data and generates personalized advice based on a prompt. An example of the prompt is, "User health data: {health_data}, Emotion: {emotion}. Provide personalized health advice based on these details."

[1130] Step 6:

[1131] Providing health advice

[1132] The generated health advice is sent from the server to the smart glasses. The user can visually confirm the advice in real time through the lenses of the smart glasses. The input is the generated health advice, and the output is the user's visual confirmation. Specifically, the display function of the smart glasses displays the advice.

[1133] 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.

[1134] 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.

[1135] 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.

[1136] [Fourth embodiment]

[1137] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1138] 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.

[1139] 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).

[1140] 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.

[1141] 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.

[1142] 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).

[1143] 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.

[1144] 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.

[1145] 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.

[1146] 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.

[1147] 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.

[1148] 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.

[1149] 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."

[1150] The present invention is a system that analyzes health checkup results and provides individualized health advice to users. This system is implemented based on a series of programs that receive health checkup data, analyze the data, acquire lifestyle information, generate health advice, and provide it.

[1151] (Natural language description of the program's processing)

[1152] Uploading and analyzing health checkup results

[1153] A user uploads a file of their health checkup results to the system from their device. This file contains information such as blood test results, blood pressure, and weight.

[1154] The server receives the uploaded file, converts it into text data using OCR (optical character recognition) technology, and analyzes the data to extract the necessary health information.

[1155] Collection of lifestyle information

[1156] The server generates lifestyle-related questions for the user and sends them to the device, such as "How often do you exercise per week?" and "Do you smoke?"

[1157] Users answer these questions using their devices and send the answers to the server, which then analyzes the received data and formats it into a standard format.

[1158] Generating health advice

[1159] The server combines the health checkup results data and lifestyle information and inputs them into the AI ​​generator, which analyzes this data and generates health advice based on the user's health status and anticipated risks.

[1160] For example, if your blood sugar level is slightly above the normal range, the AI ​​will generate advice such as, "I recommend improving the quality of your diet and doing aerobic exercise several times a week."

[1161] Providing health advice

[1162] The generated health advice is sent from the server to the user's device, where the user can check the advice and use it as a reference for reviewing their own lifestyle habits.

[1163] Re-upload and update of blood test results that will be known at a later date

[1164] The user uploads the blood test results, which are later determined, back into the system.

[1165] The server receives and analyzes this new data, updating existing health advice and providing new advice to the user based on their current situation.

[1166] (Example)

[1167] For example, suppose a user undergoes a health checkup and uploads the results to the system. The results show that their blood sugar level is slightly above the normal range, so they follow the AI's advice to review their diet and start exercising. If they then undergo another health checkup a few weeks later and re-upload the results, they will receive advice based on the new data that reads, "Your blood sugar level is improving. Please continue to maintain your current lifestyle habits."

[1168] In this way, this system supports users' health management by providing health advice tailored to each individual user based on health checkup results and lifestyle data.

[1169] The processing flow will be explained below.

[1170] Step 1:

[1171] The user obtains the results of the health check and uploads the file from the terminal using the system interface.

[1172] Step 2:

[1173] The device sends the uploaded file to a server, which contains health-related information such as blood test results, blood pressure, and weight.

[1174] Step 3:

[1175] The server receives and stores the uploaded file, then uses OCR (Optical Character Recognition) technology to extract text data from the file.

[1176] Step 4:

[1177] The server analyzes the extracted text data and extracts the necessary health information, such as blood sugar levels, cholesterol levels, and blood pressure.

[1178] Step 5:

[1179] The server generates lifestyle questions for the user, such as "How often do you exercise per week?" and "Do you smoke?"

[1180] Step 6:

[1181] The terminal displays the generated questions to the user, who then answers the questions about their lifestyle habits.

[1182] Step 7:

[1183] The user's answers are sent from the device to the server, which receives them and formats them into a standard format.

[1184] Step 8:

[1185] The server integrates the acquired health checkup data and lifestyle information, and inputs this integrated data into the generation AI.

[1186] Step 9:

[1187] The generative AI analyzes the integrated data and generates health advice based on the user's health status and anticipated risks. For example, if blood sugar levels are slightly high, it will generate advice such as "improve the quality of your diet and exercise several times a week."

[1188] Step 10:

[1189] The server compiles the generated health advice into a report for the user, which includes necessary advice and precautions.

[1190] Step 11:

[1191] The server then sends the generated report to the device, where the user can check the report and understand their own health condition and lifestyle improvements.

[1192] Step 12:

[1193] The user uploads the blood test results, which are later known, from the terminal to the system again. The terminal then sends the new result file to the server.

[1194] Step 13:

[1195] The server receives the new data, extracts the text data using OCR technology again, merges the new data with the existing data, and reanalyzes it using the generative AI.

[1196] Step 14:

[1197] Generative AI updates existing health advice based on new data, generating new advice based on the latest information.

[1198] Step 15:

[1199] The server then compiles the new health advice into a report for the user and sends it to the user's device, where the user can view the updated report.

[1200] Example 1

[1201] 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."

[1202] With conventional health management systems, it was difficult to individually manage and analyze users' health checkup data and lifestyle information, making it difficult to efficiently provide appropriate health advice based on that data. In particular, reanalysis of health checkup results and updating of advice based on that data were not performed in real time, which meant that users were unable to manage their health in a timely manner, and it was difficult to expect effective health improvement.

[1203] 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.

[1204] In this invention, the server includes means for receiving health checkup data, means for converting the health checkup data into text data using optical character recognition technology and analyzing it to extract necessary health information, means for interactively acquiring lifestyle habit information, means for integrating the acquired lifestyle habit information and the health information and generating health advice using a generative artificial intelligence model, and means for providing the health advice to the user. This enables the centralized management of a user's health checkup data and lifestyle habit information and the provision of health advice in real time.

[1205] "Health checkup data" refers to data including blood test results, blood pressure, weight, and other biological information obtained during a health checkup.

[1206] "Optical character recognition technology" is a technology that analyzes characters in an image and converts them into digital text.

[1207] "Text data" is digital data expressed as a string of characters.

[1208] "Analysis" is the process of examining data in detail and extracting the necessary information.

[1209] "Health information" is data that indicates health status based on health checkups and lifestyle habits.

[1210] "Lifestyle information" is data relating to the habits of the user in their daily life, including diet, exercise, smoking, and the like.

[1211] "Dialogue" is a method of obtaining information through an exchange of questions and answers.

[1212] "Integration" means bringing together multiple pieces of data into a consistent format.

[1213] A "generative artificial intelligence model" is a model that uses artificial intelligence technology to generate predictions or suggestions based on specific input data.

[1214] "Health advice" means instructions or suggestions provided to improve a user's health.

[1215] "User" means an individual who uses this system.

[1216] A "server" is a computer that processes information and manages data on a network.

[1217] "Terminal" means the device through which a user accesses the system.

[1218] "Real time" means that data processing and information provision are carried out without delay.

[1219] The present invention relates to a system that analyzes health checkup results and provides individualized health advice to users. This system is implemented based on a series of programs that receive health checkup data, analyze the data, acquire lifestyle information, generate health advice, and provide it.

[1220] System configuration and operation

[1221] This system consists of a server and a terminal. Users can access the system using the terminal and input their health checkup results and lifestyle information. The server processes this data and provides appropriate health advice to the user.

[1222] Uploading and analyzing health checkup results

[1223] First, the user uploads a file of their health checkup results to the system from their device. This health checkup result includes information such as blood test results, blood pressure, and weight. The server receives the uploaded file and converts it into text data using OCR (optical character recognition) technology. This OCR technology uses common cloud-based OCR software (e.g., optical character recognition API). The converted text data is then analyzed to extract the necessary health information.

[1224] Collection of lifestyle information

[1225] The server then generates lifestyle-related questions for the user and sends them to the device. For example, questions include, "How often do you exercise per week?" and "Do you smoke?" The user answers these questions using the device and sends the answers to the server. The server then analyzes the received answers and formats them into a standard format.

[1226] Generating health advice

[1227] The server integrates the health checkup result data and lifestyle information and inputs them into a generative AI model. This generative AI model, for example, uses natural language processing (NLP) technology (e.g., a generative language model). The generative AI model analyzes this data and generates health advice based on the user's health status and expected risks.

[1228] Examples of specific prompts that can be used include:

[1229] "Generate personalized health advice based on the user's health checkup results data and lifestyle information. For example, if their blood sugar level exceeds the standard value, include that number and specific advice for improvement."

[1230] Providing health advice

[1231] The generated health advice is sent from the server to the user's device, where the user can check the advice and use it as a reference for reviewing their own lifestyle habits.

[1232] Re-upload and update of blood test results that will be known at a later date

[1233] Furthermore, when the user uploads their blood test results back into the system at a later date, the server receives and analyzes this new data, updating existing health advice and providing new advice to the user based on their current situation.

[1234] Specific examples

[1235] For example, suppose a user undergoes a health checkup and uploads the results data to the system. Because their blood sugar level is slightly above the normal range, they follow the AI's advice to review their diet and start exercising. If they then undergo another health checkup a few weeks later and re-upload the results, they will receive advice based on the new data that reads, "Your blood sugar level is improving. Please continue to maintain your current lifestyle habits."

[1236] In this way, this system supports users' health management by providing health advice tailored to each individual user based on health checkup results and lifestyle data.

[1237] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1238] Step 1:

[1239] The user uses a terminal to upload a health checkup result file to the system. The health checkup result file contains information such as blood test results, blood pressure, and weight. The user clicks the file selection button on the browser, selects the appropriate file, and completes the upload. The input is the health checkup result file, and the output is that the uploaded file is sent to the server.

[1240] Step 2:

[1241] The server receives the uploaded health check result file. The received file is converted into text data using OCR technology. OCR software such as Google Cloud Vision API is used. The input is the health check result file, and the output is text data. The server analyzes this text data and extracts the necessary health information. Specifically, information such as blood test results, blood pressure, and weight is extracted.

[1242] Step 3:

[1243] The server generates lifestyle-related questions for the user and sends them to the device. For example, questions include "How often do you exercise per week?" and "Do you smoke?" The input is a question template generated within the server, and the output is a list of questions sent to the device. This prepares the user to enter their own lifestyle information.

[1244] Step 4:

[1245] The user answers questions about their lifestyle habits using a device. The user enters their lifestyle information through a UI that displays input fields and options, and then presses the send button to send the answers to the server. The input is the answer data about the user's lifestyle habits, and the output is the answer data sent to the server.

[1246] Step 5:

[1247] The server analyzes the received lifestyle information and formats it into a standard format. The input is the lifestyle information submitted by the user, and the output is formatted lifestyle data. The server then integrates this lifestyle data with the health checkup result data. This integration process creates a consistent data set.

[1248] Step 6:

[1249] The server inputs the integrated data into the generative AI model. Specifically, the user's health checkup result data and lifestyle habit information are compiled into a single prompt statement, which is then provided to the generative AI model. An example of the prompt statement is, "Please generate appropriate health advice based on this user's health checkup result and lifestyle habit information." The input is the integrated data and prompt statement, and the output is health advice generated by the generative AI model.

[1250] Step 7:

[1251] The server sends the generated health advice to the user's device. The advice is in text format and may be visualized using graphs or charts. The input is the health advice generated by the generative AI model, and the output is the advice displayed on the user's device. The user can review this advice and use it as a reference for reviewing their lifestyle habits.

[1252] Step 8:

[1253] If new blood test results become available at a later date, the user uploads the data to the system again. The upload procedure is the same as in step 1. The input is the new blood test result file, and the output is the new data sent to the server.

[1254] Step 9:

[1255] The server reanalyzes the newly received data and updates the existing health advice. The new data is integrated with the existing health information and fed back into the generative AI model to generate new advice. This updated advice is provided based on the user's latest health status. The input is the new health check data and the existing dataset, and the output is the updated health advice.

[1256] (Application example 1)

[1257] 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."

[1258] Nowadays, systems that provide personalized health advice based on health checkup data and lifestyle information are widely used. However, autonomous vehicles lack the functionality to set driving modes and suggest rest breaks based on the user's health status. As a result, long driving hours may have a negative impact on the user's health. Therefore, there is a need to add autonomous driving support functions based on the user's health status to current systems to further protect the user's safety and health.

[1259] 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.

[1260] In this invention, the server includes means for receiving health checkup data, means for analyzing the health checkup data and extracting necessary health information, means for acquiring lifestyle information, means for generating health advice based on the acquired lifestyle information and the health information, means for providing the health advice to a user, and means for generating and setting a driving mode and rest suggestions based on the user's health condition in an autonomous vehicle, thereby enabling the provision of driving support and advice according to the user's health condition.

[1261] "Health checkup data" refers to data including the results of diagnoses and tests conducted at medical institutions or the like to evaluate the user's health condition.

[1262] "Lifestyle information" is information relating to the user's daily life, and includes data such as the amount of exercise, diet, smoking status, and alcohol consumption.

[1263] "Health advice" includes specific instructions and recommendations for improving and maintaining a user's health, which are generated based on health checkup data and lifestyle information.

[1264] An "autonomous vehicle" is a vehicle that can perform autonomous driving operations without requiring user input or driver intervention.

[1265] "Driving mode" refers to the driving settings and driving method of an autonomous vehicle, and includes a driving style that corresponds to the user's health condition.

[1266] The "rest suggestion" includes a suggestion to take a rest at a specific time based on the user's health condition.

[1267] "Analysis" refers to the process of analyzing health checkup data and lifestyle information and extracting necessary health information.

[1268] The present invention relates to a system that provides personalized driving support advice based on a user's health condition based on health checkup data and lifestyle information. This system optimizes the user's health and safety through conditional driving modes and rest suggestions, particularly for autonomous vehicles.

[1269] This system is configured as follows:

[1270] 1. Processing and analysis of health examination data

[1271] The user uploads a file (e.g., PDF format) of the health checkup results from their device to the server. This file contains health information such as blood test results, blood pressure, and weight.

[1272] The server uses OCR technology to convert the uploaded file into text data, and then analyzes and extracts the necessary health information. This analysis is performed using an OCR module and a health data analysis module.

[1273] 2. Collection of lifestyle information

[1274] The server generates lifestyle questions for the user and sends them to the device, such as "How often do you exercise per week?" and "Do you smoke?"

[1275] Users answer these questions using their devices and send the answers to the server, which then analyzes the received data and formats it into a standard format.

[1276] 3. Generating health advice

[1277] The server combines the health checkup results data and lifestyle information and inputs them into a generative AI model, which analyzes this data and generates driving support advice based on the user's health condition and anticipated risks.

[1278] For example, if your blood sugar level is slightly above the normal range, the generated advice might include, "We recommend setting your driving mode to Relaxed mode and taking a break every two hours."

[1279] 4. Providing driving support

[1280] The generated driving support advice is sent from the server to the autonomous vehicle's system, which then sets a driving mode based on the user's health condition and suggests breaks at appropriate times.

[1281] For example, users with high blood sugar levels are encouraged to drive in relaxed mode and are instructed to take breaks every two hours.

[1282] Below are some specific examples of prompts for generative AI models:

[1283] Example prompt sentence:

[1284] Analyze the user's health check results and lifestyle information to generate the following driving advice:

[1285] Blood glucose level: 110 mg / dL, blood pressure: 130 / 85 mmHg

[1286] Weekly exercise time: 30 minutes, Smoking: No

[1287] In this way, this system provides driving support advice tailored to each individual user based on health checkup data and lifestyle data, making it possible to provide driving assistance that takes into account the health status of users of autonomous vehicles.

[1288] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1289] Step 1:

[1290] The user uploads the health check result file from the terminal to the server.

[1291] Input: Health check result file (PDF format)

[1292] Output: Uploaded files on the server

[1293] Specific operation: The user uses the upload function of the device to send the health check result file to the server.

[1294] Step 2:

[1295] The server uses OCR technology to convert the uploaded file into text data, and then analyzes and extracts the necessary health information.

[1296] Input: Uploaded health check result file

[1297] Output: Extracted health information (text data)

[1298] Specific operation: The server uses an OCR module (e.g., Tesseract) to extract text from the PDF file, and then uses a health data analysis module to analyze and extract the required information.

[1299] Step 3:

[1300] The server generates questions about the user's lifestyle habits and sends them to the terminal.

[1301] Input: None

[1302] Output: Generated question (displayed on terminal)

[1303] Specific operation: The server uses the lifestyle information collection module to generate questions about the user and instructs the terminal to display them.

[1304] Step 4:

[1305] The user uses the terminal to answer questions about lifestyle habits and transmits the answers to the server.

[1306] Input: User's answer (lifestyle information)

[1307] Output: Lifestyle information sent to the server

[1308] Specific operation: The user answers questions displayed on the terminal and sends the answer data to the server.

[1309] Step 5:

[1310] The server analyzes the lifestyle information it receives and formats it into a standard format.

[1311] Input: User's answer (lifestyle information)

[1312] Output: lifestyle information in a standard format

[1313] Specific operation: The server uses the lifestyle information analysis module to analyze the user's response data and convert it into a standard format.

[1314] Step 6:

[1315] The server integrates health checkup result data and lifestyle information and inputs them into the generative AI model.

[1316] Input: Health checkup result data and lifestyle information in a standard format

[1317] Output: Generated health advice

[1318] How it works: The server integrates both sets of data and generates health advice using a generative AI model (e.g., GPT-3).

[1319] Step 7:

[1320] The server transmits the generated health advice to the autonomous vehicle's system.

[1321] Input: Generated health advice

[1322] Output: Advice sent to the autonomous vehicle system

[1323] Specific operation: The server sends advice data to the autonomous vehicle and instructs it to set the vehicle's driving mode and suggest rest breaks.

[1324] Step 8:

[1325] The self-driving vehicle will set a driving mode based on the user's health status and suggest rest breaks at appropriate times.

[1326] Input: Health advice sent from the server

[1327] Output: Notification of set operation mode and break suggestion

[1328] Specific operation: Based on the advice received from the server, the autonomous vehicle sets the driving mode to relaxation mode and suggests taking a break every two hours.

[1329] In this way, driving support based on the user's health information is realized through each processing step.

[1330] 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.

[1331] The present invention is a system that analyzes health checkup results and provides individualized health advice to users. By combining this system with an emotion engine, it is possible to grasp the user's emotions and provide appropriate health advice based on those emotions. This system includes a series of programs that receive health checkup data, analyze the data, acquire lifestyle information, generate health advice, and provide it. Furthermore, the system incorporates an emotion engine and includes a function for analyzing the user's emotions and adjusting the content of the advice.

[1332] (Natural language description of the program's processing)

[1333] Uploading and analyzing health checkup results

[1334] The user uploads the health check result file, which includes blood test results, blood pressure, weight, etc., to the system from their terminal.

[1335] The server receives the uploaded file and converts it into text data using OCR (optical character recognition) technology, then analyzes the data to extract the necessary health information.

[1336] Collection of lifestyle information

[1337] The server generates lifestyle-related questions for the user and sends them to the device, such as "How often do you exercise per week?" and "Do you smoke?"

[1338] The user answers these questions using a terminal, and the answers are sent from the terminal to the server.

[1339] The server analyzes the received response data and formats it into a standard format.

[1340] Emotion analysis using an emotion engine

[1341] The server transmits text contained in the response data collected from the user to the emotion engine.

[1342] The emotion engine uses natural language processing techniques to analyze the user's emotions (e.g., stress, joy, anxiety, etc.).

[1343] Generating health advice

[1344] The server integrates health checkup results data, lifestyle information, and emotion analysis results from the emotion engine, and inputs this integrated data into the generative AI.

[1345] The generative AI analyzes this data and generates health advice based on the user's health status and anticipated risks. For example, if blood sugar levels are slightly high, it will generate advice such as "improve the quality of your diet and exercise several times a week."

[1346] Providing health advice

[1347] The generated health advice is sent from the server to the user's device, where the user can check the advice and use it as a reference for reviewing their own lifestyle habits.

[1348] Re-upload and update of blood test results that will be known at a later date

[1349] The user uploads the blood test results, which are later known, to the system from their device. The new result file is sent to the server.

[1350] The server receives the new data, extracts the text data using OCR technology, and then integrates the new data with the existing data and reanalyzes it using the generative AI.

[1351] As a result of the reanalysis, the generative AI updates existing health advice and generates new advice based on the latest information.

[1352] (Example)

[1353] For example, suppose a user undergoes a health check and uploads the results to the system. The results show that the user's blood sugar level is slightly high, and the emotion engine analyzes the data to determine that the user is under high stress. The generative AI generates advice such as, "To manage stress, try to do aerobic exercise several times a week and eat a balanced diet. We also recommend incorporating relaxation activities."

[1354] In this way, this system provides health advice tailored to each individual user based on health checkup results, lifestyle habit data, and emotional data, providing comprehensive support for the user's health management.

[1355] The processing flow will be explained below.

[1356] Step 1:

[1357] The user obtains the health check result file and uploads it from the terminal using the system interface.

[1358] Step 2:

[1359] The device sends the selected file to the server, which contains health-related information such as blood test results, blood pressure, and weight.

[1360] Step 3:

[1361] The server receives and saves the uploaded file, and depending on the format of the saved file, converts it into text data using OCR (Optical Character Recognition) technology.

[1362] Step 4:

[1363] The server analyzes the extracted text data to identify and extract the necessary health information, such as blood sugar, cholesterol, and blood pressure.

[1364] Step 5:

[1365] The server generates questions about the user's lifestyle and sends them to the device, such as "How often do you exercise per week?" or "Do you smoke?"

[1366] Step 6:

[1367] The terminal displays the generated questions to the user, who answers the questions and enters the answers using the terminal.

[1368] Step 7:

[1369] The user's answers are sent from the device to the server, which then analyzes the received answer data and formats it into a standard format.

[1370] Step 8:

[1371] The server sends the user's response data to the emotion engine, which uses natural language processing technology to analyze the user's emotions (e.g., stress, joy, anxiety, etc.).

[1372] Step 9:

[1373] The analysis results of the emotion engine are returned to the server, which then integrates the received emotion analysis results with the lifestyle information and health checkup results.

[1374] Step 10:

[1375] The server inputs the integrated data into the generation AI, which then analyzes the data. Based on the user's health and emotional state, the AI ​​generates appropriate health advice. For example, if the user's blood sugar level is slightly high and stress is high, the AI ​​generates advice such as "Improve the quality of your diet and incorporate relaxation activities to manage stress."

[1376] Step 11:

[1377] The server compiles the generated health advice into a report and sends it to the device, which includes the user's current health status and recommended actions.

[1378] Step 12:

[1379] The user can then use the device to check the sent report, and based on the report, review their lifestyle habits and plan specific actions to improve them.

[1380] Step 13:

[1381] At a later date, the user uploads the newly discovered blood test results to the system from the terminal again, and the terminal sends the new result file to the server.

[1382] Step 14:

[1383] The server receives the new data, converts it into text data using OCR technology again, then integrates the new data with the existing data and reanalyzes it using the generative AI.

[1384] Step 15:

[1385] Generative AI updates existing health advice based on new data and generates new advice that reflects the latest information.

[1386] Step 16:

[1387] The server then compiles new health advice in the form of a report and sends it to the device, where the user can check the updated report and use it to further improve their lifestyle habits.

[1388] Example 2

[1389] 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."

[1390] Conventional systems generate health advice based on health checkup data and lifestyle information, but because they do not take the user's emotional state into account, it is difficult to provide appropriate advice tailored to each individual's condition.In addition, the system lacks the functionality to update health advice to reflect blood test results that are discovered at a later date, which means that advice based on the latest health information is not provided.

[1391] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving health checkup data, means for analyzing the health checkup data and extracting necessary health information, means for acquiring lifestyle habit information, means for generating health advice based on the acquired lifestyle habit information and the health information, means for providing the health advice to the user, means for collecting and analyzing emotional data, and means for generating health advice by integrating the emotional data, lifestyle habit information, and health information. This makes it possible to provide individual health advice that takes the user's emotional state into consideration, and to update the advice to reflect the latest health information that becomes available at a later date.

[1392] "Health checkup data" refers to test result data obtained during regular health checkups that users undergo. This includes information such as blood test results, blood pressure, and weight.

[1393] "Analysis" refers to processing and analyzing the received data to extract necessary information. This processing includes converting it to text data and formatting the data.

[1394] "Health information" refers to information about the user's health condition extracted through analysis. Specifically, it includes indicators such as blood sugar level, blood pressure, and weight.

[1395] "Lifestyle information" is data related to the user's daily life, including exercise frequency, dietary habits, smoking status, etc.

[1396] "Health advice" is specific advice for maintaining or improving the user's health, generated based on health checkup data and lifestyle information.

[1397] "Providing" refers to communicating the generated health advice to the user, often via an electronic device.

[1398] "Emotional data" refers to data that indicates the user's emotional state. This data is analyzed from the user's text input and responses.

[1399] "Sentiment analysis" refers to analyzing a user's emotional state using emotional data, using natural language processing techniques.

[1400] "Integration" means combining multiple different data sets to create a single, consistent data set.

[1401] "Generative AI" is a system that uses artificial intelligence to analyze data and automatically generate appropriate health advice.

[1402] "Updating" means bringing existing information and advice up to date with new data.

[1403] The present invention is a system that analyzes health checkup results and provides individual health advice to users, and by combining it with an emotion engine, it grasps the user's emotions and provides appropriate health advice according to those emotions. Specific embodiments of the system are described in detail below.

[1404] This system mainly consists of a server, a terminal, and a user. The server receives and analyzes health checkup data and extracts health information. Specifically, it uses Microsoft Azure's OCR technology to convert the health checkup results into text data, and then analyzes the data using Python analysis libraries (e.g., Pandas, Numpy). The necessary health information is then extracted. The terminal functions as an interface for users to upload health checkup result files.

[1405] The server then begins the process of collecting lifestyle information. Using the Django framework, it generates a questionnaire for the user and sends it to the device. The user answers these questions through the device, and the response data is sent to the server. The server then uses the Python Pandas library to format the received data and standardize it.

[1406] For emotion analysis using the emotion engine, the server sends the user's response data to IBM Watson Natural Language Understanding, which analyzes the user's emotional state (e.g., stress, joy, anxiety). The emotion analysis results are sent back to the server and used to generate health advice.

[1407] The generative AI model, OpenAI GPT-4, integrates health checkup data, lifestyle information, and emotion analysis results to generate personalized health advice. The server then sends the generated health advice to the user's device, where the user can review it. This allows the user to understand their own health status and obtain specific guidelines for improvement.

[1408] Furthermore, if new data, such as blood test results, becomes available at a later date, the user can upload that data again to the system. The server then analyzes the new data again using Microsoft Azure's OCR technology, combines it with the existing data, and has the generative AI perform a re-analysis. This generates health advice based on the latest information and provides it to the user.

[1409] Specific examples

[1410] For example, suppose a user undergoes a health check and uploads the results to the system. If the blood sugar level is 105 mg / dL and the emotion engine analysis reveals a high stress level, the generative AI will generate advice such as, "To manage stress, we recommend aerobic exercise, a balanced diet, and relaxation activities."

[1411] Prompt Sentence Examples

[1412] Generate personalized health advice based on the following health checkup data, lifestyle information, and sentiment analysis results:

[1413] Health checkup result data:

[1414] Blood glucose level: 105 mg / dL

[1415] Blood pressure: 130 / 85 mmHg

[1416] Weight: 70 kg

[1417] Height: 170 cm

[1418] Lifestyle information:

[1419] Exercise frequency: twice a week

[1420] Smoking: No

[1421] Diet: Balanced diet

[1422] Emotion analysis results:

[1423] Stress level: High

[1424] Joy: Medium

[1425] Use this information to provide appropriate health advice to your users.

[1426] This system provides health advice tailored to each individual user based on the user's health checkup results, lifestyle data, and emotional data, providing comprehensive support for the user's health management.

[1427] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1428] Step 1:

[1429] The user uploads the health check result file from the terminal to the system. The user selects the health check result file (e.g. PDF, image) through the terminal's web browser and clicks the upload button. The uploaded file becomes the input and is sent to the server.

[1430] Step 2:

[1431] The server receives the file and converts it to text data using OCR technology. The server calls the Microsoft Azure OCR service to convert the uploaded file to text data. The input is the file received in step 1, and the output is the converted text data.

[1432] Step 3:

[1433] The server analyzes the health checkup data and extracts the necessary health information. The server uses Python analysis libraries (e.g., Pandas, Numpy) to analyze the text data and extract health information such as blood glucose levels, blood pressure, and weight. The input is text data converted by OCR, and the output is the extracted health information.

[1434] Step 4:

[1435] The server generates questions about the user's lifestyle and sends them to the device as a web form. The server uses the Django framework to generate questions about the user's lifestyle and sends them to the device as a web form. The input is a fixed question template, and the output is the generated web form.

[1436] Step 5:

[1437] The user answers the generated questions using the terminal. The user inputs answers to the questions displayed on the terminal's browser. The input is the user's answer data, which is sent from the terminal to the server.

[1438] Step 6:

[1439] The server analyzes the received response data and formats it into a standard format. The server uses the Python Pandas library to format the received data and unify it into a standard format. The input is the user's response data, and the output is formatted lifestyle habit information.

[1440] Step 7:

[1441] The server sends the received response data to the emotion engine. The server then calls the IBM Watson Natural Language Understanding service and sends the user's response data. The input is lifestyle habit information, and the output is the emotion analysis results.

[1442] Step 8:

[1443] The emotion engine analyzes the user's emotions and returns the analysis results to the server. The emotion engine uses natural language processing technology to analyze the user's emotional state (e.g., stress, elation, anxiety) and returns the results to the server. The input is lifestyle information, and the output is the emotion analysis results.

[1444] Step 9:

[1445] The server integrates the health checkup result data, lifestyle information, and emotion analysis results, and inputs them into the generative AI model. After integrating these data, the server inputs them into the OpenAI GPT-4 model. The inputs are the health checkup data, lifestyle information, and emotion analysis results, and the output is the input data for the generative AI model.

[1446] Step 10:

[1447] The generative AI analyzes the input data and generates health advice. The OpenAI GPT-4 model analyzes the input data and generates personalized health advice for the user. The input is the synthesized data, and the output is the generated health advice.

[1448] Step 11:

[1449] The server sends the generated health advice to the terminal. The server sends the generated health advice to the user's terminal, and the user uses the terminal to check the advice. The input is the generated health advice, and the output is the health advice displayed on the user's terminal.

[1450] Step 12:

[1451] The user uploads the blood test results, which will be known at a later date, from their terminal to the system again. The user uploads a new test result file from their terminal to the system, which is then sent to the server. The input is the new test result file, and the output is the file sent to the server.

[1452] Step 13:

[1453] The server receives the new data and extracts the text data using OCR technology again. The server then converts the new test result file into text data using Microsoft Azure's OCR service again. The input is the new test result file, and the output is text data.

[1454] Step 14:

[1455] The server integrates the new data with the existing data and reanalyzes it into the generative AI model. The server integrates the new data with the existing health information and inputs it into the OpenAI GPT-4 model again for analysis. The input is the integrated data, and the output is updated health advice.

[1456] Step 15:

[1457] The server sends the health advice generated as a result of the reanalysis to the user's device. The server then sends the updated health advice to the user's device, and the user uses the device to check the new advice. The input is the updated health advice, and the output is the new health advice displayed on the user's device.

[1458] (Application example 2)

[1459] 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."

[1460] In modern society, personal health management is important, but opportunities to receive appropriate advice based on health checkup results and lifestyle habits are limited. Furthermore, conventional health management systems rarely take into account the user's emotional state, resulting in issues with the accuracy and appropriateness of individual health advice. Furthermore, the advice provided cannot be visually confirmed immediately, making it difficult for users to use. There is a need to solve these issues and provide more effective and personalized health advice.

[1461] 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.

[1462] In this invention, the server includes means for receiving health checkup data, means for analyzing the health checkup data to extract necessary health information, means for acquiring lifestyle habit information, means for analyzing the emotional data, means for integrating the emotional data, the health information, and the lifestyle habit information and generating personalized health advice using a generative AI model, and means including a visualization device for visually presenting the generated health advice. This allows the personalized health advice to be provided taking into account the user's emotional state, and allows for quick visual confirmation.

[1463] "Medical checkup data" refers to medical data such as blood test results, blood pressure, and weight provided as a result of a medical checkup conducted by a user.

[1464] "Lifestyle information" refers to data such as the user's exercise habits, dietary habits, smoking and drinking habits in their daily lives.

[1465] "Emotion data" is data that indicates the user's emotional state, and is emotional information such as stress, joy, and anxiety extracted using natural language processing technology and image recognition technology.

[1466] A "generative artificial intelligence model" is an artificial intelligence system that analyzes a user's health and emotional state based on collected data and generates personalized health advice.

[1467] A "visual device" is a device for visually presenting the generated health advice to a user, and includes visual display devices such as head-mounted displays and smart glasses.

[1468] "Health advice" refers to specific suggestions and advice for improving the user's health, generated by a generative artificial intelligence model based on health checkup data, lifestyle information, and emotional data.

[1469] The present invention relates to a system that integrates health checkup data, lifestyle information, and user emotion data to provide personalized health advice. Specific embodiments for carrying out the present invention will be described below.

[1470] The entire system mainly consists of a server, a terminal, and smart glasses (visual device).

[1471] Receiving and analyzing health checkup data

[1472] The server first receives the health checkup data uploaded by the user via their device. This data includes information such as blood test results, blood pressure, and weight. The server then uses OCR technology to convert this data into text data and extract the necessary health information. Standard OCR software is used for the OCR technology.

[1473] Obtaining lifestyle information

[1474] The server generates lifestyle questions for the user and sends them to the device. Examples of questions include, "How often do you exercise per week?" or "Do you smoke?" The user answers the questions through the device, and the data is sent to the server. The server formats the data into a standard format.

[1475] Emotional Data Analysis

[1476] The smart glasses' built-in camera captures the user's facial expressions and tone of voice in real time, and uses emotion analysis software to analyze the user's emotional data, which includes stress, joy, anxiety, etc. This analysis is performed using an emotion analysis engine called EmotionEngine.

[1477] Generating health advice

[1478] The server integrates health checkup results, lifestyle information, and emotional data, and generates personalized health advice using a generative AI model. The generative AI model analyzes this data and provides advice based on the user's health and emotional state. For example, if blood sugar levels are high, it suggests exercise and dietary recommendations for stress management.

[1479] Example prompt sentence:

[1480] "User health data: {health_data}, Emotion: {emotion}. Provide personalized health advice based on these details."

[1481] Providing health advice

[1482] The generated health advice is sent from the server to the smart glasses, where users can visually confirm the advice in real time through the lenses of the smart glasses, enabling them to immediately take measures based on their health status.

[1483] Specific use cases

[1484] For example, after a user undergoes a health check, the results are uploaded to the system. The server analyzes the results and finds that the user's blood sugar level is slightly high. At the same time, facial expression data captured by the smart glasses reveals that the user is feeling stressed. Based on this information, the generative AI model generates advice such as, "To manage stress, try to do aerobic exercise several times a week and eat a balanced diet. We also recommend incorporating relaxation activities," and displays it on the lenses of the smart glasses.

[1485] In this way, the system of the present invention provides individual health advice based on the results of health checkups, lifestyle information, and emotional data, and provides comprehensive support for the user's health management.

[1486] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1487] Step 1:

[1488] Receiving health checkup data

[1489] The user uploads the results of their health checkup to the system from their device. The health checkup results include information such as blood test results, blood pressure, and weight. This data is sent to the server. The input is the health checkup data, and the output is text data stored on the server.

[1490] Step 2:

[1491] Analysis of health checkup data

[1492] The server uses OCR technology to convert the uploaded health checkup results into text data and extract the necessary health information. This information includes blood glucose levels, cholesterol levels, blood pressure, etc. The input is the health checkup data, and the output is the extracted health information. Specifically, the OCR software processes the scanned data and generates structured data.

[1493] Step 3:

[1494] Obtaining lifestyle information

[1495] The server generates lifestyle-related questions for the user and sends them to the device. For example, questions include, "How often do you exercise per week?" and "Do you smoke?" The user answers these questions via the device. The input is the lifestyle-related questions and the user's answers, and the output is lifestyle information. The server analyzes these answer data and formats them into a standard format.

[1496] Step 4:

[1497] Emotional Data Analysis

[1498] The camera built into the smart glasses captures the user's facial expressions and tone of voice in real time, and uses emotion analysis software to analyze the user's emotional data. Emotional data includes stress, joy, anxiety, etc. The input is the camera footage, and the output is the analyzed emotional data. Specifically, the EmotionEngine analyzes facial expressions and generates quantitative emotional data.

[1499] Step 5:

[1500] Integrating data and generating advice

[1501] The server integrates health checkup result data, lifestyle information, and emotional data. The integrated data is input into a generative AI model, which generates personalized health advice based on the user's health and emotional state. The input is the integrated data, and the output is health advice. Specifically, the AI ​​model analyzes the data and generates personalized advice based on a prompt. An example of the prompt is, "User health data: {health_data}, Emotion: {emotion}. Provide personalized health advice based on these details."

[1502] Step 6:

[1503] Providing health advice

[1504] The generated health advice is sent from the server to the smart glasses. The user can visually confirm the advice in real time through the lenses of the smart glasses. The input is the generated health advice, and the output is the user's visual confirmation. Specifically, the display function of the smart glasses displays the advice.

[1505] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1506] 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.

[1507] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1508] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1509] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1510] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1511] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1512] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1513] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1514] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1515] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1516] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1517] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1518] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1519] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1520] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1521] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1522] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1523] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1524] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1525] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1526] The following is further disclosed regarding the above embodiment.

[1527] (Claim 1)

[1528] means for receiving health checkup data;

[1529] means for analyzing the health checkup data and extracting necessary health information;

[1530] A means for obtaining lifestyle information;

[1531] a means for generating health advice based on the acquired lifestyle habit information and the health information;

[1532] means for providing said health advice to a user;

[1533] A system including:

[1534] (Claim 2)

[1535] 2. The system according to claim 1, further comprising means for acquiring the lifestyle habit information in an interactive format.

[1536] (Claim 3)

[1537] 2. The system according to claim 1, further comprising means for receiving data, such as blood test results, that become available at a later date and updating said health advice.

[1538] "Example 1"

[1539] (Claim 1)

[1540] means for receiving health checkup data;

[1541] a means for converting the health checkup data into text data using optical character recognition technology, and analyzing the data to extract necessary health information;

[1542] A means for interactively obtaining lifestyle information;

[1543] a means for integrating the acquired lifestyle habit information and the health information and generating health advice using a generative artificial intelligence model;

[1544] means for providing said health advice to a user;

[1545] A system including:

[1546] (Claim 2)

[1547] 2. The system according to claim 1, further comprising means for acquiring the lifestyle habit information in an interactive manner.

[1548] (Claim 3)

[1549] 2. The system of claim 1, further comprising means for receiving data, such as blood test results, that become available at a later date and updating the health advice.

[1550] "Application Example 1"

[1551] (Claim 1)

[1552] means for receiving health checkup data;

[1553] means for analyzing the health checkup data and extracting necessary health information;

[1554] A means for obtaining lifestyle information;

[1555] a means for generating health advice based on the acquired lifestyle habit information and the health information;

[1556] means for providing said health advice to a user;

[1557] In an autonomous vehicle, a means for generating and setting a driving mode and rest suggestion based on a user's health condition;

[1558] A system including:

[1559] (Claim 2)

[1560] 2. The system according to claim 1, further comprising means for acquiring the lifestyle habit information in an interactive format.

[1561] (Claim 3)

[1562] 2. The system according to claim 1, further comprising means for receiving data, such as blood test results, that become available at a later date and updating said health advice.

[1563] "Example 2: Combining Emotion Engines"

[1564] (Claim 1)

[1565] means for receiving health checkup data;

[1566] means for analyzing the health checkup data and extracting necessary health information;

[1567] A means for obtaining lifestyle information;

[1568] a means for generating health advice based on the acquired lifestyle habit information and the health information;

[1569] means for providing said health advice to a user;

[1570] a means for collecting and analyzing emotion data;

[1571] means for integrating the emotion data, lifestyle habit information, and health information to generate health advice;

[1572] A system including:

[1573] (Claim 2)

[1574] 2. The system according to claim 1, further comprising means for acquiring the lifestyle habit information in an interactive format.

[1575] (Claim 3)

[1576] 2. The system according to claim 1, further comprising means for receiving data, such as blood test results, that become available at a later date and updating said health advice.

[1577] "Application example 2 when combining emotion engines"

[1578] (Claim 1)

[1579] means for receiving health checkup data;

[1580] means for analyzing the health checkup data and extracting necessary health information;

[1581] A means for obtaining lifestyle information;

[1582] a means for generating health advice based on the acquired lifestyle habit information and the health information;

[1583] means for providing said health advice to a user;

[1584] a means for analyzing emotion data;

[1585] means for integrating the emotion data with health information and lifestyle information and generating personalized health advice using a generative artificial intelligence model;

[1586] means including a visual device for visually presenting the generated health advice;

[1587] A system including:

[1588] (Claim 2)

[1589] 2. The system according to claim 1, further comprising means for acquiring the lifestyle information and emotion data in an interactive manner.

[1590] (Claim 3)

[1591] 2. The system according to claim 1, further comprising means for receiving data, such as blood test results, that become available at a later date and updating said health advice. [Explanation of symbols]

[1592] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving health checkup data; means for analyzing the health checkup data and extracting necessary health information; A means for obtaining lifestyle information; a means for generating health advice based on the acquired lifestyle habit information and the health information; means for providing said health advice to a user; A system including:

2. 2. The system according to claim 1, further comprising means for acquiring said lifestyle habit information in an interactive format.

3. 2. The system according to claim 1, further comprising means for receiving data, such as blood test results, that become known at a later date and updating said health advice.

Images