System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting a medical interview sheet by a user; means for transmitting medical interview information input by the user to a server; means for receiving the medical interview information in the server and analyzing symptoms using a generated AI model; means for searching for and recommending appropriate medical institutions based on an analysis result; means for proposing home medical care and recommending commercially available drugs based on the analysis result; and means for notifying the user of a recommendation result, the proposal of home medical care, and the recommendation of commercially available drugs.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] The purpose of this invention is to improve the efficiency of medical care provision and the patient treatment process. Specifically, by providing a system that can quickly and accurately suggest appropriate medical institutions and treatments when patients feel unwell, it aims to make effective use of medical resources. It also solves the problem of selecting a medical institution, which takes a lot of time and effort in the current medical system, and the challenges of attracting customers and making money for medical institutions. [Means for solving the problem]

[0005] The present invention solves the above problems by using the following means.

[0006] The system includes a means for a user to input a medical questionnaire, a means for transmitting the medical questionnaire data input by the user to a server, a means for receiving the medical questionnaire data at the server and analyzing symptoms using a generative AI model, a means for searching for and recommending an appropriate medical institution based on the analysis results, a means for proposing home medical care and recommending over-the-counter drugs based on the analysis results, and a means for notifying the user of the recommendation results, the proposing home medical care, and the recommended over-the-counter drugs.The system also includes a means for listing appropriate medical institutions using the user's current location information and past usage history based on the analysis results, and a means for providing an interface for inputting the user's initial symptoms, thereby effectively optimizing the provision of medical care and improving the accuracy of matching patients and medical institutions.

[0007] The "means for the user to input the questionnaire" is an interface that the user uses to record their symptoms and health condition.

[0008] The "means for transmitting the medical questionnaire data entered by the user to the server" refers to a protocol or mechanism for transmitting the medical questionnaire data entered by the user to the server via the Internet.

[0009] "Means for receiving medical interview data on the server and analyzing symptoms using a generative AI model" refers to the process of examining and evaluating symptoms using a generative AI model based on the medical interview data received by the server.

[0010] "Means for searching for and recommending appropriate medical institutions based on analysis results" refers to a system that identifies appropriate medical institutions based on the results of AI analysis and provides information to users.

[0011] "Means for proposing home medical care and recommending over-the-counter medications based on the analysis results" refers to a mechanism that suggests treatments that users can perform at home and over-the-counter medications that they can purchase based on the results of symptom analysis by AI.

[0012] "Means for notifying users of recommendation results, home medical treatment suggestions, and over-the-counter drug recommendations" refers to a system that notifies and displays on the device a list of optimal medical institutions generated by AI, as well as information on home treatment and over-the-counter drugs.

[0013] "A means of listing appropriate medical institutions based on the analysis results and using the user's current location information and past usage history" is a function that selects the most suitable medical institution for the user by referring to past data and current location information.

[0014] The "means for providing an interface for inputting the user's initial symptoms" is a system that provides a form and a screen layout that allows the user to simply input the initial symptoms. [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 provides a specific embodiment of a system for efficiently evaluating a patient's symptoms and recommending appropriate medical institutions and treatments. This system is composed of multiple components, each of which works in conjunction with the other components to provide comprehensive medical care.

[0037] System configuration

[0038] 1. User fills out questionnaire

[0039] Device: The user accesses the questionnaire through a dedicated application on a smartphone or PC. The questionnaire contains questions about symptoms, onset time, frequency of symptoms, degree of pain, etc., and the user enters this information.

[0040] Example: A user enters "I've had a headache for three days" along with any related symptoms and further information.

[0041] 2. Sending medical interview data to the server

[0042] Terminal: The medical interview data entered by the user is sent to the server via the application. The data is securely encrypted before transmission, preventing unauthorized access and information leaks.

[0043] 3. Receipt and analysis of medical interview data by the server

[0044] Server: The server stores the received medical interview data in a database and automatically passes the data to a generative AI model to begin analyzing the symptoms.

[0045] Example: A server receives the data "Headache, 3 days", and a generative AI model analyzes it to determine whether it is a tension headache or a migraine.

[0046] 4. Recommendation of appropriate medical institutions

[0047] Server: Based on the analysis results, the server searches for an appropriate medical institution that can treat the symptoms from among the medical institutions registered in the database. This is highly convenient as it also takes into account the user's current location information and past usage history.

[0048] Example: If the diagnosis is "tension headache," the server will list hospitals and clinics with headache specialists.

[0049] 5. Proposal of home medical care and recommendation of over-the-counter medications

[0050] Server: If symptoms are mild and can be managed at home, the server will suggest specific home care methods and over-the-counter medications.

[0051] Example: Suggestions such as "Take over-the-counter pain medication (e.g., ibuprofen) and drink plenty of fluids" may be generated.

[0052] 6. Notification of analysis results and recommendations

[0053] Server: The server generates a message containing the analysis results and information on recommended medical institutions, home care methods, and over-the-counter medications, and sends it to the user's device.

[0054] Device: The user's device will display the received message and allow the user to review the recommended information.

[0055] Example: The user sees the message, "Your headache may be a tension headache. Please seek medical advice from the clinic listed below. We also recommend trying over-the-counter ibuprofen."

[0056] This invention allows users to easily and quickly access appropriate medical institutions and treatments, thereby improving the efficiency of medical care provision and the effective use of resources. In addition, by proposing home medical care, it also provides a way to treat mild symptoms at home, thereby contributing to reducing the burden on medical institutions.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] A user feels unwell and launches a dedicated application on their smartphone or computer. A login screen appears, and the user enters their authentication information to log in to their account.

[0060] Step 2:

[0061] The terminal displays a questionnaire screen to the logged-in user. The questionnaire includes questions about the type of symptoms, onset time, frequency, intensity, and related symptoms (e.g., headache, dizziness, nausea, etc.).

[0062] Step 3:

[0063] The user enters their symptoms into the questionnaire, for example, "I've had a headache for three days" or "I feel nauseous," and taps the "Done" button.

[0064] Step 4:

[0065] The terminal collects the medical interview data entered by the user and formats it. The formatted data is then encrypted and securely sent to the server.

[0066] Step 5:

[0067] The server decodes the received medical interview data and stores it in a database. After saving, it automatically prepares to transfer the data to the generative AI model.

[0068] Step 6:

[0069] The server's generated AI model analyzes the interview data and makes an initial assessment of the symptoms. For example, based on the input information "headache" and "3 days," it assesses the possibility of a tension headache or migraine.

[0070] Step 7:

[0071] The server receives the analysis results of the AI ​​model and initiates the process of searching for an appropriate medical institution based on the analysis results, taking into account the user's current location information and past usage history.

[0072] Step 8:

[0073] The server identifies and lists medical institutions that can treat the symptoms from the database, including information such as the name, address, contact information, and specialty of the medical institution.

[0074] Step 9:

[0075] The server then executes a process based on the analysis results to suggest home medical care or over-the-counter medications. For example, for a mild headache, it may recommend an over-the-counter painkiller (e.g., ibuprofen).

[0076] Step 10:

[0077] The server compiles a list of appropriate medical institutions, home medical care, and over-the-counter drug suggestions, generates a message for the user, and initiates a procedure to send this message to the terminal.

[0078] Step 11:

[0079] The terminal decodes the message received from the server and displays the results to the user, who can then decide which medical institution to use or whether to seek home care.

[0080] Step 12:

[0081] The user checks the presented information and decides on the next action to take, such as making an appointment with the presented hospital or purchasing over-the-counter medicine to treat the condition at home.

[0082] Example 1

[0083] 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."

[0084] In today's world, medical institutions are often used frequently, making it difficult for patients to make appointments. It is also difficult to determine whether or not a patient needs to visit a medical institution, even if their symptoms are mild. Another issue is that it takes time for patients to find an appropriate medical institution. The present invention aims to solve these problems and provide a system that enables patients to easily and quickly access appropriate medical institutions and treatments.

[0085] 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.

[0086] In this invention, the server includes an information processing device for the user to input a medical questionnaire, a communication device for transmitting the medical questionnaire data input by the user to the server, and a data processing device for receiving the medical questionnaire data in the server and analyzing symptoms using a generative AI model. This enables the user to input symptoms, have them quickly analyzed using the generative AI model, and recommend appropriate medical institutions and treatments.

[0087] An "information processing device" is a device that includes hardware and software for processing data entered by a user and enabling it to be entered into a questionnaire.

[0088] "Communication means" refers to means including protocols and infrastructure for transmitting data entered from a user's terminal to a server.

[0089] "Data processing means" refers to a system that analyzes data received by the server and performs calculations and data analysis to diagnose symptoms using a generative AI model.

[0090] The "search means" refers to the algorithms and software that search for information in the database based on the analysis results and find appropriate medical institutions.

[0091] The "recommendation method" is a system that includes data generation and recommendation functions to suggest home medical care methods and over-the-counter drug information based on the analysis results.

[0092] The "notification means" is a means including a message generation and transmission function for notifying the user of the generated recommendations and diagnostic results.

[0093] A "user interface" is a system that includes a screen and input devices through which a user enters initial symptoms and other information.

[0094] A "generative AI model" is a type of machine learning model used to diagnose symptoms and make recommendations based on input medical interview data.

[0095] This invention is a system that efficiently evaluates a patient's symptoms and recommends appropriate medical institutions and treatments. This system utilizes a user terminal, a server, and a generative AI model to analyze the medical interview data entered by the user and provide medical information based on the results.

[0096] Input and transmission of medical interview data via user terminal

[0097] Users access a dedicated application using an information processing device such as a smartphone or PC. They answer questions about symptoms, when they first started, how often they experienced symptoms, and how much pain they felt, according to a questionnaire displayed in the application. For example, a user might enter detailed information such as "I've had a headache for three days," "The pain occurs several times a day," and "The pain is moderate."

[0098] The entered data is encrypted by the user's device and securely transmitted to the server using AES encryption and the HTTPS protocol.

[0099] Data reception and analysis by the server

[0100] The server receives the encrypted medical interview data, decrypts it, and stores it in a database that stores the user's past medical information and current medical interview data.

[0101] The received data is immediately passed to the generative AI model, where analysis begins. This generative AI model is written in Python and analyzes the user's symptoms based on pre-trained medical data. Specifically, the server sends data to the generative AI model in the format "User ID 1234: Symptom Data" and receives the analysis results.

[0102] Recommendations for medical institutions and home medical care

[0103] Based on the analysis results, the server searches the registered medical institutions in the database and lists appropriate medical institutions. The server also takes into account the user's current location and past medical history. For example, if a patient is diagnosed with tension headache, the server will select a hospital or clinic with a doctor specializing in headaches.

[0104] If symptoms are mild and home medical care is recommended, specific home care methods and over-the-counter medication information will also be provided. For example, the server will generate a recommendation message such as, "Take over-the-counter painkillers (e.g., ibuprofen) and drink plenty of fluids."

[0105] User Notification

[0106] Finally, the server generates a message containing the analysis results and recommendations, and sends it to the user's device. The user's device receives the message and displays it on its screen. For example, the user's screen may display a message saying, "Your headache may be a tension headache. Please visit the hospital listed below. We also recommend trying the over-the-counter medication ibuprofen."

[0107] Example prompts for generative AI models

[0108] "The patient has had a headache for three days, with moderate pain several times a day. Based on these symptoms, what is the likely diagnosis and what medical or over-the-counter medications would you recommend?"

[0109] "Analyze the user's medical interview data and generate optimal diagnostic results and a list of recommended medical institutions."

[0110] The present invention allows users to quickly and appropriately obtain medical information, thereby improving the efficiency of medical care provision and the effective use of resources. In addition, by proposing home care, it also contributes to reducing the burden on medical institutions.

[0111] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0112] Step 1:

[0113] User accesses the questionnaire

[0114] Input: A user accesses a dedicated application using a smartphone or computer.

[0115] Specific operation: A user installs and launches the application, then enters their user ID and password on the login screen to log in to their account.

[0116] Output: The user reaches the questionnaire entry screen and the questionnaire is displayed.

[0117] Step 2:

[0118] The user fills out the questionnaire

[0119] Input: The user answers the questions displayed in the questionnaire (e.g., symptoms, time of onset, frequency of symptoms, pain level).

[0120] What happens: The user enters details such as "I have had a headache for three days," "The pain occurs several times a day," and "The pain is moderate."

[0121] Output: The entered medical interview data is temporarily saved in the application.

[0122] Step 3:

[0123] The device sends the medical interview data to the server.

[0124] Input: Interview data entered by the user.

[0125] Specific operation: The terminal encrypts the medical interview data using AES and sends the data to the server using the HTTPS protocol.

[0126] Output: The encrypted interview data is sent to the server.

[0127] Step 4:

[0128] The server receives and decrypts the interview data.

[0129] Input: Encrypted medical interview data sent from the terminal.

[0130] What it does: The server decrypts the received data and stores it in an SQL database, which contains information such as the user ID, symptom data, and the date and time of entry.

[0131] Output: The decrypted interview data is stored in the database.

[0132] Step 5:

[0133] The server starts analysis using the generated AI model

[0134] Input: Medical interview data stored in a database.

[0135] How it works: The server passes medical interview data as input to a generative AI model written in Python. For example, data is sent to the generative AI model in the form of "User ID 1234: headache lasting 3 days, frequency several times a day, moderate pain."

[0136] Output: As a result of the analysis, the generative AI model outputs information such as the diagnosis and recommended medical institution.

[0137] Step 6:

[0138] The server searches for an appropriate medical institution

[0139] Input: Analysis results obtained from a generative AI model.

[0140] Specific operation: Based on the analysis results (e.g., tension headache), the server searches for appropriate medical institutions from the database, taking into account the user's current location information and past medical institution usage history. A list of medical institutions is obtained using an SQL query.

[0141] Output: A list of recommended medical institutions is generated.

[0142] Step 7:

[0143] The server provides home care and over-the-counter medication suggestions.

[0144] Input: Analysis results of the generative AI model and medical institution search results.

[0145] Specific operation: If the server detects a "tension headache," it generates information on home care methods and over-the-counter medications (e.g., ibuprofen). For example, it generates suggestions such as "Take over-the-counter painkillers and drink plenty of fluids."

[0146] Output: A recommendation message is generated containing home care and over-the-counter medication information.

[0147] Step 8:

[0148] The server generates and sends notifications of analysis results and recommendations.

[0149] Input: Recommendation message, list of healthcare providers.

[0150] Specific operation: The server combines the recommended message with the list of medical institutions to generate a notification message for the user, and then sends the message to the user's device using an HTTP request.

[0151] Output: A notification message is sent that the user receives.

[0152] Step 9:

[0153] The device displays a notification message

[0154] Input: The notification message sent by the server.

[0155] Specific behavior: The device receives a notification and displays it to the user in the application. For example, it displays the message, "Your headache may be a tension headache. Please visit the hospital listed below. We also recommend trying the over-the-counter medication ibuprofen."

[0156] Output: User can see the recommended information.

[0157] (Application example 1)

[0158] 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."

[0159] There is a need to grasp the health status of workers in factories in real time and quickly provide them with appropriate medical institutions and treatment. However, currently, checking their health status and selecting medical institutions is time-consuming and laborious, which can delay appropriate measures. Furthermore, if symptoms are mild, it is desirable to avoid unnecessary use of medical institutions and provide home care options. Against this background, a new system is needed to streamline medical responses in factories and optimize worker health management.

[0160] 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.

[0161] In this invention, the server includes: a means for a user to input a medical questionnaire; a means for transmitting the medical questionnaire data input by the user to the server; a means for receiving the medical questionnaire data in the server and analyzing symptoms using a generative AI model; a means for searching for and recommending an appropriate medical institution based on the analysis results; a means for proposing home medical care and recommending over-the-counter medications based on the analysis results; a means for notifying the user of the recommendation results, home medical care suggestions, and over-the-counter medication recommendations; a means for checking the health status of employees while they are working in a factory and suggesting appropriate medical institutions and treatments based on the results; and a means for monitoring the health status of workers in real time and securely encrypting and transmitting the data. This enables the health status of workers in a factory to be quickly evaluated and appropriate medical institutions and treatments to be provided. Furthermore, home care options are also provided for mild symptoms, avoiding unnecessary use of medical institutions.

[0162] A "medical questionnaire" is a form in which a user enters detailed information about their health condition, such as symptoms, onset time, and frequency of pain.

[0163] "Means" refers to the devices or methods used by the system to achieve a specific function or operation.

[0164] A "user" is someone who uses the system to assess their own health status and receive recommendations for appropriate medical institutions and treatments.

[0165] "Data" refers to health and symptom information entered by users, including details of symptoms, onset time, frequency, and pain level.

[0166] "Server" refers to the computer system that receives and analyzes data sent by users, thereby storing the data and analyzing it using AI models.

[0167] A "generative AI model" refers to a machine learning model that analyzes received data and recommends appropriate treatments and medical institutions based on the user's symptoms.

[0168] "Analysis" refers to the process of using a generative AI model to evaluate and interpret the user's medical interview data and identify the most appropriate treatment and medical institution based on the symptoms.

[0169] "Medical institution" refers to a facility that provides specialized medical services, such as a hospital, clinic, or medical office.

[0170] "Home medical care" refers to medical care and treatments that users can receive at home for mild symptoms. The system will suggest home medical care according to the situation.

[0171] "Over-the-counter drugs" refer to medicines that can be purchased at general pharmacies and drug stores and do not require a prescription.

[0172] "Recommendation" refers to the act of presenting the most appropriate medical institution or treatment method to the user based on the analysis results of the generative AI model.

[0173] "Notification" refers to the process of communicating analysis results and recommendations to users, allowing them to obtain the information they need in a timely manner.

[0174] "Employees" refers specifically to workers in factories, who can use the system to check their own health status and obtain appropriate medical care and treatment.

[0175] "Real-time" means that health status checks, data transmission, and analysis are carried out immediately, allowing for rapid health management of workers.

[0176] "Monitoring" refers to the process of continuously monitoring the health status of workers and responding promptly to any abnormalities.

[0177] "Data encryption" refers to the technology that securely protects data being transmitted in order to protect user privacy, thereby preventing unauthorized access and information leaks.

[0178] MODE FOR CARRYING OUT THE INVENTION

[0179] The present invention provides a system that monitors the health status of workers in factories in real time and promptly provides appropriate medical institutions and treatments. This system automates the process in which workers check their own health status and receive recommendations for medical institutions and treatments based on the results.

[0180] System configuration

[0181] This system consists of the following components:

[0182] 1. Input method for the questionnaire:

[0183] Workers access the questionnaire using the touch display of the factory robot, a smartphone, or a tablet, and enter information such as symptoms, when they first started, how often they are sick, and how much pain they are in.

[0184] 2. How to send medical interview data to the server:

[0185] The medical interview data entered by the worker is securely encrypted and sent to the server using the Cryptography library, and the Requests library is used to send the data.

[0186] 3. Means of receiving and analyzing interview data:

[0187] The server stores the received medical interview data in a database and simultaneously begins analysis using a generative AI model, which identifies appropriate treatments and medical institutions based on the symptom data.

[0188] 4. Medical institution recommendation methods:

[0189] Based on the analysis results, the server searches for and recommends the best medical facility for the patient's symptoms from among the medical institutions registered in the database, taking into account the user's current location and past usage history.

[0190] 5. Home care suggestions and over-the-counter medication recommendations:

[0191] If symptoms are mild, the server will suggest specific home care methods and over-the-counter medications based on the analysis results.

[0192] 6. Means of communication of analysis results and recommendations:

[0193] The server generates a message containing the analysis results and information on recommended medical institutions, home care methods, and over-the-counter medications, decrypts it, and sends it to the worker's device, allowing the worker to receive the appropriate information quickly.

[0194] Hardware and Software Configuration

[0195] Hardware:

[0196] Touch display for factory robots

[0197] Workers' smartphones and tablets

[0198] software:

[0199] Python

[0200] Cryptography Library

[0201] Requests library

[0202] Specific examples

[0203] As the factory nears closing time, an employee types in "I've had a headache for three days." The data is sent to the server using the following prompt statement:

[0204] Example prompt sentence:

[0205] Symptom: Headache

[0206] Onset period: 3 days

[0207] Severity: Moderate

[0208] The server performs the analysis and provides the following information:

[0209] Analysis result: "Possible tension headache."

[0210] Recommended medical institution: "XX Hospital (has a headache specialist)"

[0211] Home care suggestion: "I recommend taking over-the-counter pain medication (e.g., ibuprofen) and staying hydrated."

[0212] This allows workers to take appropriate action quickly.

[0213] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0214] Step 1:

[0215] Users access the questionnaire from the touch display of a robot in the factory or their own smartphone and enter information such as symptoms, onset time, frequency of symptoms, and pain level. This information is collected using the questionnaire's input method. The input data is recorded on the terminal as text prompts such as "Symptoms: headache," "Duration: 3 days," and "Severity: moderate."

[0216] Step 2:

[0217] The terminal encrypts the entered medical interview data and sends it to the server. The encryption is performed using the Cryptography library, and the data is sent to the server using the Request library. The text data received as input is sent to the server in encrypted form, and the actual data sent is an encrypted string.

[0218] Step 3:

[0219] The server receives the encrypted data sent from the device, decrypts it, and stores it in the database. The received data is an encrypted string, and the server decrypts it and stores it in the database as "Symptom: Headache," "Duration: 3 days," and "Severity: Moderate."

[0220] Step 4:

[0221] The server uses a generative AI model to analyze the medical interview data in the database and identify appropriate treatments and medical institutions based on the user's symptoms. This model receives symptom data as input and outputs a symptom diagnosis and recommendation by optimizing its objective function. Specifically, the model receives the data "headache," "3 days," and "moderate severity," and obtains the analysis result "high probability of tension-type headache."

[0222] Step 5:

[0223] Based on the analysis results of the generative AI model, the server searches for appropriate facilities from among the medical institutions registered in the database and recommends them to the user. The server also takes into consideration the user's current location information and past usage history to generate a list of optimal medical institutions. Based on the analysis results and location information as input, it generates a list of medical institutions such as "XX Hospital (with headache specialists)" as output.

[0224] Step 6:

[0225] Based on the analysis results, the server also generates recommendations for home care methods and over-the-counter medications. For mild symptoms, specific advice such as "take over-the-counter painkillers (e.g., ibuprofen) and drink plenty of fluids" is generated. In this process, the generative AI model's subsystem takes symptom data and analysis results as input and outputs recommended care methods.

[0226] Step 7:

[0227] The server generates a message notifying the user of the analysis results, recommended medical institutions, and home medical care suggestions, and sends it securely encrypted. The user's device receives this notification and displays the analysis results and recommendations. The output message contains specific information such as "You may have a tension headache," "Visit XX Hospital (where a headache specialist is located)," and "Take over-the-counter painkillers."

[0228] This series of processing steps allows users to assess their health status in real time and quickly receive recommendations for necessary medical institutions and treatments.

[0229] 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.

[0230] The present invention provides a specific embodiment of a system that efficiently evaluates a patient's symptoms and emotions and recommends appropriate medical institutions and treatments. This system consists of multiple components, such as a medical questionnaire input system, an emotion recognition engine, AI analysis, medical institution recommendation, and home care proposal system, which work in conjunction with each other.

[0231] System configuration

[0232] 1. User fills out questionnaire

[0233] Device: The user accesses the questionnaire through a dedicated application on a smartphone or PC. The questionnaire contains questions about symptoms, onset time, frequency, intensity, related symptoms, etc., and the user enters this information.

[0234] Example: A user enters "I've had a headache for three days" along with any related symptoms and further information.

[0235] 2. Sending medical interview data to the server

[0236] Terminal: The medical interview data entered by the user is sent to the server via the application. The data is securely encrypted before transmission, preventing unauthorized access and information leaks.

[0237] 3. Emotion engine for recognizing user emotions

[0238] On the device: As the user types, the emotion engine analyzes the user's facial expressions, tone of voice, typing speed, etc. to recognize their emotional state (e.g., stress, urgency, anxiety, etc.).

[0239] Example: As the user continues to type, the emotion engine detects anxiety in the user's tone of voice and tags it as "anxious."

[0240] 4. Integrated transmission of medical interview data and emotion data to the server

[0241] Terminal: The interview data and the recognized emotion data are integrated and sent to the server.

[0242] Server: The server stores the received data in a database and passes the data to the generative AI model to begin analysis.

[0243] 5. Analysis of medical interview data and emotional data by the server

[0244] Server: The generative AI model comprehensively analyzes the medical interview data and emotional data to evaluate symptoms and emotional state.

[0245] Example: Based on data on "headache, 3 days" and "anxiety," AI determines the possibility of tension headache and mental stress.

[0246] 6. Recommendation of appropriate medical institutions

[0247] Server: Based on the analysis results, the server searches for an appropriate medical institution that can treat the user's symptoms and emotional state from among the medical institutions registered in the database, taking into account the user's current location and past usage history.

[0248] Example: If the diagnosis is "tension headache" and "mental stress," a list of hospitals and clinics with headache specialists and psychiatrists will be provided.

[0249] 7. Proposal of home medical care and recommendation of over-the-counter medications

[0250] Server: If symptoms are mild and can be managed at home, the server will suggest specific home care methods and over-the-counter medications.

[0251] Example: Suggestions such as "Take over-the-counter painkillers (e.g., ibuprofen) and drink plenty of fluids" may be generated, along with suggestions for relaxation techniques and mental health support.

[0252] 8. Notification of analysis results and recommendations

[0253] Server: The server generates a message containing the analysis results and information on recommended medical institutions, home care methods, and over-the-counter medications, and sends it to the user's device.

[0254] Device: The user's device will display the received message and allow the user to review the recommended information.

[0255] Example: The user sees a message on their screen saying, "Your headache may be a tension headache. Please seek medical advice from the hospital listed below. We also recommend relaxation techniques along with over-the-counter ibuprofen."

[0256] This invention allows users to easily and quickly access appropriate medical institutions and treatments, thereby realizing more efficient medical care and effective use of resources. Furthermore, the introduction of an emotion engine enables comprehensive diagnosis and proposals that take into account the user's mental state, realizing the provision of more personalized medical services.

[0257] The processing flow will be explained below.

[0258] Step 1:

[0259] A user feels unwell and launches a dedicated application on their smartphone or computer. A login screen appears, and the user enters their authentication information to log in to their account.

[0260] Step 2:

[0261] The terminal displays a questionnaire screen to the logged-in user. The questionnaire includes questions about the type of symptoms, onset time, frequency, intensity, and related symptoms (e.g., headache, dizziness, nausea, etc.).

[0262] Step 3:

[0263] The user enters their symptoms into the questionnaire, for example, "I've had a headache for three days" or "I feel nauseous," and taps the "Done" button.

[0264] Step 4:

[0265] The terminal collects the medical interview data entered by the user and formats it. The formatted data is then encrypted and securely sent to the server.

[0266] Step 5:

[0267] The device activates an emotion engine while the user is typing, analyzing the user's facial expressions, tone of voice, typing speed, etc. to recognize their emotional state (e.g., stress, anxiety).

[0268] Step 6:

[0269] The device integrates the recognized emotion data with the medical interview data and sends it to the server, allowing both symptom and emotion data to be managed centrally.

[0270] Step 7:

[0271] The server stores the received medical interview data and emotion data in a database and passes it to the generative AI model to begin analysis.

[0272] Step 8:

[0273] The server's generated AI model analyzes the medical interview data and emotional data in an integrated manner to evaluate symptoms and emotional state. For example, it analyzes data such as "headache lasting three days" and "state of anxiety."

[0274] Step 9:

[0275] The server receives the analysis results of the AI ​​model and initiates the process of searching for an appropriate medical institution based on the analysis results, taking into account the user's current location information and past usage history.

[0276] Step 10:

[0277] The server identifies and lists medical institutions from a database that can treat the patient's symptoms and emotional state, including information such as the name, address, contact information, and specialty of the institution.

[0278] Step 11:

[0279] The server then executes a process based on the analysis results to suggest home medical care and over-the-counter medications. For example, for a mild headache, it may recommend an over-the-counter painkiller (e.g., ibuprofen). It also generates suggestions for relaxation techniques and mental health support based on emotion data.

[0280] Step 12:

[0281] The server compiles a list of appropriate medical institutions, home medical care, and over-the-counter drug suggestions, generates a message for the user, and initiates a procedure to send this message to the terminal.

[0282] Step 13:

[0283] The terminal decodes the message received from the server and displays the results to the user, who can then decide which medical institution to use or whether to seek home care.

[0284] Step 14:

[0285] The user checks the presented information and decides on the next action to take, such as making an appointment with the presented hospital or purchasing over-the-counter medicine to treat the condition at home.

[0286] Example 2

[0287] 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."

[0288] Current healthcare systems lack the ability to efficiently evaluate patients' symptoms and quickly recommend appropriate medical institutions and treatments. Furthermore, because patients' emotional states are not reflected in diagnoses, it is difficult to provide personalized healthcare services. This can lead to worsening symptoms and inefficient use of healthcare resources.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0290] In this invention, the server includes: [means for using an emotion engine to recognize the user's emotions;] [means for integrating the emotion data with medical interview data and sending it to the server; and] [means for receiving the medical interview data and emotion data in the server and analyzing the symptoms and emotional state using a generative AI model.] This makes it possible [to comprehensively evaluate the patient's symptoms and emotional state and quickly and individually recommend appropriate medical institutions and treatments].

[0291] A "medical questionnaire" is a paper or electronic questionnaire that a patient fills out to record their symptoms and medical history.

[0292] "User" refers to an individual who uses the system to complete a medical questionnaire and receive medical assistance.

[0293] A "server" is a central computer system that receives data sent by users, analyzes it, and returns the results.

[0294] An "emotion engine" is software or hardware that analyzes a user's facial expressions, voice, and input speed to recognize their emotional state.

[0295] "Medical Interview Data" refers to symptoms, medical history, and other health-related information entered by a user into a medical interview form.

[0296] "Emotion data" is data relating to the user's emotional state analyzed by the emotion engine.

[0297] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze data and evaluate symptoms and emotional states.

[0298] "Analysis results" refers to the evaluation and results of the generative AI model based on the medical interview data and emotional data.

[0299] "Healthcare facility" means a hospital, clinic, medical office, or other facility that provides health care services.

[0300] "Home healthcare" refers to medical care and treatments that can be performed at home.

[0301] "Over-the-counter" refers to drugs that can be purchased without a doctor's prescription.

[0302] "Encryption" refers to the process of using cryptographic techniques to securely transmit and receive data.

[0303] "Listing" refers to selecting appropriate options or items in list form based on specific conditions.

[0304] "Interface" refers to the means or device by which a user interacts with a system.

[0305] This invention provides a specific embodiment of a system that efficiently evaluates a patient's symptoms and emotions and recommends appropriate medical institutions and treatments. This system consists of multiple components, such as user questionnaire input, emotion recognition, AI analysis, medical institution recommendation, and home care suggestion, which work in conjunction with each other.

[0306] First, the user accesses a dedicated application using a smartphone or computer. Through this application, the user enters information about their symptoms, onset time, frequency, intensity, related symptoms, etc. into a questionnaire. For example, they enter detailed information such as "I have had a headache for three days, it started at the beginning of the week, and it is moderate in intensity."

[0307] The device then securely encrypts the medical interview data entered by the user and transmits it to the server using the SSL / TLS protocol. During this data transmission process, the user's data is always protected and is protected from unauthorized access and information leaks.

[0308] While the user continues to fill out the questionnaire, the emotion recognition engine analyzes the user's facial expressions, voice tone, and input speed in real time through the device's built-in camera, microphone, and touchscreen. For example, the emotion recognition engine will detect the user's anxious voice or impatient expression and tag it as "anxiety."

[0309] The device then combines the emotion data and the medical interview data and sends them back to the server in encrypted form. The server receives this data and stores it in a database (e.g., MySQL, PostgreSQL). The data is then input into a generative AI model (e.g., GPT-4, BERT) to begin analysis.

[0310] The generative AI model comprehensively analyzes the medical interview data and emotional data to evaluate symptoms and emotional state. For example, based on the data "headache, 3 days" and the emotional data "anxiety," it determines the possibility of tension headache and mental stress.

[0311] Next, the server searches the database for appropriate medical institutions based on the analysis results. Taking into account the user's current location and past usage history, it generates a list of the most suitable medical institutions. For example, it may list hospitals and clinics that can treat "tension headaches" and "mental stress."

[0312] Furthermore, if symptoms are mild and home care is appropriate, the server will suggest over-the-counter medications and specific home care methods to the user, such as taking over-the-counter painkillers (e.g., ibuprofen), staying hydrated, and providing specific advice on relaxation techniques.

[0313] Finally, the server generates a message containing the analysis results and recommended information (medical institution, home care, over-the-counter medication), encrypts it, and sends it to the user's device. The user's device displays this message, allowing them to check information about appropriate medical institutions and treatment options. For example, the message might read, "Your headache may be a tension headache. Please visit the hospital listed below. We also recommend taking over-the-counter medication ibuprofen and drinking plenty of fluids."

[0314] As a specific example, if a user enters "I have had a headache for three days" into a questionnaire and the emotion recognition engine detects "anxiety," the server will determine that the headache is likely tension headache or mental stress, and will recommend a medical institution that can treat the condition, as well as suggest over-the-counter medications and home care methods.

[0315] An example of a prompt sentence is, "I have had a headache for three days and would like you to recommend an appropriate medical institution. I am currently feeling a little anxious."

[0316] This system enables users to quickly find the appropriate medical institution and treatment for their symptoms, thereby improving the efficiency of medical care delivery and the effective use of resources.

[0317] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0318] Step 1:

[0319] Users access a dedicated application using a smartphone or computer. They enter information about their symptoms, onset time, frequency, intensity, related symptoms, etc. into a questionnaire. This generates interview data. For example, users enter detailed information such as "I have had a headache for three days, it started at the beginning of the week, and it is moderate in intensity."

[0320] Input: Information about the user's symptoms (e.g., headache, onset time, frequency, intensity)

[0321] Output: Interview data (e.g., headache lasting 3 days, onset since the beginning of the week, moderate severity)

[0322] Step 2:

[0323] The terminal sends the medical interview data entered by the user to the server using encryption technology (e.g., SSL / TLS). The data is protected securely during the data transmission process.

[0324] Input: Medical interview data

[0325] Output: Encrypted interview data

[0326] Step 3:

[0327] While the user continues to fill out the questionnaire, the device activates an emotion recognition engine via the camera, microphone, and touchscreen to analyze the user's facial expressions, voice tone, and typing speed in real time. For example, it detects an anxious voice or a panicked expression and tags it as "anxious."

[0328] Input: User facial expressions, tone of voice, and typing speed

[0329] Output: Emotion data (e.g., anxiety, stress)

[0330] Step 4:

[0331] The device combines the emotional data and the medical interview data and transmits them to the server in an encrypted form.

[0332] Input: Integrated interview data and emotion data

[0333] Output: Encrypted consolidated data

[0334] Step 5:

[0335] The server receives the encrypted interview data and emotion data and stores them in a database (e.g., MySQL, PostgreSQL). The data is then input into a generative AI model (e.g., GPT-4, BERT) to begin analysis. For example, it analyzes "headache, 3 days" and "anxiety" to determine the likelihood of tension headache and mental stress.

[0336] Input: Encrypted integrated data

[0337] Output: Analysis results from the generative AI model (e.g., tension headache, mental stress determination)

[0338] Step 6:

[0339] Based on the analysis results, the server searches the database for appropriate medical institutions, taking into account the user's current location and past usage history. For example, it may list hospitals and clinics that can treat tension headaches and mental stress.

[0340] Input: Analysis results of the generated AI model, user's current location information, past usage history

[0341] Output: A list of recommended medical facilities

[0342] Step 7:

[0343] If symptoms are mild and home care is appropriate, the server will suggest over-the-counter medications and specific home care methods to the user, such as taking over-the-counter painkillers (e.g., ibuprofen), staying hydrated, and taking relaxation techniques.

[0344] Input: Analysis results, severity of user's symptoms

[0345] Output: Over-the-counter medication suggestions, home care methods

[0346] Step 8:

[0347] The server generates a message containing the analysis results and recommended information (medical institutions, home care, over-the-counter medications), encrypts it, and sends it to the user's device. The user's device displays this message, allowing the user to check information about appropriate medical institutions and treatment options.

[0348] Input: Analysis results and recommendations

[0349] Output: A message to be displayed on the user's device (e.g., "Your headache may be a tension headache. Please seek medical advice from the clinic listed below. We also recommend taking over-the-counter ibuprofen and drinking plenty of fluids.")

[0350] By following specific steps in order, users can quickly and accurately find medical institutions and treatments that suit their symptoms.

[0351] (Application example 2)

[0352] 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."

[0353] Conventional medical interview systems only require users to input specific symptoms and do not take into account their emotional state, making it difficult to recommend the most appropriate medical institution. Furthermore, they do not offer home care or dietary suggestions, making them unable to address individual user needs. Furthermore, there are no systems that can recommend appropriate meals based on the user's health condition.

[0354] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving medical interview data and emotion data and analyzing symptoms and emotion state using a generative AI model, means for searching for and recommending appropriate medical institutions or meals based on the analysis results, and means for recommending home care methods or over-the-counter drugs, or means for recommending appropriate menus, based on the analysis results. This makes it possible to comprehensively analyze the user's health condition and emotional state and recommend medical institutions, home care, and even appropriate meals.

[0355] A "medical questionnaire" is a paper or digital form that a user fills out to describe their symptoms and health status.

[0356] "Emotion data" is data that indicates the emotional state of a user, obtained from their facial expressions and voice quality.

[0357] A "generative AI model" is an artificial intelligence model used to analyze medical interview data and emotional data.

[0358] "Server" is a central processing unit for receiving, analyzing, and making recommendations on data sent by users.

[0359] "Analysis results" are the assessment of symptoms and emotional state obtained by the generative AI model.

[0360] A "medical institution" is a place that provides medical services, such as a hospital or clinic.

[0361] "Home care methods" are suggestions for health management and treatment methods that can be carried out at home.

[0362] "Over-the-counter drugs" are drugs sold in pharmacies and stores.

[0363] A "menu" is a list of food options or dishes.

[0364] "Notification" refers to the act or means of informing the user of the analysis results and recommendations.

[0365] This invention relates to a system that analyzes medical interview data and emotional data to recommend appropriate medical institutions and meals to users. This system uses a generative AI model to analyze the health and emotional state input by the user and provides optimal recommendations.

[0366] System Overview

[0367] The system consists of the following steps:

[0368] 1. Input of user interview data and emotion data

[0369] 2. Sending data to the server

[0370] 3. Data analysis using generative AI models

[0371] 4. Recommendations for medical institutions and meals based on analysis results

[0372] 5. Notice to Users

[0373] Detailed embodiment

[0374] Input of user interview data and emotion data

[0375] The user accesses the questionnaire through a dedicated application using a smartphone or smart glasses. The questionnaire contains questions about the user's health condition (e.g., stomachache, fatigue, insomnia, etc.), and the user enters this information. In addition, the camera and microphone on the smart glasses or smartphone analyze the user's facial expressions and voice tone as they enter the information, capturing emotional data.

[0376] Sending data to the server

[0377] Health and emotional data entered by users is encrypted through the application and sent to a server, preventing unauthorized access and information leaks.

[0378] Data analysis with generative AI models

[0379] The server receives the data and stores it in a database. It then passes the data to a generative AI model that analyzes the health and emotional state of the person, using the Hugging Face Transformer library.

[0380] Recommendations for medical institutions and meals based on analysis results

[0381] Based on the analysis results, the server searches the database for medical institutions and meal menus that can accommodate the user's condition, and lists the most suitable options, taking into account the user's current location and past usage history.

[0382] User Notification

[0383] The final analysis results in recommendations for appropriate medical institutions, home care methods, over-the-counter medicines, or meal plans, which are then sent from the server to the user's device and displayed for easy access.

[0384] Specific examples

[0385] For example, if a user inputs "stomach pain" and "anxiety" is detected from the emotional data, the generative AI model will use the user's analysis data to recommend a gastroenterologist near the user. It will also suggest "porridge" or "chamomile tea" as home care.

[0386] Prompt Sentence Examples

[0387] The user can enter messages into the application such as:

[0388] "I have a terrible stomach ache and I feel anxious."

[0389] This allows the system to comprehensively analyze the user's health and emotional state and provide the most appropriate response.

[0390] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0391] Step 1:

[0392] The user accesses the questionnaire through a dedicated application using a smartphone or smart glasses. The user inputs their health condition (e.g., stomachache, fatigue, insomnia, etc.). The input data is temporarily stored on the user's device. At the same time, the camera and microphone on the smart glasses or smartphone capture the user's facial expressions and voice quality to generate emotional data. This emotional data is also stored on the device.

[0393] Input: User's health condition data, facial expression data, voice quality data

[0394] Output: Temporary storage of medical interview data and emotional data

[0395] Step 2:

[0396] The questionnaire data entered by the user and the acquired emotion data are sent to the server in a securely encrypted format via a dedicated application. The encryption is performed using standard security protocols (e.g., AES encryption). The server receives this data and stores it in a database.

[0397] Input: User interview data, emotion data

[0398] Output: Encrypted data sent to server, stored in database

[0399] Step 3:

[0400] The server passes the received medical interview data and emotional data to the generative AI model for analysis. The generative AI model evaluates the user's symptoms and emotional state based on the data. For example, it analyzes data on "stomach pain" and "anxiety" and selects the most appropriate medical institution and meal options based on that combination.

[0401] Input: medical interview data, emotion data

[0402] Output: Symptom and emotional state assessment results

[0403] Step 4:

[0404] Based on the analysis results, the server searches the database and lists appropriate medical institutions or meal menus. The user's current location and past usage history are also taken into consideration. For example, "gastroenterologists" or "meals that are good for the stomach (such as porridge)" may be listed.

[0405] Input: Symptom and emotional state assessment results, current location information, past usage history

[0406] Output: List of medical institutions or meal menus

[0407] Step 5:

[0408] The server sends a list of medical institutions, meal menus, home care methods, and over-the-counter drug recommendations to the user's device. The sent information is notified to the user's smartphone. The user receives a notification and can view the most suitable medical institutions and meal suggestions.

[0409] Input: Listed medical institutions, meal menus

[0410] Output: Notification to user terminal, confirmation by user

[0411] For example, if a user inputs "I have a severe stomachache and I'm feeling anxious," the system will analyze this input and recommend a gastroenterologist near the user or a stomach-friendly meal (such as porridge). The user will receive a notification and be able to confirm the recommendation.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] [Second embodiment]

[0416] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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."

[0428] The present invention provides a specific embodiment of a system for efficiently evaluating a patient's symptoms and recommending appropriate medical institutions and treatments. This system is composed of multiple components, each of which works in conjunction with the other components to provide comprehensive medical care.

[0429] System configuration

[0430] 1. User fills out questionnaire

[0431] Device: The user accesses the questionnaire through a dedicated application on a smartphone or PC. The questionnaire contains questions about symptoms, onset time, frequency of symptoms, degree of pain, etc., and the user enters this information.

[0432] Example: A user enters "I've had a headache for three days" along with any related symptoms and further information.

[0433] 2. Sending medical interview data to the server

[0434] Terminal: The medical interview data entered by the user is sent to the server via the application. The data is securely encrypted before transmission, preventing unauthorized access and information leaks.

[0435] 3. Receipt and analysis of medical interview data by the server

[0436] Server: The server stores the received medical interview data in a database and automatically passes the data to a generative AI model to begin analyzing the symptoms.

[0437] Example: A server receives the data "Headache, 3 days", and a generative AI model analyzes it to determine whether it is a tension headache or a migraine.

[0438] 4. Recommendation of appropriate medical institutions

[0439] Server: Based on the analysis results, the server searches for an appropriate medical institution that can treat the symptoms from among the medical institutions registered in the database. This is highly convenient as it also takes into account the user's current location information and past usage history.

[0440] Example: If the diagnosis is "tension headache," the server will list hospitals and clinics with headache specialists.

[0441] 5. Proposal of home medical care and recommendation of over-the-counter medications

[0442] Server: If symptoms are mild and can be managed at home, the server will suggest specific home care methods and over-the-counter medications.

[0443] Example: Suggestions such as "Take over-the-counter pain medication (e.g., ibuprofen) and drink plenty of fluids" may be generated.

[0444] 6. Notification of analysis results and recommendations

[0445] Server: The server generates a message containing the analysis results and information on recommended medical institutions, home care methods, and over-the-counter medications, and sends it to the user's device.

[0446] Device: The user's device will display the received message and allow the user to review the recommended information.

[0447] Example: The user sees the message, "Your headache may be a tension headache. Please seek medical advice from the clinic listed below. We also recommend trying over-the-counter ibuprofen."

[0448] This invention allows users to easily and quickly access appropriate medical institutions and treatments, thereby improving the efficiency of medical care provision and the effective use of resources. In addition, by proposing home medical care, it also provides a way to treat mild symptoms at home, thereby contributing to reducing the burden on medical institutions.

[0449] The processing flow will be explained below.

[0450] Step 1:

[0451] A user feels unwell and launches a dedicated application on their smartphone or computer. A login screen appears, and the user enters their authentication information to log in to their account.

[0452] Step 2:

[0453] The terminal displays a questionnaire screen to the logged-in user. The questionnaire includes questions about the type of symptoms, onset time, frequency, intensity, and related symptoms (e.g., headache, dizziness, nausea, etc.).

[0454] Step 3:

[0455] The user enters their symptoms into the questionnaire, for example, "I've had a headache for three days" or "I feel nauseous," and taps the "Done" button.

[0456] Step 4:

[0457] The terminal collects the medical interview data entered by the user and formats it. The formatted data is then encrypted and securely sent to the server.

[0458] Step 5:

[0459] The server decodes the received medical interview data and stores it in a database. After saving, it automatically prepares to transfer the data to the generative AI model.

[0460] Step 6:

[0461] The server's generated AI model analyzes the interview data and makes an initial assessment of the symptoms. For example, based on the input information "headache" and "3 days," it assesses the possibility of a tension headache or migraine.

[0462] Step 7:

[0463] The server receives the analysis results of the AI ​​model and initiates the process of searching for an appropriate medical institution based on the analysis results, taking into account the user's current location information and past usage history.

[0464] Step 8:

[0465] The server identifies and lists medical institutions that can treat the symptoms from the database, including information such as the name, address, contact information, and specialty of the medical institution.

[0466] Step 9:

[0467] The server then executes a process based on the analysis results to suggest home medical care or over-the-counter medications. For example, for a mild headache, it may recommend an over-the-counter painkiller (e.g., ibuprofen).

[0468] Step 10:

[0469] The server compiles a list of appropriate medical institutions, home medical care, and over-the-counter drug suggestions, generates a message for the user, and initiates a procedure to send this message to the terminal.

[0470] Step 11:

[0471] The terminal decodes the message received from the server and displays the results to the user, who can then decide which medical institution to use or whether to seek home care.

[0472] Step 12:

[0473] The user checks the presented information and decides on the next action to take, such as making an appointment with the presented hospital or purchasing over-the-counter medicine to treat the condition at home.

[0474] Example 1

[0475] 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."

[0476] In today's world, medical institutions are often used frequently, making it difficult for patients to make appointments. It is also difficult to determine whether or not a patient needs to visit a medical institution, even if their symptoms are mild. Another issue is that it takes time for patients to find an appropriate medical institution. The present invention aims to solve these problems and provide a system that enables patients to easily and quickly access appropriate medical institutions and treatments.

[0477] 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.

[0478] In this invention, the server includes an information processing device for the user to input a medical questionnaire, a communication device for transmitting the medical questionnaire data input by the user to the server, and a data processing device for receiving the medical questionnaire data in the server and analyzing symptoms using a generative AI model. This enables the user to input symptoms, have them quickly analyzed using the generative AI model, and recommend appropriate medical institutions and treatments.

[0479] An "information processing device" is a device that includes hardware and software for processing data entered by a user and enabling it to be entered into a questionnaire.

[0480] "Communication means" refers to means including protocols and infrastructure for transmitting data entered from a user's terminal to a server.

[0481] "Data processing means" refers to a system that analyzes data received by the server and performs calculations and data analysis to diagnose symptoms using a generative AI model.

[0482] The "search means" refers to the algorithms and software that search for information in the database based on the analysis results and find appropriate medical institutions.

[0483] The "recommendation method" is a system that includes data generation and recommendation functions to suggest home medical care methods and over-the-counter drug information based on the analysis results.

[0484] The "notification means" is a means including a message generation and transmission function for notifying the user of the generated recommendations and diagnostic results.

[0485] A "user interface" is a system that includes a screen and input devices through which a user enters initial symptoms and other information.

[0486] A "generative AI model" is a type of machine learning model used to diagnose symptoms and make recommendations based on input medical interview data.

[0487] This invention is a system that efficiently evaluates a patient's symptoms and recommends appropriate medical institutions and treatments. This system utilizes a user terminal, a server, and a generative AI model to analyze the medical interview data entered by the user and provide medical information based on the results.

[0488] Input and transmission of medical interview data via user terminal

[0489] Users access a dedicated application using an information processing device such as a smartphone or PC. They answer questions about symptoms, when they first started, how often they experienced symptoms, and how much pain they felt, according to a questionnaire displayed in the application. For example, a user might enter detailed information such as "I've had a headache for three days," "The pain occurs several times a day," and "The pain is moderate."

[0490] The entered data is encrypted by the user's device and securely transmitted to the server using AES encryption and the HTTPS protocol.

[0491] Data reception and analysis by the server

[0492] The server receives the encrypted medical interview data, decrypts it, and stores it in a database that stores the user's past medical information and current medical interview data.

[0493] The received data is immediately passed to the generative AI model, where analysis begins. This generative AI model is written in Python and analyzes the user's symptoms based on pre-trained medical data. Specifically, the server sends data to the generative AI model in the format "User ID 1234: Symptom Data" and receives the analysis results.

[0494] Recommendations for medical institutions and home medical care

[0495] Based on the analysis results, the server searches the registered medical institutions in the database and lists appropriate medical institutions. The server also takes into account the user's current location and past medical history. For example, if a patient is diagnosed with tension headache, the server will select a hospital or clinic with a doctor specializing in headaches.

[0496] If symptoms are mild and home medical care is recommended, specific home care methods and over-the-counter medication information will also be provided. For example, the server will generate a recommendation message such as, "Take over-the-counter painkillers (e.g., ibuprofen) and drink plenty of fluids."

[0497] User Notification

[0498] Finally, the server generates a message containing the analysis results and recommendations, and sends it to the user's device. The user's device receives the message and displays it on its screen. For example, the user's screen may display a message saying, "Your headache may be a tension headache. Please visit the hospital listed below. We also recommend trying the over-the-counter medication ibuprofen."

[0499] Example prompts for generative AI models

[0500] "The patient has had a headache for three days, with moderate pain several times a day. Based on these symptoms, what is the likely diagnosis and what medical or over-the-counter medications would you recommend?"

[0501] "Analyze the user's medical interview data and generate optimal diagnostic results and a list of recommended medical institutions."

[0502] The present invention allows users to quickly and appropriately obtain medical information, thereby improving the efficiency of medical care provision and the effective use of resources. In addition, by proposing home care, it also contributes to reducing the burden on medical institutions.

[0503] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0504] Step 1:

[0505] User accesses the questionnaire

[0506] Input: A user accesses a dedicated application using a smartphone or computer.

[0507] Specific operation: A user installs and launches the application, then enters their user ID and password on the login screen to log in to their account.

[0508] Output: The user reaches the questionnaire entry screen and the questionnaire is displayed.

[0509] Step 2:

[0510] The user fills out the questionnaire

[0511] Input: The user answers the questions displayed in the questionnaire (e.g., symptoms, time of onset, frequency of symptoms, pain level).

[0512] What happens: The user enters details such as "I have had a headache for three days," "The pain occurs several times a day," and "The pain is moderate."

[0513] Output: The entered medical interview data is temporarily saved in the application.

[0514] Step 3:

[0515] The device sends the medical interview data to the server.

[0516] Input: Interview data entered by the user.

[0517] Specific operation: The terminal encrypts the medical interview data using AES and sends the data to the server using the HTTPS protocol.

[0518] Output: The encrypted interview data is sent to the server.

[0519] Step 4:

[0520] The server receives and decrypts the interview data.

[0521] Input: Encrypted medical interview data sent from the terminal.

[0522] What it does: The server decrypts the received data and stores it in an SQL database, which contains information such as the user ID, symptom data, and the date and time of entry.

[0523] Output: The decrypted interview data is stored in the database.

[0524] Step 5:

[0525] The server starts analysis using the generated AI model

[0526] Input: Medical interview data stored in a database.

[0527] How it works: The server passes medical interview data as input to a generative AI model written in Python. For example, data is sent to the generative AI model in the form of "User ID 1234: headache lasting 3 days, frequency several times a day, moderate pain."

[0528] Output: As a result of the analysis, the generative AI model outputs information such as the diagnosis and recommended medical institution.

[0529] Step 6:

[0530] The server searches for an appropriate medical institution

[0531] Input: Analysis results obtained from a generative AI model.

[0532] Specific operation: Based on the analysis results (e.g., tension headache), the server searches for appropriate medical institutions from the database, taking into account the user's current location information and past medical institution usage history. A list of medical institutions is obtained using an SQL query.

[0533] Output: A list of recommended medical institutions is generated.

[0534] Step 7:

[0535] The server provides home care and over-the-counter medication suggestions.

[0536] Input: Analysis results of the generative AI model and medical institution search results.

[0537] Specific operation: If the server detects a "tension headache," it generates information on home care methods and over-the-counter medications (e.g., ibuprofen). For example, it generates suggestions such as "Take over-the-counter painkillers and drink plenty of fluids."

[0538] Output: A recommendation message is generated containing home care and over-the-counter medication information.

[0539] Step 8:

[0540] The server generates and sends notifications of analysis results and recommendations.

[0541] Input: Recommendation message, list of healthcare providers.

[0542] Specific operation: The server combines the recommended message with the list of medical institutions to generate a notification message for the user, and then sends the message to the user's device using an HTTP request.

[0543] Output: A notification message is sent that the user receives.

[0544] Step 9:

[0545] The device displays a notification message

[0546] Input: The notification message sent by the server.

[0547] Specific behavior: The device receives a notification and displays it to the user in the application. For example, it displays the message, "Your headache may be a tension headache. Please visit the hospital listed below. We also recommend trying the over-the-counter medication ibuprofen."

[0548] Output: User can see the recommended information.

[0549] (Application example 1)

[0550] 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."

[0551] There is a need to grasp the health status of workers in factories in real time and quickly provide them with appropriate medical institutions and treatment. However, currently, checking their health status and selecting medical institutions is time-consuming and laborious, which can delay appropriate measures. Furthermore, if symptoms are mild, it is desirable to avoid unnecessary use of medical institutions and provide home care options. Against this background, a new system is needed to streamline medical responses in factories and optimize worker health management.

[0552] 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.

[0553] In this invention, the server includes: a means for a user to input a medical questionnaire; a means for transmitting the medical questionnaire data input by the user to the server; a means for receiving the medical questionnaire data in the server and analyzing symptoms using a generative AI model; a means for searching for and recommending an appropriate medical institution based on the analysis results; a means for proposing home medical care and recommending over-the-counter medications based on the analysis results; a means for notifying the user of the recommendation results, home medical care suggestions, and over-the-counter medication recommendations; a means for checking the health status of employees while they are working in a factory and suggesting appropriate medical institutions and treatments based on the results; and a means for monitoring the health status of workers in real time and securely encrypting and transmitting the data. This enables the health status of workers in a factory to be quickly evaluated and appropriate medical institutions and treatments to be provided. Furthermore, home care options are also provided for mild symptoms, avoiding unnecessary use of medical institutions.

[0554] A "medical questionnaire" is a form in which a user enters detailed information about their health condition, such as symptoms, onset time, and frequency of pain.

[0555] "Means" refers to the devices or methods used by the system to achieve a specific function or operation.

[0556] A "user" is someone who uses the system to assess their own health status and receive recommendations for appropriate medical institutions and treatments.

[0557] "Data" refers to health and symptom information entered by users, including details of symptoms, onset time, frequency, and pain level.

[0558] "Server" refers to the computer system that receives and analyzes data sent by users, thereby storing the data and analyzing it using AI models.

[0559] A "generative AI model" refers to a machine learning model that analyzes received data and recommends appropriate treatments and medical institutions based on the user's symptoms.

[0560] "Analysis" refers to the process of using a generative AI model to evaluate and interpret the user's medical interview data and identify the most appropriate treatment and medical institution based on the symptoms.

[0561] "Medical institution" refers to a facility that provides specialized medical services, such as a hospital, clinic, or medical office.

[0562] "Home medical care" refers to medical care and treatments that users can receive at home for mild symptoms. The system will suggest home medical care according to the situation.

[0563] "Over-the-counter drugs" refer to medicines that can be purchased at general pharmacies and drug stores and do not require a prescription.

[0564] "Recommendation" refers to the act of presenting the most appropriate medical institution or treatment method to the user based on the analysis results of the generative AI model.

[0565] "Notification" refers to the process of communicating analysis results and recommendations to users, allowing them to obtain the information they need in a timely manner.

[0566] "Employees" refers specifically to workers in factories, who can use the system to check their own health status and obtain appropriate medical care and treatment.

[0567] "Real-time" means that health status checks, data transmission, and analysis are carried out immediately, allowing for rapid health management of workers.

[0568] "Monitoring" refers to the process of continuously monitoring the health status of workers and responding promptly to any abnormalities.

[0569] "Data encryption" refers to the technology that securely protects data being transmitted in order to protect user privacy, thereby preventing unauthorized access and information leaks.

[0570] MODE FOR CARRYING OUT THE INVENTION

[0571] The present invention provides a system that monitors the health status of workers in factories in real time and promptly provides appropriate medical institutions and treatments. This system automates the process in which workers check their own health status and receive recommendations for medical institutions and treatments based on the results.

[0572] System configuration

[0573] This system consists of the following components:

[0574] 1. Input method for the questionnaire:

[0575] Workers access the questionnaire using the touch display of the factory robot, a smartphone, or a tablet, and enter information such as symptoms, when they first started, how often they are sick, and how much pain they are in.

[0576] 2. How to send medical interview data to the server:

[0577] The medical interview data entered by the worker is securely encrypted and sent to the server using the Cryptography library, and the Requests library is used to send the data.

[0578] 3. Means of receiving and analyzing interview data:

[0579] The server stores the received medical interview data in a database and simultaneously begins analysis using a generative AI model, which identifies appropriate treatments and medical institutions based on the symptom data.

[0580] 4. Medical institution recommendation methods:

[0581] Based on the analysis results, the server searches for and recommends the best medical facility for the patient's symptoms from among the medical institutions registered in the database, taking into account the user's current location and past usage history.

[0582] 5. Home care suggestions and over-the-counter medication recommendations:

[0583] If symptoms are mild, the server will suggest specific home care methods and over-the-counter medications based on the analysis results.

[0584] 6. Means of communication of analysis results and recommendations:

[0585] The server generates a message containing the analysis results and information on recommended medical institutions, home care methods, and over-the-counter medications, decrypts it, and sends it to the worker's device, allowing the worker to receive the appropriate information quickly.

[0586] Hardware and Software Configuration

[0587] Hardware:

[0588] Touch display for factory robots

[0589] Workers' smartphones and tablets

[0590] software:

[0591] Python

[0592] Cryptography Library

[0593] Requests library

[0594] Specific examples

[0595] As the factory nears closing time, an employee types in "I've had a headache for three days." The data is sent to the server using the following prompt statement:

[0596] Example prompt sentence:

[0597] Symptom: Headache

[0598] Onset period: 3 days

[0599] Severity: Moderate

[0600] The server performs the analysis and provides the following information:

[0601] Analysis result: "Possible tension headache."

[0602] Recommended medical institution: "XX Hospital (has a headache specialist)"

[0603] Home care suggestion: "I recommend taking over-the-counter pain medication (e.g., ibuprofen) and staying hydrated."

[0604] This allows workers to take appropriate action quickly.

[0605] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0606] Step 1:

[0607] Users access the questionnaire from the touch display of a robot in the factory or their own smartphone and enter information such as symptoms, onset time, frequency of symptoms, and pain level. This information is collected using the questionnaire's input method. The input data is recorded on the terminal as text prompts such as "Symptoms: headache," "Duration: 3 days," and "Severity: moderate."

[0608] Step 2:

[0609] The terminal encrypts the entered medical interview data and sends it to the server. The encryption is performed using the Cryptography library, and the data is sent to the server using the Request library. The text data received as input is sent to the server in encrypted form, and the actual data sent is an encrypted string.

[0610] Step 3:

[0611] The server receives the encrypted data sent from the device, decrypts it, and stores it in the database. The received data is an encrypted string, and the server decrypts it and stores it in the database as "Symptom: Headache," "Duration: 3 days," and "Severity: Moderate."

[0612] Step 4:

[0613] The server uses a generative AI model to analyze the medical interview data in the database and identify appropriate treatments and medical institutions based on the user's symptoms. This model receives symptom data as input and outputs a symptom diagnosis and recommendation by optimizing its objective function. Specifically, the model receives the data "headache," "3 days," and "moderate severity," and obtains the analysis result "high probability of tension-type headache."

[0614] Step 5:

[0615] Based on the analysis results of the generative AI model, the server searches for appropriate facilities from among the medical institutions registered in the database and recommends them to the user. The server also takes into consideration the user's current location information and past usage history to generate a list of optimal medical institutions. Based on the analysis results and location information as input, it generates a list of medical institutions such as "XX Hospital (with headache specialists)" as output.

[0616] Step 6:

[0617] Based on the analysis results, the server also generates recommendations for home care methods and over-the-counter medications. For mild symptoms, specific advice such as "take over-the-counter painkillers (e.g., ibuprofen) and drink plenty of fluids" is generated. In this process, the generative AI model's subsystem takes symptom data and analysis results as input and outputs recommended care methods.

[0618] Step 7:

[0619] The server generates a message notifying the user of the analysis results, recommended medical institutions, and home medical care suggestions, and sends it securely encrypted. The user's device receives this notification and displays the analysis results and recommendations. The output message contains specific information such as "You may have a tension headache," "Visit XX Hospital (where a headache specialist is located)," and "Take over-the-counter painkillers."

[0620] This series of processing steps allows users to assess their health status in real time and quickly receive recommendations for necessary medical institutions and treatments.

[0621] 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.

[0622] The present invention provides a specific embodiment of a system that efficiently evaluates a patient's symptoms and emotions and recommends appropriate medical institutions and treatments. This system consists of multiple components, such as a medical questionnaire input system, an emotion recognition engine, AI analysis, medical institution recommendation, and home care proposal system, which work in conjunction with each other.

[0623] System configuration

[0624] 1. User fills out questionnaire

[0625] Device: The user accesses the questionnaire through a dedicated application on a smartphone or PC. The questionnaire contains questions about symptoms, onset time, frequency, intensity, related symptoms, etc., and the user enters this information.

[0626] Example: A user enters "I've had a headache for three days" along with any related symptoms and further information.

[0627] 2. Sending medical interview data to the server

[0628] Terminal: The medical interview data entered by the user is sent to the server via the application. The data is securely encrypted before transmission, preventing unauthorized access and information leaks.

[0629] 3. Emotion engine for recognizing user emotions

[0630] On the device: As the user types, the emotion engine analyzes the user's facial expressions, tone of voice, typing speed, etc. to recognize their emotional state (e.g., stress, urgency, anxiety, etc.).

[0631] Example: As the user continues to type, the emotion engine detects anxiety in the user's tone of voice and tags it as "anxious."

[0632] 4. Integrated transmission of medical interview data and emotion data to the server

[0633] Terminal: The interview data and the recognized emotion data are integrated and sent to the server.

[0634] Server: The server stores the received data in a database and passes the data to the generative AI model to begin analysis.

[0635] 5. Analysis of medical interview data and emotional data by the server

[0636] Server: The generative AI model comprehensively analyzes the medical interview data and emotional data to evaluate symptoms and emotional state.

[0637] Example: Based on data on "headache, 3 days" and "anxiety," AI determines the possibility of tension headache and mental stress.

[0638] 6. Recommendation of appropriate medical institutions

[0639] Server: Based on the analysis results, the server searches for an appropriate medical institution that can treat the user's symptoms and emotional state from among the medical institutions registered in the database, taking into account the user's current location and past usage history.

[0640] Example: If the diagnosis is "tension headache" and "mental stress," a list of hospitals and clinics with headache specialists and psychiatrists will be provided.

[0641] 7. Proposal of home medical care and recommendation of over-the-counter medications

[0642] Server: If symptoms are mild and can be managed at home, the server will suggest specific home care methods and over-the-counter medications.

[0643] Example: Suggestions such as "Take over-the-counter painkillers (e.g., ibuprofen) and drink plenty of fluids" may be generated, along with suggestions for relaxation techniques and mental health support.

[0644] 8. Notification of analysis results and recommendations

[0645] Server: The server generates a message containing the analysis results and information on recommended medical institutions, home care methods, and over-the-counter medications, and sends it to the user's device.

[0646] Device: The user's device will display the received message and allow the user to review the recommended information.

[0647] Example: The user sees a message on their screen saying, "Your headache may be a tension headache. Please seek medical advice from the clinic listed below. We also recommend relaxation techniques along with over-the-counter ibuprofen."

[0648] This invention allows users to easily and quickly access appropriate medical institutions and treatments, thereby realizing more efficient medical care and effective use of resources. Furthermore, the introduction of an emotion engine enables comprehensive diagnosis and proposals that take into account the user's mental state, realizing the provision of more personalized medical services.

[0649] The processing flow will be explained below.

[0650] Step 1:

[0651] A user feels unwell and launches a dedicated application on their smartphone or computer. A login screen appears, and the user enters their authentication information to log in to their account.

[0652] Step 2:

[0653] The terminal displays a questionnaire screen to the logged-in user. The questionnaire includes questions about the type of symptoms, onset time, frequency, intensity, and related symptoms (e.g., headache, dizziness, nausea, etc.).

[0654] Step 3:

[0655] The user enters their symptoms into the questionnaire, for example, "I've had a headache for three days" or "I feel nauseous," and taps the "Done" button.

[0656] Step 4:

[0657] The terminal collects the medical interview data entered by the user and formats it. The formatted data is then encrypted and securely sent to the server.

[0658] Step 5:

[0659] The device activates an emotion engine while the user is typing, analyzing the user's facial expressions, tone of voice, typing speed, etc. to recognize their emotional state (e.g., stress, anxiety).

[0660] Step 6:

[0661] The device integrates the recognized emotion data with the medical interview data and sends it to the server, allowing both symptom and emotion data to be managed centrally.

[0662] Step 7:

[0663] The server stores the received medical interview data and emotion data in a database and passes it to the generative AI model to begin analysis.

[0664] Step 8:

[0665] The server's generated AI model analyzes the medical interview data and emotional data in an integrated manner to evaluate symptoms and emotional state. For example, it analyzes data such as "headache lasting three days" and "state of anxiety."

[0666] Step 9:

[0667] The server receives the analysis results of the AI ​​model and initiates the process of searching for an appropriate medical institution based on the analysis results, taking into account the user's current location information and past usage history.

[0668] Step 10:

[0669] The server identifies and lists medical institutions from a database that can treat the patient's symptoms and emotional state, including information such as the name, address, contact information, and specialty of the institution.

[0670] Step 11:

[0671] The server then executes a process based on the analysis results to suggest home medical care and over-the-counter medications. For example, for a mild headache, it may recommend an over-the-counter painkiller (e.g., ibuprofen). It also generates suggestions for relaxation techniques and mental health support based on emotion data.

[0672] Step 12:

[0673] The server compiles a list of appropriate medical institutions and home medical and over-the-counter drug suggestions, generates a message for the user, and initiates a procedure to send this message to the terminal.

[0674] Step 13:

[0675] The terminal decodes the message received from the server and displays the results to the user, who can then decide which medical institution to use or whether to seek home care.

[0676] Step 14:

[0677] The user checks the presented information and decides on the next action to take, such as making an appointment at the suggested hospital or purchasing over-the-counter medicine to treat the condition at home.

[0678] Example 2

[0679] 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."

[0680] Current healthcare systems lack the ability to efficiently evaluate patients' symptoms and quickly recommend appropriate medical institutions and treatments. Furthermore, because patients' emotional states are not reflected in diagnoses, it is difficult to provide personalized healthcare services. This can lead to worsening symptoms and inefficient use of healthcare resources.

[0681] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0682] In this invention, the server includes: [means for using an emotion engine to recognize the user's emotions;] [means for integrating the emotion data with medical interview data and sending it to the server; and] [means for receiving the medical interview data and emotion data in the server and analyzing the symptoms and emotional state using a generative AI model.] This makes it possible [to comprehensively evaluate the patient's symptoms and emotional state and quickly and individually recommend appropriate medical institutions and treatments].

[0683] A "medical questionnaire" is a paper or electronic questionnaire that a patient fills out to record their symptoms and medical history.

[0684] "User" refers to an individual who uses the system to complete a medical questionnaire and receive medical assistance.

[0685] A "server" is a central computer system that receives data sent by users, analyzes it, and returns the results.

[0686] An "emotion engine" is software or hardware that analyzes a user's facial expressions, voice, and input speed to recognize their emotional state.

[0687] "Medical Interview Data" refers to symptoms, medical history, and other health-related information entered by a user into a medical interview form.

[0688] "Emotion data" is data relating to the user's emotional state analyzed by the emotion engine.

[0689] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze data and evaluate symptoms and emotional states.

[0690] "Analysis results" refers to the evaluation and results of the generative AI model based on the medical interview data and emotional data.

[0691] "Healthcare facility" means a hospital, clinic, medical office, or other facility that provides health care services.

[0692] "Home healthcare" refers to medical care and treatments that can be performed at home.

[0693] "Over-the-counter" refers to drugs that can be purchased without a doctor's prescription.

[0694] "Encryption" refers to the process of using cryptographic techniques to securely transmit and receive data.

[0695] "Listing" refers to selecting appropriate options or items in list form based on specific conditions.

[0696] "Interface" refers to the means or device by which a user interacts with a system.

[0697] This invention provides a specific embodiment of a system that efficiently evaluates a patient's symptoms and emotions and recommends appropriate medical institutions and treatments. This system consists of multiple components, such as user questionnaire input, emotion recognition, AI analysis, medical institution recommendation, and home care suggestion, which work in conjunction with each other.

[0698] First, the user accesses a dedicated application using a smartphone or computer. Through this application, the user enters information about their symptoms, onset time, frequency, intensity, related symptoms, etc. into a questionnaire. For example, they enter detailed information such as "I have had a headache for three days, it started at the beginning of the week, and it is moderate in intensity."

[0699] The device then securely encrypts the medical interview data entered by the user and transmits it to the server using the SSL / TLS protocol. During this data transmission process, the user's data is always protected and is protected from unauthorized access and information leaks.

[0700] While the user continues to fill out the questionnaire, the emotion recognition engine analyzes the user's facial expressions, voice tone, and input speed in real time through the device's built-in camera, microphone, and touchscreen. For example, the emotion recognition engine will detect the user's anxious voice or impatient expression and tag it as "anxiety."

[0701] The device then combines the emotion data and the medical interview data and sends them back to the server in encrypted form. The server receives this data and stores it in a database (e.g., MySQL, PostgreSQL). The data is then input into a generative AI model (e.g., GPT-4, BERT) to begin analysis.

[0702] The generative AI model comprehensively analyzes the medical interview data and emotional data to evaluate symptoms and emotional state. For example, based on the data "headache, 3 days" and the emotional data "anxiety," it determines the possibility of tension headache and mental stress.

[0703] Next, the server searches the database for appropriate medical institutions based on the analysis results. Taking into account the user's current location and past usage history, it generates a list of the most suitable medical institutions. For example, it may list hospitals and clinics that can treat "tension headaches" and "mental stress."

[0704] Furthermore, if symptoms are mild and home care is appropriate, the server will suggest over-the-counter medications and specific home care methods to the user, such as taking over-the-counter painkillers (e.g., ibuprofen), staying hydrated, and providing specific advice on relaxation techniques.

[0705] Finally, the server generates a message containing the analysis results and recommended information (medical institution, home care, over-the-counter medication), encrypts it, and sends it to the user's device. The user's device displays this message, allowing them to check information about appropriate medical institutions and treatment options. For example, the message might read, "Your headache may be a tension headache. Please visit the hospital listed below. We also recommend taking over-the-counter medication ibuprofen and drinking plenty of fluids."

[0706] As a specific example, if a user enters "I have had a headache for three days" into a questionnaire and the emotion recognition engine detects "anxiety," the server will determine that the headache is likely tension headache or mental stress, and will recommend a medical institution that can treat the condition, as well as suggest over-the-counter medications and home care methods.

[0707] An example of a prompt sentence is, "I have had a headache for three days and would like you to recommend an appropriate medical institution. I am currently feeling a little anxious."

[0708] This system enables users to quickly find the appropriate medical institution and treatment for their symptoms, thereby improving the efficiency of medical care delivery and the effective use of resources.

[0709] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0710] Step 1:

[0711] Users access a dedicated application using a smartphone or computer. They enter information about their symptoms, onset time, frequency, intensity, related symptoms, etc. into a questionnaire. This generates interview data. For example, users enter detailed information such as "I have had a headache for three days, it started at the beginning of the week, and it is moderate in intensity."

[0712] Input: Information about the user's symptoms (e.g., headache, onset time, frequency, intensity)

[0713] Output: Interview data (e.g., headache lasting 3 days, onset since the beginning of the week, moderate severity)

[0714] Step 2:

[0715] The terminal sends the medical interview data entered by the user to the server using encryption technology (e.g., SSL / TLS). The data is protected securely during the data transmission process.

[0716] Input: Medical interview data

[0717] Output: Encrypted interview data

[0718] Step 3:

[0719] While the user continues to fill out the questionnaire, the device activates an emotion recognition engine via the camera, microphone, and touchscreen to analyze the user's facial expressions, voice tone, and typing speed in real time. For example, it detects an anxious voice or a panicked expression and tags it as "anxious."

[0720] Input: User facial expressions, tone of voice, and typing speed

[0721] Output: Emotion data (e.g., anxiety, stress)

[0722] Step 4:

[0723] The device combines the emotional data and the medical interview data and transmits them to the server in an encrypted form.

[0724] Input: Integrated interview data and emotion data

[0725] Output: Encrypted consolidated data

[0726] Step 5:

[0727] The server receives the encrypted interview data and emotion data and stores them in a database (e.g., MySQL, PostgreSQL). The data is then input into a generative AI model (e.g., GPT-4, BERT) to begin analysis. For example, it analyzes "headache, 3 days" and "anxiety" to determine the likelihood of tension headache and mental stress.

[0728] Input: Encrypted integrated data

[0729] Output: Analysis results from the generative AI model (e.g., tension headache, mental stress determination)

[0730] Step 6:

[0731] Based on the analysis results, the server searches the database for appropriate medical institutions, taking into account the user's current location and past usage history. For example, it may list hospitals and clinics that can treat tension headaches and mental stress.

[0732] Input: Analysis results of the generated AI model, user's current location information, past usage history

[0733] Output: A list of recommended medical facilities

[0734] Step 7:

[0735] If symptoms are mild and home care is appropriate, the server will suggest over-the-counter medications and specific home care methods to the user, such as taking over-the-counter painkillers (e.g., ibuprofen), staying hydrated, and taking relaxation techniques.

[0736] Input: Analysis results, severity of user's symptoms

[0737] Output: Over-the-counter medication suggestions, home care methods

[0738] Step 8:

[0739] The server generates a message containing the analysis results and recommended information (medical institutions, home care, over-the-counter medications), encrypts it, and sends it to the user's device. The user's device displays this message, allowing the user to check information about appropriate medical institutions and treatment options.

[0740] Input: Analysis results and recommendations

[0741] Output: A message to be displayed on the user's device (e.g., "Your headache may be a tension headache. Please seek medical advice from the clinic listed below. We also recommend taking over-the-counter ibuprofen and drinking plenty of fluids.")

[0742] By following specific steps in order, users can quickly and accurately find medical institutions and treatments that suit their symptoms.

[0743] (Application example 2)

[0744] 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."

[0745] Conventional medical interview systems only require users to input specific symptoms and do not take into account their emotional state, making it difficult to recommend the most appropriate medical institution. Furthermore, they do not offer home care or dietary suggestions, making them unable to address individual user needs. Furthermore, there are no systems that can recommend appropriate meals based on the user's health condition.

[0746] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving medical interview data and emotion data and analyzing symptoms and emotion state using a generative AI model, means for searching for and recommending appropriate medical institutions or meals based on the analysis results, and means for recommending home care methods or over-the-counter drugs, or means for recommending appropriate menus, based on the analysis results. This makes it possible to comprehensively analyze the user's health condition and emotional state and recommend medical institutions, home care, and even appropriate meals.

[0747] A "medical questionnaire" is a paper or digital form that a user fills out to describe their symptoms and health status.

[0748] "Emotion data" is data that indicates the emotional state of a user, obtained from their facial expressions and voice quality.

[0749] A "generative AI model" is an artificial intelligence model used to analyze medical interview data and emotional data.

[0750] "Server" is a central processing unit for receiving, analyzing, and making recommendations on data sent by users.

[0751] "Analysis results" are the assessment of symptoms and emotional state obtained by the generative AI model.

[0752] A "medical institution" is a place that provides medical services, such as a hospital or clinic.

[0753] "Home care methods" are suggestions for health management and treatment methods that can be carried out at home.

[0754] "Over-the-counter drugs" are drugs sold in pharmacies and stores.

[0755] A "menu" is a list of food options or dishes.

[0756] "Notification" refers to the act or means of informing the user of the analysis results and recommendations.

[0757] This invention relates to a system that analyzes medical interview data and emotional data to recommend appropriate medical institutions and meals to users. This system uses a generative AI model to analyze the health and emotional state input by the user and provides optimal recommendations.

[0758] System Overview

[0759] The system consists of the following steps:

[0760] 1. Input of user interview data and emotion data

[0761] 2. Sending data to the server

[0762] 3. Data analysis using generative AI models

[0763] 4. Recommendations for medical institutions and meals based on analysis results

[0764] 5. Notice to Users

[0765] Detailed embodiment

[0766] Input of user interview data and emotion data

[0767] The user accesses the questionnaire through a dedicated application using a smartphone or smart glasses. The questionnaire contains questions about the user's health condition (e.g., stomachache, fatigue, insomnia, etc.), and the user enters this information. In addition, the camera and microphone on the smart glasses or smartphone analyze the user's facial expressions and voice tone as they enter the information, capturing emotional data.

[0768] Sending data to the server

[0769] Health and emotional data entered by users is encrypted through the application and sent to a server, preventing unauthorized access and information leaks.

[0770] Data analysis with generative AI models

[0771] The server receives the data and stores it in a database. It then passes the data to a generative AI model that analyzes the health and emotional state of the person, using the Hugging Face Transformer library.

[0772] Recommendations for medical institutions and meals based on analysis results

[0773] Based on the analysis results, the server searches the database for medical institutions and meal menus that can accommodate the user's condition, and lists the most suitable options, taking into account the user's current location and past usage history.

[0774] User Notification

[0775] The final analysis results in recommendations for appropriate medical institutions, home care methods, over-the-counter medicines, or meal plans, which are then sent from the server to the user's device and displayed for easy access.

[0776] Specific examples

[0777] For example, if a user inputs "stomach pain" and "anxiety" is detected from the emotional data, the generative AI model will use the user's analysis data to recommend a gastroenterologist near the user. It will also suggest "porridge" or "chamomile tea" as home care.

[0778] Prompt Sentence Examples

[0779] The user can enter messages into the application such as:

[0780] "I have a terrible stomach ache and I feel anxious."

[0781] This allows the system to comprehensively analyze the user's health and emotional state and provide the most appropriate response.

[0782] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0783] Step 1:

[0784] The user accesses the questionnaire through a dedicated application using a smartphone or smart glasses. The user inputs their health condition (e.g., stomachache, fatigue, insomnia, etc.). The input data is temporarily stored on the user's device. At the same time, the camera and microphone on the smart glasses or smartphone capture the user's facial expressions and voice quality to generate emotional data. This emotional data is also stored on the device.

[0785] Input: User's health condition data, facial expression data, voice quality data

[0786] Output: Temporary storage of medical interview data and emotional data

[0787] Step 2:

[0788] The questionnaire data entered by the user and the acquired emotion data are sent to the server in a securely encrypted format via a dedicated application. The encryption is performed using standard security protocols (e.g., AES encryption). The server receives this data and stores it in a database.

[0789] Input: User interview data, emotion data

[0790] Output: Encrypted data sent to server, stored in database

[0791] Step 3:

[0792] The server passes the received medical interview data and emotional data to the generative AI model for analysis. The generative AI model evaluates the user's symptoms and emotional state based on the data. For example, it analyzes data on "stomach pain" and "anxiety" and selects the most appropriate medical institution and meal options based on that combination.

[0793] Input: medical interview data, emotion data

[0794] Output: Symptom and emotional state assessment results

[0795] Step 4:

[0796] Based on the analysis results, the server searches the database and lists appropriate medical institutions or meal menus. The user's current location and past usage history are also taken into consideration. For example, "gastroenterologists" or "meals that are good for the stomach (such as porridge)" may be listed.

[0797] Input: Symptom and emotional state assessment results, current location information, past usage history

[0798] Output: List of medical institutions or meal menus

[0799] Step 5:

[0800] The server sends a list of medical institutions, meal menus, home care methods, and over-the-counter drug recommendations to the user's device. The sent information is notified to the user's smartphone. The user receives a notification and can view the most suitable medical institutions and meal suggestions.

[0801] Input: Listed medical institutions, meal menus

[0802] Output: Notification to user terminal, confirmation by user

[0803] For example, if a user inputs "I have a severe stomachache and I'm feeling anxious," the system will analyze this input and recommend a gastroenterologist near the user or a stomach-friendly meal (such as porridge). The user will receive a notification and be able to confirm the recommendation.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] [Third embodiment]

[0808] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0809] 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.

[0810] 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).

[0811] 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.

[0812] 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.

[0813] 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).

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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."

[0820] The present invention provides a specific embodiment of a system for efficiently evaluating a patient's symptoms and recommending appropriate medical institutions and treatments. This system is composed of multiple components, each of which works in conjunction with the other components to provide comprehensive medical care.

[0821] System configuration

[0822] 1. User fills out questionnaire

[0823] Device: The user accesses the questionnaire through a dedicated application on a smartphone or PC. The questionnaire contains questions about symptoms, onset time, frequency of symptoms, degree of pain, etc., and the user enters this information.

[0824] Example: A user enters "I've had a headache for three days" along with any related symptoms and further information.

[0825] 2. Sending medical interview data to the server

[0826] Terminal: The medical interview data entered by the user is sent to the server via the application. The data is securely encrypted before transmission, preventing unauthorized access and information leaks.

[0827] 3. Receipt and analysis of medical interview data by the server

[0828] Server: The server stores the received medical interview data in a database and automatically passes the data to a generative AI model to begin analyzing the symptoms.

[0829] Example: A server receives the data "Headache, 3 days", and a generative AI model analyzes it to determine whether it is a tension headache or a migraine.

[0830] 4. Recommendation of appropriate medical institutions

[0831] Server: Based on the analysis results, the server searches for an appropriate medical institution that can treat the symptoms from among the medical institutions registered in the database. This is highly convenient as it also takes into account the user's current location information and past usage history.

[0832] Example: If the diagnosis is "tension headache," the server will list hospitals and clinics with headache specialists.

[0833] 5. Proposal of home medical care and recommendation of over-the-counter medications

[0834] Server: If symptoms are mild and can be managed at home, the server will suggest specific home care methods and over-the-counter medications.

[0835] Example: Suggestions such as "Take over-the-counter pain medication (e.g., ibuprofen) and drink plenty of fluids" may be generated.

[0836] 6. Notification of analysis results and recommendations

[0837] Server: The server generates a message containing the analysis results and information on recommended medical institutions, home care methods, and over-the-counter medications, and sends it to the user's device.

[0838] Device: The user's device will display the received message and allow the user to review the recommended information.

[0839] Example: The user sees the message, "Your headache may be a tension headache. Please seek medical advice from the clinic listed below. We also recommend trying over-the-counter ibuprofen."

[0840] This invention allows users to easily and quickly access appropriate medical institutions and treatments, thereby improving the efficiency of medical care provision and the effective use of resources. In addition, by proposing home medical care, it also provides a way to treat mild symptoms at home, thereby contributing to reducing the burden on medical institutions.

[0841] The processing flow will be explained below.

[0842] Step 1:

[0843] A user feels unwell and launches a dedicated application on their smartphone or computer. A login screen appears, and the user enters their authentication information to log in to their account.

[0844] Step 2:

[0845] The terminal displays a questionnaire screen to the logged-in user. The questionnaire includes questions about the type of symptoms, onset time, frequency, intensity, and related symptoms (e.g., headache, dizziness, nausea, etc.).

[0846] Step 3:

[0847] The user enters their symptoms into the questionnaire, for example, "I've had a headache for three days" or "I feel nauseous," and taps the "Done" button.

[0848] Step 4:

[0849] The terminal collects the medical interview data entered by the user and formats it. The formatted data is then encrypted and securely sent to the server.

[0850] Step 5:

[0851] The server decodes the received medical interview data and stores it in a database. After saving, it automatically prepares to transfer the data to the generative AI model.

[0852] Step 6:

[0853] The server's generated AI model analyzes the interview data and makes an initial assessment of the symptoms. For example, based on the input information "headache" and "3 days," it assesses the possibility of a tension headache or migraine.

[0854] Step 7:

[0855] The server receives the analysis results of the AI ​​model and initiates the process of searching for an appropriate medical institution based on the analysis results, taking into account the user's current location information and past usage history.

[0856] Step 8:

[0857] The server identifies and lists medical institutions that can treat the symptoms from the database, including information such as the name, address, contact information, and specialty of the medical institution.

[0858] Step 9:

[0859] The server then executes a process based on the analysis results to suggest home medical care or over-the-counter medications. For example, for a mild headache, it may recommend an over-the-counter painkiller (e.g., ibuprofen).

[0860] Step 10:

[0861] The server compiles a list of appropriate medical institutions, home medical care, and over-the-counter drug suggestions, generates a message for the user, and initiates a procedure to send this message to the terminal.

[0862] Step 11:

[0863] The terminal decodes the message received from the server and displays the results to the user, who can then decide which medical institution to use or whether to seek home care.

[0864] Step 12:

[0865] The user checks the presented information and decides on the next action to take, such as making an appointment with the presented hospital or purchasing over-the-counter medicine to treat the condition at home.

[0866] Example 1

[0867] 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."

[0868] In today's world, medical institutions are often used frequently, making it difficult for patients to make appointments. It is also difficult to determine whether or not a patient needs to visit a medical institution, even if their symptoms are mild. Another issue is that it takes time for patients to find an appropriate medical institution. The present invention aims to solve these problems and provide a system that enables patients to easily and quickly access appropriate medical institutions and treatments.

[0869] 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.

[0870] In this invention, the server includes an information processing device for the user to input a medical questionnaire, a communication device for transmitting the medical questionnaire data input by the user to the server, and a data processing device for receiving the medical questionnaire data in the server and analyzing symptoms using a generative AI model. This enables the user to input symptoms, have them quickly analyzed using the generative AI model, and recommend appropriate medical institutions and treatments.

[0871] An "information processing device" is a device that includes hardware and software for processing data entered by a user and enabling it to be entered into a questionnaire.

[0872] "Communication means" refers to means including protocols and infrastructure for transmitting data entered from a user's terminal to a server.

[0873] "Data processing means" refers to a system that analyzes data received by the server and performs calculations and data analysis to diagnose symptoms using a generative AI model.

[0874] The "search means" refers to the algorithms and software that search for information in the database based on the analysis results and find appropriate medical institutions.

[0875] The "recommendation method" is a system that includes data generation and recommendation functions to suggest home medical care methods and over-the-counter drug information based on the analysis results.

[0876] The "notification means" is a means including a message generation and transmission function for notifying the user of the generated recommendations and diagnostic results.

[0877] A "user interface" is a system that includes a screen and input devices through which a user enters initial symptoms and other information.

[0878] A "generative AI model" is a type of machine learning model used to diagnose symptoms and make recommendations based on input medical interview data.

[0879] This invention is a system that efficiently evaluates a patient's symptoms and recommends appropriate medical institutions and treatments. This system utilizes a user terminal, a server, and a generative AI model to analyze the medical interview data entered by the user and provide medical information based on the results.

[0880] Input and transmission of medical interview data via user terminal

[0881] Users access a dedicated application using an information processing device such as a smartphone or PC. They answer questions about symptoms, when they first started, how often they experienced symptoms, and how much pain they felt, according to a questionnaire displayed in the application. For example, a user might enter detailed information such as "I've had a headache for three days," "The pain occurs several times a day," and "The pain is moderate."

[0882] The entered data is encrypted by the user's device and securely transmitted to the server using AES encryption and the HTTPS protocol.

[0883] Data reception and analysis by the server

[0884] The server receives the encrypted medical interview data, decrypts it, and stores it in a database that stores the user's past medical information and current medical interview data.

[0885] The received data is immediately passed to the generative AI model, where analysis begins. This generative AI model is written in Python and analyzes the user's symptoms based on pre-trained medical data. Specifically, the server sends data to the generative AI model in the format "User ID 1234: Symptom Data" and receives the analysis results.

[0886] Recommendations for medical institutions and home medical care

[0887] Based on the analysis results, the server searches the registered medical institutions in the database and lists appropriate medical institutions. The server also takes into account the user's current location and past medical history. For example, if a patient is diagnosed with tension headache, the server will select a hospital or clinic with a doctor specializing in headaches.

[0888] If symptoms are mild and home medical care is recommended, specific home care methods and over-the-counter medication information will also be provided. For example, the server will generate a recommendation message such as, "Take over-the-counter painkillers (e.g., ibuprofen) and drink plenty of fluids."

[0889] User Notification

[0890] Finally, the server generates a message containing the analysis results and recommendations, and sends it to the user's device. The user's device receives the message and displays it on its screen. For example, the user's screen may display a message saying, "Your headache may be a tension headache. Please visit the hospital listed below. We also recommend trying the over-the-counter medication ibuprofen."

[0891] Example prompts for generative AI models

[0892] "The patient has had a headache for three days, with moderate pain several times a day. Based on these symptoms, what is the likely diagnosis and what medical or over-the-counter medications would you recommend?"

[0893] "Analyze the user's medical interview data and generate optimal diagnostic results and a list of recommended medical institutions."

[0894] The present invention allows users to quickly and appropriately obtain medical information, thereby improving the efficiency of medical care provision and the effective use of resources. In addition, by proposing home care, it also contributes to reducing the burden on medical institutions.

[0895] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0896] Step 1:

[0897] User accesses the questionnaire

[0898] Input: A user accesses a dedicated application using a smartphone or computer.

[0899] Specific operation: A user installs and launches the application, then enters their user ID and password on the login screen to log in to their account.

[0900] Output: The user reaches the questionnaire entry screen and the questionnaire is displayed.

[0901] Step 2:

[0902] The user fills out the questionnaire

[0903] Input: The user answers the questions displayed in the questionnaire (e.g., symptoms, time of onset, frequency of symptoms, pain level).

[0904] What happens: The user enters details such as "I have had a headache for three days," "The pain occurs several times a day," and "The pain is moderate."

[0905] Output: The entered medical interview data is temporarily saved in the application.

[0906] Step 3:

[0907] The device sends the medical interview data to the server.

[0908] Input: Interview data entered by the user.

[0909] Specific operation: The terminal encrypts the medical interview data using AES and sends the data to the server using the HTTPS protocol.

[0910] Output: The encrypted interview data is sent to the server.

[0911] Step 4:

[0912] The server receives and decrypts the interview data.

[0913] Input: Encrypted medical interview data sent from the terminal.

[0914] What it does: The server decrypts the received data and stores it in an SQL database, which contains information such as the user ID, symptom data, and the date and time of entry.

[0915] Output: The decrypted interview data is stored in the database.

[0916] Step 5:

[0917] The server starts analysis using the generated AI model

[0918] Input: Medical interview data stored in a database.

[0919] How it works: The server passes medical interview data as input to a generative AI model written in Python. For example, data is sent to the generative AI model in the form of "User ID 1234: headache lasting 3 days, frequency several times a day, moderate pain."

[0920] Output: As a result of the analysis, the generative AI model outputs information such as the diagnosis and recommended medical institution.

[0921] Step 6:

[0922] The server searches for an appropriate medical institution

[0923] Input: Analysis results obtained from a generative AI model.

[0924] Specific operation: Based on the analysis results (e.g., tension headache), the server searches for appropriate medical institutions from the database, taking into account the user's current location information and past medical institution usage history. A list of medical institutions is obtained using an SQL query.

[0925] Output: A list of recommended medical institutions is generated.

[0926] Step 7:

[0927] The server provides home care and over-the-counter medication suggestions.

[0928] Input: Analysis results of the generative AI model and medical institution search results.

[0929] Specific operation: If the server detects a "tension headache," it generates information on home care methods and over-the-counter medications (e.g., ibuprofen). For example, it generates suggestions such as "Take over-the-counter painkillers and drink plenty of fluids."

[0930] Output: A recommendation message is generated containing home care and over-the-counter medication information.

[0931] Step 8:

[0932] The server generates and sends notifications of analysis results and recommendations.

[0933] Input: Recommendation message, list of healthcare providers.

[0934] Specific operation: The server combines the recommended message with the list of medical institutions to generate a notification message for the user, and then sends the message to the user's device using an HTTP request.

[0935] Output: A notification message is sent that the user receives.

[0936] Step 9:

[0937] The device displays a notification message

[0938] Input: The notification message sent by the server.

[0939] Specific behavior: The device receives a notification and displays it to the user in the application. For example, it displays the message, "Your headache may be a tension headache. Please visit the hospital listed below. We also recommend trying the over-the-counter medication ibuprofen."

[0940] Output: User can see the recommended information.

[0941] (Application example 1)

[0942] 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."

[0943] There is a need to grasp the health status of workers in factories in real time and quickly provide them with appropriate medical institutions and treatment. However, currently, checking their health status and selecting medical institutions is time-consuming and laborious, which can delay appropriate measures. Furthermore, if symptoms are mild, it is desirable to avoid unnecessary use of medical institutions and provide home care options. Against this background, a new system is needed to streamline medical responses in factories and optimize worker health management.

[0944] 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.

[0945] In this invention, the server includes: a means for a user to input a medical questionnaire; a means for transmitting the medical questionnaire data input by the user to the server; a means for receiving the medical questionnaire data in the server and analyzing symptoms using a generative AI model; a means for searching for and recommending an appropriate medical institution based on the analysis results; a means for proposing home medical care and recommending over-the-counter medications based on the analysis results; a means for notifying the user of the recommendation results, home medical care suggestions, and over-the-counter medication recommendations; a means for checking the health status of employees while they are working in a factory and suggesting appropriate medical institutions and treatments based on the results; and a means for monitoring the health status of workers in real time and securely encrypting and transmitting the data. This enables the health status of workers in a factory to be quickly evaluated and appropriate medical institutions and treatments to be provided. Furthermore, home care options are also provided for mild symptoms, avoiding unnecessary use of medical institutions.

[0946] A "medical questionnaire" is a form in which a user enters detailed information about their health condition, such as symptoms, onset time, and frequency of pain.

[0947] "Means" refers to the devices or methods used by the system to achieve a specific function or operation.

[0948] A "user" is someone who uses the system to assess their own health status and receive recommendations for appropriate medical institutions and treatments.

[0949] "Data" refers to health and symptom information entered by users, including details of symptoms, onset time, frequency, and pain level.

[0950] A "server" refers to a computer system that receives and analyzes data sent by users, thereby storing the data and analyzing it using AI models.

[0951] A "generative AI model" refers to a machine learning model that analyzes received data and recommends appropriate treatments and medical institutions based on the user's symptoms.

[0952] "Analysis" refers to the process of using a generative AI model to evaluate and interpret the user's medical interview data and identify the most appropriate treatment and medical institution based on the symptoms.

[0953] "Medical institution" refers to a facility that provides specialized medical services, such as a hospital, clinic, or medical office.

[0954] "Home medical care" refers to medical care and treatments that users can receive at home for mild symptoms. The system will suggest home medical care according to the situation.

[0955] "Over-the-counter drugs" refer to medicines that can be purchased at general pharmacies and drug stores and do not require a prescription.

[0956] "Recommendation" refers to the act of presenting the most appropriate medical institution or treatment method to the user based on the analysis results of the generative AI model.

[0957] "Notification" refers to the process of communicating analysis results and recommendations to users, allowing them to obtain the information they need in a timely manner.

[0958] "Employees" refers specifically to workers in factories, who can use the system to check their own health status and obtain appropriate medical care and treatment.

[0959] "Real-time" means that health status checks, data transmission, and analysis are carried out immediately, allowing for rapid health management of workers.

[0960] "Monitoring" refers to the process of continuously monitoring the health status of workers and responding promptly to any abnormalities.

[0961] "Data encryption" refers to the technology that securely protects data being transmitted in order to protect user privacy, thereby preventing unauthorized access and information leaks.

[0962] MODE FOR CARRYING OUT THE INVENTION

[0963] The present invention provides a system that monitors the health status of workers in factories in real time and promptly provides appropriate medical institutions and treatments. This system automates the process in which workers check their own health status and receive recommendations for medical institutions and treatments based on the results.

[0964] System configuration

[0965] This system consists of the following components:

[0966] 1. Input method for the questionnaire:

[0967] Workers access the questionnaire using the touch display of the factory robot, a smartphone, or a tablet, and enter information such as symptoms, when they first started, how often they are sick, and how much pain they are in.

[0968] 2. How to send medical interview data to the server:

[0969] The medical interview data entered by the worker is securely encrypted and sent to the server using the Cryptography library, and the Requests library is used to send the data.

[0970] 3. Means of receiving and analyzing interview data:

[0971] The server stores the received medical interview data in a database and simultaneously begins analysis using a generative AI model, which identifies appropriate treatments and medical institutions based on the symptom data.

[0972] 4. Medical institution recommendation methods:

[0973] Based on the analysis results, the server searches for and recommends the best medical facility for the patient's symptoms from among the medical institutions registered in the database, taking into account the user's current location and past usage history.

[0974] 5. Home care suggestions and over-the-counter medication recommendations:

[0975] If symptoms are mild, the server will suggest specific home care methods and over-the-counter medications based on the analysis results.

[0976] 6. Means of communication of analysis results and recommendations:

[0977] The server generates a message containing the analysis results and information on recommended medical institutions, home care methods, and over-the-counter medications, decrypts it, and sends it to the worker's device, allowing the worker to receive the appropriate information quickly.

[0978] Hardware and Software Configuration

[0979] Hardware:

[0980] Touch display for factory robots

[0981] Workers' smartphones and tablets

[0982] software:

[0983] Python

[0984] Cryptography Library

[0985] Requests library

[0986] Specific examples

[0987] As the factory nears closing time, an employee types in "I've had a headache for three days." The data is sent to the server using the following prompt statement:

[0988] Example prompt sentence:

[0989] Symptom: Headache

[0990] Onset period: 3 days

[0991] Severity: Moderate

[0992] The server performs the analysis and provides the following information:

[0993] Analysis result: "Possible tension headache."

[0994] Recommended medical institution: "XX Hospital (has a headache specialist)"

[0995] Home care suggestion: "I recommend taking over-the-counter pain medication (e.g., ibuprofen) and staying hydrated."

[0996] This allows workers to take appropriate action quickly.

[0997] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0998] Step 1:

[0999] Users access the questionnaire from the touch display of a robot in the factory or their own smartphone and enter information such as symptoms, onset time, frequency of symptoms, and pain level. This information is collected using the questionnaire's input method. The input data is recorded on the terminal as text prompts such as "Symptoms: headache," "Duration: 3 days," and "Severity: moderate."

[1000] Step 2:

[1001] The terminal encrypts the entered medical interview data and sends it to the server. The encryption is performed using the Cryptography library, and the data is sent to the server using the Request library. The text data received as input is sent to the server in encrypted form, and the actual data sent is an encrypted string.

[1002] Step 3:

[1003] The server receives the encrypted data sent from the device, decrypts it, and stores it in the database. The received data is an encrypted string, and the server decrypts it and stores it in the database as "Symptom: Headache," "Duration: 3 days," and "Severity: Moderate."

[1004] Step 4:

[1005] The server uses a generative AI model to analyze the medical interview data in the database and identify appropriate treatments and medical institutions based on the user's symptoms. This model receives symptom data as input and outputs a symptom diagnosis and recommendation by optimizing its objective function. Specifically, the model receives the data "headache," "3 days," and "moderate severity," and obtains the analysis result "high probability of tension-type headache."

[1006] Step 5:

[1007] Based on the analysis results of the generative AI model, the server searches for appropriate facilities from among the medical institutions registered in the database and recommends them to the user. The server also takes into consideration the user's current location information and past usage history to generate a list of optimal medical institutions. Based on the analysis results and location information as input, it generates a list of medical institutions such as "XX Hospital (with headache specialists)" as output.

[1008] Step 6:

[1009] Based on the analysis results, the server also generates recommendations for home care methods and over-the-counter medications. For mild symptoms, specific advice such as "take over-the-counter painkillers (e.g., ibuprofen) and drink plenty of fluids" is generated. In this process, the generative AI model's subsystem takes symptom data and analysis results as input and outputs recommended care methods.

[1010] Step 7:

[1011] The server generates a message notifying the user of the analysis results, recommended medical institutions, and home medical care suggestions, and sends it securely encrypted. The user's device receives this notification and displays the analysis results and recommendations. The output message contains specific information such as "You may have a tension headache," "Visit XX Hospital (where a headache specialist is located)," and "Take over-the-counter painkillers."

[1012] This series of processing steps allows users to assess their health status in real time and quickly receive recommendations for necessary medical institutions and treatments.

[1013] 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.

[1014] The present invention provides a specific embodiment of a system that efficiently evaluates a patient's symptoms and emotions and recommends appropriate medical institutions and treatments. This system consists of multiple components, such as a medical questionnaire input system, an emotion recognition engine, AI analysis, medical institution recommendation, and home care proposal system, which work in conjunction with each other.

[1015] System configuration

[1016] 1. User fills out questionnaire

[1017] Device: The user accesses the questionnaire through a dedicated application on a smartphone or PC. The questionnaire contains questions about symptoms, onset time, frequency, intensity, related symptoms, etc., and the user enters this information.

[1018] Example: A user enters "I've had a headache for three days" along with any related symptoms and further information.

[1019] 2. Sending medical interview data to the server

[1020] Terminal: The medical interview data entered by the user is sent to the server via the application. The data is securely encrypted before transmission, preventing unauthorized access and information leaks.

[1021] 3. Emotional engine for recognizing user emotions

[1022] On the device: As the user types, the emotion engine analyzes the user's facial expressions, tone of voice, typing speed, etc. to recognize their emotional state (e.g., stress, urgency, anxiety, etc.).

[1023] Example: As the user continues to type, the emotion engine detects anxiety in the user's tone of voice and tags it as "anxious."

[1024] 4. Integrated transmission of medical interview data and emotion data to the server

[1025] Terminal: The interview data and the recognized emotion data are integrated and sent to the server.

[1026] Server: The server stores the received data in a database and passes the data to the generative AI model to begin analysis.

[1027] 5. Analysis of medical interview data and emotional data by the server

[1028] Server: The generative AI model comprehensively analyzes the medical interview data and emotional data to evaluate symptoms and emotional state.

[1029] Example: Based on data on "headache, 3 days" and "anxiety," AI determines the possibility of tension headache and mental stress.

[1030] 6. Recommendation of appropriate medical institutions

[1031] Server: Based on the analysis results, the server searches for an appropriate medical institution that can treat the user's symptoms and emotional state from among the medical institutions registered in the database, taking into account the user's current location and past usage history.

[1032] Example: If the diagnosis is "tension headache" and "mental stress," a list of hospitals and clinics with headache specialists and psychiatrists will be provided.

[1033] 7. Proposal of home medical care and recommendation of over-the-counter medications

[1034] Server: If symptoms are mild and can be managed at home, the server will suggest specific home care methods and over-the-counter medications.

[1035] Example: Suggestions such as "Take over-the-counter painkillers (e.g., ibuprofen) and drink plenty of fluids" may be generated, along with suggestions for relaxation techniques and mental health support.

[1036] 8. Notification of analysis results and recommendations

[1037] Server: The server generates a message containing the analysis results and information on recommended medical institutions, home care methods, and over-the-counter medications, and sends it to the user's device.

[1038] Device: The user's device will display the received message and allow the user to review the recommended information.

[1039] Example: The user sees a message on their screen saying, "Your headache may be a tension headache. Please seek medical advice from the clinic listed below. We also recommend relaxation techniques along with over-the-counter ibuprofen."

[1040] This invention allows users to easily and quickly access appropriate medical institutions and treatments, thereby realizing more efficient medical care and effective use of resources. Furthermore, the introduction of an emotion engine enables comprehensive diagnosis and proposals that take into account the user's mental state, realizing the provision of more personalized medical services.

[1041] The processing flow will be explained below.

[1042] Step 1:

[1043] A user feels unwell and launches a dedicated application on their smartphone or computer. A login screen appears, and the user enters their authentication information to log in to their account.

[1044] Step 2:

[1045] The terminal displays a questionnaire screen to the logged-in user. The questionnaire includes questions about the type of symptoms, onset time, frequency, intensity, and related symptoms (e.g., headache, dizziness, nausea, etc.).

[1046] Step 3:

[1047] The user enters their symptoms into the questionnaire, for example, "I've had a headache for three days" or "I feel nauseous," and taps the "Done" button.

[1048] Step 4:

[1049] The terminal collects the medical interview data entered by the user and formats it. The formatted data is then encrypted and securely sent to the server.

[1050] Step 5:

[1051] The device activates an emotion engine while the user is typing, analyzing the user's facial expressions, tone of voice, typing speed, etc. to recognize their emotional state (e.g., stress, anxiety).

[1052] Step 6:

[1053] The device integrates the recognized emotion data with the medical interview data and sends it to the server, allowing both symptom and emotion data to be managed centrally.

[1054] Step 7:

[1055] The server stores the received medical interview data and emotion data in a database and passes it to the generative AI model to begin analysis.

[1056] Step 8:

[1057] The server's generated AI model analyzes the medical interview data and emotional data in an integrated manner to evaluate symptoms and emotional state. For example, it analyzes data such as "headache lasting three days" and "state of anxiety."

[1058] Step 9:

[1059] The server receives the analysis results of the AI ​​model and initiates the process of searching for an appropriate medical institution based on the analysis results, taking into account the user's current location information and past usage history.

[1060] Step 10:

[1061] The server identifies and lists medical institutions from a database that can treat the patient's symptoms and emotional state, including information such as the name, address, contact information, and specialty of the institution.

[1062] Step 11:

[1063] The server then executes a process based on the analysis results to suggest home medical care and over-the-counter medications. For example, for a mild headache, it may recommend an over-the-counter painkiller (e.g., ibuprofen). It also generates suggestions for relaxation techniques and mental health support based on emotion data.

[1064] Step 12:

[1065] The server compiles a list of appropriate medical institutions and home medical and over-the-counter drug suggestions, generates a message for the user, and initiates a procedure to send this message to the terminal.

[1066] Step 13:

[1067] The terminal decodes the message received from the server and displays the results to the user, who can then decide which medical institution to use or whether to seek home care.

[1068] Step 14:

[1069] The user checks the presented information and decides on the next action to take, such as making an appointment at the suggested hospital or purchasing over-the-counter medicine to treat the condition at home.

[1070] Example 2

[1071] 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."

[1072] Current healthcare systems lack the ability to efficiently evaluate patients' symptoms and quickly recommend appropriate medical institutions and treatments. Furthermore, because patients' emotional states are not reflected in diagnoses, it is difficult to provide personalized healthcare services. This can lead to worsening symptoms and inefficient use of healthcare resources.

[1073] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1074] In this invention, the server includes: [means for using an emotion engine to recognize the user's emotions;] [means for integrating the emotion data with medical interview data and sending it to the server; and] [means for receiving the medical interview data and emotion data in the server and analyzing the symptoms and emotional state using a generative AI model.] This makes it possible [to comprehensively evaluate the patient's symptoms and emotional state and quickly and individually recommend appropriate medical institutions and treatments].

[1075] A "medical questionnaire" is a paper or electronic questionnaire that a patient fills out to record their symptoms and medical history.

[1076] "User" refers to an individual who uses the system to complete a medical questionnaire and receive medical assistance.

[1077] A "server" is a central computer system that receives data sent by users, analyzes it, and returns the results.

[1078] An "emotion engine" is software or hardware that analyzes a user's facial expressions, voice, and input speed to recognize their emotional state.

[1079] "Medical Interview Data" refers to symptoms, medical history, and other health-related information entered by a user into a medical interview form.

[1080] "Emotion data" is data relating to the user's emotional state analyzed by the emotion engine.

[1081] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze data and evaluate symptoms and emotional states.

[1082] "Analysis results" refers to the evaluation and results of the generative AI model based on the medical interview data and emotional data.

[1083] "Healthcare facility" means a hospital, clinic, medical office, or other facility that provides health care services.

[1084] "Home healthcare" refers to medical care and treatments that can be performed at home.

[1085] "Over-the-counter" refers to drugs that can be purchased without a doctor's prescription.

[1086] "Encryption" refers to the process of using cryptographic techniques to securely transmit and receive data.

[1087] "Listing" refers to selecting appropriate options or items in list form based on specific conditions.

[1088] "Interface" refers to the means or device by which a user interacts with a system.

[1089] This invention provides a specific embodiment of a system that efficiently evaluates a patient's symptoms and emotions and recommends appropriate medical institutions and treatments. This system consists of multiple components, such as user questionnaire input, emotion recognition, AI analysis, medical institution recommendation, and home care suggestion, which work in conjunction with each other.

[1090] First, the user accesses a dedicated application using a smartphone or computer. Through this application, the user enters information about their symptoms, onset time, frequency, intensity, related symptoms, etc. into a questionnaire. For example, they enter detailed information such as "I have had a headache for three days, it started at the beginning of the week, and it is moderate in intensity."

[1091] The device then securely encrypts the medical interview data entered by the user and transmits it to the server using the SSL / TLS protocol. During this data transmission process, the user's data is always protected and is protected from unauthorized access and information leaks.

[1092] While the user continues to fill out the questionnaire, the emotion recognition engine analyzes the user's facial expressions, voice tone, and input speed in real time through the device's built-in camera, microphone, and touchscreen. For example, the emotion recognition engine will detect the user's anxious voice or impatient expression and tag it as "anxiety."

[1093] The device then combines the emotion data and the medical interview data and sends them back to the server in encrypted form. The server receives this data and stores it in a database (e.g., MySQL, PostgreSQL). The data is then input into a generative AI model (e.g., GPT-4, BERT) to begin analysis.

[1094] The generative AI model comprehensively analyzes the medical interview data and emotional data to evaluate symptoms and emotional state. For example, based on the data "headache, 3 days" and the emotional data "anxiety," it determines the possibility of tension headache and mental stress.

[1095] Next, the server searches the database for appropriate medical institutions based on the analysis results. Taking into account the user's current location and past usage history, it generates a list of the most suitable medical institutions. For example, it may list hospitals and clinics that can treat "tension headaches" and "mental stress."

[1096] Furthermore, if symptoms are mild and home care is appropriate, the server will suggest over-the-counter medications and specific home care methods to the user, such as taking over-the-counter painkillers (e.g., ibuprofen), staying hydrated, and providing specific advice on relaxation techniques.

[1097] Finally, the server generates a message containing the analysis results and recommended information (medical institution, home care, over-the-counter medication), encrypts it, and sends it to the user's device. The user's device displays this message, allowing them to check information about appropriate medical institutions and treatment options. For example, the message might read, "Your headache may be a tension headache. Please visit the hospital listed below. We also recommend taking over-the-counter medication ibuprofen and drinking plenty of fluids."

[1098] As a specific example, if a user enters "I have had a headache for three days" into a questionnaire and the emotion recognition engine detects "anxiety," the server will determine that the headache is likely tension headache or mental stress, and will recommend a medical institution that can treat the condition, as well as suggest over-the-counter medications and home care methods.

[1099] An example of a prompt sentence is, "I have had a headache for three days and would like you to recommend an appropriate medical institution. I am currently feeling a little anxious."

[1100] This system enables users to quickly find the appropriate medical institution and treatment for their symptoms, thereby improving the efficiency of medical care delivery and the effective use of resources.

[1101] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1102] Step 1:

[1103] Users access a dedicated application using a smartphone or computer. They enter information about their symptoms, onset time, frequency, intensity, related symptoms, etc. into a questionnaire. This generates interview data. For example, users enter detailed information such as "I have had a headache for three days, it started at the beginning of the week, and it is moderate in intensity."

[1104] Input: Information about the user's symptoms (e.g., headache, onset time, frequency, intensity)

[1105] Output: Interview data (e.g., headache lasting 3 days, onset since the beginning of the week, moderate severity)

[1106] Step 2:

[1107] The terminal sends the medical interview data entered by the user to the server using encryption technology (e.g., SSL / TLS). The data is protected securely during the data transmission process.

[1108] Input: Medical interview data

[1109] Output: Encrypted interview data

[1110] Step 3:

[1111] While the user continues to fill out the questionnaire, the device activates an emotion recognition engine via the camera, microphone, and touchscreen to analyze the user's facial expressions, voice tone, and typing speed in real time. For example, it detects an anxious voice or a panicked expression and tags it as "anxious."

[1112] Input: User facial expressions, tone of voice, and typing speed

[1113] Output: Emotion data (e.g., anxiety, stress)

[1114] Step 4:

[1115] The device combines the emotional data and the medical interview data and transmits them to the server in an encrypted form.

[1116] Input: Integrated interview data and emotion data

[1117] Output: Encrypted consolidated data

[1118] Step 5:

[1119] The server receives the encrypted interview data and emotion data and stores them in a database (e.g., MySQL, PostgreSQL). The data is then input into a generative AI model (e.g., GPT-4, BERT) to begin analysis. For example, it analyzes "headache, 3 days" and "anxiety" to determine the likelihood of tension headache and mental stress.

[1120] Input: Encrypted integrated data

[1121] Output: Analysis results from the generative AI model (e.g., tension headache, mental stress determination)

[1122] Step 6:

[1123] Based on the analysis results, the server searches the database for appropriate medical institutions, taking into account the user's current location and past usage history. For example, it may list hospitals and clinics that can treat tension headaches and mental stress.

[1124] Input: Analysis results of the generated AI model, user's current location information, past usage history

[1125] Output: A list of recommended medical facilities

[1126] Step 7:

[1127] If symptoms are mild and home care is appropriate, the server will suggest over-the-counter medications and specific home care methods to the user, such as taking over-the-counter painkillers (e.g., ibuprofen), staying hydrated, and taking relaxation techniques.

[1128] Input: Analysis results, severity of user's symptoms

[1129] Output: Over-the-counter medication suggestions, home care methods

[1130] Step 8:

[1131] The server generates a message containing the analysis results and recommended information (medical institutions, home care, over-the-counter medications), encrypts it, and sends it to the user's device. The user's device displays this message, allowing the user to check information about appropriate medical institutions and treatment options.

[1132] Input: Analysis results and recommendations

[1133] Output: A message to be displayed on the user's device (e.g., "Your headache may be a tension headache. Please seek medical advice from the clinic listed below. We also recommend taking over-the-counter ibuprofen and drinking plenty of fluids.")

[1134] By following specific steps in order, users can quickly and accurately find medical institutions and treatments that suit their symptoms.

[1135] (Application example 2)

[1136] 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."

[1137] Conventional medical interview systems only require users to input specific symptoms and do not take into account their emotional state, making it difficult to recommend the most appropriate medical institution. Furthermore, they do not offer home care or dietary suggestions, making them unable to address individual user needs. Furthermore, there are no systems that can recommend appropriate meals based on the user's health condition.

[1138] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving medical interview data and emotion data and analyzing symptoms and emotion state using a generative AI model, means for searching for and recommending appropriate medical institutions or meals based on the analysis results, and means for recommending home care methods or over-the-counter drugs, or means for recommending appropriate menus, based on the analysis results. This makes it possible to comprehensively analyze the user's health condition and emotional state and recommend medical institutions, home care, and even appropriate meals.

[1139] A "medical questionnaire" is a paper or digital form that a user fills out to describe their symptoms and health status.

[1140] "Emotion data" is data that indicates the emotional state of a user, obtained from their facial expressions and voice quality.

[1141] A "generative AI model" is an artificial intelligence model used to analyze medical interview data and emotional data.

[1142] "Server" is a central processing unit for receiving, analyzing, and making recommendations on data sent by users.

[1143] "Analysis results" are the assessment of symptoms and emotional state obtained by the generative AI model.

[1144] A "medical institution" is a place that provides medical services, such as a hospital or clinic.

[1145] "Home care methods" are suggestions for health management and treatment methods that can be carried out at home.

[1146] "Over-the-counter drugs" are drugs sold in pharmacies and stores.

[1147] A "menu" is a list of food options or dishes.

[1148] "Notification" refers to the act or means of informing the user of the analysis results and recommendations.

[1149] This invention relates to a system that analyzes medical interview data and emotional data to recommend appropriate medical institutions and meals to users. This system uses a generative AI model to analyze the health and emotional state input by the user and provides optimal recommendations.

[1150] System Overview

[1151] The system consists of the following steps:

[1152] 1. Input of user interview data and emotion data

[1153] 2. Sending data to the server

[1154] 3. Data analysis using generative AI models

[1155] 4. Recommendations for medical institutions and meals based on analysis results

[1156] 5. Notice to Users

[1157] Detailed embodiment

[1158] Input of user interview data and emotion data

[1159] The user accesses the questionnaire through a dedicated application using a smartphone or smart glasses. The questionnaire contains questions about the user's health condition (e.g., stomachache, fatigue, insomnia, etc.), and the user enters this information. In addition, the camera and microphone on the smart glasses or smartphone analyze the user's facial expressions and voice tone as they enter the information, capturing emotional data.

[1160] Sending data to the server

[1161] Health and emotional data entered by users is encrypted through the application and sent to a server, preventing unauthorized access and information leaks.

[1162] Data analysis with generative AI models

[1163] The server receives the data and stores it in a database. It then passes the data to a generative AI model that analyzes the health and emotional state of the person, using the Hugging Face Transformer library.

[1164] Recommendations for medical institutions and meals based on analysis results

[1165] Based on the analysis results, the server searches the database for medical institutions and meal menus that can accommodate the user's condition, and lists the most suitable options, taking into account the user's current location and past usage history.

[1166] User Notification

[1167] The final analysis results in recommendations for appropriate medical institutions, home care methods, over-the-counter medicines, or meal plans, which are then sent from the server to the user's device and displayed for easy access.

[1168] Specific examples

[1169] For example, if a user inputs "stomach pain" and "anxiety" is detected from the emotional data, the generative AI model will use the user's analysis data to recommend a gastroenterologist near the user. It will also suggest "porridge" or "chamomile tea" as home care.

[1170] Prompt Sentence Examples

[1171] The user can enter messages into the application such as:

[1172] "I have a terrible stomach ache and I feel anxious."

[1173] This allows the system to comprehensively analyze the user's health and emotional state and provide the most appropriate response.

[1174] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1175] Step 1:

[1176] The user accesses the questionnaire through a dedicated application using a smartphone or smart glasses. The user inputs their health condition (e.g., stomachache, fatigue, insomnia, etc.). The input data is temporarily stored on the user's device. At the same time, the camera and microphone on the smart glasses or smartphone capture the user's facial expressions and voice quality to generate emotional data. This emotional data is also stored on the device.

[1177] Input: User's health condition data, facial expression data, voice quality data

[1178] Output: Temporary storage of medical interview data and emotional data

[1179] Step 2:

[1180] The questionnaire data entered by the user and the acquired emotion data are sent to the server in a securely encrypted format via a dedicated application. The encryption is performed using standard security protocols (e.g., AES encryption). The server receives this data and stores it in a database.

[1181] Input: User interview data, emotion data

[1182] Output: Encrypted data sent to server, stored in database

[1183] Step 3:

[1184] The server passes the received medical interview data and emotional data to the generative AI model for analysis. The generative AI model evaluates the user's symptoms and emotional state based on the data. For example, it analyzes data on "stomach pain" and "anxiety" and selects the most appropriate medical institution and meal options based on that combination.

[1185] Input: medical interview data, emotion data

[1186] Output: Symptom and emotional state assessment results

[1187] Step 4:

[1188] Based on the analysis results, the server searches the database and lists appropriate medical institutions or meal menus. The user's current location and past usage history are also taken into consideration. For example, "gastroenterologists" or "meals that are good for the stomach (such as porridge)" may be listed.

[1189] Input: Symptom and emotional state assessment results, current location information, past usage history

[1190] Output: List of medical institutions or meal menus

[1191] Step 5:

[1192] The server sends a list of medical institutions, meal menus, home care methods, and over-the-counter drug recommendations to the user's device. The sent information is notified to the user's smartphone. The user receives a notification and can view the most suitable medical institutions and meal suggestions.

[1193] Input: Listed medical institutions, meal menus

[1194] Output: Notification to user terminal, confirmation by user

[1195] For example, if a user inputs "I have a severe stomachache and I'm feeling anxious," the system will analyze this input and recommend a gastroenterologist near the user or a stomach-friendly meal (such as porridge). The user will receive a notification and be able to confirm the recommendation.

[1196] 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.

[1197] 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.

[1198] 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.

[1199] [Fourth embodiment]

[1200] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1201] 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.

[1202] 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).

[1203] 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.

[1204] 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.

[1205] 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).

[1206] 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.

[1207] 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.

[1208] 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.

[1209] 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.

[1210] 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.

[1211] 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.

[1212] 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."

[1213] The present invention provides a specific embodiment of a system for efficiently evaluating a patient's symptoms and recommending appropriate medical institutions and treatments. This system is composed of multiple components, each of which works in conjunction with the other components to provide comprehensive medical care.

[1214] System configuration

[1215] 1. User fills out questionnaire

[1216] Device: The user accesses the questionnaire through a dedicated application on a smartphone or PC. The questionnaire contains questions about symptoms, onset time, frequency of symptoms, degree of pain, etc., and the user enters this information.

[1217] Example: A user enters "I've had a headache for three days" along with any related symptoms and further information.

[1218] 2. Sending medical interview data to the server

[1219] Terminal: The medical interview data entered by the user is sent to the server via the application. The data is securely encrypted before transmission, preventing unauthorized access and information leaks.

[1220] 3. Receipt and analysis of medical interview data by the server

[1221] Server: The server stores the received medical interview data in a database and automatically passes the data to a generative AI model to begin analyzing the symptoms.

[1222] Example: A server receives the data "Headache, 3 days", and a generative AI model analyzes it to determine whether it is a tension headache or a migraine.

[1223] 4. Recommendation of appropriate medical institutions

[1224] Server: Based on the analysis results, the server searches for an appropriate medical institution that can treat the symptoms from among the medical institutions registered in the database. This is highly convenient as it also takes into account the user's current location information and past usage history.

[1225] Example: If the diagnosis is "tension headache," the server will list hospitals and clinics with headache specialists.

[1226] 5. Proposal of home medical care and recommendation of over-the-counter medications

[1227] Server: If symptoms are mild and can be managed at home, the server will suggest specific home care methods and over-the-counter medications.

[1228] Example: Suggestions such as "Take over-the-counter pain medication (e.g., ibuprofen) and drink plenty of fluids" may be generated.

[1229] 6. Notification of analysis results and recommendations

[1230] Server: The server generates a message containing the analysis results and information on recommended medical institutions, home care methods, and over-the-counter medications, and sends it to the user's device.

[1231] Device: The user's device will display the received message and allow the user to review the recommended information.

[1232] Example: The user sees the message, "Your headache may be a tension headache. Please seek medical advice from the clinic listed below. We also recommend trying over-the-counter ibuprofen."

[1233] This invention allows users to easily and quickly access appropriate medical institutions and treatments, thereby improving the efficiency of medical care provision and the effective use of resources. In addition, by proposing home medical care, it also provides a way to treat mild symptoms at home, thereby contributing to reducing the burden on medical institutions.

[1234] The processing flow will be explained below.

[1235] Step 1:

[1236] A user feels unwell and launches a dedicated application on their smartphone or computer. A login screen appears, and the user enters their authentication information to log in to their account.

[1237] Step 2:

[1238] The terminal displays a questionnaire screen to the logged-in user. The questionnaire includes questions about the type of symptoms, onset time, frequency, intensity, and related symptoms (e.g., headache, dizziness, nausea, etc.).

[1239] Step 3:

[1240] The user enters their symptoms into the questionnaire, for example, "I've had a headache for three days" or "I feel nauseous," and taps the "Done" button.

[1241] Step 4:

[1242] The terminal collects the medical interview data entered by the user and formats it. The formatted data is then encrypted and securely sent to the server.

[1243] Step 5:

[1244] The server decodes the received medical interview data and stores it in a database. After saving, it automatically prepares to transfer the data to the generative AI model.

[1245] Step 6:

[1246] The server's generated AI model analyzes the interview data and makes an initial assessment of the symptoms. For example, based on the input information "headache" and "3 days," it assesses the possibility of a tension headache or migraine.

[1247] Step 7:

[1248] The server receives the analysis results of the AI ​​model and initiates the process of searching for an appropriate medical institution based on the analysis results, taking into account the user's current location information and past usage history.

[1249] Step 8:

[1250] The server identifies and lists medical institutions that can treat the symptoms from the database, including information such as the name, address, contact information, and specialty of the medical institution.

[1251] Step 9:

[1252] The server then executes a process based on the analysis results to suggest home medical care or over-the-counter medications. For example, for a mild headache, it may recommend an over-the-counter painkiller (e.g., ibuprofen).

[1253] Step 10:

[1254] The server compiles a list of appropriate medical institutions and home medical and over-the-counter drug suggestions, generates a message for the user, and initiates a procedure to send this message to the terminal.

[1255] Step 11:

[1256] The terminal decodes the message received from the server and displays the results to the user, who can then decide which medical institution to use or whether to seek home care.

[1257] Step 12:

[1258] The user checks the presented information and decides on the next action to take, such as making an appointment at the suggested hospital or purchasing over-the-counter medicine to treat the condition at home.

[1259] Example 1

[1260] 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."

[1261] In today's world, medical institutions are often used frequently, making it difficult for patients to make appointments. It is also difficult to determine whether or not a patient needs to visit a medical institution, even if their symptoms are mild. Another issue is that it takes time for patients to find an appropriate medical institution. The present invention aims to solve these problems and provide a system that enables patients to easily and quickly access appropriate medical institutions and treatments.

[1262] 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.

[1263] In this invention, the server includes an information processing device for the user to input a medical questionnaire, a communication device for transmitting the medical questionnaire data input by the user to the server, and a data processing device for receiving the medical questionnaire data in the server and analyzing symptoms using a generative AI model. This enables the user to input symptoms, have them quickly analyzed using the generative AI model, and recommend appropriate medical institutions and treatments.

[1264] An "information processing device" is a device that includes hardware and software for processing data entered by a user and enabling it to be entered into a questionnaire.

[1265] "Communication means" refers to means including protocols and infrastructure for transmitting data entered from a user's terminal to a server.

[1266] "Data processing means" refers to a system that analyzes data received by the server and performs calculations and data analysis to diagnose symptoms using a generative AI model.

[1267] The "search means" refers to the algorithms and software that search for information in the database based on the analysis results and find appropriate medical institutions.

[1268] The "recommendation method" is a system that includes data generation and recommendation functions to suggest home medical care methods and over-the-counter drug information based on the analysis results.

[1269] The "notification means" is a means including a message generation and transmission function for notifying the user of the generated recommendations and diagnostic results.

[1270] A "user interface" is a system that includes a screen and input devices through which a user enters initial symptoms and other information.

[1271] A "generative AI model" is a type of machine learning model used to diagnose symptoms and make recommendations based on input medical interview data.

[1272] This invention is a system that efficiently evaluates a patient's symptoms and recommends appropriate medical institutions and treatments. This system utilizes a user terminal, a server, and a generative AI model to analyze the medical interview data entered by the user and provide medical information based on the results.

[1273] Input and transmission of medical interview data via user terminal

[1274] Users access a dedicated application using an information processing device such as a smartphone or PC. They answer questions about symptoms, when they first started, how often they experienced symptoms, and how much pain they felt, according to a questionnaire displayed in the application. For example, a user might enter detailed information such as "I've had a headache for three days," "The pain occurs several times a day," and "The pain is moderate."

[1275] The entered data is encrypted by the user's device and securely transmitted to the server using AES encryption and the HTTPS protocol.

[1276] Data reception and analysis by the server

[1277] The server receives the encrypted medical interview data, decrypts it, and stores it in a database that stores the user's past medical information and current medical interview data.

[1278] The received data is immediately passed to the generative AI model, where analysis begins. This generative AI model is written in Python and analyzes the user's symptoms based on pre-trained medical data. Specifically, the server sends data to the generative AI model in the format "User ID 1234: Symptom Data" and receives the analysis results.

[1279] Recommendations for medical institutions and home medical care

[1280] Based on the analysis results, the server searches the registered medical institutions in the database and lists appropriate medical institutions. The server also takes into account the user's current location and past medical history. For example, if a patient is diagnosed with tension headache, the server will select a hospital or clinic with a doctor specializing in headaches.

[1281] If symptoms are mild and home medical care is recommended, specific home care methods and over-the-counter medication information will also be provided. For example, the server will generate a recommendation message such as, "Take over-the-counter painkillers (e.g., ibuprofen) and drink plenty of fluids."

[1282] User Notification

[1283] Finally, the server generates a message containing the analysis results and recommendations, and sends it to the user's device. The user's device receives the message and displays it on its screen. For example, the user's screen may display a message saying, "Your headache may be a tension headache. Please visit the hospital listed below. We also recommend trying the over-the-counter medication ibuprofen."

[1284] Example prompts for generative AI models

[1285] "The patient has had a headache for three days, with moderate pain several times a day. Based on these symptoms, what is the likely diagnosis and what medical or over-the-counter medications would you recommend?"

[1286] "Analyze the user's medical interview data and generate optimal diagnostic results and a list of recommended medical institutions."

[1287] The present invention allows users to quickly and appropriately obtain medical information, thereby improving the efficiency of medical care provision and the effective use of resources. In addition, by proposing home care, it also contributes to reducing the burden on medical institutions.

[1288] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1289] Step 1:

[1290] User accesses the questionnaire

[1291] Input: A user accesses a dedicated application using a smartphone or computer.

[1292] Specific operation: A user installs and launches the application, then enters their user ID and password on the login screen to log in to their account.

[1293] Output: The user reaches the questionnaire entry screen and the questionnaire is displayed.

[1294] Step 2:

[1295] The user fills out the questionnaire

[1296] Input: The user answers the questions displayed in the questionnaire (e.g., symptoms, time of onset, frequency of symptoms, pain level).

[1297] What happens: The user enters details such as "I have had a headache for three days," "The pain occurs several times a day," and "The pain is moderate."

[1298] Output: The entered medical interview data is temporarily saved in the application.

[1299] Step 3:

[1300] The device sends the medical interview data to the server.

[1301] Input: Interview data entered by the user.

[1302] Specific operation: The terminal encrypts the medical interview data using AES and sends the data to the server using the HTTPS protocol.

[1303] Output: The encrypted interview data is sent to the server.

[1304] Step 4:

[1305] The server receives and decrypts the interview data.

[1306] Input: Encrypted medical interview data sent from the terminal.

[1307] What it does: The server decrypts the received data and stores it in an SQL database, which contains information such as the user ID, symptom data, and the date and time of entry.

[1308] Output: The decrypted interview data is stored in the database.

[1309] Step 5:

[1310] The server starts analysis using the generated AI model

[1311] Input: Medical interview data stored in a database.

[1312] How it works: The server passes medical interview data as input to a generative AI model written in Python. For example, data is sent to the generative AI model in the form of "User ID 1234: headache lasting 3 days, frequency several times a day, moderate pain."

[1313] Output: As a result of the analysis, the generative AI model outputs information such as the diagnosis and recommended medical institution.

[1314] Step 6:

[1315] The server searches for an appropriate medical institution

[1316] Input: Analysis results obtained from a generative AI model.

[1317] Specific operation: Based on the analysis results (e.g., tension headache), the server searches for appropriate medical institutions from the database, taking into account the user's current location information and past medical institution usage history. A list of medical institutions is obtained using an SQL query.

[1318] Output: A list of recommended medical institutions is generated.

[1319] Step 7:

[1320] The server provides home care and over-the-counter medication suggestions.

[1321] Input: Analysis results of the generative AI model and medical institution search results.

[1322] Specific operation: If the server detects a "tension headache," it generates information on home care methods and over-the-counter medications (e.g., ibuprofen). For example, it generates suggestions such as "Take over-the-counter painkillers and drink plenty of fluids."

[1323] Output: A recommendation message is generated containing home care and over-the-counter medication information.

[1324] Step 8:

[1325] The server generates and sends notifications of analysis results and recommendations.

[1326] Input: Recommendation message, list of healthcare providers.

[1327] Specific operation: The server combines the recommended message with the list of medical institutions to generate a notification message for the user, and then sends the message to the user's device using an HTTP request.

[1328] Output: A notification message is sent that the user receives.

[1329] Step 9:

[1330] The device displays a notification message

[1331] Input: The notification message sent by the server.

[1332] Specific behavior: The device receives a notification and displays it to the user in the application. For example, it displays the message, "Your headache may be a tension headache. Please visit the hospital listed below. We also recommend trying the over-the-counter medication ibuprofen."

[1333] Output: User can see the recommended information.

[1334] (Application example 1)

[1335] 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."

[1336] There is a need to grasp the health status of workers in factories in real time and quickly provide them with appropriate medical institutions and treatment. However, currently, checking their health status and selecting medical institutions is time-consuming and laborious, which can delay appropriate measures. Furthermore, if symptoms are mild, it is desirable to avoid unnecessary use of medical institutions and provide home care options. Against this background, a new system is needed to streamline medical responses in factories and optimize worker health management.

[1337] 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.

[1338] In this invention, the server includes: a means for a user to input a medical questionnaire; a means for transmitting the medical questionnaire data input by the user to the server; a means for receiving the medical questionnaire data in the server and analyzing symptoms using a generative AI model; a means for searching for and recommending an appropriate medical institution based on the analysis results; a means for proposing home medical care and recommending over-the-counter medications based on the analysis results; a means for notifying the user of the recommendation results, home medical care suggestions, and over-the-counter medication recommendations; a means for checking the health status of employees while they are working in a factory and suggesting appropriate medical institutions and treatments based on the results; and a means for monitoring the health status of workers in real time and securely encrypting and transmitting the data. This enables the health status of workers in a factory to be quickly evaluated and appropriate medical institutions and treatments to be provided. Furthermore, home care options are also provided for mild symptoms, avoiding unnecessary use of medical institutions.

[1339] A "medical questionnaire" is a form in which a user enters detailed information about their health condition, such as symptoms, onset time, and frequency of pain.

[1340] "Means" refers to the devices or methods used by the system to achieve a specific function or operation.

[1341] A "user" is someone who uses the system to assess their own health status and receive recommendations for appropriate medical institutions and treatments.

[1342] "Data" refers to health and symptom information entered by users, including details of symptoms, onset time, frequency, and pain level.

[1343] A "server" refers to a computer system that receives and analyzes data sent by users, thereby storing the data and analyzing it using AI models.

[1344] A "generative AI model" refers to a machine learning model that analyzes received data and recommends appropriate treatments and medical institutions based on the user's symptoms.

[1345] "Analysis" refers to the process of using a generative AI model to evaluate and interpret the user's medical interview data and identify the most appropriate treatment and medical institution based on the symptoms.

[1346] "Medical institution" refers to a facility that provides specialized medical services, such as a hospital, clinic, or medical office.

[1347] "Home medical care" refers to medical care and treatments that users can receive at home for mild symptoms. The system will suggest home medical care according to the situation.

[1348] "Over-the-counter drugs" refer to medicines that can be purchased at general pharmacies and drug stores and do not require a prescription.

[1349] "Recommendation" refers to the act of presenting the most appropriate medical institution or treatment method to the user based on the analysis results of the generative AI model.

[1350] "Notification" refers to the process of communicating analysis results and recommendations to users, allowing them to obtain the information they need in a timely manner.

[1351] "Employees" refers specifically to workers in factories, who can use the system to check their own health status and obtain appropriate medical care and treatment.

[1352] "Real-time" means that health status checks, data transmission, and analysis are carried out immediately, allowing for rapid health management of workers.

[1353] "Monitoring" refers to the process of continuously monitoring the health status of workers and responding promptly to any abnormalities.

[1354] "Data encryption" refers to the technology that securely protects data being transmitted in order to protect user privacy, thereby preventing unauthorized access and information leaks.

[1355] MODE FOR CARRYING OUT THE INVENTION

[1356] The present invention provides a system that monitors the health status of workers in factories in real time and promptly provides appropriate medical institutions and treatments. This system automates the process in which workers check their own health status and receive recommendations for medical institutions and treatments based on the results.

[1357] System configuration

[1358] This system consists of the following components:

[1359] 1. Input method for the questionnaire:

[1360] Workers access the questionnaire using the touch display of the factory robot, a smartphone, or a tablet, and enter information such as symptoms, when they first started, how often they are sick, and how much pain they are in.

[1361] 2. How to send medical interview data to the server:

[1362] The medical interview data entered by the worker is securely encrypted and sent to the server using the Cryptography library, and the Requests library is used to send the data.

[1363] 3. Means of receiving and analyzing interview data:

[1364] The server stores the received medical interview data in a database and simultaneously begins analysis using a generative AI model, which identifies appropriate treatments and medical institutions based on the symptom data.

[1365] 4. Medical institution recommendation methods:

[1366] Based on the analysis results, the server searches for and recommends the best medical facility for the patient's symptoms from among the medical institutions registered in the database, taking into account the user's current location and past usage history.

[1367] 5. Home care suggestions and over-the-counter medication recommendations:

[1368] If symptoms are mild, the server will suggest specific home care methods and over-the-counter medications based on the analysis results.

[1369] 6. Means of communication of analysis results and recommendations:

[1370] The server generates a message containing the analysis results and information on recommended medical institutions, home care methods, and over-the-counter medications, decrypts it, and sends it to the worker's device, allowing the worker to receive the appropriate information quickly.

[1371] Hardware and Software Configuration

[1372] Hardware:

[1373] Touch display for factory robots

[1374] Workers' smartphones and tablets

[1375] software:

[1376] Python

[1377] Cryptography Library

[1378] Requests library

[1379] Specific examples

[1380] As the factory nears closing time, an employee types in "I've had a headache for three days." The data is sent to the server using the following prompt statement:

[1381] Example prompt sentence:

[1382] Symptom: Headache

[1383] Onset period: 3 days

[1384] Severity: Moderate

[1385] The server performs the analysis and provides the following information:

[1386] Analysis result: "Possible tension headache."

[1387] Recommended medical institution: "XX Hospital (has a headache specialist)"

[1388] Home care suggestion: "I recommend taking over-the-counter pain medication (e.g., ibuprofen) and staying hydrated."

[1389] This allows workers to take appropriate action quickly.

[1390] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1391] Step 1:

[1392] Users access the questionnaire from the touch display of a robot in the factory or their own smartphone and enter information such as symptoms, onset time, frequency of symptoms, and pain level. This information is collected using the questionnaire's input method. The input data is recorded on the terminal as text prompts such as "Symptoms: headache," "Duration: 3 days," and "Severity: moderate."

[1393] Step 2:

[1394] The terminal encrypts the entered medical interview data and sends it to the server. The encryption is performed using the Cryptography library, and the data is sent to the server using the Request library. The text data received as input is sent to the server in encrypted form, and the actual data sent is an encrypted string.

[1395] Step 3:

[1396] The server receives the encrypted data sent from the device, decrypts it, and stores it in the database. The received data is an encrypted string, and the server decrypts it and stores it in the database as "Symptom: Headache," "Duration: 3 days," and "Severity: Moderate."

[1397] Step 4:

[1398] The server uses a generative AI model to analyze the medical interview data in the database and identify appropriate treatments and medical institutions based on the user's symptoms. This model receives symptom data as input and outputs a symptom diagnosis and recommendation by optimizing its objective function. Specifically, the model receives the data "headache," "3 days," and "moderate severity," and obtains the analysis result "high probability of tension-type headache."

[1399] Step 5:

[1400] Based on the analysis results of the generative AI model, the server searches for appropriate facilities from among the medical institutions registered in the database and recommends them to the user. The server also takes into consideration the user's current location information and past usage history to generate a list of optimal medical institutions. Based on the analysis results and location information as input, it generates a list of medical institutions such as "XX Hospital (with headache specialists)" as output.

[1401] Step 6:

[1402] Based on the analysis results, the server also generates recommendations for home care methods and over-the-counter medications. For mild symptoms, specific advice such as "take over-the-counter painkillers (e.g., ibuprofen) and drink plenty of fluids" is generated. In this process, the generative AI model's subsystem takes symptom data and analysis results as input and outputs recommended care methods.

[1403] Step 7:

[1404] The server generates a message notifying the user of the analysis results, recommended medical institutions, and home medical care suggestions, and sends it securely encrypted. The user's device receives this notification and displays the analysis results and recommendations. The output message contains specific information such as "You may have a tension headache," "Visit XX Hospital (where a headache specialist is located)," and "Take over-the-counter painkillers."

[1405] This series of processing steps allows users to assess their health status in real time and quickly receive recommendations for necessary medical institutions and treatments.

[1406] 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.

[1407] The present invention provides a specific embodiment of a system that efficiently evaluates a patient's symptoms and emotions and recommends appropriate medical institutions and treatments. This system consists of multiple components, such as a medical questionnaire input system, an emotion recognition engine, AI analysis, medical institution recommendation, and home care proposal system, which work in conjunction with each other.

[1408] System configuration

[1409] 1. User fills out questionnaire

[1410] Device: The user accesses the questionnaire through a dedicated application on a smartphone or PC. The questionnaire contains questions about symptoms, onset time, frequency, intensity, related symptoms, etc., and the user enters this information.

[1411] Example: A user enters "I've had a headache for three days" along with any related symptoms and further information.

[1412] 2. Sending medical interview data to the server

[1413] Terminal: The medical interview data entered by the user is sent to the server via the application. The data is securely encrypted before transmission, preventing unauthorized access and information leaks.

[1414] 3. Emotion engine for recognizing user emotions

[1415] On the device: As the user types, the emotion engine analyzes the user's facial expressions, tone of voice, typing speed, etc. to recognize their emotional state (e.g., stress, urgency, anxiety, etc.).

[1416] Example: As the user continues to type, the emotion engine detects anxiety in the user's tone of voice and tags it as "anxious."

[1417] 4. Integrated transmission of medical interview data and emotion data to the server

[1418] Terminal: The interview data and the recognized emotion data are integrated and sent to the server.

[1419] Server: The server stores the received data in a database and passes the data to the generative AI model to begin analysis.

[1420] 5. Analysis of medical interview data and emotional data by the server

[1421] Server: The generative AI model comprehensively analyzes the medical interview data and emotional data to evaluate symptoms and emotional state.

[1422] Example: Based on data on "headache, 3 days" and "anxiety," AI determines the possibility of tension headache and mental stress.

[1423] 6. Recommendation of appropriate medical institutions

[1424] Server: Based on the analysis results, the server searches for an appropriate medical institution that can treat the user's symptoms and emotional state from among the medical institutions registered in the database, taking into account the user's current location and past usage history.

[1425] Example: If the diagnosis is "tension headache" and "mental stress," a list of hospitals and clinics with headache specialists and psychiatrists will be provided.

[1426] 7. Proposal of home medical care and recommendation of over-the-counter medications

[1427] Server: If symptoms are mild and can be managed at home, the server will suggest specific home care methods and over-the-counter medications.

[1428] Example: Suggestions such as "Take over-the-counter painkillers (e.g., ibuprofen) and drink plenty of fluids" may be generated, along with suggestions for relaxation techniques and mental health support.

[1429] 8. Notification of analysis results and recommendations

[1430] Server: The server generates a message containing the analysis results and information on recommended medical institutions, home care methods, and over-the-counter medications, and sends it to the user's device.

[1431] Device: The user's device will display the received message and allow the user to review the recommended information.

[1432] Example: The user sees a message on their screen saying, "Your headache may be a tension headache. Please seek medical advice from the clinic listed below. We also recommend relaxation techniques along with over-the-counter ibuprofen."

[1433] This invention allows users to easily and quickly access appropriate medical institutions and treatments, thereby realizing more efficient medical care and effective use of resources. Furthermore, the introduction of an emotion engine enables comprehensive diagnosis and proposals that take into account the user's mental state, realizing the provision of more personalized medical services.

[1434] The processing flow will be explained below.

[1435] Step 1:

[1436] A user feels unwell and launches a dedicated application on their smartphone or computer. A login screen appears, and the user enters their authentication information to log in to their account.

[1437] Step 2:

[1438] The terminal displays a questionnaire screen to the logged-in user. The questionnaire includes questions about the type of symptoms, onset time, frequency, intensity, and related symptoms (e.g., headache, dizziness, nausea, etc.).

[1439] Step 3:

[1440] The user enters their symptoms into the questionnaire, for example, "I've had a headache for three days" or "I feel nauseous," and taps the "Done" button.

[1441] Step 4:

[1442] The terminal collects the medical interview data entered by the user and formats it. The formatted data is then encrypted and securely sent to the server.

[1443] Step 5:

[1444] The device activates an emotion engine while the user is typing, analyzing the user's facial expressions, tone of voice, typing speed, etc. to recognize their emotional state (e.g., stress, anxiety).

[1445] Step 6:

[1446] The device integrates the recognized emotion data with the medical interview data and sends it to the server, allowing both symptom and emotion data to be managed centrally.

[1447] Step 7:

[1448] The server stores the received medical interview data and emotion data in a database and passes it to the generative AI model to begin analysis.

[1449] Step 8:

[1450] The server's generated AI model analyzes the medical interview data and emotional data in an integrated manner to evaluate symptoms and emotional state. For example, it analyzes data such as "headache lasting three days" and "state of anxiety."

[1451] Step 9:

[1452] The server receives the analysis results of the AI ​​model and initiates the process of searching for an appropriate medical institution based on the analysis results, taking into account the user's current location information and past usage history.

[1453] Step 10:

[1454] The server identifies and lists medical institutions from a database that can treat the patient's symptoms and emotional state, including information such as the name, address, contact information, and specialty of the institution.

[1455] Step 11:

[1456] The server then executes a process based on the analysis results to suggest home medical care and over-the-counter medications. For example, for a mild headache, it may recommend an over-the-counter painkiller (e.g., ibuprofen). It also generates suggestions for relaxation techniques and mental health support based on emotion data.

[1457] Step 12:

[1458] The server compiles a list of appropriate medical institutions and home medical and over-the-counter drug suggestions, generates a message for the user, and initiates a procedure to send this message to the terminal.

[1459] Step 13:

[1460] The terminal decodes the message received from the server and displays the results to the user, who can then decide which medical institution to use or whether to seek home care.

[1461] Step 14:

[1462] The user checks the presented information and decides on the next action to take, such as making an appointment at the suggested hospital or purchasing over-the-counter medicine to treat the condition at home.

[1463] Example 2

[1464] 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."

[1465] Current healthcare systems lack the ability to efficiently evaluate patients' symptoms and quickly recommend appropriate medical institutions and treatments. Furthermore, because patients' emotional states are not reflected in diagnoses, it is difficult to provide personalized healthcare services. This can lead to worsening symptoms and inefficient use of healthcare resources.

[1466] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1467] In this invention, the server includes: [means for using an emotion engine to recognize the user's emotions;] [means for integrating the emotion data with medical interview data and sending it to the server; and] [means for receiving the medical interview data and emotion data in the server and analyzing the symptoms and emotional state using a generative AI model.] This makes it possible [to comprehensively evaluate the patient's symptoms and emotional state and quickly and individually recommend appropriate medical institutions and treatments].

[1468] A "medical questionnaire" is a paper or electronic questionnaire that a patient fills out to record their symptoms and medical history.

[1469] "User" refers to an individual who uses the system to complete a medical questionnaire and receive medical assistance.

[1470] A "server" is a central computer system that receives data sent by users, analyzes it, and returns the results.

[1471] An "emotion engine" is software or hardware that analyzes a user's facial expressions, voice, and input speed to recognize their emotional state.

[1472] "Medical Interview Data" refers to symptoms, medical history, and other health-related information entered by a user into a medical interview form.

[1473] "Emotion data" is data relating to the user's emotional state analyzed by the emotion engine.

[1474] A "generative AI model" is an algorithm that uses artificial intelligence technology to analyze data and evaluate symptoms and emotional states.

[1475] "Analysis results" refers to the evaluation and results of the generative AI model based on the medical interview data and emotional data.

[1476] "Healthcare facility" means a hospital, clinic, medical office, or other facility that provides health care services.

[1477] "Home healthcare" refers to medical care and treatments that can be performed at home.

[1478] "Over-the-counter" refers to drugs that can be purchased without a doctor's prescription.

[1479] "Encryption" refers to the process of using cryptographic techniques to securely transmit and receive data.

[1480] "Listing" refers to selecting appropriate options or items in list form based on specific conditions.

[1481] "Interface" refers to the means or device by which a user interacts with a system.

[1482] This invention provides a specific embodiment of a system that efficiently evaluates a patient's symptoms and emotions and recommends appropriate medical institutions and treatments. This system consists of multiple components, such as user questionnaire input, emotion recognition, AI analysis, medical institution recommendation, and home care suggestion, which work in conjunction with each other.

[1483] First, the user accesses a dedicated application using a smartphone or computer. Through this application, the user enters information about their symptoms, onset time, frequency, intensity, related symptoms, etc. into a questionnaire. For example, they enter detailed information such as "I have had a headache for three days, it started at the beginning of the week, and it is moderate in intensity."

[1484] The device then securely encrypts the medical interview data entered by the user and transmits it to the server using the SSL / TLS protocol. During this data transmission process, the user's data is always protected and is protected from unauthorized access and information leaks.

[1485] While the user continues to fill out the questionnaire, the emotion recognition engine analyzes the user's facial expressions, voice tone, and input speed in real time through the device's built-in camera, microphone, and touchscreen. For example, the emotion recognition engine will detect the user's anxious voice or impatient expression and tag it as "anxiety."

[1486] The device then combines the emotion data and the medical interview data and sends them back to the server in encrypted form. The server receives this data and stores it in a database (e.g., MySQL, PostgreSQL). The data is then input into a generative AI model (e.g., GPT-4, BERT) to begin analysis.

[1487] The generative AI model comprehensively analyzes the medical interview data and emotional data to evaluate symptoms and emotional state. For example, based on the data "headache, 3 days" and the emotional data "anxiety," it determines the possibility of tension headache and mental stress.

[1488] Next, the server searches the database for appropriate medical institutions based on the analysis results. Taking into account the user's current location and past usage history, it generates a list of the most suitable medical institutions. For example, it may list hospitals and clinics that can treat "tension headaches" and "mental stress."

[1489] Furthermore, if symptoms are mild and home care is appropriate, the server will suggest over-the-counter medications and specific home care methods to the user, such as taking over-the-counter painkillers (e.g., ibuprofen), staying hydrated, and providing specific advice on relaxation techniques.

[1490] Finally, the server generates a message containing the analysis results and recommended information (medical institution, home care, over-the-counter medication), encrypts it, and sends it to the user's device. The user's device displays this message, allowing them to check information about appropriate medical institutions and treatment options. For example, the message might read, "Your headache may be a tension headache. Please visit the hospital listed below. We also recommend taking over-the-counter medication ibuprofen and drinking plenty of fluids."

[1491] As a specific example, if a user enters "I have had a headache for three days" into a questionnaire and the emotion recognition engine detects "anxiety," the server will determine that the headache is likely tension headache or mental stress, and will recommend a medical institution that can treat the condition, as well as suggest over-the-counter medications and home care methods.

[1492] An example of a prompt sentence is, "I have had a headache for three days and would like you to recommend an appropriate medical institution. I am currently feeling a little anxious."

[1493] This system enables users to quickly find the appropriate medical institution and treatment for their symptoms, thereby improving the efficiency of medical care delivery and the effective use of resources.

[1494] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1495] Step 1:

[1496] Users access a dedicated application using a smartphone or computer. They enter information about their symptoms, onset time, frequency, intensity, related symptoms, etc. into a questionnaire. This generates interview data. For example, users enter detailed information such as "I have had a headache for three days, it started at the beginning of the week, and it is moderate in intensity."

[1497] Input: Information about the user's symptoms (e.g., headache, onset time, frequency, intensity)

[1498] Output: Interview data (e.g., headache lasting 3 days, onset since the beginning of the week, moderate severity)

[1499] Step 2:

[1500] The terminal sends the medical interview data entered by the user to the server using encryption technology (e.g., SSL / TLS). The data is protected securely during the data transmission process.

[1501] Input: Medical interview data

[1502] Output: Encrypted interview data

[1503] Step 3:

[1504] While the user continues to fill out the questionnaire, the device activates an emotion recognition engine via the camera, microphone, and touchscreen to analyze the user's facial expressions, voice tone, and typing speed in real time. For example, it detects an anxious voice or a panicked expression and tags it as "anxious."

[1505] Input: User facial expressions, tone of voice, and typing speed

[1506] Output: Emotion data (e.g., anxiety, stress)

[1507] Step 4:

[1508] The device combines the emotional data and the medical interview data and transmits them to the server in an encrypted form.

[1509] Input: Integrated interview data and emotion data

[1510] Output: Encrypted consolidated data

[1511] Step 5:

[1512] The server receives the encrypted interview data and emotion data and stores them in a database (e.g., MySQL, PostgreSQL). The data is then input into a generative AI model (e.g., GPT-4, BERT) to begin analysis. For example, it analyzes "headache, 3 days" and "anxiety" to determine the likelihood of tension headache and mental stress.

[1513] Input: Encrypted integrated data

[1514] Output: Analysis results from the generative AI model (e.g., tension headache, mental stress determination)

[1515] Step 6:

[1516] Based on the analysis results, the server searches the database for appropriate medical institutions, taking into account the user's current location and past usage history. For example, it may list hospitals and clinics that can treat tension headaches and mental stress.

[1517] Input: Analysis results of the generated AI model, user's current location information, past usage history

[1518] Output: A list of recommended medical facilities

[1519] Step 7:

[1520] If symptoms are mild and home care is appropriate, the server will suggest over-the-counter medications and specific home care methods to the user, such as taking over-the-counter painkillers (e.g., ibuprofen), staying hydrated, and taking relaxation techniques.

[1521] Input: Analysis results, severity of user's symptoms

[1522] Output: Over-the-counter medication suggestions, home care methods

[1523] Step 8:

[1524] The server generates a message containing the analysis results and recommended information (medical institutions, home care, over-the-counter medications), encrypts it, and sends it to the user's device. The user's device displays this message, allowing the user to check information about appropriate medical institutions and treatment options.

[1525] Input: Analysis results and recommendations

[1526] Output: A message to be displayed on the user's device (e.g., "Your headache may be a tension headache. Please seek medical advice from the clinic listed below. We also recommend taking over-the-counter ibuprofen and drinking plenty of fluids.")

[1527] By following specific steps in order, users can quickly and accurately find medical institutions and treatments that suit their symptoms.

[1528] (Application example 2)

[1529] 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."

[1530] Conventional medical interview systems only require users to input specific symptoms and do not take into account their emotional state, making it difficult to recommend the most appropriate medical institution. Furthermore, they do not offer home care or dietary suggestions, making them unable to address individual user needs. Furthermore, there are no systems that can recommend appropriate meals based on the user's health condition.

[1531] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving medical interview data and emotion data and analyzing symptoms and emotion state using a generative AI model, means for searching for and recommending appropriate medical institutions or meals based on the analysis results, and means for recommending home care methods or over-the-counter drugs, or means for recommending appropriate menus, based on the analysis results. This makes it possible to comprehensively analyze the user's health condition and emotional state and recommend medical institutions, home care, and even appropriate meals.

[1532] A "medical questionnaire" is a paper or digital form that a user fills out to describe their symptoms and health status.

[1533] "Emotion data" is data that indicates the emotional state of a user, obtained from their facial expressions and voice quality.

[1534] A "generative AI model" is an artificial intelligence model used to analyze medical interview data and emotional data.

[1535] "Server" is a central processing unit for receiving, analyzing, and making recommendations on data sent by users.

[1536] "Analysis results" are the assessment of symptoms and emotional state obtained by the generative AI model.

[1537] A "medical institution" is a place that provides medical services, such as a hospital or clinic.

[1538] "Home care methods" are suggestions for health management and treatment methods that can be carried out at home.

[1539] "Over-the-counter drugs" are drugs sold in pharmacies and stores.

[1540] A "menu" is a list of food options or dishes.

[1541] "Notification" refers to the act or means of informing the user of the analysis results and recommendations.

[1542] This invention relates to a system that analyzes medical interview data and emotional data to recommend appropriate medical institutions and meals to users. This system uses a generative AI model to analyze the health and emotional state input by the user and provides optimal recommendations.

[1543] System Overview

[1544] The system consists of the following steps:

[1545] 1. Input of user interview data and emotion data

[1546] 2. Sending data to the server

[1547] 3. Data analysis using generative AI models

[1548] 4. Recommendations for medical institutions and meals based on analysis results

[1549] 5. Notice to Users

[1550] Detailed embodiment

[1551] Input of user interview data and emotion data

[1552] The user accesses the questionnaire through a dedicated application using a smartphone or smart glasses. The questionnaire contains questions about the user's health condition (e.g., stomachache, fatigue, insomnia, etc.), and the user enters this information. In addition, the camera and microphone on the smart glasses or smartphone analyze the user's facial expressions and voice tone as they enter the information, capturing emotional data.

[1553] Sending data to the server

[1554] Health and emotional data entered by users is encrypted through the application and sent to a server, preventing unauthorized access and information leaks.

[1555] Data analysis with generative AI models

[1556] The server receives the data and stores it in a database. It then passes the data to a generative AI model that analyzes the health and emotional state of the person, using the Hugging Face Transformer library.

[1557] Recommendations for medical institutions and meals based on analysis results

[1558] Based on the analysis results, the server searches the database for medical institutions and meal menus that can accommodate the user's condition, and lists the most suitable options, taking into account the user's current location and past usage history.

[1559] User Notification

[1560] The final analysis results in recommendations for appropriate medical institutions, home care methods, over-the-counter medicines, or meal plans, which are then sent from the server to the user's device and displayed for easy access.

[1561] Specific examples

[1562] For example, if a user inputs "stomach pain" and "anxiety" is detected from the emotional data, the generative AI model will use the user's analysis data to recommend a gastroenterologist near the user. It will also suggest "porridge" or "chamomile tea" as home care.

[1563] Prompt Sentence Examples

[1564] The user can enter messages into the application such as:

[1565] "I have a terrible stomach ache and I feel anxious."

[1566] This allows the system to comprehensively analyze the user's health and emotional state and provide the most appropriate response.

[1567] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1568] Step 1:

[1569] The user accesses the questionnaire through a dedicated application using a smartphone or smart glasses. The user inputs their health condition (e.g., stomachache, fatigue, insomnia, etc.). The input data is temporarily stored on the user's device. At the same time, the camera and microphone on the smart glasses or smartphone capture the user's facial expressions and voice quality to generate emotional data. This emotional data is also stored on the device.

[1570] Input: User's health condition data, facial expression data, voice quality data

[1571] Output: Temporary storage of medical interview data and emotional data

[1572] Step 2:

[1573] The questionnaire data entered by the user and the acquired emotion data are sent to the server in a securely encrypted format via a dedicated application. The encryption is performed using standard security protocols (e.g., AES encryption). The server receives this data and stores it in a database.

[1574] Input: User interview data, emotion data

[1575] Output: Encrypted data sent to server, stored in database

[1576] Step 3:

[1577] The server passes the received medical interview data and emotional data to the generative AI model for analysis. The generative AI model evaluates the user's symptoms and emotional state based on the data. For example, it analyzes data on "stomach pain" and "anxiety" and selects the most appropriate medical institution and meal options based on that combination.

[1578] Input: medical interview data, emotion data

[1579] Output: Symptom and emotional state assessment results

[1580] Step 4:

[1581] Based on the analysis results, the server searches the database and lists appropriate medical institutions or meal menus. The user's current location and past usage history are also taken into consideration. For example, "gastroenterologists" or "meals that are good for the stomach (such as porridge)" may be listed.

[1582] Input: Symptom and emotional state assessment results, current location information, past usage history

[1583] Output: List of medical institutions or meal menus

[1584] Step 5:

[1585] The server sends a list of medical institutions, meal menus, home care methods, and over-the-counter drug recommendations to the user's device. The sent information is notified to the user's smartphone. The user receives a notification and can view the most suitable medical institutions and meal suggestions.

[1586] Input: Listed medical institutions, meal menus

[1587] Output: Notification to user terminal, confirmation by user

[1588] For example, if a user inputs "I have a severe stomachache and I'm feeling anxious," the system will analyze this input and recommend a gastroenterologist near the user or a stomach-friendly meal (such as porridge). The user will receive a notification and be able to confirm the recommendation.

[1589] 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.

[1590] 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.

[1591] 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.

[1592] 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.

[1593] 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.

[1594] 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.

[1595] 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).

[1596] 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.

[1597] 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."

[1598] 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.

[1599] 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).

[1600] 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.

[1601] 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.

[1602] 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.

[1603] 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.

[1604] 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.

[1605] 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.

[1606] 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.

[1607] 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.

[1608] 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.

[1609] 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.

[1610] The following is further disclosed regarding the above embodiment.

[1611] (Claim 1)

[1612] [Means for a user to input a questionnaire;

[1613] [Means for transmitting the questionnaire data entered by the user to a server;

[1614] [Means for receiving the medical interview data in a server and analyzing symptoms using a generating AI model;

[1615] [Means for searching and recommending appropriate medical institutions based on the analysis results,

[1616] [Means for proposing home medical care and recommending over-the-counter drugs based on the analysis results, and

[1617] [Means for notifying users of the recommendation results, home medical care suggestions, and over-the-counter drug recommendations;

[1618] A system including:

[1619] (Claim 2)

[1620] [The system according to claim 1 includes a means for listing appropriate medical institutions based on the analysis results and using the user's current location information and past usage history.]

[1621] (Claim 3)

[1622] The system of claim 1, further comprising: a means for providing an interface for inputting a user's initial symptoms.

[1623] "Example 1"

[1624] (Claim 1)

[1625] [information processing means for a user to input a questionnaire;

[1626] [a communication means for transmitting the medical interview data input by the user to a server;

[1627] [a data processing means for receiving the interview data in a server and analyzing symptoms using a generating AI model;

[1628] [A search tool that searches for and recommends appropriate medical institutions based on the analysis results;

[1629] [Based on the analysis results, we will propose home medical care and recommend over-the-counter drugs.]

[1630] [A notification means for notifying the user of the recommendation results, home medical care suggestions, and over-the-counter drug recommendations;

[1631] A system including:

[1632] (Claim 2)

[1633] [The system according to claim 1 includes a means for listing appropriate medical institutions based on the analysis results and using the user's current location information and past usage history.]

[1634] (Claim 3)

[1635] The system of claim 1, further comprising: a means for providing a user interface for inputting initial symptoms of a user.

[1636] "Application Example 1"

[1637] (Claim 1)

[1638] [Means for a user to input a questionnaire;

[1639] [Means for transmitting the questionnaire data entered by the user to a server;

[1640] [Means for receiving the medical interview data in a server and analyzing symptoms using a generating AI model;

[1641] [Means for searching and recommending appropriate medical institutions based on the analysis results,

[1642] [Means for proposing home medical care and recommending over-the-counter drugs based on the analysis results, and

[1643] [Means for notifying users of the recommendation results, home medical care suggestions, and over-the-counter drug recommendations;

[1644] [Means for checking the health status of employees while they are working in the factory and recommending appropriate medical institutions and treatments based on the results; and

[1645] [Means for monitoring worker health status in real time and transmitting data securely and encrypted;

[1646] A system including:

[1647] (Claim 2)

[1648] [The system according to claim 1 includes a means for listing appropriate medical institutions based on the analysis results and using the user's current location information and past usage history.]

[1649] (Claim 3)

[1650] The system of claim 1, further comprising: a means for providing an interface for inputting a user's initial symptoms.

[1651] "Example 2: Combining Emotion Engines"

[1652] (Claim 1)

[1653] [Means for a user to input a questionnaire;

[1654] [Means for transmitting the questionnaire data entered by the user to a server;

[1655] [using an emotion engine to recognize a user's emotion;

[1656] [Means for integrating emotion data with questionnaire data and transmitting the data to a server;

[1657] [Means for receiving the interview data and emotion data in a server and analyzing the symptoms and emotion state using a generative AI model;

[1658] [Means for searching and recommending appropriate medical institutions based on the analysis results,

[1659] [Means for proposing home medical care and recommending over-the-counter drugs based on the analysis results, and

[1660] [Means for notifying users of the recommendation results, home medical care suggestions, and over-the-counter drug recommendations;

[1661] A system including:

[1662] (Claim 2)

[1663] [The system according to claim 1 includes a means for listing appropriate medical institutions based on the analysis results and using the user's current location information and past usage history.]

[1664] (Claim 3)

[1665] The system of claim 1, further comprising: a means for providing an interface for inputting a user's initial symptoms.

[1666] "Application example 2 when combining emotion engines"

[1667] (Claim 1)

[1668] [Means for a user to input a questionnaire;

[1669] [Means for transmitting the questionnaire data and emotion data input by the user to a server;

[1670] [Means for receiving the interview data and emotion data in a server and analyzing the symptoms and emotion state using a generative AI model;

[1671] [Means for searching and recommending appropriate medical institutions or meals based on the analysis results;

[1672] [Means for recommending home care methods and over-the-counter medications, or for recommending an appropriate menu based on the analysis results, and

[1673] [Means for notifying the user of the recommendation results and home care suggestions, over-the-counter drug recommendations, or dietary recommendations; and

[1674] A system including:

[1675] (Claim 2)

[1676] [The system according to claim 1 includes a means for listing appropriate medical institutions or meals based on the analysis results and using the user's current location information and past usage history.]

[1677] (Claim 3)

[1678] The system of claim 1, further comprising: means for providing an interface for inputting a user's initial symptoms and emotional state. [Explanation of symbols]

[1679] 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. A means for a user to input a questionnaire; means for transmitting the medical interview data input by the user to a server; A server receives the medical interview data and analyzes the symptoms using a generative AI model; A means for searching for and recommending appropriate medical institutions based on the analysis results; Based on the analysis results, we propose home medical care and recommend over-the-counter drugs. a means for notifying the user of the recommendation results, home medical treatment suggestions, and over-the-counter drug recommendations; A system including:

2. 2. The system according to claim 1, further comprising a means for listing appropriate medical institutions based on the analysis results and using the user's current location information and past usage history.

3. 10. The system of claim 1, further comprising means for providing an interface for inputting a user's initial symptoms.

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